Maximum deflections measured by laser sensor.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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About 80–90% of failures in steel structures are related to fatigue and fracture [1]. Despite the deterioration caused by environmental factors, the increasing traffic volume and weight pose a premier threat to steel highway bridges. The total number of truck passages in the 75 year life of a highway bridge could exceed 100 million [2]. With the aging of existing steel highway bridges and the accumulated damage under truck loading, the fatigue assessment for continuing service has become important for decision makings on the structure maintenance, component replacement, and other major retrofits.
\nGiven the uncertainties of the complicated traffic loading and the complexity of the bridge structure, fatigue evaluation based on field measurements under actual traffic flow is recommended by many researchers. As the quality and quantity of the available long-term traffic monitoring data and information have been improved, a set of methodologies has been developed to obtain a more realistic vehicular live load. The knowledge of actual traffic loading may reduce the uncertainty involved in the evaluation of the load-carrying capacity, estimation of the rate of deterioration, and prediction of remaining fatigue life. However, there are still some difficulties in field measurements. For example, some highway bridges are not accessible for field tests; the maintenance of monitoring system is difficult and costly, especially for long-term monitoring; some highway bridges will not even be considered for field tests with economic concerns.
\nThe results of several NCHRP reports, written by Dr. John Fisher in 1970s, have confirmed that for welded details, fatigue life is primarily a function of stress range, detail category, and the number of applied cycles. The live load of a bridge includes static and dynamic parts while the early research and studies focused on the static portion. Schilling [3] and Raju et al. [4] suggested to improve the accuracy of the fatigue truck model by adjusting the fatigue truck axle weights in proportion to an equivalent total weight calculated from the specific site load distribution. The collected weigh station measurements, or data measured in stationary weight scales, were used by Nowak et al. [5] to determine the truck-load spectra for highway bridges on highways I-75 and I-94. Later, Laman and Nowak [6] developed a fatigue-load model from weigh station measurements and calculated the statistical parameters of stress for girder bridges. The results indicated that magnitude and frequency of truck load spectra are strongly site-specific and the live load stress spectra are strongly component-specific. With the advantage of weigh-in-motion (WIM) technology, Miao and Chan developed a methodology by using 10 year WIM data for deriving highway bridge live load models for short span bridges in Hong Kong [7]. NCHRP developed a set of protocols and methodologies for using available nation-wide, state-specific, or site-specific truck traffic data collected at different U.S. sites to obtain live load models for LRFD superstructure design, fatigue design, deck design, and design for overload permits [8].
\nIn the early studies, it was commonly assumed that a certain percentage of the total weight was loaded on the front axle or rear axle for the magnitude and configuration. Further, there was no real traffic simulation considering the truck flow pattern. Bridge behavior simulations under truck loading were usually performed using the Monte Carlo method, which is a statistical projection approach with generic nature and does not consider any vehicle and driver behavior models when simulating truck traffic flow. In recent years, traffic flow simulation method has been applied to provide instantaneous information of individual vehicle by many researchers. Chen and Wu developed a general framework of modeling the live load from traffic for a long-span bridge by using the cellular automation (CA) traffic flow simulation technique. A typical four-lane long-span bridge was studied using the proposed method. Each lane was divided into cells with an equal length of 7.5 m. Three conditions, the free flow, the moderate flow, and the congested flow, were considered in the simulation. A simple comparison between the simulated static traffic load and the AASHTO LRFD HL-93 design load was conducted. The results showed that the HL-93 may be insufficient for the congested flow condition [9].
\nThis research has developed a framework for the fatigue assessment of steel highway bridges based on simulated truck loading. The proposed methodology is implemented on a steel highway girder bridge on interstate 270 (I-270) over Middlebrook Road in Germantown, Maryland. With the help of the available long-term monitoring traffic data, truck loading was also obtained through the probability-based model. Then, the three-dimensional finite element (FE) global bridge models were studied subjected to the simulated truck loading. Meanwhile, the preliminary field test and the long-term monitoring test were also conducted. The FE models were calibrated with the collected field measurements through monitoring systems, and the simulated numerical structural responses were validated. Lastly, this model has been used for identifying the cause of fatigue cracks reported in the biennial bridge inspection. Thus, the proposed methodology could be used to realistically simulate the fatigue behavior of steel highway bridges under current or future truck loading, to direct the experimental designs and instrumentation plans before performing experiments on laboratory or on site, and to better understand the fatigue mechanism and prevent the fatigue damage of steel highway bridges.
\nThe I-270 Bridge over Middlebrook road (MD Bridge No.15042) is a simple-span composite steel I-girder bridge with a span length of 140 ft. This bridge is comprised of two structures for the northbound (NB) and southbound (SB) roadways respectively, separated at the centerline. It carries three traffic lanes in the southbound and four traffic lanes in the northbound with equal lane widths of 12′-0″.
\nThe southbound superstructure provides a curb-to-curb roadway width of 61′-2″ and consists of eight identical welded steel plate girders with a composite reinforced concrete deck constructed with shear connectors. The eight girders are equally spaced at 7′-11″ and each girder has a constant web depth of 60″ throughout the entire bridge. The northbound superstructure provides a curb-to-curb roadway width of 73′-1″ and consists of nine identical welded steel plate girders with a composite reinforced concrete deck constructed with shear connector. The nine girders are equally spaced at 8′-5″ and each girder has a constant web depth of 60″ throughout the entire bridge. This bridge has a 76 degree parallel skew of its bearing lines (or 14 degree measured from normal). The cross-frames are inverted K-type braces with bottom chords only. All of them are parallel to the bearing lines. Girders of the southbound superstructure are numbered as G1 through G8 from the exterior to the centerline of the bridge. The cross section is depicted in Figure 1.
