Results of standard mechanical tests of pyrolytic products and their blends with reference bitumen [20].
\r\n\t
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Dr. Giovana obtained a Ph.D. in Sciences from the Heart Institute, Medical School, University of São Paulo State (USP), Brazil, in 2008; a postdoctoral degree from the Department of Gynecology, UNIFESP, in 2014; and a postdoctoral degree from Laboratory of Angiogenesis and Neurovascular Link, Vesalius Research Center–KU Leuven, Belgium, in 2015. 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Bitumen in asphalt mixtures ages already during the production, transport and installation of asphalt mixture and the process of ageing continues during the use of asphalt. Ageing processes are influenced by several factors, the most important of them are: temperature, UV radiation and oxygen exposure. Subsequently, bitumen becomes harder and more brittle, its viscosity increases, adhesion and cohesion deteriorate, which leads to the ravelling and the formation of cracks in asphalt mixture [1, 2, 3]. With the use of special additives, the so-called rejuvenator, bitumen restores its basic properties [4, 5]. Furthermore, rejuvenators allow mixing and installation at lower temperatures, which reduces production costs and energy consumption, therefore the production of asphalt is more environmentally friendly. The rejuvenator enables to re-install the aged bitumen from reclaimed asphalt (RA) into the fresh asphalt [6]. The first rejuvenator was used as early as 1960 [7]. Over the years, several different products have been used as rejuvenators: emulsions, oil components with a high content of maltene, soft bitumen with high penetration, tertiary amines, waste vegetable oils, waste motor oils and various combinations of these materials [8, 9]. A rejuvenator made from waste tires by a pyrolysis process, a pyrolytic rejuvenator, was developed in our study [10].
Pyrolysis in technical terms is a process in which the chemical decomposition of a substance takes place at elevated temperatures and without the presence of oxygen. Because of the increasing number of vehicles, demand for car tires is also increasing, and as a consequence their production is increasing (324 million tires were sold in 2019 in Europe [11]), resulting in an increasing number of waste tires at the end of their service life. European Directive on the landfill of waste 1999/31/EC [12] has prohibited the disposal of waste tires at landfill sites since 2006, as their improper storage may endanger human health and pose a threat to the environment, e.g. fire, the spread of rodents and insects and dangerous emissions that can be released into the air or the earth. In addition to this directive, waste management is also significantly influenced by the waste directive 2008/98/ES [13] which sets out a 5-step waste hierarchy, namely: prevention, preparing for re-use, recycling, other recovery (e.g. energy recovery) and disposal. Pyrolysis is a suitable process for the treatment of waste tires, as it has a small impact on the environment, and at the same time obtained products that can be used further. In the past, pyrolytic products from waste tires have been mainly used as additives to reduce the temperature sensitivity of bitumen [14, 15, 16, 17]. The pyrolytic product from waste tires pyrolysis may contain high concentrations of polycyclic aromatic hydrocarbons (PAH). Therefore, the slow pyrolysis process of waste tires was performed at a relatively low temperature where the highest treatment temperature (HTT) did not exceed 500°C. Consequently, a product with low PAH content, i.e. lower than those reported in existing studies [18, 19] was obtained.
In the first phase of the presented study, several different pyrolytic products were developed [20]. Among them, the most appropriate for the purpose of rejuvenator was selected. In the second part, the influence of the pyrolytic rejuvenator on the properties of the non-aged and aged bitumen was examined. Results of the tests proved that the pyrolytic rejuvenator revived aged bitumen [21]. In the last phase, the pyrolytic rejuvenator was used in asphalt mixtures, to which the percentage of reclaimed asphalt was gradually increased [22].
