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1. Introduction
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In 2013, the American Society for Civil Engineers (ASCE) released an updated Infrastructure Report Card that found nearly 25% of the nation’s bridges to be either structurally deficient or functionally obsolete. A bridge is considered structurally deficient (SD) when it is in need of significant maintenance, rehabilitation, or replacement due to deteriorated physical conditions and is considered functionally obsolete (FO) when it does not meet current standards, such as vertical clearances or lane widths. To make these condition assessments, the Federal Highway Administration uses information from inspection reports that are hosted by state and federal bridge management systems (BMS). BMS are heavily dependent on field inspectors, who collect information on bridge elements and bridge components, evaluate their condition, and enter this data into the BMS database. Among the various tasks of BMS, field inspection is the most essential in evaluating the current condition of steel bridges, which are vulnerable to fatigue-induced damage: the process of material degradation and/or cracking by repeated loads. Fatigue damage occurs over a long period of time and is the primary failure mechanism in steel bridges reaching their original design life [1]. Fatigue damage is largely dependent on the size of the traffic loadings, the frequency of the loads, and the type of detail under examination [2]. The damage usually initiates at the fatigue-prone areas of the bridge: the bridge connections, attachments, and details, such as welds connecting connection plates to steel girders. The defects begin to grow under repetitive loads until a bridge inspector finds the crack in a visual inspection. If the crack is not attended to, it will continue to grow until the structural component is capable of fracture and is also considered to be at the end of its total fatigue life.
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Currently, the Federal Highway Administration (FHWA) uses fatigue life estimations to predict the performance of steel bridge members [3]. These fatigue estimates describe the onset of a crack by correlating the magnitude of the stress ranges with the number of load cycles the member has experienced. However, once cracking has occurred, there are no federal or state specifications for crack analysis or crack growth predictions. The fatigue life assessment can be more accurately characterized when crack growth analysis is also included in the assessment. This paper presents a fatigue life assessment method that combines the stress-cycle approach, currently used in AASHTO LRFD Bridge Design Specifications 2014, with a fracture mechanics approach. The damage accumulation results are integrated with current condition state classifications used in BMS.
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2. Fatigue life assessment modeling
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The fatigue life of a member is the number of load cycles a member can endure before confronting the structure’s serviceability limit state. Within a structure’s fatigue life, a structure is considered to experience deterioration in two different periods in time: crack initiation and crack propagation. The crack initiation period describes the time when cracks are just beginning to initiate from points of stress concentrations in structural details. Starting with an inclusion in the material, an initial microscopic crack grows a microscopically small amount in size each time a load is applied. The crack initiation period ends when a microscopic crack reaches a predefined critical crack size, typically a crack that is visible in size. The initiation period covers a significant part of the fatigue life. Once a fatigue crack has initiated, applied repeated stresses cause propagation, or growth, of a crack across the section of the member until the member is capable of fracture. The crack propagation period ends when a crack has reached a critical size or final crack size, determined from the material fracture toughness. When a structure has experienced a crack size at the end of the propagation life, the structure is capable of fracture and is also considered to be at the end of its total fatigue life. It is technically significant to consider the crack initiation and crack propagation stages separately because the practical conditions that have a large influence on the crack initiation period are different from the conditions that will influence the crack propagation period [4].
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2.1 Fatigue crack initiation period
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The crack initiation period corresponds to the onset of a fatigue crack in a component under traffic loads due to an applied stress. To properly account for the dynamic effects in traffic loads, it is necessary to gather a realistic set of data on the stress history that depends upon bridge traffic [5]. This can be accomplished through structural health monitoring (SHM).
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2.1.1 Structural evaluation using structural health monitoring
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A SHM system gathers real-time measurements of a structure behavior under the effects of varying vehicle weights and their random combinations in multiple lanes. Therefore, the measured strain data reflects the loading conditions in the particular location of the strain gage. SHM methodologies can be divided into two main categories: a statistical/data model-based approach and a physical model-based approach. In the statistical model-based approach, only the measured response of the structure is considered for an assessment, while a physical model-based approach concentrates on the understanding of the structure from its physical model, and a finite element analysis is frequently employed and validated through SHM [6]. In the physical model-based approach, the field measurements verify and validate the finite element models, and a simulation of traffic loads can be used to conduct a structural damage assessment.
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To accurately characterize load histories, the content of a measured signal should be summarized and quantified in a meaningful way. The rainflow cycle counting method is recognized as the most accurate way of representing variable amplitude loading [7] and is preferred for statistical analysis of load-time histories, as described in the standard of the American Society for Testing and Materials [8]. Rainflow counting method is advantageous to other range counting methods because it offers realistic counting results while preserving the amplitudes of the acquired stress ranges. As part of the cycle counting process, it is customary to remove small oscillations that are negligible contributors to fatigue damage. Further, the stress ranges caused from smaller vehicles are often considered negligible compared to trucks. This is not only established in AASHTO Standard Specifications for Highway Bridges [9], but the NCHRP Report, Fatigue Evaluation of Steel Bridges [10], also pays distinct attention to truckloads when estimating fatigue life, stating “the effective stress range shall be estimated as either the measured stress range or a calculated stress range value determined by using a fatigue truck as specified in the AASHTO LRFD Bridge Design Specification 2014 [11].” Because of the significance of truckloads compared with smaller vehicular passages, it is rational to neglect stress cycles below 1 ksi [12].
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2.1.2 Bridge global model
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Alongside structural health monitoring, a three-dimensional finite element global model can be developed for linear elastic structural analyses. For a typical steel highway bridge, the global model includes the deck, girders, connection plates, and the cross frames to the girders. The global model contains only the main components of the bridge and is primarily used for modal analysis, finding the displacement output of the whole bridge, and critical fatigue location determination known as hotspots, i.e., the locations of known high tensile strength. Field measurements were taken to calibrate the finite element model, accelerometers were used to capture the bridge frequency, laser sensors and potentiometers were used to measure the dynamic deflection of the bridge, and strain gages were used on connection plates to capture the stresses of bridge components. The simulation of truckloads on the global model will output the stresses of all the components on the bridge.
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2.1.3 Global simulation modeling
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Global simulation modeling uses a three-dimensional model of a bridge with a traffic simulation to estimate fatigue damage. The fidelity of the fatigue assessment is dependent on the accuracy of the traffic load model and the accuracy of the structural model. Since larger loads (i.e., truckloads) are major contributors to fatigue damage and the global simulation model requires computational complexity, the traffic simulation only considers truck loading data for the fatigue assessment. There are two main components of truckloads to consider: the loading configuration (i.e., axle weights and axle spacing) and the traffic patterns. Weigh stations and traffic monitoring systems are often used by State Transportation Departments to acquire loading configuration and traffic pattern data. This data can be used to develop a traffic load simulation, also referred to as the truckload spectra.
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To generate load configuration data, the Guide Specifications for Fatigue Evaluation of Existing Steel Bridges [13] recommends collecting data through weigh station measurements. A weigh station is a checkpoint equipped with truck scales. Trucks and commercial vehicles are subject to passing the scales at a very low speed and return to the highway after inspection. Data collected from weigh station measurements includes the number of axles and the axle spacing. The collection of truck traffic data at weigh stations can be used to calculate the effective gross weight of the truck spectra:
where \n\n\nf\ni\n\n\n is the fraction of gross weights within an interval and \n\n\nW\ni\n\n\n is the midwidth of the interval.
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The traffic patterns are another influence to fatigue damage. The actual traffic flow through a bridge is affected by the traffic on the connecting roadways. Automatic traffic recorders can be used to realistically capture the actual traffic patterns, such as vehicle speed, lane distribution, and vehicle position. Time-varying vehicular count data combined with weigh station measurements are used to develop a probabilistic-based truck simulation model. After obtaining the time-history spectra, the fatigue life and the remaining fatigue life for this detail can be calculated as a function of stress range and number of cycles. Detailed traffic load simulation is reported in a separate companion paper, Fatigue Assessment of Highway Bridges under Traffic Loading Using Microscopic Traffic Simulation.
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2.1.4 Crack initiation life prediction
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The crack initiation period is characterized by the S-N curve. S-N curves are used to relate the stress range (S) vs. number of loading cycles (ni) and ultimately define the fatigue life of the material. S-N curves comprise the influence of material, the geometry of the local structure, and the surface condition. Failure for the crack initiation period is defined by a crack that is of a critical size. Until the onset of this fatigue crack, the specimen can be characterized by the amount of current fatigue damage in terms of its fatigue life. So, the specimen may be at x% of its fatigue life, or the specimen can be classified to have (100 – x)% remaining useful life. This damage may not be visible upon inspection but is still present in the material.
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Since the data in S-N curves were developed under constant amplitude cyclic loading, an effective stress range should be calculated to equivalently represent the variable amplitude cyclic loading on bridge structures. The effective stress range for a variable amplitude spectrum is defined as the constant amplitude stress range that would result in the same fatigue life as the variable amplitude spectrum. For steel structures, the root mean cube stress range (Eq. 2) is calculated from a variable amplitude stress range histogram and is used with the constant amplitude S-N curves for fatigue life analyses [14]:
where Sri is the midwidth of the ith bar, or interval, in the frequency-of-occurrence histogram, 3 is the reciprocal of the slope in the constant S-N curve, and \n\n\nγ\ni\n\n\n is the fraction of stress ranges in that same interval [15].
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2.1.5 Damage accumulation: crack initiation period
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The damage accumulation of crack initiation period, \n\n\nd\ni\n\n\n, is calculated by comparing the effective stress range to the predefined laboratory values of specimens which are used to construct the S-N curve. Thus, the cumulative damage from the crack initiation life is written as a percentage of the fatigue life by dividing the number of current cycles at the effective stress range, \n\n\nN\ne\n\n\n, by the number of stress cycles to fatigue failure, \n\n\nN\nf\n\n\n:
In the crack propagation period, the crack is considered to be a macro-crack and is now growing through the material. The rate of this crack growth is highly dependent on the material type. While the nature of the material cracking is a nonelastic deformation, the region beyond the crack (at the crack tip) experiences a linear elastic stress field under load.