\nCross section with lane positions.
Designed in 1988, the I-270 Bridge over Middlebrook Road has been in-service for over 20 years. In addition to the deterioration caused by environmental factors, the bridge structure has also been subjected to increasing traffic volume and weight. Four fatigue cracks as marked on Figure 2 were reported in the June 2011 Bridge Inspection Report, and all in the welded connection between the lower end of the cross frame (Figure 3) connection plate and the girder bottom flange of the southbound superstructure. Figure 4(a) shows one of the four crack locations at G3B2D3 (Girder 3 Bay 2 Diaphragm 3). Therefore, only the southbound superstructure will be discussed in the following sections. Most bridge components with fatigue cracks are repaired or replaced shortly after the crack is found in an inspection. However, since the crack on the I-270 Bridge was identified on a secondary bridge member, and delaying repair would not jeopardize the safety of drivers, this crack was selected for research purposes and long-term monitoring.
\nCrack locations and sensor placements on the framing plan.
Cross frame detail.
Crack locations and sensor placements: (a) details at G3B2D3 and (b) details at G3B3D3.
The field test of the I-270 Bridge was conducted through a Wireless Integrated Structural Health Monitoring System sponsored by the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R). This smart bridge condition monitoring system, termed the ISHM system, features a number of technology innovations, including remote sensing capability, piezo paint acoustic emission sensors, wind and solar based energy harvesting devices to power the sensor network, high-speed wireless sensing ability and advanced data analysis methods for remaining life estimation of aging bridges. Through successful advancement and commercialization in the state-of-the-art technology of remote infrastructure sensing, the ISHM system is promising to reduce life cycle costs while significantly maintaining the sustainability of the highway infrastructure in the US.
\nThe main data acquisition systems used in this test consisted of a PXI-based data acquisition system by National Instruments, which was used for data collection by the BDI strain transducers, string pots and Acoustic Emission (AE) sensors, and a multi-channel data acquisition equipment CR5000 manufactured by Campbell Scientific, Inc., which was used for the extra BDI strain transducer. Types of sensors used in this project were: (1) piezoelectric paint AE sensors; (2) wireless accelerometers; (3) laser sensor; (4) ultrasonic distance sensors; (5) BDI strain transducers; and (6) string pots. Sensors were strategically placed where the cracks on the SB bridge were identified and their related strain, AE or supplemental data can be collected by the data acquisition system and later used for validating the FE models. The instrumentation plan is shown in Figure 2. Girder displacement and stress range records due to truck traffic were part of the field measurements in this study.
\nA total of four wireless accelerometers were used to monitor the vibration responses of the bridge. Wireless sensors were installed on four girders (Girders 2–5) and acceleration data were acquired at 100 Hz sampling rate synchronically. The acceleration data were used to provide modal frequency information that was used to calibrate the finite element model of the bridge. The fundamental frequency measured is 3.22 Hz, which was very close to the value of the first vertical mode from the finite element analysis of 3.24 Hz discussed in the following sections.
\nBoth laser sensor and ultrasonic distance sensors were used to measure the dynamic deflection of the bridge. Only one laser sensor and one ultrasonic distance sensor were used each time. The measured deflection value from the laser sensor agreed well with the string pot, and its accuracy was also validated by the fundamental frequency indicated by fast Fourier transform (FFT) of the laser sensor measured deflection data. The measured maximum deflection of the I-270 bridge over Middlebrook Road under traffic loading is summarized in Table 1.
\nGirder number | \n3 | \n4 | \n5 | \n
---|---|---|---|
Max D (in) | \n0.2598 | \n0.2717 | \n0.2480 | \n
Maximum deflections measured by laser sensor.
Cracks occur in the direction perpendicular to the direction of principal tensile stress. To assess the driving force of the fatigue cracks in the connection welds, strain gages were placed vertically on the connection plate just beyond the tip of the existing crack. Strain gages were also placed longitudinally on the girder flanges to correlate with the occurrence of vehicular loads. For comparison with the results from analytical methods, field testing is applied as it is the most accurate approach since no assumptions need to be made for uncertainties in load distribution such as unintended composite action between structural components, contribution of nonstructural members, stiffness of various connections, and behavior of the concrete deck in tension. The actual strain histories experienced by bridge components are directly measured by strain gages at the areas of concern. The effects of varying vehicle weights and their random combinations in multiple lanes are also reflected in the measured strains.
\nBDI 1-4 strain transducers were placed on both sides of the connection plates while BDI 5-8 strain transducers were placed at the top and bottom flanges on Girders 3 and 4 (Figure 2). Figure 5 shows the measured stresses on the flanges and connection plates, respectively. The maximum stress measured at the bottom flange was 1.604 ksi in tension for BDI 3215 on the bottom flange of Girder 3 due to regular traffic loading, which was very low comparatively. As for the connection plates, the maximum stresses were 16.18 ksi in tension for BDI 1641 on Girder 3 and 16.1 ksi in tension for BDI 1644 on Girder 4 (Figure 5).