Pyrolytic products in the research were obtained by the process of slow pyrolysis. In the first phase, the conditions of the pyrolysis process e.g. the duration (from 10 min to 150min) and the pyrolysis temperature (from 280–500°C) were changed in order to obtain different pyrolytic products. In the second phase, the pyrolytic products were modified with various oils and crushed rubber. All composed pyrolytic products were similar to bitumen. Their properties and properties of their blends with reference bitumen were evaluated by standard European mechanical tests, which are usually used to determine the properties of bitumen. As reference bitumen and as a matrix of the blends a 50/70 penetration grade bitumen (B50/70) was used. All the blends were laboratory produced by adding a controlled quantity of the pyrolytic product to the bitumen. The blends of bitumen and pyrolytic product were commonly produced by mixing two of the components in ratios of 1:1 (labelling them B+No. of pyrolytic product). Only the blends of pyrolytic product No. 11 were also prepared in smaller concentrations (11_x%, where x represents a share of the pyrolytic product).
The softening properties were determined by using the Ring and Ball method (RB) according to the EN 1427 [23]. Fraass breaking point test according to the EN 12593 [24] was used to determine the brittleness of the products at low temperatures. The tensile properties of the bitumen and pyrolytic products were determined by the force ductility method in accordance with EN 13589 [25].
For the production of asphalt, it is important to determine the optimum mixing temperature for specific bitumen, which is the temperature to be maintained at the asphalt plant during mixing. For laying of asphalt the compaction temperature, at which the mixture has to be compacted on sites has to be determined. The mixing and compaction temperatures for pure pyrolytic rejuvenators and bitumen blends were determined with the rotational viscometer Haake RS50. Both temperatures determine recommended viscosity of bitumen: 0.170 ± 0.02 Pas form mixing and 0.260 ± 0.03 Pas for compaction, respectively [26].
Results in Table 1 show most pyrolytic products lowered the softening point of the blends in comparison with reference bitumen. That means the pyrolytic product could also lower the high softening point of the extracted bitumen in reclaimed asphalt. For our product, this is a good feature, as we want to use it as a rejuvenator. In addition, almost all pyrolytic products lowered the Fraass breaking point, meaning the temperature range is extended.
RB | Fraass breaking point | Force ductility | Mixing temperature (@ η = 0.17 Pas) | Compaction temperature (@ η = 0.26 Pas) | ||||
---|---|---|---|---|---|---|---|---|
T | Elongation | Force (max) | Energy E’0,4-E’0,2 | |||||
[°C] | [°C] | [°C] | [mm] | [N] | [J/cm2] | [°C] | [°C] | |
B 50/70 | 50.5 | −11 | 25 | 1500* | 0.97 | 0.07 | 141.2 | 121.9 |
1 | 44.6 | −14 | 25 | 236 | 0.1 | 0.00 | 153.6 | 127.1 |
2 | 59.1 | 2 | 25 | 187 | 1.01 | 0.06 | 192.9 | 159.9 |
3 | 54.6 | −18 | 25 | 137 | 0.34 | 0.02 | 169.2 | 141.2 |
4 | 61.3 | −12 | 25 | 257 | 1.41 | 0.10 | 185.6 | 156.8 |
5 | 60.6 | −8 | 25 | 354 | 1.41 | 0.12 | 187.8 | 158.6 |
6 | 47.3 | −20 | 25 | 232 | 0.27 | 0.02 | 169.3 | 138.8 |
7 | 45.9 | −14 | 25 | 827 | 0.41 | 0.02 | 139.4 | 116.4 |