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2.2.1 Linear elastic fracture mechanics
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Because the stresses at the crack tip are so small in fatigue problems, the plastic zone is limited, and linear elastic fracture mechanics (LEFM) can be used to assess fatigue crack propagation. Paris model is most widely used model in linear elastic fracture mechanics for the prediction of crack growth. In this model, the range of the stress intensity factor is the main factor driving the crack growth with two parameters C and m that reflect the material properties:
where a is the initial crack size, N is the number of fatigue loading cycles, C and m are material properties, and \n\n∆\nK\n\n is the stress intensification factor. For a given initial crack size, once the crack growth rate is determined, then the existing crack size can be easily calculated through a summation over crack size increments starting from the known size.
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2.2.2 Stress intensity factor
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The stress in the local crack tip is described as a function of the applied stress in the form of a stress intensity factor (SIF). SIFs are used to describe the severity of a stress distribution around a crack tip, the rate of crack growth, and the onset of fracture [16]. Even at relatively low loads, there will be a high concentration of stress at the crack tip, and plastic deformation can occur [17]. The simplest form to describe the “intensity” of a stress distribution around a crack tip can be written as
\n\n\n\nK\n=\nβ\nS\n\n\nπ\na\n\n\n\n\n\nE5
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where \n\nS\n\n is the remote loading stress, \n\na\n\n is the crack length, and \n\nβ\n\n is a dimensionless factor depending on the geometry of the specimen or structural component. One important feature this equation illustrates is that the stress distribution around the crack tip can be described as a linear function.
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For many ordinary cases of cracking, the calculations of stress intensification factors for various crack geometries and loading cases have already been computed and can be obtained from previously published literature, e.g., elliptical cracks embedded in very large bodies [4]. However, for cases with more complex geometries, more accurate K values should be independently calculated. Finite element modeling (FEM) offers a variety of techniques and efficient computation and has proven to offer satisfactory results for the stress intensification factors [4]. In finite element models, the crack is treated as an integral part of the structure and can be modeled in as much detail as necessary to accurately reflect the structural load paths, both near and far from the crack tip.
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2.2.3 Fracture toughness
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When the crack grows to a particular size, the stresses at the crack tip are too high for the material to endure, and fracture takes place. This critical stress intensity value is more often referred to as the fracture toughness, \n\n\nK\nIc\n\n\n, where I denotes opening mode and c represents critical. Fracture toughness is a measured material property, just like Poisson’s ratio or Young’s modulus, and is usually measured through standard compact specimens. The fracture toughness is used to describe the ability of an already cracked material to resist fracture or to indicate the sensitivity of the material and the material’s susceptibility to experiencing cracks under loading [4]. Thus, SIFs can be compared with the fracture toughness variables to determine if the crack will propagate and to determine the size of crack a material can endure until fracture [18]. When the applied stress intensity equals or exceeds the material fracture resistance, \n\n\nK\nIC\n\n\n, fracture is predicted.
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2.2.4 Crack propagation period cumulative damage
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Models that predict fatigue crack growth propagation emphasize that crack growth is largely dependent on the cycle-by-cycle process. Prediction models are referred to as interaction models and non-interaction models. Interaction effects imply that the crack growth rate in a particular cycle is also dependent on the load history of the preceding cycles rather than an independent effect from one cycle. A non-interaction prediction model is used if the interaction effects in the variable amplitude history are assumed to be absent. In a non-interaction model, crack growth in each cycle is assumed to be dependent on the severity of the current cycle only and not on the load history in the preceding cycles. While it is expected that a non-interaction model will lead to a more conservative life prediction than models that account for interaction effects, considering interaction effects account for retardation in crack growth, a non-interaction model can provide quick and useful information about fatigue crack growth behavior, particularly crack growth rates [4]. The non-interaction prediction model leads to a simple numerical summation in Eq. (6), where \n\n\n\n∆\na\n=\nda\n/\ndN\n\n\n\n:
The accumulation of damage for fatigue crack growth models is consequent of the change in crack size, a, where \n\n\na\n0\n\n\n is the initial crack size, \n\n∆\n\na\ni\n\n\n is the change in crack size per cycle, and \n\n\na\nn\n\n\n is the updated crack size [4]. Thus, the cumulative damage from the fatigue crack propagation period, \n\n\nd\np\n\n\n, is written as a percentage of the fatigue life by dividing the current crack size,\n\n\n\na\nn\n\n,\n\n by the critical crack size at failure,\n\n\n\na\ncrit\n\n\n:
The assessment for the crack initiation period and the assessment for the crack propagation period can be combined to determine a damage prognosis, \n\n\nD\nTotal\n\n\n, for the entire fatigue life:
where \n\n\nN\ne\n\n\n is the number of cycles the element has currently experienced, \n\n\nN\nf\n\n\n is the number of cycles to failure, \n\n\nd\ni\n\n\n is obtained from Eq. (3) and \n\n\nd\np\n\n\n is obtained from Eq. (7), and \n\n\nα\nI\n\n\n and \n\n\nα\nP\n\n\n are rate adjustment factors since the crack initiation period and crack propagation period are not equal in time. These factors can be altered to reflect the rate of damage. Figure 1 displays the various aspects of fatigue analyses that are considered in the derivation of a fatigue damage prognosis. The diagram summarizes the analyses that are detailed in the preceding sections of this paper. As seen in the diagram, the damage accumulation model that defines crack initiation is informed by a structural evaluation, which can be conducted by means of a global simulation model that is validated with structural health monitoring. SHM gathers information about the actual load distributions and operating conditions of the bridge components. This information is processed and evaluated with damage tolerance information, which describes the material characteristics and material properties, such as the number of stress cycles a structural element can endure before cracking. The damage accumulation model that defines the crack propagation period is informed by finite element models of fatigue hotspots with existing cracks. The finite element modeling provides insight of the stress rate at the crack tip. Fracture toughness is then used to determine the critical crack size, at which the structure is described to be at the end of its fatigue life. Ultimately, the damage accumulation models in the crack initiation period and crack propagation period are used to determine the structure’s damage prognosis (remaining useful life).
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Figure 1.
Fatigue damage prognoses with structural health monitoring.
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3.1 Integration of damage prognosis with bridge management systems
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Currently, most US state Departments of Transportation (DOTs) report their bridge inspection findings using AASHTO Pontis software, which poses the guidelines for capturing damage of bridge elements. The conditions of bridge elements are categorized into element condition states to reflect these damages. The AASHTO Pontis software is most useful for state DOTs, since it provides an internal tool for mapping the element condition states back into the national condition ratings. Table 1 summarizes the four condition states related to fatigue damage. These condition states are found in the Maryland Pontis Element Data Collection Manual [19].
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National bridge element condition states
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Defect
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Condition state 1 (good)
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Condition state 2 (fair)
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Condition state 3 (poor)
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Condition state 4 (severe)
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Cracking/fatigue
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None
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Fatigue damage
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Fatigue damage (Analysis warranted)
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Severe fatigue damage
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Fatigue damage exists but has been repaired or arrested. The element may still be fatigue prone
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Fatigue damage exists which is not arrested. Condition state used for first time element is identified with crack
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Fatigue damage exists which warrants analysis of the element to ascertain the serviceability of the element or bridge
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Table 1.
Pontis system condition states related to fatigue [19].
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The condition states in Table 1 can be used with the fatigue life curve (Figure 2) to gather quantitative information of the fatigue life. An element in condition state one is considered a new element or in “like new” condition; it has no fatigue damage present. This element falls within the early stages of the crack life-initiation period. Condition state two recognizes fatigue damage. This damage could be found from a stress-cycle analysis that showed the structure was nearing the end of the crack initiation life or could be the result of a visual inspection from of a small crack that is not considered to be in immediate need of repair. An element in condition state two will be approaching the critical crack size of the crack initiation period and is merging into the crack propagation period. Thus, an element is in the propagation period in condition state three, which explicitly calls for additional analyses. In many state DOTs, it is suggested that deterioration modeling be used to assess the fatigue damage and evaluate the probability of transitioning from condition states [19]. A stress-cycle history can be used to obtain information about the daily or yearly cycle count and stress ranges on the structure. In the event there is enough information about the crack, crack growth models can be used to obtain information about the crack growth rate. This is particularly important information to obtain if the fatigue damage is on a primary component of the structure. Finally, an element in condition state four is in need of immediate rehabilitation or replacement. Analysis can still be used to understand the problem with this section of the bridge to make appropriate changes and to increase the bridge life.
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Figure 2.
BME condition states integrated into fatigue life curve.
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A description of the national bridge element condition states is described in Table 1 and is used in parallel with the FHWA Bridge Preservation Guide, which hosts the commonly employed feasible actions that inspectors and state DOTs should take, given the condition state of their bridge. The purpose of the FHWA Bridge Preservation Guide is to provide a framework for a preventive maintenance program for bridge owners or agencies [20].
\n
\n
\n
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4. Case study
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The fatigue assessment in this paper was conducted as part of the University of Maryland project to design and implement an integrated structural health monitoring system that is particularly suited for fatigue detection on highway bridges. Data for the analyses was acquired from a highway bridge carrying traffic from interstate 270 (I-270) over Middlebrook Road in Germantown, MD, seen in Figure 3. This bridge is referred to as the Middlebrook Bridge.
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Figure 3.
Maryland bridge carrying I-270 over Middlebrook Road.
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The Middlebrook Bridge was built in 1980 and reconstructed in 1991. With help from Maryland bridge inspectors, this bridge was selected as a good candidate for fatigue monitoring due to the average daily truck traffic, the bridge’s maintenance history, the geometric configuration, and the identification of existing fatigue cracks on the connection plates.
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The Middlebrook Bridge is a composite steel I-girder bridge consisting of 17 welded steel plate girders with a span length of 140 ft. The bridge has three traffic lanes in the southbound roadway and five traffic lanes in the northbound, i.e., a high occupancy vehicle lane, an exit lane, and three travel lanes. Four fatigue cracks were reported in the Maryland State Highway June 2011 Bridge Inspection Report. These four cracks were all found in the welded connections between the lower end of the cross brace connection plate and the girder bottom flange.
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The Middlebrook Bridge is built with skewed supports to accommodate the roadway below the bridge. Due to the skewed supports, the corresponding cross frames are also built with skewed angles. The Middlebrook Bridge was built with K-brace cross frame, seen in Figure 4.
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Figure 4.