\nBDI strain transducer measurements of connection plates and flanges (positive indicates compression; 1641 G3 cracked side; 1642 G3 uncracked side, 1643 G4 uncracked side and 1644 G4 cracked side).
The traffic data that is used to simulate the traffic flow were the time-varying vehicle count data obtained from the Traffic Monitoring System Program (TMSP); operated and maintained by the Highway Information Services Division under Maryland Department of Transportation, State Highway Administration (MDSHA) [10]. The TMSP has been responsible for the collection, processing, analysis, and management of Maryland highway traffic data since 1997. Under this program, MDSHA has implemented 79 permanent continuous automatic traffic recorders (ATRs) counting traffic continuously throughout the year, and over 3800 short-term (48 hour) program count locations throughout the state, with data taken during the week on either Tuesday and Wednesday or Wednesday and Thursday to reflect typical weekday travel patterns. These monitoring systems are installed across Maryland and monitor most of the arterials, freeways, and interstates. Figure 6 displays the location of several ATRs on I-270.
\nObservation points on I-270 in Montgomery County.
The TMSP has also created an Internet Traffic Monitoring System (I-TMS) that provides access to detailed traffic count data. On the I-TMS, the user can select an individual location to view reports (class, volume, lane distribution, etc.). Based on the hourly traffic volume, one typical day was divided into four different time periods: midnight, early morning and night, morning peak hour, and noon to evening, as shown in Table 2. The durations for these four time periods are 5, 5, 5, 9 hours, respectively. The average hourly volume varied from 505 to 4215, and the truck percentage also varied from 10.39 to 20.10%. Lane distribution of I-270 Bridge over Middlebrook Road is shown in Table 3. The main purpose of this time division is to realistically simulate the major characteristics of the traffic flow for each time period.
\nTime period | \nTime | \nAverage total volume (no. of vehicles per hour) | \nPassenger car (no. of vehicles per hour) | \nTruck by axle number (no. of vehicles per hour) | \nTruck percentage | \n||||
---|---|---|---|---|---|---|---|---|---|
2 | \n3 | \n4 | \n5 | \n6 | \n|||||
Midnight | \n23:00–24:00 0:00–3:00 (5 hours) | \n505 | \n403 | \n25 | \n2 | \n3 | \n69 | \n3 | \n20.10% | \n
Early morning and night | \n4:00–5:00 19:00–23:00 (5 hours) | \n1934 | \n1712 | \n55 | \n4 | \n7 | \n150 | \n6 | \n11.40% | \n
Moring peak | \n5:00–10:00 (5 hours) | \n4215 | \n3759 | \n113 | \n7 | \n14 | \n310 | \n12 | \n10.82% | \n
Noon to evening | \n10:00–19:00 (9 hours) | \n3021 | \n2707 | \n78 | \n5 | \n9 | \n213 | \n8 | \n10.39% | \n
Traffic condition under different time period.
Vehicle type | \nLeft lane (%) | \nMiddle lane (%) | \nRight lane (%) | \n
---|---|---|---|
Total (passage car and truck) | \n31.87 | \n30.62 | \n37.51 | \n
Truck | \n1.45 | \n44.84 | \n53.71 | \n
Lane distribution of one typical day.
Following the specifications in the Guide Specifications for Fatigue Evaluation of Existing Steel Bridges (1989), weigh station measurements were collected at Hyattstown Weigh Station. From these measurements, a gross-weights histogram was obtained for the truck traffic, which was used to calculate the effective gross weight of the fatigue truck. The Hyattstown Weigh and Inspection Station is located approximately 10 miles north of I-270 Bridge over Middlebrook Road, along Interstate 270 (I-270). Around 2200 samples during 1 year were chosen as the database to generate the truck weight and configuration. The measured data were filtered before the statistical analyses were made, where five samples were deleted. All the trucks were cataloged into seven classes based the number of axles (Figure 7). It is clear that 2-axle trucks and 5-axle trucks were the majority, which occupies about 24.87 and 67.99%, respectively. The 3-axle trucks, 4-axle trucks and the heaviest 6-axle trucks and over accounted for 1.61, 2.98 and 2.55%, respectively, which adds up to 7.14% in total.
\nTruck class distribution.
Because of the limitation of the traffic simulation software CORSIM, only three different types of trucks can be defined during traffic simulation: the small truck, the medium truck, and the large truck. Since 2-axle trucks and 5-axle trucks were the majority, the small truck was defined to consist 2-axle trucks and 3-axle trucks, and the medium truck to include 4-axle trucks and 5-axle trucks. For safety consideration, the heaviest 6-axle trucks and over were also considered as the third type, although it only takes a very small percentage.
\nThe effective gross weight of the fatigue truck was computed from Eq. (1)
where fi is the fraction of gross weights within an interval and Wi is the midwidth of the interval. The gross weight was distributed to axles in accordance with the site data. The final fatigue truck configurations were shown in Figure 8.
\nFatigue truck configurations: (a) small truck; (b) medium truck; and (c) large truck.