8 | 65.6 | +1 | 25 | 1309 | 12.59 | 0.91 | 177.6 | 153.1 |
9 | 42.0 | −20 | 15 | 145 | 0.14 | 0.01 | 190.6 | 150.3 |
10 | 30.4 | −23 | 15 | 202 | 0.04 | 0.00 | 155.7 | 121.5 |
11 | — | −1 | 15 | — | — | — | 202.3 | 154.4 |
12 | 43.9 | −21 | 15 | 325 | 0.07 | 0.01 | 212.4 | 173.6 |
13 | 54.9 | −21 | 15 | 322 | 0.29 | 0.05 | 255.5 | 207.5 |
14 | 37.5 | −22 | 15 | 230 | 0.0 | 0.00 | 150.3 | 122.4 |
B + 1 | 43.3 | −13 | 25 | 902 | 0.31 | 0.02 | 152.1 | 125.8 |
B + 2 | 46.4 | −8 | 25 | 534 | 0.52 | 0.03 | 146.0 | 123.3 |
B + 3 | 47.1 | −18 | 25 | 335 | 0.58 | 0.04 | 143.6 | 121.9 |
B + 4 | 46.9 | −11 | 25 | 1500 | 0.96 | 0.06 | 146.6 | 123.3 |
B + 5 | 46.6 | −10 | 25 | 1492 | 0.82 | 0.05 | 152.1 | 128.0 |
B + 6 | 44.8 | −18 | 25 | 760 | 0.43 | 0.03 | 142.2 | 119.2 |
B + 7 | 45.7 | −14 | 25 | 1500* | 0.72 | 0.04 | 140.1 | 118.2 |
B + 8 | 52.3 | −6 | 25 | 1500* | 3.15 | 0.22 | 150.7 | 129.1 |
B + 9 | 38.1 | −24 | 15 | 1500* | 0.67 | 0.10 | 158.6 | 129.3 |
B + 10 | 37.2 | −22 | 15 | 1500* | 0.61 | 0.06 | 135.3 | 113.8 |
B + 11 | — | −24 | — | — | — | — | 140.3 | 105.2 |
11_5% | 42.8 | −17 | 15 | 1343 | 2.6 | 0.23 | 183.3 | 117.2 |
11_10% | 36.6 | −23 | 15 | 746 | 0.45 | 0.03 | 134.8 | 111.8 |
11_20% | 22.4 | −23 | — | — | — | — | 129.6 | 102.5 |
B+12 | 40.2 | −21 | 15 | 1326 | 0.10 | 176.8 | 143.5 | |
B+13 | 45.4 | −14 | 15 | 1500* | 0.28 | 0.03 | 169.9 | 140.8 |
B+14 | 42.6 | −19 | 15 | 1122 | 0.20 | 0.00 | 139.6 | 117.2 |
Results of standard mechanical tests of pyrolytic products and their blends with reference bitumen [20].
* Maximum elongation.
Results of final elongation at force ductility tests show that all pyrolytic products broke before they reached the maximum possible length (1500 mm, designated 1500* in Table 1) to the contrary of reference bitumen. The pyrolytic products 4, 5, 7, 8, 9, 10, and 11 (in adequate concentration) retained the elongation ability of the reference bitumen in the blends. All other pyrolytic products shortened the elongation of the blends. Except for the pyrolytic product no. 8, all other values of maximum force measurements were in the range around 1 N or even smaller. The ductility test was performed at 25°C for the pyrolytic products (and their blends) from 1 to 8. For others, the test temperature was lowered, to 15°C. Pyrolytic product 11 and its blends, B+11 and 11_20%, could not be tested even at the temperature of 15°C, so we did no performed test for those three samples.
The results of the mixing and the compaction temperatures of the pyrolytic products are higher than the reference bitumen’s. Although this indicates that the pyrolytic products have a higher viscosity, also the homogeneity of the sample influenced the viscosity. Inhomogeneous samples (2, 9, 10, and 11) have higher viscosity. Blends of pyrolytic products 7, 10, 11 and 14 have lower mixing and compaction temperatures.
The viscosity of the blends was determined at three different temperatures and was measured at the constant shear rate. Results (Table 2) show that the viscosity of the bitumen and the blends is decreasing with increasing temperature. In general, the viscosity of all blends is lower than the viscosity of the references bitumen at 60 °C and 100°C. At 150°C viscosity of almost all blends was at least as high as the reference’s bitumen; nevertheless, the absolute values of the viscosity were very small.