K-type cross brace on Middlebrook Bridge.
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The skew angle of the cross frames are built to code and are in accordance with AASHTO LRFD Bridge Design Specifications [11], so long as the skew angle is less than 20 degrees. A bridge with skewed cross braces is more prone to fatigue damages because its geometric configuration enhances the live load effects. The connections of the skewed cross braces are bent at an angle to connect with the transverse stiffeners of the bridge girders. When the bridge girders deflect, this angle introduces a bending effect into the transverse stiffeners.
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4.1 Structural health monitoring and data processing
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A connection plate of a steel girder highway bridge is selected for long-term monitoring, shown in Figure 5.
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Figure 5.
Connection plate with known crack (left) and schematic of strain gage location (right).
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This connection plate was identified by Maryland State Bridge inspectors in 2011 to have an existing active crack, i.e., a crack that is growing in size. The crack was described in inspection reports as “… very fine crack in the weld that connects the web stiffener to the top of the lower flange. The crack runs along the top of the weld material next to the stiffener and begins at the toe of the weld” [21].
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Only one strain transducer was used to continue monitoring the bridge in a long-term monitoring evaluation. The strain transducer was placed on one of the stiffeners that showed to high tension stress. The bridge itself is loaded in bending by the dynamic effects caused from the vehicle passage. Specifically, Figure 6 displays a sample of the acquired stress data as a function of time that was taken from a connection plate. The variation in loading of the load spectrum on the connection plate is dependent on the number of vehicles passing the bridge and the weight of the vehicle. Given that the traffic volumes and patterns are sporadic, the captured bridge loads are also sporadic. Strain data was collected from the bridge over the course of 1 year.
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Figure 6.
Illustration of variable amplitude loading.
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4.2 Fatigue analysis
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The acquired variable amplitude strain data is converted to stress for linear damage accumulation models, where stress ranges are the main contributor to fatigue damage. In addition, methods of extrapolation were used to fill in missing points of data. The method of extrapolation that has been applied to the fatigue data is done in the rainflow domain. The results of the extrapolated rainflow matrix were modeled from a measured rainflow history, where the density of rainflow cycles was calculated. The calculation of this density provided the number of stress cycles and stress ranges that were to be estimated for each specific hour of the day. The data was then processed with the rainflow cycle counting method to count the number of stress ranges. Figure 7 displays a histogram of measured stress ranges. This particular histogram displays the traffic data that was accumulated on the bridge over 8 days.
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Figure 7.
Histogram of measured stress ranges.
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With variable amplitude stress history, the variable stress cycles are associated with a particular stress range value that will map the measured data with the S-N curves. The measured histograms showed an un-proportionally large amount of cycles occur at smaller stress ranges. Therefore the stress ranges are truncated, and an effective stress range is solved for; with S-N curves the number of cycles to failure is based on the effective stress range. For this case study, the effective stress range (\n\n\nS\nre\n\n\n) was found to be = 7.2 ksi, and the number of cycles over the course of 1 year were approximately 5.8 million cycles.
\n
In accordance with the histograms for this case study, as the effective stress range increases, the number of stress cycles decreases dramatically. Without including an increase in traffic volumes, the effective stress range and number of cycles are assumed consistent for each year. Under this assumption, the estimated fatigue life for the crack initiation period was 18.0 years. Figure 8 displays the yearly accumulation until failure is reached on the S-N curve.
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Figure 8.
AASHTO S-N curve with cumulative points plotted until failure.
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4.3 Global model and simulation
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A three-dimensional global model of the southbound direction, seen in Figure 9, was created to evaluate a bridge’s response to loading. The model of the southbound superstructure consisted of eight I-girders. The concrete deck, the eight I-girders, and connection plates which connected cross frames to the girders were modeled by shell elements, while all the cross frames were modeled by spatial frames along their center of gravity. Special link members were defined to connect girder elements and concrete deck elements at the actual spatial points where these members intersect. The translations in the x-, y-, and z-directions were fixed at the abutments to represent the actual characteristics of support and continuity.
\n
Figure 9.
Global model of Middlebrook Bridge and location of local model [22].
\n
To study the dynamic effects of the Middlebrook Bridge, simulated truckloads were applied to the global finite element model through traffic simulation software, Traffic Software Integrated System (TSIS) 6.0. The data that was used to simulate the truckloads were taken from Maryland State Highway Administration’s Internet Traffic Monitoring System (ITMS) and a local weigh station that is approximately 10 miles north of the Middlebrook Bridge but on the same interstate [23]. The ITSM features permanent Automatic Traffic Recorders that count traffic continuously throughout the year and breaks down the traffic count data by class, volume, and lane distribution [24]. The average hourly volume varied from 505 to 4215, and the truck percentages also varied from about 10.5 to 20%. The weigh station-collected weight data of the truck traffic and the trucks were categorized into seven classes based on the number of axles. The majority of trucks were 2-axle which made up 25% of trucks and 5-axle, which made up 68% of trucks. The simulated truck network contained the mainline section of the highway with the Middlebrook Bridge in the center and adjacent ramps. Three classes of trucks were used for the simulation, shown in Figure 10. From the collected data, the simulation included the axle weight, axle spacing, vehicle position, and speed at each time step in the simulation.
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Figure 10.
Fatigue truck configurations (a), small truck, (b) medium truck, and (c) large truck.
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The loading data from the simulation matched the loading data from field monitoring, and the simulated truckloads on the global modeL of the Middlebrook Bridge confirmed high tensile stresses between cross-frame connection plates and girder bottom flanges. These stresses are highest at the outer edge of the connection plate where the existing fatigue crack on the I-270 Bridge over Middlebrook Road was located. More detailed traffic load simulation is reported in a separate companion paper, Fatigue Assessment of Highway Bridges Under Traffic Loading Using Microscopic Traffic Simulation.
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4.4 Fracture analysis
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Since the interest is to obtain a SIF, the global model cannot be any more refined, and a local model of this critical region was created for the purpose of understanding the stress field around the crack. A local model was created by applying the resulting deflections from the global model as resulting displacements in the local model. Since the deflections are a result of simulated traffic loads, applying the deflections simulates the loads transferred across a free-body section of the global model where the local model resides.
\n
Additionally, the stress loads at the location of the strain gage were applied to the local model at the corresponding perimeter location. Figure 9 displays the location of the local model within the global structure. This location is described with white lines that outline the local model geometry. Figure 11 displays the local model with applied displacements and forces. A dashed rectangle outlines the location of the existing crack. A fine mesh is created around the previously identified existing crack, and a radial mesh is created around the crack tip. The crack was modeled with an assumed depth of 0.05 inch, which is slightly greater than a largest depth of micro-crack (0.05 mm <a <1 mm) and approximately the length of the penetration of the fusion in a fillet weld [25]. Figure 12 displays the stress contour of the y-component of the cross section and a magnified view at the location of the crack.
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Figure 11.
FEM local model with applied displacements and forces.
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Figure 12.
Stress contour of crack to illustrate plastic zone at crack tip.
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4.4.1 Damage tolerance and fracture toughness
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The specifications of the American Society for Testing and Materials for A572 Grade 50 steel require a minimum yield strength value of 50 ksi. The fracture toughness for the steel on the Middlebrook Bridge is \n\n\n\n\nK\nIC\n\n=\n56\nksi\n\nin\n\n\n\n\n. The critical crack length that corresponds to the fracture toughness comes from the fracture mechanics equation for critical SIF. Under the parameters that fit the Middlebrook Bridge, the critical crack size is \n\n\n\n\na\ncrit\n\n=\n\n\n\nK\nIC\n\n\nπ\n\nβ\n2\n\n\nσ\n2\n\n\n\n\n=\n.15\nin\n\n\n\n.
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4.4.2 Crack growth and total cumulative damage
\n
The computed SIF from the local model was used alongside Paris law to solve for the yearly crack growth rate. Rearranging Eq. (5), the crack size, a, at any given time, is a function of the SIF and the effective stress range. The accumulation of damage for fatigue crack growth models (shown in Eq. 6) is consequent of the change in crack size, \n\n∆\n\na\ni\n\n\n; then the crack would reach the critical size after 9.6 years. Since the bridge inspectors first noticed the bridge cracking in 2011, at the time of testing (2012–2013), the crack had been present for about 1–2 years. The crack was repaired in 2014, at which time the remaining useful life for this bridge element was calculated to be 6.6 years to failure.
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4.5 Integration of damage with Maryland condition states
\n
The case study was estimated from measured and extrapolated load distributions to assess the life of the bridge. The fatigue life of the crack initiation period was found to be 18 years, and the fatigue life of the crack propagation period was found to be about 9 years. Accordingly, rate adjustment factors were selected to be \n\n\nα\nI\n\n\n = 0.7 and \n\n\nα\nP\n\n\n = 0.3. The second row in Table 2 illustrates the amount of damage for each condition state. The third row is a simplified explanation of the condition states which are found in the Maryland Pontis Element Data Collection Manual, and the last row is the feasible actions for these condition states from the FHWA Bridge Preservation Guide. In 2014, when the crack was repaired, the calculated percent damage was 87.2%, correlating to condition state 4, “Fatigue damage exists which warrants analysis of the element to ascertain the serviceability of the element or bridge.”
Damage accumulation mapped to bridge condition states.
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5. Summary and conclusions
\n
This paper proposes a damage accumulation model to more accurately characterize fatigue damage prognoses of bridge elements. The fatigue life has been described and divided into two periods: the initiation period and the propagation period. An empirical correlation approach, characterized by the S-N curve, is used to analyze the initiation period, and the data acquired from SHM and traffic simulation models are used to inform the crack initiation analyses. SHM is shown to have a significant contribution in damage prognosis, where the sensing information instrumentation is used to validate FEM models and acquire information about a bridge’s response to loads. It is shown how this data can be particularly useful when processed through cycle counting algorithms, and methods of extrapolation are applied to gather information on stress range distributions to estimate future traffic loads of the bridge. Fatigue damage assessments in the crack initiation period can be supplemented with a fracture mechanics analysis, which defines the crack propagation period and estimates crack growth. It is also shown how finite element modeling can be used to solve for the SIF, which is then used to estimate the growth rate. A case study is presented to illustrate the application of the fatigue damage prognoses on a steel highway bridge element. The damage accumulation models are used to estimate the onset of a fatigue crack and fatigue crack growth rates and ultimately derive a damage prognosis of the bridge element.