As an intercorrelated component of a whole transportation network, the actual traffic flow through a bridge is affected by the traffic on the connecting roadway segments. Therefore, to realistically capture the major characteristics of the traffic flow, a road network system consisting of the bridge, highway, and two neighboring ramps was studied in the present work. The detailed procedure is summarized in four steps:
Build the simulation network (Figure 9) in TSIS 5.1 [11] around the I-270 over Middlebrook Road based on the background map obtained from Google Maps. The background map is adjusted to the correct scale, and the simulation network is drawn along the real roadway segment. The network contained the mainline of I-270 and adjacent on-ramps of the bridge in the study. Since the focus is on the southbound of the bridge, the network only contains one-way southbound link. The simulation time is set to be 1 hour.
Use the time varying vehicle count data collected from nearby detectors, which were placed on the I-TMS website, and combine with the weigh station measurements collected from the Hyattstown southbound station as the input data for the simulation model. The truck count data from the vehicle count report are converted to truck percentage (truck count/total vehicle count) as the input for CORSIM simulation.
Set three different types of trucks corresponding to fatigue trucks generated in the last section. Install three loop detectors at the bridge in the created simulation network, one for each lane to record the speed, type, and passage time of the detected vehicles.
After the simulation network is created, the traffic demand is input and calibrated, and the detectors are installed, the CORSIM simulation can begin. The simulation provides the following meaningful results for the analysis. First, it records the animation of the simulation, which is used to observe the passage time of the trucks. Second, it provides text output including the volume and speed statistics by each interval (set to be 1 s here). Combining the above two outputs, the passage time and the lane occurred and speed of the truck could be successfully matched.
Traffic simulation network.
The results (Table 4) could provide vehicle position and speed at each time step of the simulation. It was found from the results that the 20 min simulation period currently used can lead to stable pattern and matched field monitoring results. Details of the field monitoring are reported in a separate companion paper, “Integrating Bridge Management Systems with Fatigue Damage Assessments.”
\nTime periods | \nCORSIM | \n||
---|---|---|---|
Average speed (mph) | \nNumber | \nAverage headway (s) | \n|
Midnight | \n53.69 | \n32 | \n37.5 | \n
Early morning and night | \n52.94 | \n78 | \n15.38 | \n
Morning peak | \n35.45 | \n165 | \n7.27 | \n
Noon to evening | \n42.07 | \n98 | \n12.24 | \n
CORSIM simulation results.
To investigate the fatigue performance of the bridge, a three-dimensional finite element model was developed for linear-elastic structural analysis using the CSiBridge [12], as depicted in Figure 10. The model of the southbound superstructure consisted 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-, z-directions were fixed at the abutments to represent the actual characteristics of support and continuity. It is complicated to establish a comprehensive finite element model of a large practical structure for fatigue damage analysis, since the finite element model should embody the sectional properties of structural members (e.g., the weld between two members). In addition, fatigue damage is a local failure mode and often occurs around welded regions. Therefore, a global model with refined meshing around the welded connection between the connection plates and the bottom flanges was constructed for analysis.
\nFinite element model of I-270 Bridge in CSiBridge: (a) isometric view of FEM for I-270 Bridge and (b) zoom-in view (refined meshing around the welds).
The accuracy of a finite element analysis depends on the mesh size of the elements; the smaller the size of the elements, the greater the accuracy of the analysis. However, the desire for increased calculation accuracy can significantly increase the computational time. Therefore, in finite element analysis, a convergence test is used to determine appropriate mesh size for a model without increasing the computation time. The measurement of a finite element model’s mesh size depends on the purpose of the model. Since this bridge model is to investigate the vertical stress or shear stress in the cracked connection weld, it needs to have a very fine mesh in the connection area but needs to also transit gradually to coarser meshes because otherwise the model would become unnecessarily too large. A more uniform mesh may then be used along the rest of the bridge length for all the girders. However, there are multiple parameters related to the accuracy of a two-dimensional or three-dimensional finite element model, including the dimensions and aspect ratios of the elements for the girder top flange, bottom flange, and web, as well as the bridge deck.
\nTo simplify the convergence test for these finite element models of the I-270 Bridge over Middlebrook Road, a consistent refined mesh around the weld region was employed in all the models, and the maximum element size was used to control the uniform mesh along the bridge longitudinal length for all the girders and the deck. The determination of the first natural frequency was used as the measurement during the convergence test. As the maximum mesh size changed from 1000 in to 0.5 in, the results of the first natural frequency gradually increased from 2 to 3.20 Hz. The results of the first natural frequency were all beyond 3 Hz when the maximum mesh size of the finite element models was smaller than 200 in, which means that the error rate of the first natural frequency was under 6.25%. When the maximum mesh size was equal to or less than 50 in, the results of the first natural frequency were accurate enough with an error rate less than 2%, and were therefore used as the basis for the selection of an accurate finite element mesh in CSiBridge.
\nModal analysis is used to determine the vibration modes of a structure. These modes are useful to understand the behavior of the structure. They can also be used as the basis for modal superposition in response-spectrum and modal time-history load cases. An eigenvector analysis was used to determine the undamped free-vibration mode shapes and frequencies of the system.
\nThe first six mode shapes of torsion, vertical and lateral modes are shown in Figure 11. To validate the finite element models, experimental data from the field test and numerical results from CSiBridge were studied. In the numerical study, the bridge was only subjected to dead load. The results obtained from the finite element model and field measurements were compared, and the differences of most of the compared frequencies were less than 6%, which was considered acceptable for the finite element analysis. All the mode shapes matched well with each other. Therefore, the CSiBridge model was considered reasonably accurate for the purposes of this study.