Viscosity | |||
---|---|---|---|
@ T = 60°C | @ T = 100°C | @ T = 150°C | |
Label | [Pas] | [Pas] | [Pas] |
B 50/70 | 209.37 | 2.03 | 0.12 |
B + 1 | 71.50 | 1.56 | 0.23 |
B + 2 | 104.19 | 1.67 | 0.17 |
B + 3 | 110.84 | 1.67 | 0.15 |
B + 4 | 92.59 | 1.59 | 0.17 |
B + 5 | 99.02 | 3.36 | 0.18 |
B + 6 | 61.04 | 1.31 | 0.14 |
B + 7 | 71.39 | 1.21 | 0.13 |
B + 8 | 234.85 | 2.60 | 0.21 |
B + 9 | 56.31 | 1.74 | 0.29 |
B + 10 | 30.91 | 0.90 | 0.12 |
B + 11 | 6.93 | 0.43 | 0.15 |
11_5% | 73.03 | 1.35 | 0.12 |
11_10% | 31.14 | 0.90 | 0.09 |
11_20% | 9.01 | 0.53 | 0.09 |
B + 12 | 94.83 | 2.78 | 0.58 |
B + 13 | 144.06 | 3.33 | 0.45 |
B + 14 | 59.34 | 1.25 | 0.12 |
Results of viscosity measurements of the blends (pyrolytic products with reference bitumen) [21].
Based on the results of these tests we decided that out of the fourteen manufactured and modified pyrolytic products, the most suitable pyrolytic product for the role of a pyrolytic rejuvenator was number 14.
After selecting the appropriate pyrolytic rejuvenator, the focus of the study was to evaluate its effect on paving grade bitumen. When reclaimed asphalt (with aged bitumen) is added to the new asphalt mixture, stone aggregate and a calculated amount of fresh (non-aged) bitumen are also added at the same time. Consequently, researches on non-aged bitumen and on laboratory aged bitumen were conducted. The short term ageing, to which bitumen is subjected during mixing, transport and installation of asphalt, is in laboratory simulated by rolling thin film oven test (RTFOT) method according to EN 12607 [27]. According to this method, bitumen is aged under the influence of high temperatures and constant air flow. The ageing conditions are not exactly the same as in asphalt production, but the ageing results are comparable [28]. Ongoing ageing of bitumen during road use was simulated by the pressure ageing vessel (PAV) method according to EN 14769 [29]. PAV ageing at elevated pressure and temperature was performed on RTFOT aged bitumen. PAV simulates ageing according to climatic conditions (temperature, UV, etc.), but cannot take into account variables in the asphalt, such as the proportion of air voids, the type of aggregate and the absorbency of the aggregate.
Blends of non-aged bitumen and pyrolytic rejuvenator were laboratory prepared in different concentrations: 3%, 5%, 10% and 20% rejuvenator based on the mass of the reference bitumen (Table 3). All prepared samples and concentrations of the blends are presented in Table 3.
Label | Proportion of the reference bitumen | Proportion of the pyrolytic rejuvenator | RB | Penetration | Fraass breaking point | |
---|---|---|---|---|---|---|
[%](m/m) | [%](m/m) | [°C] | [1/10 mm] | [°C] | ||
Non- aged bitumen | B 50/70 | 100 | 0 | 50.5 | 53 | −11 |
B50/70_3% | 97 | 3 | 50.2 | 55 | −11 | |
B50/70_5% | 95 | 5 | 50.3 | 59 | −20 | |
B50/70_10% | 90 | 10 | 49.0 | 60 | −16 | |
B50/70_20% | 80 | 20 | 47.3 | 75 | −16 | |
B50/70_50% | 50 | 50 | 42.6 | 115 | −19 | |
Pyro-lytic rejuve-nator | PR | 0 | 100 | 37.5 | 233 | −22 |
Aged bitumen | B_PAV | 100 | 0 | 70.4 | 21 | −10 |
B_PAV_3% | 97 | 3 | 69.0 | 22 | −6 | |
B_PAV_5% | 95 | 5 | 67.7 | 22 | −6 | |
B_PAV_10% | 90 | 10 | 65.6 | 26 | −7 | |
B_PAV_20% | 80 | 20 | 61.6 | 34 | −7 |
Tested blends of pyrolytic rejuvenator (labels, RB and penetration) [21].