\n
\n
Acknowledgments
\n
This research was partially sponsored by the US Department of Transportation’s Office of the Assistant Secretary for Research and Technology (USDOT/OST-R), under The Commercial Remote Sensing and Spatial Information (CRS&SI) Technologies Program. This support is acknowledged and greatly appreciated.
\n
\n',keywords:"fatigue, fatigue damage, structural health monitoring, damage prognoses, fatigue assessment, bridge management systems, condition ratings",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64838.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64838.xml",downloadPdfUrl:"/chapter/pdf-download/64838",previewPdfUrl:"/chapter/pdf-preview/64838",totalDownloads:370,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"July 9th 2018",dateReviewed:"October 15th 2018",datePrePublished:"December 18th 2018",datePublished:"February 5th 2020",dateFinished:null,readingETA:"0",abstract:"Fatigue damage is one of the primary safety concerns for steel bridges reaching the end of their design life. Currently, US federal requirements mandate regular inspection of steel bridges for fatigue cracks; however, these inspections rely on visual inspection, which is subjective to the inspector’s physically inherent limitations. Structural health monitoring (SHM) can be implemented on bridges to collect data between inspection intervals and gather supplementary information on the bridges’ response to loads. Combining SHM with finite element analyses, this paper integrates two analysis methods to assess fatigue damage in the crack initiation and crack propagation periods of fatigue life. The crack initiation period is evaluated using S-N curves, a process that is currently used by the FHWA and AASHTO to assess fatigue damage. The crack propagation period is evaluated with linear elastic fracture mechanic-based finite element models, which have been widely used to predict steady-state crack growth behavior. Ultimately, the presented approach will determine the fatigue damage prognoses of steel bridge elements and damage prognoses are integrated with current condition state classifications used in bridge management systems. A case study is presented to demonstrate how this approach can be used to assess fatigue damage on an existing steel bridge.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64838",risUrl:"/chapter/ris/64838",book:{slug:"bridge-optimization-inspection-and-condition-monitoring"},signatures:"Timothy Saad, Chung C. Fu, Gengwen Zhao and Chaoran Xu",authors:[{id:"258099",title:"Dr.",name:"Chung",middleName:null,surname:"Fu",fullName:"Chung Fu",slug:"chung-fu",email:"ccfu@umd.edu",position:null,institution:null},{id:"266667",title:"Dr.",name:"Tim",middleName:null,surname:"Saad",fullName:"Tim Saad",slug:"tim-saad",email:"timsaad@gmail.com",position:null,institution:null},{id:"266724",title:"Dr.",name:"Gengwen",middleName:null,surname:"Zhao",fullName:"Gengwen Zhao",slug:"gengwen-zhao",email:"gw.eileen.zhao@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Fatigue life assessment modeling",level:"1"},{id:"sec_2_2",title:"2.1 Fatigue crack initiation period",level:"2"},{id:"sec_2_3",title:"2.1.1 Structural evaluation using structural health monitoring",level:"3"},{id:"sec_3_3",title:"2.1.2 Bridge global model",level:"3"},{id:"sec_4_3",title:"2.1.3 Global simulation modeling",level:"3"},{id:"sec_5_3",title:"2.1.4 Crack initiation life prediction",level:"3"},{id:"sec_6_3",title:"2.1.5 Damage accumulation: crack initiation period",level:"3"},{id:"sec_8_2",title:"2.2 Fatigue crack propagation period",level:"2"},{id:"sec_8_3",title:"2.2.1 Linear elastic fracture mechanics",level:"3"},{id:"sec_9_3",title:"2.2.2 Stress intensity factor",level:"3"},{id:"sec_10_3",title:"2.2.3 Fracture toughness",level:"3"},{id:"sec_11_3",title:"2.2.4 Crack propagation period cumulative damage",level:"3"},{id:"sec_14",title:"3. Damage prognoses fatigue life",level:"1"},{id:"sec_14_2",title:"3.1 Integration of damage prognosis with bridge management systems",level:"2"},{id:"sec_16",title:"4. Case study",level:"1"},{id:"sec_16_2",title:"4.1 Structural health monitoring and data processing",level:"2"},{id:"sec_17_2",title:"4.2 Fatigue analysis",level:"2"},{id:"sec_18_2",title:"4.3 Global model and simulation",level:"2"},{id:"sec_19_2",title:"4.4 Fracture analysis",level:"2"},{id:"sec_19_3",title:"4.4.1 Damage tolerance and fracture toughness",level:"3"},{id:"sec_20_3",title:"4.4.2 Crack growth and total cumulative damage",level:"3"},{id:"sec_22_2",title:"4.5 Integration of damage with Maryland condition states",level:"2"},{id:"sec_24",title:"5. Summary and conclusions",level:"1"},{id:"sec_25",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'FHWA. Focus Accelerating Infrastructure Innovations. Federal Highway Administration Launches Steel Bridge Testing Program. 2011\n'},{id:"B2",body:'Haldipur P, Jalinoos F. Detection and Characterization of Fatigue Cracks in Steel Bridges. 2010. Available from: www.structuralfaultsandrepair.com\n\n'},{id:"B3",body:'FHWA. Bridge Inspector’s Reference Manual. Vol. 2. Washington DC: FHWA NHI Publication No. 12-050; 2012\n'},{id:"B4",body:'Schijve J. Fatigue of Structures and Materials. 2nd ed. Netherlands: Springer Science+Business Media B.V.; 2009\n'},{id:"B5",body:'Mohammadi J, Guralnick SA, Polepeddi R. Bridge fatigue life estimation from field data. Practice Periodical on Structural Design and Construction. 1998;3(3):128-133\n'},{id:"B6",body:'Zhou YL, Maia NM, Sampaio RP, Wahab MA. Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure. Structural Health Monitoring. Sage Publication. 2017;16(6):711-731. Available from: https://www.nafems.org/downloads/FENet_Meetings/Trieste_Italy_Sep_2002/FENET_Trieste_Sept2002_DLE_Zafosnik.pdf/\n\n'},{id:"B7",body:'Shantz CR. Uncertainty Quantification in Crack Growth Modeling Under Multi-Axial Variable Amplitude Loading. Nashville, Tennessee: Graduate School of Vanderbilt University; 2010\n'},{id:"B8",body:'ASTM E-1049. Standard Practices for Cycle Counting in Fatigue Analysis. West Conshohocken: ASTM International; 2011\n'},{id:"B9",body:'AASHTO. Standard Specifications for Highway Bridges. Washington, D.C.: American Association of State Highway and Transportation Officials. 2002\n'},{id:"B10",body:'NCHRP. Fatigue Evaluation of Steel Bridges. Washington, D.C.: National Academy of Sciences, Transportation Research Board. 2012\n'},{id:"B11",body:'AASHTO. LRFD Bridge Design Specifications. 7th ed. Washington DC: American Association of State Highway and Transportation Officials. 2014\n'},{id:"B12",body:'Massarelli PJ, Baber TT. Fatigue Reliability of Steel Highway Bridge Details. US DOT FHWA, Charlottesville, Virginia: Virginia Transportation Research Council; Virginia DOT. 2001\n'},{id:"B13",body:'AASHTO. Guide Specifications for Fatigue Evaluation of Existing Steel Bridges. Washington DC: American Association of State Highway and Transportation Officials; 1990\n'},{id:"B14",body:'Zhou YE. Assessment of bridge remaining fatigue life through field strain measurement. Journal of Bridge Engineering. 2006;11(6):737-744\n'},{id:"B15",body:'Keating PB, Fisher JW. Fatigue Tests and Design Criteria. Bethlehem, PA: National Cooperative Highway Research Program and Fritz Engineering Laboratory; 1986\n'},{id:"B16",body:'Zafosnik B, Ren Z, Ulbin M, Flasker J. Evaluation of stress intensity factors using finite elements. FENet: A NAFEMS Project; 2002\n'},{id:"B17",body:'Mertz D. Steel Bridge Design Handbook: Design for Fatigue. Washington, D.C.: FHWA-IF-12-052-Vol.12; 2012\n'},{id:"B18",body:'CAE Associates. Fracture Mechanics in Workbench v14.5 ANSYS e-Learning Session. Middlebury. 2013\n'},{id:"B19",body:'MDSHA. Pontis Element Data Collection Manual. Baltimore, MD: Bridge Inspection and Remedial Engineering Division, Office of Bridge Development; 2003\n'},{id:"B20",body:'FHWA. Bridge Preservation Guide. U.S. Department of Transportation Federal Highway Administration. New Jersey Avenue, SE Washington, DC. 2011\n'},{id:"B21",body:'MDSHA. Maryland State Highway Administration Bridge Inspection Report. MDSHA, Maryland State Highway Administration. North Calvert Street, Baltimore, Maryland. 2013\n'},{id:"B22",body:'Fu CC, Wang S. Computational Analysis and Design of Bridge Structures. Boca Raton, FL: CRC Press Taylor and Francis Group; 2014\n'},{id:"B23",body:'SHA. Internet Traffic Monitoring System. Maryland State Highway Administration. 29 July 2015. [Online]. Available from: http://shagbhisdadt.mdot.state.md.us/ITMS_Public/default.aspx\n\n'},{id:"B24",body:'Zhao G. Truck Loading Simulation for the Fatigue Assessment of Steel Highway Bridges. College Park: University of Maryland; 2015\n'},{id:"B25",body:'Janosch J. Investigation into the Fatigue Strength of Fillet Welded Assemblies of E-36-4 Steel as a Function of the Penetration of the Weld Subjected to Tensile and Bending Loads. 1993\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Timothy Saad",address:null,affiliation:'
The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, USA
'},{corresp:"yes",contributorFullName:"Chung C. Fu",address:"ccfu@umd.edu",affiliation:'
The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, USA
The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, USA
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1. Introduction: veterinary pharmaceuticals and antimicrobials
Veterinary pharmaceuticals include drugs, medications, and other substances in use to treat or prevent animal diseases for health, growth promotion, and productivity [1]. These drugs can be broadly divided into categories according to the different pathogens or targeted infections. They include antiparasitic drugs, antiinflammatory, reproductive medication, surgical medications, anesthetics, nutritional drugs, and feed additives sometimes used as growth promoters (Table 1). Among commonly used drug in veterinary medicine are antibiotics. These drugs and medicaments can be administered in form of injectable, tablet, bolus, drench, and bath/wash or added to feed and drinking water. There are documented evidence of earlier norms and practices of animal husbandry regarding how shepherd and nomads provide medication for livestock. Some were written document by priests in monasteries, such as the use of garlic (Allium sativum L.) and ointment made from honey and grease [2]. This is similar to what has now been recognized in modern veterinary medicine as ethno-veterinary or alternative medicine in human and according to the World Health Organization (WHO); 75% of the world’s population are using herbs for basic healthcare [3]. Such practices predate modern day pharmacopeia, which has, however, refined and synthesized the delivery of veterinary and human medicines. Some global industrial leaders in modern veterinary pharmaceuticals include Zoetis (formerly Pfizer Animal Health), Merck, Bayer, Elanco Animal Health, Boehringer Ingelheim Animal Health (Merial), Norvatis Animal Health, and many others. These companies and their subsidiaries are engaged in the multibillion dollars profitable business of drug distribution in developing countries (and also from Asia to Africa) in millions of doses some of which may be overused and contributing to drug resistance [4, 5].