\nMode shapes of I-270 Bridge over Middlebrook Road in CSiBridge: (a) mode shape 1 (first torsion), (b) mode shape 2 (first vertical), (c) mode shape 3 (second torsion), (d) mode shape 4 (first lateral), (e) mode shape 5 (second vertical), and (f) mode shape 6 (third torsion).
Possible driving forces for the fatigue cracks shown in Figure 4 are vertical tensile stress, horizontal shear stress, or the principal tensile stress due to their combined actions along the connection welds. Live load induced stresses from the welded connections between the cross-frame connection plates and the girder bottom flanges were extracted in the refined portion of the finite element models. A total of four different traffic loading cases obtained from the traffic simulation were studied as described below and the key results were summarized in Table 5. For all the four cases analyzed, the longitudinal positions of trucks remained the same as for the previous deflection studies.
\nTime period | \nMax vertical stress | \nMax shear stress | \nMax principal stress | \n
---|---|---|---|
Midnight | \n6.665 | \n2.165 | \n7.629 | \n
Early morning and night | \n7.586 | \n2.563 | \n8.70 | \n
Morning peak | \n12.94 | \n4.327 | \n14.84 | \n
Noon to evening | \n7.905 | \n2.664 | \n9.061 | \n
Stresses in cross frame connection plate-to-girder bottom flange connections at G3 without dynamic impact (FE results) (ksi).
There were many live load cases that could have produced significant tensile stresses in the connections of concern. The simulated truck loading contained most of the possible truck loading patterns. Magnitudes of tensile stresses in the connection plates depend on the magnitudes and positions of the wheel loads of crossing vehicles. The stresses listed in Table 5 are for illustration and are taken from the connection plates at Girder 3 for the four different time periods. A comparison of live load cases for the four different time periods suggest that live loads during morning peak may have caused the highest tensile stress of 12.94 ksi in the connection of concern. All the shear stresses in the connection welds were much lower than the vertical stresses at the same spot during each time period. Considering a factor of dynamic load allowance, the dynamic maximum vertical stress was 16.822 ksi, which perfectly matched with the field measurements.
\nThe results of the finite element analysis were verified and validated with the field test data; all the cracks were located on the western sides of the connection plates. The vertical stress near the welded edges of the connection plates followed the same pattern; the western sides of the connection plates were under tension; and the eastern sides of the connection plates were under compression. To further discuss the cause of this phenomenon, a series of controlled FEM tests were established for the comparison study.
\nAccording to the design drawings and the existing bridge construction, cross-frame connection plates and bearing stiffeners are normal to the girders and connection plates connecting the cross-frames are bent. Therefore, for the original FE model, all the connection plates were normal (90°) to the girders and the cross-frames are parallel to the two abutments with a skew angel of 76°. For the controlled model, all the connection plates were parallel to the cross frames with the same skew angel of 76 degree (Figure 12).
\nSkewed (right) and non-skewed (left) connection plates.
The K-type bracing system was modeled for studying the influence of the bracing system configuration on the stress distribution in the connection plates. The K-type cross-frame without top chord was modeled in the original FE model, while the K-type cross-frame with top chord was modeled in the controlled model. The cross section of the diagonal and bottom chords was employed for the additional top chords (Figure 13). All the models were subjected to the same live load case. The live load case was defined as an HS20 truck in the right traffic lane passing across the bridge from north to south at the speed limit of 55 mph. The vertical stress at the crack location (Girder 3 Diaphragm 3) and the axial forces in the top chord located at Diaphragm 3 Bay 2, directly connecting with the crack side, were analyzed and are shown in Table 6. Maximum vertical stresses in the model with the non-skewed connection plates were much higher than the stresses in the model with the skewed connection plates. The maximum axial forces in the models during the load time history analysis were quite small; the values were only 3.47 and 1.12 kip. The values of maximum vertical stresses did not change much due to the addition of a top chord. It demonstrates that the connection plate configuration has a significant influence on the stress distribution in the connection plates, while the top chord of K-type bracing plays a negligible role in this situation. Further, the results showed that X-type or K-type bracing made no difference on the vertical stress at the crack location.
\nConnection plates configuration | \nBracing system configuration | \nMaximum axial force (kip) | \nMaximum vertical stress of crack location (ksi) | \n
---|---|---|---|
Non-skewed connection plats | \nK-frame without top chord | \n— | \n13.50 | \n
K-frame with top chord | \n3.47 | \n12.66 | \n|
Skewed connection plats | \nK-frame without top chord | \n— | \n0.33 | \n
K-frame with top chord | \n1.12 | \n0.30 | \n
Maximum vertical stress and axial force through simulated numerical analyses.
K-frame without top chord (left) and K-frame with top chord (right).