In addition to before mentioned standard test on samples, determination of the sample’s consistency by needle penetration test according to the EN 1426 [30] at 25°C was also performed. The elastic recovery of the samples was determined according to the EN 13398 [31]. According to this standard, a specimen was first elongated to 20 cm and then cut in the middle to obtain two halves of the thread. After the predetermined time (30 minutes) for recovery has elapsed, the shortening of the half threads was measured and expressed as the percentage of the elongation length.
To check the possible phase separation in the blend of bitumen and pyrolytic products, a storage stability test according to EN 13399 [32] was performed only for the blend of pyrolytic product No. 14 and reference bitumen. In the test, the sample of the blend is maintained in the vertical vessel at 180°C for three days. After the sample is cooled down, it is cut into three equal parts. The two ends (top and bottom) are further analysed to evaluate possible differences in characteristics. The affinity between pyrolytic rejuvenator and stone aggregate (limestone) was checked by standard rolling bottle method according to EN 12697–11 [33] and compared with the affinity of reference bitumen (non-aged) and the blend.
The properties of samples in the low-temperature range were characterized with the bending beam rheometer (BBR) according to EN 14771 [34]. Bending tests are suitable for testing brittle materials when measurements at tensile load do not provide insight into the properties of the material or are not feasible. The stress relaxation in bitumen is significantly slower at low temperatures, which can lead to the formation of cracks in the asphalt and loss of binder functionality. During the BBR test a bitumen beam is bent under a constant load and deformation of bitumen is measured. The flexural creep stiffness Sm(t) is calculated at time t = 60 s. The characteristic parameter is also value m60, the slope of the curve S (t) at t = 60 s, which indicates the relaxation capacity of the bitumen stress. We presumed that the adequate quality of bitumen at low temperatures is ensured by the maximum value of S60 = 300 MPa and the minimum value of m60 = 0.300.
The results of softening point, Fraass breaking point and penetration of reference bitumen (non-aged, ‘B 50/70’ and laboratory aged, ‘B_PAV’), pyrolytic rejuvenator ‘PR’ and their blends are shown in Table 3. Comparison of penetration and Fraass breaking point between ‘B 50/70’ and pyrolytic rejuvenator ‘PR’ indicates on their different chemical composition. Pyrolytic rejuvenator shows a significantly lower value of softening point and Fraass breaking point and at the same time a much higher value of penetration.
Results on non-aged samples show the effect of the rejuvenator on the standard mechanical properties (penetration, softening point, Fraass breaking point) of bitumen. Penetration values increased, at the same time softening values decreased with increasing rejuvenator proportion. All added amounts of rejuvenator to non-aged bitumen decreased the values of the Fraass breaking point.
Original B 50/070 was RTFOT+PAV aged, simulating naturally aged bitumen in reclaimed asphalt. Then the same proportions of pyrolytic rejuvenator No. 14 (as in the case of non-aged bitumen) were added and the blends were tested.
A comparison of non-aged and aged bitumen blends shows the impact of laboratory ageing. Aged B 50/70 (B_PAV) and all bitumen blends became stiffer since the penetration of aged bitumen decreased and the softening point increased. Unexpectedly, all proportions of rejuvenator which were added to RTFOT+PAV aged B 50/70 decreased the values of the Fraass breaking point. It should be noticed that the repeatability of the Fraass breaking point test is 3°C.