Antibiotics
Antiparasitic
Antiinflammatory
Anesthetics
Growth promoters
Terramycin
Banminth
Ibuprofen
Phenobarbital
Feed grade antibiotics
Penicillin
Ivermectin
Meloxicam
Thiamylal
Probiotics
Streptomycin
Diminazene aceturate
Dexamethasone
Xylazine hydrochloride
Dihydropyridine
Colistin
Amprolium
Prednisone
Chlorpromazine
Organic acids
Erythromycin
Piperazine
Prednisolone
Diazepam
Amino acids
Doxycycline
Albendazole
Aspirin
Thiopental sodium
Racto-amine
Enrofloxacin
Closantel
Phenylbutazone
Pentobarbital
Sodium-bicarbonate
Tylosin
Dermatocide
Dimethylsulfoxide
Chloral hydrate
Potassium chloride
Oxytetracycline
Diazinon
Flunixin
Methohexital
Fatty acids
Amoxycillin
Nitroxynil
Meglumine
Methoxyflurane
Zytomil
Gentamycin
Cypermethrin
Cortisone
Halothane
Renature-Z oral powder
Chloramphenicol
Pyrantel pamoate
Methimazole
Diethyl ether
Vita-Sel-E oral solution
Ciprofloxacin
Praziquantel
Celecoxib
Isoflurane
Eucament plus oral solution
Griseofulvin
Mectizan
Colchicine
Enflurane
Chicktonic
Norfloxacin
Nitroxyl
Cyclooxygenase
Nitrous oxide
Aminogrow WS
Rifampin
Diclazuril
Pylorus
Glyceryl quiacolate
Electromix WS
Novidium chloride
Mavacoxib
Succinyl choline
Introvit A+ WS
Isomethadone
Tepoxalin
Curare
Introvit-ES-200 WS
Furazolidone
Homidium chloride
Piroxicam
Lidocaine
Introvit-K-200 WS
Table 1.
Some veterinary pharmaceuticals distributed in Nigeria.
Source: survey of commonly use veterinary antimicrobials in Nigeria, courtesy of Dr. Jolly Amoche of National Veterinary Research Institute, Vom.
Globally, there are more livestock in the world than human, with livestock systems occupying about 30% of the planet’s ice-free terrestrial surface area [6]. Most of these animals are kept in free range husbandry systems in under-developed countries where the enterprise supports the livelihood of about 600 million small holders [7]. The livestock sector in developing countries is also evolving in response to rapidly increasing demand for livestock products with changes in the demand for livestock products being driven among other factors by human population growth, urbanization, and increasing income [8, 9].
A major limiting factor in profitable livestock production in developing country is the burden of infectious diseases. These livestock diseases cause great socioeconomic impact, and the burdens are most of the time exasperated by poor biosecurity in both intensive and open production systems. This has made the use of antimicrobials for treatment of diseases indispensable [10]. It is important to emphasize that the reduction in the burden of infectious livestock diseases has been possible due in part to the use of a wide range effective drugs and vaccines and improvements in diagnostic techniques and services [11].
Therapeutic treatments are targeted at animals that are diseased. In food animals, it is usually often more convenient to treat entire groups by administering medication through feed or water, though individual animals may also be treated. For animals like poultry and fish, mass medication is the most feasible means of treatment but with the possibility of drug dispersal into the environment via leaching and agricultural wastewater [12]. Furthermore, certain mass-medication procedures called metaphylaxis, aimed at treatment of sick animals while medicating others in the group that may not be sick but exposed, can also be counterproductive. Other prophylactic antimicrobial treatments are typically used during high-risk periods for infectious diseases even, while the animals may not be infected also described as nonspecific infection prevention [13]. These practices, however plausible, are currently considered as contributing to emergence of antimicrobial resistance due to subtherapeutic exposure to veterinary pharmaceuticals by both infected and noninfected animals, as well as the environment [14].
Antimicrobial resistance has been described as the ability of bacterial, parasites, viruses, and fungi to survive and spread despite treatment with specific and combination therapy that are normally used against them [15]. The World Health Organization also emphasized that resistance happens when microorganisms change when they are exposed to antimicrobial drugs (such as antibiotics, antifungals, antivirals, antimalarials, and antihelmintics). These microorganisms that develop antimicrobial resistance are sometimes referred to as “superbugs”. Antimicrobial resistance may be spontaneous and occur as a natural process, and resistance to antimicrobials dates back as far as when the first generations of antibiotics including penicillin were introduced in 1943/44 by Alexander Fleming [13]. In evolution, selection pressure is bound to cause subpopulation of microorganism with resistance genes to emerge [16]. This selective pressure has been ascribed to appropriate and inappropriate use of antimicrobials but aggravated by (1) intensity of usage, (2) persistence of usage, (3) under usage and subtherapeutic doses that animals are exposed to in prophylactic treatment, and (4) unintended human exposure through antimicrobials in food residues and the environment [10].
The burden of infectious diseases in developing countries and intensive use of antimicrobials to combat this has also been stressed in a study that suggested that up to a third of the global increase (67%) in antibiotic consumption will be in food animals, over the period 2010–2030 and attributable to low-middle income countries [17]. This challenge is in view of the high burden of foodborne infectious and zoonotic diseases especially also in developing countries [18]. Veterinary practices use drugs for mitigating these diseases in animals, including food animals that have to be maintained in health and productivity (meat, egg, and milk). To prevent these drugs from getting into the food chain and being consumed by humans, “withdrawal time,” which is the last time any drug may be administered before egg/milk and meat from such animals are collected and consumed is specified. The withdrawal time for antimicrobials is intended to prevent harmful drug residues in meat, milk, and eggs [19]. These waiting periods need to be observed from the time of treatment to when the animals are slaughtered for food. This is important because food products that contain antimicrobial residues not metabolized leaves residues beyond permissible limits at the end of the withdrawal period may be considered unwholesome for consumption and may contribute to antimicrobial resistance in humans [20].
Veterinary pharmaceuticals, therefore, contribute in many ways to the emergence of antimicrobial resistance either directly in suboptimal usage in animals or indirectly in human who consume subtherapeutic doses in animal products [13]. When resistant organism emerges, it has also been argued that human sources also seed these resistant bacteria to animals and the environment through sewage [21]. A recent study by Marcelino et al. [22] described high levels of antibiotic resistance gene expression among birds living in a wastewater treatment plants. The study observed that birds feeding at a wastewater treatment plant carried greatest resistance gene burden, suggesting that human waste, even after treatment, contributes to the spread of antibiotic resistance genes to the wild. Domestic and wild animals, including rodents, and birds, can acquire these environmental contaminants and pass them on via their excreta to grazing land or feed of food animals, which may in turn end up in human through the food chain [23]. While it is imperative to canvass AMR stewardship through rational and circumspect usage of antimicrobial in animals, it is important to bear in mind that human also present risk to animals. The USFD described the phenomenon of antimicrobial resistance as a very complex and nonvictimless phenomenon, affecting both human and animal health [13].
2. Livestock diseases and the application of veterinary pharmaceuticals
In the management of infectious and noninfectious diseases of livestock in developing countries, a number of veterinary pharmaceuticals are administered. The choice of drugs is often determined by efficacy, availability, and cost. These factors are explored by manufacturers mostly based in developed countries from where the drugs are exported to developing countries. This distribution chain is also largely driven by business interest such that drug companies sell volumes that are targeted at frequent, intensive usage that may have deleterious effect such as emergence of AMR.
Intensive use of veterinary chemotherapy on the other hand may be justifiable considering that many bacterial, viral, and parasitic diseases like mycoplasmosis, Newcastle disease, avian influenza, anthrax, coccidiosis, brucellosis, foot and mouth disease (FMD), rift valley fever, etc. threatens socioeconomics, instills fear that shock systems, either by suddenly and rapidly killing large number of animals or causes large-scale drop in demand through fear of zoonotic diseases [24, 25]. On the other hand, the growing concern that animals are major sources of human diseases and that around 60% of all animal diseases are zoonotic [26] make treatment of such diseases in animals an essential control measure before it is transmitted to human, and to reduce their capacity to cause epidemics and pandemics.
The livability and economic impacts of animal disease disaster is well documented, for instance, highly pathogenic avian influenza recently killed millions of poultry birds in Nigeria (including other countries in West Africa) and wiped out entire farms [27]. The costs of epidemic African swine fever in Cote d’Ivoire was estimated at $9.2 million; Nipah virus in Malaysia $114 million, while contagious bovine pleuropneumonia in Botswana costs about $300 million [25]. In the absence of preventive measures such as biosecurity and vaccination, the use of antimicrobial especially for nonviral infections is essential for profitable livestock production and to prevent infections that may be transmitted from animals to human as attested to by WHO [28].