The measured high vertical tensile stress around the connection plate welds was proven caused by the configuration of the connection plates instead of the configuration of the cross-frames. The connection plates, which were bent to be parallel to the skewed abutment, induced torsion in the connection plate welds. Differential displacements between the girders caused one diagonal cross frame to be in tension and the other diagonal to be in compression. Measured vertical tensile stresses from field tests up to 16.1 ksi in the connection plate explains why fatigue cracks have occurred at their connections to the girder bottom flange. Girders 3 and 4 are located under the slow-moving lane which most heavy trucks are using while Girders 1 and 2 support a shoulder and thus larger differential deflections typically occur between Girders 2 and 3 (with up to 0.5″ to 0.75″ vertical deflections due to observed live load). The connection plate configuration is a key factor in the stress distribution that results in the connection plates.
\nThe passage of trucks on the bridge deck can cause vertical tensile stresses in the welded connections between cross-frame connection plates and girder bottom flanges. These stresses were highest at the outer edge of the connection plate where all the existing four fatigue cracks on the I-270 Bridge over Middlebrook Road were located. Girder 4 located at the center left of the middle traffic lane, and Girder 3 located at the center right of the right traffic lane, are the most critical locations for the bridge deflections and the resulting stresses.
\nThe live load-induced stresses in the connection plates were localized around the welded connections and would not be anticipated to spread from the bottom to the top of connection plates. At the same face of the connection plate, both tensile and compressive stresses were observed at the symmetric positions around the girder web. The cracked side of the connection plates was always under tensile stress, while the uncracked side was always under compressive stress during each time period. At the same location of the cracked side, the north face and the south face sustained the same stresses, (although opposite directions). It was proved that the high vertical tensile stress around the connection plate welds was caused by the configuration of the connection plates instead of by the configuration of the cross-frames. The connection plates, which were bent to be parallel to the skewed abutment, induced torsion in the connection plate welds. The connection plate configuration is a key factor in the stress distribution that results in the connection plates.
\nDifferent from the explicit equation-based method, the proposed approach combines a comprehensive traffic loading model, which includes information on vehicle types, axle weights, axle spacing, and the lane occupation, and a detailed 3D FE model, which enables fatigue analysis on unreachable or complicated details where complex stress conditions may exist. The proposed approach may be used as a tool accompanying a monitoring program to find the stresses in unmonitored details or to reduce the frequency of structural health monitoring resulting in lower costs in fatigue assessment. In such case, the proposed approach also provides a tool to predict the fatigue reliabilities of these hard-to-reach details. When combined with the fracture damage mechanics, the proposed approach can help understand the accumulation of fatigue damage and crack propagation.
\nThis work 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.
\nRetail stores manage millions of items on a day to day basis to deliver to their customers. Point of Sales (POS) systems with barcodes were among the first technologies used to track products across the supply chain and in stores. Barcodes, as an identification technology, are not utilized at item-level but usually represent a group of products. Retailers need to scan products at pallet level at the point of receiving shipments, in inventories entrance and exit places, and at the POS to keep track of what is coming into and leaving stores [1]. With barcode systems, inventory inaccuracy is created because stores barcode scanning are not always performed at the right time and the right location. This inventory inaccuracy leads to a significant loss at retailers. Retailers needed to explore new ways of tracking their items to lower the inventory inaccuracy and prevent consequent losses. Radio Frequency Identification (RFID) technology appears to be the new technology solution that could improve the inventory record accuracy of stores for various items.
\nRFID technology applications have been recognized in many areas such as healthcare, finance, manufacturing, and retail. The share of the RFID market in retail is projected to be the largest of all sectors with about 34% by 2026, followed by transportation sector (25%), financial and security services (22%), and other industries such as healthcare and manufacturing at smaller portions [2]. RFID tags can store more information about each item at real time and can have individualized identification for items versus barcodes with a small data storage capacity that can only identify a group of items. RFID readers do not need to be on the line of sight to read RFID tags information which means items can be scanned more frequently and faster at any movement. These capabilities allow little mistakes in tracking records and largely eliminates inventory inaccuracy.
\nRFID technology’s benefits to retailers were identified early at the beginning of the 21st century. However, RFID’s applications in retail stores on a large scale took a while to be implemented. This paper reviews utilizing RFID, as an ideal solution to retail operations, since earlier this century and will cover a twenty-year horizon (2001–2020) divided into three equally long periods of 2001–2007, 2008–2014, and 2015–2020.
\nIn studies done earlier in this century, RFID was recognized as the next major identification technology to replace barcode systems in the retail industry [3, 4, 5, 6, 7]. Barcode systems have been used to track customer purchases, to manage inventory records, and to offer promotion and advertising in retail since 1970 [8]. Barcode tags, however, need to be on the line of readers to be read, a requirement that makes physical inventory counting a labor-intensive task and prevents stores from updating their inventory records frequently and on time. Therefore, with barcode systems, inventory inaccuracy is significant [9]. Inventory inaccuracy refers to the difference between inventory on record and the actual number of items on hand in stores. Inventory inaccuracy is caused by many factors such as transaction errors in the POS system, or shrinkage caused by possible employee/customer theft. Inventory inaccuracy means that stores may not be able to place inventory orders on time, resulting in out-of- stock conditions and consequently losing sales and hurting customer shopping experience. RFID technology, on the other hand, enhances product visibility in store operations and across the supply chain through the ease of reading RFID tag information and updating inventory records on a real-time basis.
\nStudies have investigated RFID benefits in different areas of retail operations, such as supply chain management, and show how inventory inaccuracy and consequently out-of-stock conditions are improved with the implementation of RFID across the supply chain [10, 11, 12]. Enhanced information visibility, provided by RFID in the supply chain, decreases uncertainties and lowers high inventory costs associated with the uncertainties [13, 14].