Table 4 presents results of tensile properties of bitumen, pyrolytic rejuvenator and their blends. Non-aged bitumen and its blends with rejuvenator elongated to the maximum length (1500 mm). The elongation of aged bitumen was prolonged with the addition of a rejuvenator. When mixing materials such as bitumen, two consequences can be observed: the mixing effect (mostly linear change) and the structural-interaction effect (mostly nonlinear change) [21]. Maximum force decreased proportionally with the added rejuvenator indicating a linear change occurred. The effect of nonlinearity is not observed, as the elongation at maximum force, Fmax, is the same for all samples with non-aged bitumen, as well as for all samples with aged bitumen, regardless of the amount of rejuvenator added. The elongation of the non-aged bitumen was about 1.5 times greater than the elongation of the aged bitumen with rejuvenator, indicating that aged bitumen was not completely restored. The results show that due to the added rejuvenator, the mechanical properties of aged bitumen approached the values of non-aged bitumen, but a complete restoration was not achieved.
Force Ductility at 25°C | Elastic recovery | ||||
---|---|---|---|---|---|
Elongation | Force Fmax | Elongation at Fmax | Energy | ||
Label | [mm] | [N] | [mm] | [J/cm2] | [%] |
B 50/70 | 1500* | 0.97 | 17.18 | 0.07 | / |
B50/70_3% | 1500* | 1.58 | 7.22 | 0.11 | 13 |
B50/70_5% | 1500* | 1.27 | 7.21 | 0.10 | 13 |
B50/70_10% | 1500* | 0.99 | 7.21 | 0.07 | 12 |
B50/70_20% | 1500* | 0.71 | 7.11 | 0.05 | 12 |
PR | 230 | 0.04 | 17.12 | 0.00 | −38 |
B_PAV | 184 | 21.66 | 12.01 | 1.34 | 30 |
B_PAV_3% | 304 | 20.03 | 12.10 | 1.37 | 27 |
B_PAV_5% | 469 | 17.08 | 12.40 | 1.28 | 21 |
B_PAV_10% | 307 | 13.38 | 12.71 | 0.95 | 21 |
B_PAV_20% | 370 | 8.27 | 12.89 | 0.62 | 22 |
Tested blends of pyrolytic rejuvenator (ductility, elastic recovery) [21].
* Maximum elongation.
The elastic recovery did not change significantly for non-aged bitumen regardless of the amount of rejuvenator. In the case of aged bitumen, the elastic recovery decreases with the amount of rejuvenator and thus approached the value of the reference aged bitumen. The elastic recovery of the rejuvenator had a negative value, so the sample was stretching after the test, meaning that the rejuvenator had no elastic properties and all energy was lost. We expected that due to rubber content in car tires some elasticity will remain in our product, but it is evident that all rubber from tires decomposed during the pyrolytic process.
Mixing and compaction temperatures (Table 5) of aged bitumen were higher than temperatures of non-aged bitumen, and in both cases, the temperatures decreased with the increasing amount of added rejuvenator.
Temperature | Mixing temperature (η = 0.17 Pas) | Compaction temperature (η = 0.26 Pas) | BBR T at S60 = 300 MPa | BBR T at m60 = 0.300 | Higher T (BBR) |
---|---|---|---|---|---|
Label | [°C] | [°C] | [°C] | [°C] | [°C] |
B 50/70 | 141 | 122 | −19.3 | −20.8 | −19.3 |
B50/70_3% | 140 | 120 | −19.6 | −21.5 | −19.6 |
B50/70_5% | 140 | 120 | −19.8 | −22.0 | −19.8 |
B50/70_10% | 138 | 118 | −20.7 | −22.7 | −20.7 |
B50/70_20% | 139 | 118 | −22.6 | −24.5 | −22.6 |
PR | 150 | 122 | −31.3 | / | / |
B_PAV | 155 | 139 | −16.1 | −11.7 | −11.7 |
B_PAV_3% | 154 | 137 | −16.9 | −12.9 | −12.9 |
B_PAV_5% | 153 | 137 | −17.5 | −12.8 | −12.8 |
B_PAV_10% | 151 | 134 | −18.3 | −13.3 | −13.3 |
B_PAV_20% | 150 | 132 | −19.6 | −18.8 | −18.8 |
Results of rheological tests [21].