3. Development of AMR in veterinary practice
The global antimicrobial resistance (AMR) crisis is predicted to kill roughly 10 million people annually by 2050 due to antibiotic-resistant infections, with Africa alone accounting for about 4.15 million [29]. This is estimated to cost the global economy about $100 trillion [30] with about 28.3 million people pushed into extreme poverty [31]. The alarming rate of AMR in developing countries can be attributed to gross misuse of antimicrobials in human and animals [32]. Although resistance can still develop even at an appropriate antimicrobial use, however the situation can be made worse whenever there is excessive and unnecessary usage [33]. The global revolution in livestock and aquaculture is an underlying factor for frequent antimicrobial use and subsequent development of AMR. This is also driven by population increase, urbanization, improving economic conditions, and globalization. Countries like Brazil, China, and India are currently the hotspots for livestock intensification, while Nigeria, Myanmar, Peru, and Vietnam are future spots (Van [17]). In developing countries, most nonhuman-medical use of antimicrobials is almost certainly in livestock and farmed fish production and it is likely that most veterinary use is in intensive production rather than pastoralist or small-holder systems [34]. In Nigeria and other developing countries in sub-Saharan Africa, Asia and Middle East, there is paucity of information on antimicrobial drug resistance in farm animals, although little information exists on residue level [35, 36, 37, 38]. However, there is information on antimicrobial drug resistant microbes isolated from human patients from different parts of Nigeria [39, 40]. Previous report by Adesokan et al. [41] on pattern of antimicrobial usage in livestock production in three states of South-Western Nigeria between the period of 2010 and 2012 showed an increased use of tetracyclines (33.6%) followed by fluoroquinolones (26.5%) and beta-lactams/aminoglycosides (20.4%). Similar trend was also reported in Africa for tetracycline & beta-lactams [42]. However, studies by Idowu et al. [35] showed level of tetracycline residues between the ranges of 0.1–1.0% in chicken eggs.
The process of AMR development is very complex, and all of the factors that contributed to the events are not fully understood. It is clear that genetic change or mutation in microbial DNA may often cause resistance to antimicrobial agents, and this change might also be passed to the offspring or transferred to other related or even unrelated microorganisms [43]. This is known as “selection pressure” where the use of antimicrobial drugs in health care, agriculture, or industrial settings favor the survival of resistant strains (or genes) over susceptible ones, thus leading to a relative increase in resistant bacteria within microbial communities [44]. This is because no matter how effective an antimicrobial is, it rarely kills 100% of the organisms, meaning some may still survive due to genetic change, which can be passed forward. Currently, science has not fully proved the causes of different types of AMR that are causing great public health risks. The widespread use of antimicrobials in food production system especially in food-producing animals is another cause of AMR [45]. The extensive use of antimicrobials in animal production as growth promoters widely exposes the microbes to the drugs, thus enhancing the development of microbial resistance causing health consequences in both animals and humans. However, the scientific evidence of how and to what extent such drug exposure affects human health still remains unclear. It is interesting to note that antimicrobial resistance would not develop in animals if antimicrobial drugs were never used in them [12].
There is danger to public health if resistant organism from animals can also cause disease in human exposed by a way of food consumption or direct contact with food-producing animals, companion animals, or through environmental spread [46]. The threat to public health also exists even if the organisms do not cause disease in human, because they may still be able to transfer the resistant genes from food-producing animals to unrelated human pathogenic bacteria as well as normal commensals [47]. It is then clear that the increase use of antimicrobials in animal production for variety of purposes such as for therapeutic and nontherapeutic use has contributed to increasing AMR in bacteria affecting man and animals [48]. In Africa and other developing countries, studies have suggested a strong correlation between the use of antimicrobials in veterinary practice and the development of AMR [49], because it is shown that a larger proportion of antimicrobial medications have been used in animals than humans mainly for food production purposes [50]. There is presence of high antimicrobial residue in meat and milk meant for human consumption correlating with the detection of multidrug resistance (MDR) bacteria in animals and their products [51] as well as in humans in contact with the animals [52, 53, 54]. This is also because a large proportion of the population in developing countries lives in close proximity with livestock, which enhance the chances of transfer of resistant microorganisms from animals to humans [55, 56]. Similarly, the increasing use of antimicrobials as prophylaxis in aquaculture in developing countries further contributed to the emergence of AMR causing problems in human, animal, and environmental health [57]. The risk to humans further exists especially when similar antimicrobial is used in both animals and humans, or there is presence of cross resistance between antimicrobial used in human and veterinary practice. Using antimicrobials that are also used in human medicine for growth promotion is especially conducive to AMR because exposure of many animals to low dosages makes resistance more likely to emerge [34].
For some antimicrobials, there is development of resistance by bacteria through plasmid-mediated transferable resistance [58]. The minimum inhibitory concentrations (MIC) for a target pathogen might be considerably different from those of commensals, and thus, the resistance gene in commensals may be selected and transferred to humans and then to human pathogens leading to development of AMR [59]. Despite the fact that the exchange of genetic materials and the short generation time of organism contributed to the development of AMR by many bacteria [60], some drugs such as penicillin still retains excellent activity on certain organism (e.g., Streptococcus agalactiae) after about 6 decades of usage [61].
AMR development can often be caused by inhibition of specific antimicrobial pathways such as cell wall synthesis, nucleic acid synthesis, ribosome function, protein synthesis, foliate metabolism, and cell membrane function by the organism [62, 63, 64]. The various steps involved in the production, distribution, prescription, dispensing, and finally consumption of the drug by human patient or its use in animal production often contributed to the emergence of AMR especially when there is imprudent or irresponsible practice along the supply chain [65]. Part of veterinary medical education is to understand how antimicrobials affect microorganisms, and how they can be used responsibly to protect human and animal health [66]. In food production systems, veterinarians are on the frontline when it comes to keeping nation’s food supply safe. Advances in animal health care and management have greatly improved food safety over the years and have reduced the need for antimicrobials in food production systems [67]. Nevertheless, antimicrobials are an important part of the veterinarian’s toolkit, and so veterinarians are aware that they should be used judiciously and in the best interest of animal and public health [66]. More importantly in the development of AMR is the quality of antimicrobials. Though difficult to implement, it has been suggested that incidence of microbial resistance can be reduced if the antimicrobials that are used in human health are not used in veterinary practice [68]. Moreover, the practices of mass treatment of all animals in a group when only one animal is sick (metaphylaxis) as well as the treatment of all animals when they are exposed to conditions that can make them likely to be ill (prophylaxis) will result in an increased antimicrobial use and as such would encourage the development of resistance [69].
Although the development of animal-related AMR is associated with the quality and quantity of antimicrobial usage in veterinary practice, there are other underlining factors that can influence AMR development:
Lack of awareness: in developing countries, there is little or no awareness or concern in the use of antimicrobials as compared to developed nations who recognize AMR as a global challenge. Omulo et al. [70] showed that only 24% of studies in Africa are related to AMR in animals or their products. In East Africa for example, despite the relevance of antibiotic procurement in health budgets, there was still a slow progress in research focusing on AMR of enteric pathogens. There is still lack of awareness among many veterinarians and other food producing personnel on the negative impact to human health as a result of extensive use of antimicrobials in animals [71].
Lack of information: the information is lacking in developing countries concerning the existence and prevalence of AMR in animals and animal products and the negative health consequence as well as the cost of AMR illness in people and animals.
Fake and substandard drugs: there is much concern over counterfeits and substandard drugs in animal health care, but there is insufficient data to understand its importance. Counterfeits and substandard products, which contain active ingredient at a lower level, will increase the chance of developing resistance. There is no comprehensive information on fake/substandard veterinary drugs.
Lack of adequate ‘One Health’ integration between animal and human healthcare: in developing countries, there is poor collaboration in healthcare sectors between human and veterinary practice especially on collection and sharing of data on antimicrobial usage. However, at international level, good collaboration exists in the area of AMR between human and animal world health bodies like WHO, OIE, and FAO.
Lack of substitution to the use of antimicrobials: alternative to the use of antimicrobials is lacking in developing countries unlike the developed nations that had successfully banned the use of antimicrobials as growth promoters and replaced with alternative growth promoters and good practice without having negative impact on the performance of their livestock industries. This could hardly be achieved in developing nations that have propensity to source antimicrobials from the black markets, which may be of poor quality, thus exacerbating the problem and creating a considerable increase in disease, with consequent mortality and morbidity losses [34].
4. Prevention of AMR in veterinary practice
In a bid to ensure measurable containment of AMR, there is a global formal declaration on AMR calling for the development of action plans on AMR by both international and national bodies. The Global Action Plan on Antimicrobial Resistance was approved in May 2015 by the World Health Assembly with the key strategies to increase global AMR awareness as well as developing policies that will attract more investment in the area of new medical interventions [72]. There is also a call to all Member States for establishing National Action Plans for AMR by 2017 of which about 57 countries have formalized such plans so far. The 2016 meeting of the UN General Assembly was another milestone focusing on multidisciplinary solution to the problems of AMR [73]. Moreover, the G20 called for the creation of a Global Research and Development (R&D) Collaboration Hub on AMR in July 2017 that could coordinate international funding efforts [74], and the search for the appropriate individuals to lead that hub began early this year. In line with the global agreement to develop National Action Plan on AMR, Nigeria (with some other developing nations) keyed into this agreement in 2017 through a ‘One Health’ approach [75] and then enrolled into a Global Antimicrobial Resistance Surveillance System (GLASS). The Action Plan addresses five strategic objectives:
improving awareness and understanding of AMR through effective communication, education, and training
strengthening the knowledge and evidence base through surveillance and research
reducing the incidence of infection through effective sanitation, hygiene, and preventive measures
optimizing the use of antibiotics in human and animal health
preparing the economic case for sustainable investment and increasing investment in new medicine, diagnostic tools, vaccines, and other interventions
Several challenges exist regarding AMR containment in developing country like Nigeria; however, the development of this action plan is an important positive step in the right direction as it aims to address the problem at all level of governance and society [75].