\nMany pilot studies during this period investigated and explored the applications of this technology at the pallet level, case level, and item levels in stores [1, 6, 12, 15, 16]. [1] conducted a case study in 2005 to analyze pallet and case-level implementation of RFID and enhanced visibility generated at the receiving gates and entrance doors from backstore to sales floors. They demonstrated that safety stock and inventory holdings can be significantly reduced and RFID benefits are broad, ranging from labor efficiencies to inventory management improvements. Cost–benefit analyses in this period showed that pallet-level implementations of RFID were more cost effective than case-level implementations.
\nMetro Group in Germany conducted some case studies in their stores to show item-level RFID applications can improve customers’ shopping experiences as well. They introduced some tools provided by RFID technology such as automatic checkout, smart carts that help customers navigate stores and find their items easier and faster, and smart dressing rooms that help customers find their desired apparel items more conveniently [6, 16]. They demonstrated that utilizing these tools significantly enhances customers’ shopping experience.
\nWalmart retail stores in the US were the first retailers that decided to mandate the implementation of RFID at pallet and case level across some of their supply chain in 2005. Walmart also did a pilot study with 24 stores over a period of around 6 months to measure how RFID can improve inventory management. They demonstrated out-of-stock conditions were significantly reduced with the implementation of RFID technology [12].
\nIn Asia, two Singaporean fashion retailers piloted item-level RFID on their apparel stores and reported significant reduction in stocking time from hours to minutes that consequently increased the frequency of counting items with handheld readers and improved inventory accuracy [17].
\nFinancial crises and the great recession that started in 2008 did not work to the advantage of retailers that were planning to implement RFID applications in their stores. During financial crises, businesses tend to adopt strategies that could help them sustain and survive by spending low and investing less. RFID technology implementation plans were mostly postponed or slowed down during the financial crises. However, this period was the best time to develop some foundations with respect to policies, regulations, and standardization of the technology.
\nPrivacy issues raised by consumer protection agencies and standardization issues across different platforms put forward by case studies and pilot projects led to the development of some regulations and privacy policies by governments, institutions, and businesses. European Commission (EC) took an active role by funding many initiatives across Europe [18]. Initiatives such as Coordinating European efforts for promoting the European RFID value chain (CE RFID) [19] and Building Radio frequency Identification solutions for the Global Environment (BRIDGE) [20], conducted from 2006 to 2009, highlighted that wide implementation of RFID technology needs some regulations, standardizations, and privacy policies in place. For example, the BRIDGE project, coordinated by GS1, helped the industry to develop standardizations such as establishing a common format for the data stored on RFID tags, or the availability of possible frequency bands.
\nRFID tags can store identifiable consumers’ private data, which need to be protected. Therefore, EU members signed an agreement on the Privacy Impact Assessment framework in order to protect consumer privacy [21, 22, 23]. This agreement established some rules to be followed in the design of smart chips such as RFID tags to protect the privacy of consumers’ data. Consumers should be informed if RFID tags are utilized in stores. In addition, tags must be deactivated at the point of sales at no cost [24]. This framework was later expanded to cover some rules for smart meters as well. In the United States, lawsuits against RFID application patents as well as privacy issues in 2011–2013 were setbacks for large-scale implementations of the technology. The National Institute of Standards and Technology (NIST) in the US has helped to establish some guidelines to help retailers; however, most of the development of policies and standardizations have been initiated by corporations in the US.
\nIn addition to developing policies and standardizations, businesses had more chances to identify and learn broader applications of RFID technology in retail. The focus of most of earlier pilot studies was how this new tracking technology helps manage inventories better in order to avoid out-of-stock conditions. However, the applications of the technology go beyond only inventory management and tracking items throughout the supply chain. As shown in the balanced scorecard developed in [25], RFID benefits extend to marketing and merchandising operations in retail as well (Figure 1).
\nBalanced scorecard for RFID applications in retail.
In marketing, stores can monitor the behavior of consumers better when customers use tools such as smart carts or smart dressing rooms provided by RFID. Retailers can learn about consumers’ preferences and reflect that in the promotion and advertising offered to customers in real time while they shop. The available tools such as smart dressing rooms and smart carts also enhance customer shopping experience. Use of these tools enables customers to find their desired items more conveniently and faster, which eventually leads to higher customer satisfaction and increase in sales.
\nIn merchandising, enhanced visibility on consumer behavior in stores provided by RFID can help retailers identify better assortments of products. In addition, an enhanced visibility means better shelf-replenishment; that is stores can reduce the shelf space since enhanced visibility on shelves allows retailers to replenish them as soon as they become emptied. Less shelf space leads to holding less number of items on shelves at any given time and consequently less inventory and capital held in stores, which allows retailers to invest in carrying more variety for products in stores.
\nThere were also more studies during this period conducting cost–benefit analysis of the implementation of the technology. The fixed cost of implementation includes middleware, fixed antennas, sensors, and readers and the variable cost includes the cost of tags per item. The cost of tags can be added to the cost of each product but then the big question is who has to pay for that cost. Should the cost be transferred to consumers or should that be shared between retailers and manufacturers? The tag cost as the variable cost of utilizing the technology is huge and cost–benefit analysis studies have shown it to be a major barrier to the implementation of the technology during this period. [26] showed that the cost of tags in item-level implementation of RFID, as the variable cost, is cumbersome. Moreover, the cost can exceed the benefits in some cases depending on the extent to which stores implement RFID applications. The cost barrier was expected to weigh less as the cost of tags became lower over time.