S60 and m60 are criteria that determine the lower limit of the bitumen application temperature. When the conditions: S60 ≤ 300 MPa and m60 ≥ 0.300 are met at the same time, the bitumen shows sufficient low temperature resistance. Table 5 shows the results, i.e. the minimum temperatures for all samples. For non-aged bitumen, the critical temperature was determined by S60, while for aged bitumen the temperature at parameter m60 was decisive. With the addition of the rejuvenator, the critical temperature was lowered. Aged bitumen with the highest amount of rejuvenator additive achieved similarly low temperatures as the reference non-aged bitumen.
Storage stability was checked only on a blend with the highest amount of pyrolytic rejuvenator, B50/70_50%. The test results showed that although an immense quantity of the rejuvenator was added to the reference bitumen, the blend remained homogeneous. This was evident from the very small changes in the penetration and softening values of the binder in the upper and lower parts of the tube (Table 6). Results also show that due to storage at high temperature the characteristics (pen, RB) did not change much.
Conditions | RB | Penetration | |
---|---|---|---|
Label | [°C] | [1/10 mm] | |
B50/70_50% | Ambient | 42.2 | 115 |
B50/70_50% upper part | 72 h at 180°C | 43.0 | 118 |
B50/70_50% lower part | 72 h at 180°C | 42.8 | 117 |
Results of storage stability test [21].
The result of the affinity test (Table 7) shows that after the first 6 hours there was no difference between the tested samples. The binder detached only slightly from the aggregate (limestone) in all samples. After one day of testing in the rolling bottle, differences between the samples appeared more obvious. The pyrolytic rejuvenator had the best affinity with aggregate. After two days, the reference bitumen and rejuvenator covered the aggregate equally well, and after three days the aggregate was best covered with pyrolytic rejuvenator and worst with a mixture of reference bitumen and pyrolytic rejuvenator.
Affinity [%] | Time of test [h] | ||||
---|---|---|---|---|---|
Sample | 0 | 6 | 24 | 48 | 72 |
B50/70 | 100 | 95 | 80 | 55 | 40 |
B50/70_50% | 100 | 95 | 80 | 50 | 30 |
PR | 100 | 95 | 90 | 55 | 45 |
Results of affinity test.
To verify the relationship between rheological and mechanical measured properties, presented in Tables 3–5, linear relationships between individual properties were examined. Linearity between properties was evaluated with statistical parameter R2 (Table 8). The results show that there is no linear relationship between BBR measurements and empirical mechanical tests. Correlation between the parameters S60 and m60 and the Fraass breaking point was expected since all measurements were performed in the low temperature range. Similarly, we expected a relationship between the properties measured in the medium and high temperature range. The softening point temperature was compared with the mixing and compaction temperatures. It turned out that there is no linear correlation for the samples of non-aged bitumen, and there is a good linear dependence for the samples of aged bitumen. There is also a good relationship between the results of the two most basic mechanical tests, penetration and softening point, for both non-aged and aged paving grade bitumen samples.
RB – penetration (Table 3) | RB – mixing temperature (Tables 3 and 5) | RB – compaction temperature (Tables 3 and 5) | BBR S60 - Fraass breaking point (Tables 3 and 5) | BBR m60 - Fraass breaking point (Tables 3 and 5 | |
---|---|---|---|---|---|
Non-aged bitumen | 0.91 | 0.00 | 0.02 | 0.10 | 0.20 |
Aged bitumen | 0.94 | 0.91 | 0.96 | 0.06 | 0.00 |
The R2 values for various properties of non-aged and aged bitumen with rejuvenator.