Veterinarians play an important role in limiting and minimizing the spread of antimicrobial resistance (AMR). Because vets are often the first point of contact for livestock owners seeking animal medical attention, they can therefore play a part in addressing the problem of AMR [45]. One of the ways to reduce the risk of transfer of AMR from animals to humans is by minimizing the zoonotic transfer of bacteria [76]. This could be achieved by practicing stringent hygiene in the farms and any meat processing plants including the abattoirs and the markets. Thorough and effective cooking of meat product can also reduce the risk of AMR [77]. There is need to strengthen the information resources in developing countries to support health workers, patients, animal owners, and attendants as well as the general public to help in reducing the risk of AMR arising from the use of antimicrobials in animals. This will enable the society to better understand the importance and value of antimicrobials. The excessive and inappropriate use of antimicrobials in veterinary practice should be discouraged. Because antimicrobials are an extremely valuable resource in livestock production, their prudent use in animals will continue to provide benefits to society and will help ensure high standards of welfare for those animals in the care of veterinarians [78]. Since exposure of bacteria to subtherapeutic concentrations of antimicrobials is thought to increase the speed of the selection of resistance, this should always be avoided [14, 15, 79]. Appropriate pharmacokinetic and pharmacodynamic relations for antimicrobials used in animals should be developed [12]. Optimal dosage strategies for eliminating zoonotic organisms in animals will reduce the risk of transferring resistance to humans [80].
According to Delia [34], broad consensus on the management of AMR in human and animal healthcare will require to:
reduce antibiotic use in humans and animals through public health improvement such as hygiene and sanitation, immunization, infection control, as well as good housing and environment.
regulate the sale and use of antibiotics through prescription.
encourage research and development of new antimicrobials.
minimize the level of environmental contamination of antimicrobials emanating from manufacturing process as well as agricultural, hospital, and community use.
develop integrated global policies on the use of antibiotics.
ban the nontherapeutic use of antimicrobials as growth promoters in agriculture.
Currently, there is no adequate information on animal production losses due to disease burden and the extent at which it could be prevented through proper use of antibiotics or their alternatives.
Although in Europe and other developed nations, the use of alternatives to antimicrobials as growth promoters is a success; their applicability in developing countries is not fully understood.
Despite the huge investments in the control of diseases in developing countries through vaccination, vector control, and the use of resistant breeds, evaluation from the angle of reduction in the usage of veterinary drug is lacking. In developing countries, the incidence and composition of substandard and fake drugs as well as their effects on treatment failure and resistance development is not well known. Similarly, the level of resilience of livestock farmers in developing countries to ban or restrict access to antimicrobials is equally not well known. It should be noted that policy and regulation alone is unlikely to improve use of vet drugs and the options for improving the use of vet drugs in agriculture and their effectiveness, feasibility, and affordability are not well understood.
4.1 Rational drug use
There have been success factors in the improvement of drug use in human health through wide range of intervention studies. Similarly, the World Animal Health Organization (OIE) and other world veterinary bodies also developed frameworks on rational use of vet drugs to which there is a limit veterinarians can make profit from antimicrobial sale for food animal production [71]. This is not the case in developing countries where the sale and use of veterinary antimicrobials is facing challenges for improvement. It was found from series of intervention studies that training remains the most common strategy for improving drug use, but this gave little success unless when combined with other strategies like changing the market condition [1].
4.2 Governance of antimicrobial use
Antimicrobial use in human and veterinary practice requires holistic approach in order to improve drug governance. There is need to list the critical or essential drugs in human and veterinary practice with requirement for prescription and guidelines such as banning the use of medically important antibiotics in agricultural practice and off-label use of antimicrobials as well as monitoring antimicrobial use and resistance. Not much success has been recorded in this regard in developing countries especially in livestock production and aquaculture due to little investments. According to OIE, better governance of veterinary antimicrobials comes from empowering veterinarians and limiting prescription to them. Most of the private veterinary service providers in developing countries are not operating at a significant scale and as such are often employed directly by agriculture and agro-allied companies making them to be less independent. The few that are successful are not operating with the guidelines of current OIE policy [81]. The community animal health workers (CAHWs), that have proven to be effective, are very expensive to train and may not be politically acceptable [82]. This is because there is lack of resources to support them by public veterinary services, and the private veterinarians often see them as potential competitors. A study investigated rational drug use by farmers and found that farmers in West Africa were mainly responsible for buying and using antimicrobials, and providing simple information on correct drug use could lead to improved drug usage as well as reduced amount of underdosages, which is an important factor for the development of AMR [83].
4.3 Antimicrobial alternatives in veterinary practice
As previously mentioned, developed countries banned the use of medically important antibiotics as well as growth promoters in animal production, which has led to better farming practices as well as reduction in AMR of medically important microbes found in farm animals. With this natural experiment, it demonstrated that routine antimicrobial usage is not a precondition for healthy animals as long as there is better hygiene and sanitation with good housing condition, and the use of antibiotics is only limited to clinical condition. The benefit of antimicrobials as growth promoters may sound reasonable only under poor management and hygiene situations [71]. Although the type of intensive livestock production in developing countries makes them rely more on nontherapeutic use of antimicrobials, there are many other promising innovations that could support profitable and productive agriculture with less reliance on antimicrobials use such as:
The use of nonantibiotic growth promoters like enzymes in feed, competitive exclusion products as well as probiotics and prebiotics
The use of other animal health technologies such as vaccines, vector control, disinfectants, phyto-therapy, as well as phage-therapy, which are underutilized in developing countries. The phage products can readily be designed to thwart development of resistance. They have been used as antibacterial agents for nearly 100 years in the former Soviet Union, and they are now undergoing a renaissance in other countries due to the growing AMR problem [33, 84, 85, 86, 87].
The use of robust diagnostic techniques for improved drug selection and identification of AMR pathogens
The management and bio-security innovations like all-in-all-out systems, pathogen-free systems, stocking density reduction, and improved waste management systems.
The use of genetically disease resistant animals as well as avoidance of monocultures of genetically similar animals.
All these intervention strategies will improve animal welfare as well as reducing environmental externalities of animal agriculture. A more radical suggestion is to decrease the amount of consumption of animal source food or shift from intensive to organic animal production.
5. Veterinary antimicrobial stewardship in developing countries
The safeguarding of antimicrobial agents for future generations is of utmost priority as AMR threatens the very core of modern medicines and the sustainability of an effective, global public health response to the enduring threats from infectious diseases [72]. In many developing countries of the world, gaps exist among health care professionals on the current status of antibiotic resistance in their area due to lack of a systematic surveillance at country, provincial, and district level [88]. There is a paucity of clinical data on antibiotic resistance, and this is particularly the case in resource-poor settings. Tons of antibiotics are used annually in clinical and agricultural settings worldwide. The estimates of the total annual global consumption of antimicrobials in animal production vary considerably due to poor surveillance and data collection in many countries [89]. In 2013, food animals alone consumed over 130,000 tons of antibiotics [90]. It cannot be ignored that two-thirds of the estimated future growth of usage of antimicrobials is estimated to be within the animal production sector, with use in pig and poultry production predicted to double [89]. Nigeria, Pakistan, India, Bangladesh, China, and Egypt are the developing countries with massive consumption of antibiotics [88].
The implementation of rational and restricted use of antibiotics is lacking in most developing countries where you have the largest market of antimicrobial drugs and reports of the highest rate of antibiotic resistance [86, 87]. Due to these developments, antimicrobial stewardship programs have emerged as an essential means to attenuate the threat of a real possibility of the specter of a “postantibiotic era” [91, 92].
Antimicrobial stewardship is a harmonized program (the optimal selection, dosage, and drug regimen) that fosters the proper use of antimicrobials (including antibiotics) with the goal of optimizing clinical outcomes, reducing microbial resistance, and lessening the spread of infections produced by multidrug-resistant organisms. The main objectives of antimicrobial stewardship are to attain excellent patient outcomes associated with antimicrobial use while reducing toxicity and other unfavorable events, thereby curbing the discriminatory pressure on bacterial population that propels the emergence of multidrug-resistant strains [93, 94].
Antimicrobial stewardship programs (ASPs) are a cornerstone of the response to the AMR crisis in human medicine but are still largely underdeveloped in veterinary medicine [95]. Antimicrobial stewardship is important to both animal health and food safety. Just like humans, animals get infections that require treatment with antibiotics. The rise of antimicrobial resistance is a serious threat to public health [30]. It is imperative that antibiotic stewardship programs seeking to preserve the effectiveness of existing antibiotics in human health also consider strategies that reduce overuse of antibiotics in the agricultural sector as antimicrobials are used in terrestrial animal production practices to preserve animal and public health, but also as growth promoters at a subtherapeutic level [89]. Other aspects to be considered with regard to antimicrobial use include the distinction between therapeutic and nontherapeutic use, between the diverse existing production systems and between specifics related to the different animal species and their eco-geographical location [72, 89].
According to the WHO, FAO, and OIE global tripartite database for antimicrobial resistance country self-assessment in 2016–2017, 42% of the countries on question regarding antimicrobial stewardship and regulation in animals and crop production responded that no national policy or legislation regarding the quality and efficacy of antimicrobials and their use in animals, and crops was available [101]. Responses to other veterinary-related questions showed a huge gap in the preparedness for combating AMR and also the lack of policy making and implementation of a successful antimicrobial stewardship program.
Various strategies have been shown to improve appropriateness of antimicrobial use and cure rates, decrease failure rates, and reduce healthcare-related costs in human hospitals [96, 97, 98]. According to Guardabassi & Prescott [95], the following successful strategies used in human hospitals can be adopted with focus on their implementation in veterinary practice.
educational approaches
development and implementation of guidelines
preprescription approval
postprescription review
computer-based decision support
It should be noted that one strategy does not exclude the other and that multiple strategies can be successfully used in combination.
A good antimicrobial stewardship program (ASP) needs remarkable input in research and training by all stakeholders including national and international veterinary organizations, funding concerns, and animal health industries [95]. At governmental levels, the growth and execution of ASPs need coordination of the task of national public health and veterinary authorities, veterinary clinics, organizations, and private practitioners. The concept of antimicrobial stewardship and of its continuous improvement is in its relative infancy in various sectors of veterinary practice in developing countries, but every veterinary component of the agricultural sector has the responsibility and access to a wide range of resources to develop an ASP.
Stewardship of antimicrobial drugs in human healthcare and veterinary settings is essential to slow the emergence of resistance and extending the useful life of effective antimicrobials according to FDA Center for Veterinary Medicine [99]. All developing countries should be committed to advancing efforts to implement good antimicrobial stewardship practices in veterinary settings as part of their role to protect human and animal health. Each program must be region-specific and constantly under review given that resistance patterns change, requiring changes to local policy of, for example, empirical antibiotic choice [100].