\nSurveys of businesses show the implementation of RFID has picked up in this period. A survey of 60 retail executives throughout the United States and Europe showed about 73% of retailers had plans to implement RFID in 2016 [27]. Another survey in 2018 [28], however, showed that 92% of retailers in North America plan on implementing RFID, which is about a 20% increase from the 2016 results.
\nThe cost of implementation has been decreasing over time, as expected, and at the same time retailers have learned how to partially implement the technology. Retailers realized that they do not need to fully implement the technology. In some cases, only tags and hand-held readers are used to add visibility of items in stores without many of the infrastructures such as antennas. Cloud services, on the other hand, have allowed retailers to eliminate some of the middleware cost as well. The leading European fashion stores C & A is one of many retailers that explored lower cost implementations with partial utilization of the technology [29].
\nRFID platforms can generate big data that are the records of tracking items throughout the supply chain and stores in real time. Businesses need big data and business analytics capabilities to fully utilize technologies [30]. The results of the analysis of such data can help retailers improve their processes such as shelf-replenishment process as well as variety and assortment planning that have been used in the same format for many years. A new timely replenishment process can result in better management of physical space, layouts, and lowering holding costs in stores. Furthermore, a better variety and assortment planning means fulfilling customers’ expectations and eliminating unpopular items that releases some capital and allow investment opportunities in other areas in retail.
\nCompeting technologies to RFID have been developed and utilized over time as well. For example, Quick Response (QR) codes give retailers better ability to manage items compared to barcodes, Near-Field Communication (NFC) technology has some capabilities compared to UHF RFID, and most recently Amazon’s cameras increase product visibilities in stores for fast checkouts. In addition, retailers have different priorities in investing in new technologies and there is competition for dollars invested in various technologies by retailers. For instance, a retail chain can focus on improving inventory operations, but another retailer may be focused on improving marketing operations and customer shopping experience in stores by developing new apps that can assist customers make decisions during their shopping time in stores. In a different example, Gucci as an Italian luxury brand name does not suffer from inventory inaccuracy issues but their priority is customer shopping experience and they have utilized RFID tags to protect customers against counterfeiting across the supply chain until products reach their customers [31].
\nOmnichannel retail has been widely available during this period of time. Retailers’ customers can shop at any time, in any place, and via any shopping channel. Omnichannel retailing needs accurate inventories and enhanced product visibility more than any other time. Item-level RFID can, therefore, accommodate the needs of omnichannel retail more than other technologies available [32]. In addition, blockchain as the latest technology in retail can provide automatic exchange of product data carried by RFID tags between different partners across the supply chain. The blockchain in retail solutions are currently being studied in a consortium of large retailers such as Nike, Macy’s, and Dillard’s in an RFID lab at Auburn University [33].
\nThe 3-S model (substitution, scale, and structure) introduced in [34] discussed and projected different phases of the adoption of RFID earlier when this technology was introduced. Later the 3-S model was adapted by [35] to describe the current stage of retailers’ implementation of RFID applications in retail (Figure 2). The substitution and scale stages are covering mostly what has been achieved during the three periods discussed in this paper. In the substitution phase, the RFID technology was utilized to replace the applications of barcode systems in tracking products. In the scale phase, the RFID applications are enabling retailers to manage their operations with more accuracy, efficiencies, and at a higher speed and scale. The structure phase, that is re-engineering processes and completely overhauling retail operations, is still underway. The RFID technology will enable retailers to accomplish things they could not imagine before and allows retailers to tap into completely new domains and applications.
\n3-S model for RFID retail adoption.
Retailers have different needs based on the way they operate in stores. Some retailers must manage large inventories in stores. An enhanced visibility on their products help them improve inventory accuracies and avoid out-of-stock and increase their efficiencies. On the other hand, some retailers have small backstore inventories and every item they receive is put directly on their shelves and available to their customers. Inventory management is not their priority, but they need to focus more on customer shopping experience. Therefore, the enhanced visibility of items in stores is expected to promote retailers’ marketing operations. Depending on the way retailers operate and what their priorities are, retailers have to plan on implementing appropriate applications of RFID technology.
\nAs discussed in this paper, RFID has been utilized broadly with various applications. As a revolutionary technology, RFID’s implementation can go beyond improving the current processes in retail operations. The current processes can innovatively change to debut completely new applications that are only possible with the enhanced visibility of items in real time. The ensuing big data that is derived from the visibility provided by RFID tags can be analyzed, leading to innovations in retail operations.
\nImplementing item-level RFID needs to be part of omnichannel strategy in the retail sector. With the wide-spread usage of online retail services such as Amazon, the competition in retail is tougher than ever before. In omnichannel services, retailers need to grant their customers easy access via different channels and make their products available in a variety of delivery services. The accessibility and fast delivery will not be possible with the level of visibility provided by barcode systems. Utilization of technology in retail is evolving quickly and RFID technology is the one that can definitely help retailers win in this overhaul.
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