Therefore, the goals in all countries should be to align antimicrobial drug product use with the principles of antimicrobial stewardship, foster antimicrobial stewardship in veterinary settings, and enhance monitoring of antimicrobial resistance and use in animals to further preserve antimicrobial drugs to ensure human and animal health [99].
6. Conclusion and recommendation
Resistance to antimicrobial agents arises in some instance through excessive use in animals as chemotherapeutics, and as subtherapeutic additives in feeds. Prolong exposure of microorganisms to sublethal doses of antimicrobials can result in spontaneous emergence of resistance gene and its subsequent transfer among animals, environment and animal products in food chain, and transfer of resistance to human. A pragmatic approach to slow down the development of antimicrobial resistance is to control abuse of antimicrobials through a number of measures. First, it is important to recognize that veterinary pharmaceuticals are important beyond animals and include human health and the environment, hence the need for “One Health” guidiance and regulation. Secondly, it is necessary to reduce drugs that are used as prophylaxis and should rather improve research and innovation for vaccine development, application and explore other alternatives to chemotherapies. The use of feed grade antibiotics and additives in feed as growth promoters also need to be discouraged in developing countries and instead promote organic, home grown livestock husbandry to complement intensive and factory farming. Alternatives to growth-promoting and prophylactic uses of antimicrobials in agriculture include improved management practices, wider use of vaccines, probiotics, and phage virus. Monitoring programs, prudent usage that are controlled, and educational campaigns are some of the approaches that can minimize further development of antimicrobial resistance in developing countries especially. These can be achieved through mutual and ‘One Health’ understanding of the challenges and informed solution through antibiotic stewardship by promoting collective action of all parties with interest including producers, consumers, and mediators.
\n',keywords:"veterinary drugs, antimicrobial resistance, stewardship, developing countries",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66512.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66512.xml",downloadPdfUrl:"/chapter/pdf-download/66512",previewPdfUrl:"/chapter/pdf-preview/66512",totalDownloads:821,totalViews:0,totalCrossrefCites:5,dateSubmitted:"August 23rd 2018",dateReviewed:"February 1st 2019",datePrePublished:"June 5th 2019",datePublished:"March 11th 2020",dateFinished:null,readingETA:"0",abstract:"Veterinary pharmaceuticals include a wide range of anti-infectives and additives in the use for animal health, nutrition, reproduction, and productivity. Antimicrobials are among the most extensively used drugs in developing countries largely due to large population of livestock and the burden of infectious diseases. The introduction of penicillin in 1943 and other antibiotics thereafter provided remedies for many infections in humans and animals, reducing mortality and productivity losses. Since then, a repertoire of antibiotics and antimicrobials has been introduced as chemotherapeutics and/or prophylaxis. This success notwithstanding, many pathogens of consequences are no longer susceptible owing to emergence of antimicrobial-resistant (AMR) microorganisms. This has made treatment of infectious diseases less effective. Beside spontaneous emergence of mutant microorganisms, scientists are wary of AMR caused by intensive use of antibiotics in humans and animals, sometimes in subtherapeutic doses as preventive medicine. In developing countries, environmental exposure and persistent use of antibiotics in food animals may leave residues in the food chain. The consequences include development of AMR. In this chapter, we reviewed antimicrobial use in veterinary medicine and sequela in the emergence of AMR and described the imperative of antimicrobial stewardship in veterinary practice to combat AMR in developing countries.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66512",risUrl:"/chapter/ris/66512",signatures:"Meseko Clement, Makanju Olabisi, Ehizibolo David and Muraina Issa",book:{id:"8634",title:"Veterinary Medicine and Pharmaceuticals",subtitle:null,fullTitle:"Veterinary Medicine and Pharmaceuticals",slug:"veterinary-medicine-and-pharmaceuticals",publishedDate:"March 11th 2020",bookSignature:"Samuel Oppong Bekoe, Mani Saravanan, Reimmel Kwame Adosraku and P K Ramkumar",coverURL:"https://cdn.intechopen.com/books/images_new/8634.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"186990",title:"Dr.",name:"Samuel Oppong",middleName:null,surname:"Bekoe",slug:"samuel-oppong-bekoe",fullName:"Samuel Oppong Bekoe"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",fullName:"Clement Meseko",slug:"clement-meseko",email:"cameseko@yahoo.com",position:null,institution:{name:"National Veterinary Research Institute",institutionURL:null,country:{name:"Poland"}}}],sections:[{id:"sec_1",title:"1. Introduction: veterinary pharmaceuticals and antimicrobials",level:"1"},{id:"sec_2",title:"2. Livestock diseases and the application of veterinary pharmaceuticals",level:"1"},{id:"sec_3",title:"3. Development of AMR in veterinary practice",level:"1"},{id:"sec_4",title:"4. Prevention of AMR in veterinary practice",level:"1"},{id:"sec_4_2",title:"4.1 Rational drug use",level:"2"},{id:"sec_5_2",title:"4.2 Governance of antimicrobial use",level:"2"},{id:"sec_6_2",title:"4.3 Antimicrobial alternatives in veterinary practice",level:"2"},{id:"sec_8",title:"5. Veterinary antimicrobial stewardship in developing countries",level:"1"},{id:"sec_9",title:"6. Conclusion and recommendation",level:"1"}],chapterReferences:[{id:"B1",body:'Shah NM et al. Can interventions improve health services from private providers in low and middle-income countries? A comprehensive review of the literature. Health Policy and Planning. 2011;26(4):275-287. 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Strategies to reduce the use of antibiotics in animals. The Pharmaceutical Journal. 2014;293(7836). DOI: 10.1211/PJ.2014.20067064. Available from: https://www.pharmaceutical-journal.com/news-and-analysis/features/strategies-to-reduce-the-use-of-antibiotics-in-animals/20067064.article?firstPass=false'},{id:"B77",body:'Brad S, Gail RH, Avinash K, Carmen DC, Lance BP, James RJ. Antibiotic Resistance in Humans and Animals. Discussion Paper of National Academy of Medicine. 2016. Available from: https://nam.edu/wp-content/uploads/2016/07/Antibiotic-Resistance-in-Humans-and-Animals.pdf'},{id:"B78",body:'Barza MD et al. The need to improve antimicrobial use in agriculture: Ecological and human health consequences. Barza M, Gorbach SL, editors. Clinical Infectious Diseases. 2002;34(Suppl 3):S76-S77. Arlington, VA: Infectious Diseases Society of America'},{id:"B79",body:'Lessar TS, Rotschafer JC, Strand LM, Solem LD, Zaske DF. Gentamicin dosing errors with four commonly used nomograms. 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Bacteriophage. 2011;1:66-85'},{id:"B85",body:'Kutter E, De Vos D, Gvasalia G, Alavidze Z, Gogokhia L, Kuhl S, et al. Phage therapy in clinical practice: Treatment of human infections. Current Pharmaceutical Biotechnology. 2010;11:69-86'},{id:"B86",body:'Reardon S. Phage therapy gets revitalized. Nature. 2014;510:15-16'},{id:"B87",body:'Reardon S. Antibiotic resistance sweeping developing world. Nature. 2014;509:141-142'},{id:"B88",body:'Syed MA, Bana NF. Developing countries need action plans to combat the challenge of antimicrobial resistance. iMedPub Journals. 2016;7(2):12. Available from: http://www.acmicrob.com/'},{id:"B89",body:'FAO. Antimicrobial Resistance: Animal Production. 2018. Available from: www.fao.org/antimicrobial-resistance/key-sector/animal-production/en. [Accessed: 03 November 2018]'},{id:"B90",body:'Van Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, et al. Reducing antimicrobial use in food animals. Science. 2017;357(6358):1350-1352'},{id:"B91",body:'Gallagher J. Antibiotic Resistance: World on Cusp of ‘Post-Antbiotic Era’. BBC News, Health; 2015. Available from: www.bbc.co.uk'},{id:"B92",body:'O’Brien DJ, Gould IM. Maximizing the impact of antimicrobial stewardship: The role of diagnostics, national and international efforts. Current Opinion in Infectious Diseases. 2013;26:352-358. DOI: 10.1097/QCO.0b013e3283631046'},{id:"B93",body:'Gerding DN. The search for good antimicrobial stewardship. Joint Commission Journal on Quality Improvement. 2001;27(8):403-404'},{id:"B94",body:'Neil F, Society for Healthcare Epidemiology of America, Infectious Diseases Society of America. Policy statement on antimicrobial stewardship by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), and the Pediatric Infectious Diseases Society (PIDS). Infection Control and Hospital Epidemiology. 2012;33:322-327'},{id:"B95",body:'Guardabassi L, Prescott JF. Antimicrobial stewardship in small animal veterinary practice: From theory to practice. Veterinary Clinics: Small Animal Practice. 2015;45:361-376. DOI: 10.1016/j.cvsm.2014.11.005'},{id:"B96",body:'MacDougall C, Polk RE. Antimicrobial stewardship programs in health care systems. Clinical Microbiology Reviews. 2005;18:638-656'},{id:"B97",body:'Owens RC Jr. Antimicrobial stewardship: Concepts and strategies in the 21st century. Diagnostic Microbiology and Infectious Disease. 2008;61:110-128'},{id:"B98",body:'Tamma PD, Cosgrove SE. Antimicrobial stewardship. Infectious Disease Clinics of North America. 2011;25:245-260'},{id:"B99",body:'Food and Drug Administration-Centre for Veterinary Medicine (FDA-CVM). The Judicious Use of Medically Important Antimicrobial Drugs in Food-Producing Animals; 2012. Available from: https://www.fda.gov/downloads/AnimalVeterinary/GuidanceComplianceEnforcement/GuidanceforIndustry/UCM216936.pdf'},{id:"B100",body:'Aryee A, Price N. Antimicrobial stewardship—Can we afford to do without it? British Journal of Clinical Pharmacology. 2014;79(2):173-181. DOI: 10.1111/bcp.12417'},{id:"B101",body:'WHO, FAO, OIE. Global Tripartite Database for Antimicrobial Resistance Country Self-Assessment. Export of Year One Data 2016-2017. 2017. Available from: https://amrcountryprogress.org/ [Accessed: 23 May 2017]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Meseko Clement",address:"cameseko@yahoo.com",affiliation:'
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