Asphalt mixtures have to withstand dynamic loads as well as high and low temperatures without cracking or rutting. Several standardized laboratory tests enable the evaluation of these asphalt characteristics. Before implementation, characteristics of reclaimed asphalt (RA) had to be established. Several samples of reclaimed asphalt from the same stockpile were sieved into sub fractions and extracted bitumen was investigated. The results showed that the bitumen content in RA was 4.5%. Based on this, the required amount of fresh bitumen and rejuvenator for each asphalt mixture type AC 8 surf were calculated.
The bitumen content of all prepared asphalt mixtures was determined at 5% of the mass regarding the total asphalt mixture mass. Regarding to the established effects of pyrolytic rejuvenator on (non-aged and laboratory aged) bitumen, the addition of 20% of pyrolytic rejuvenator to the bitumen from reclaimed asphalt was established. Control asphalt mixture of fresh materials (0% RA) and asphalt mixtures with 20%, 40% and 60% of RA according to the weight of the stone aggregate were prepared in laboratory (set of asphalt mixture samples is described in Table 9). Each asphalt mixture with RA was produced without and with a pyrolytic rejuvenator. All together seven asphalt mixtures were prepared with the same B50/70 bitumen (from the same producer) as used in previous research.
Sample | Proportion of the RA | Addition of the pyrolytic rejuvenator | Measured bitumen content | The bulk density | The maximal density | Void content |
---|---|---|---|---|---|---|
[%] (m/m) | / | [%] (m/m) | [kg/m3] | [kg/m3] | [%] (V/V) | |
0% RA | 0 | No | 5.0 | 2398 | 2478 | 3.2 |
20% RA | 20 | No | 5.0 | 2403 | 2490 | 3.5 |
20% RA + rej. | 20 | Yes | 5.0 | 2407 | 2492 | 3.4 |
40% RA | 40 | No | 5.0 | 2407 | 2494 | 3.5 |
40% RA + rej. | 40 | Yes | 5.0 | 2394 | 2492 | 3.9 |
60% RA | 60 | No | 5.0 | 2425 | 2502 | 3.1 |
60% RA + rej. | 60 | Yes | 5.0 | 2417 | 2501 | 3.4 |
Standard properties of asphalt mixtures [22].
All asphalt mixtures samples were mixed in a laboratory and compacted in accordance with EN 12697–30 [35]. Basic information about asphalt mixtures was gained by determining bulk density, EN 12697–6 [36], maximal density, EN 12697–5 [37], void content, EN 12697–8 [38], and indirect tensile strength (ITS), EN 12697–23 [39]. ITS is maximum tensile stress applied to a cylindrical specimen loaded diametrically until the break. Cylindrical specimens (nominal diameter 100 mm) were for ITS compacted by 50 impacts on each side at temperature 150°C. Water sensitivity tests were completed according to EN 12697–12 [40], in order to evaluate the effect of moister. Water sensitivity is expressed by ITSR – indirect tensile strength ratio. Two sets of four cylindrical specimens were prepared, compacted by 35 impacts on each side at temperature 150°C. One set of specimens was conditioned in water for three days; the other set was kept dry. The ratio between their ITS values expresses water sensitivity.
For determination of characteristics at low temperature thermal stress restrained specimen test (TSRST, EN 12697–46 [41]) was carried out. Asphalt mixtures were compacted in form of slabs, EN 12697–33 [42], from which prismatic specimens were cut. In TSRST, the specimen, whose length is held constant during the test, is subjected to a temperature decrease with a constant temperature rate. Due to the confined thermal shrinkage, cryogenic stress builds up in the specimen. The results of the tests are the progression of the cryogenic stress over the temperature range until break, σcry(T), and the failure stress, σcry,failure, at the failure temperature, Tfailure.
To check the behaviour of asphalt mixtures at elevated temperatures the formation of wheel tracking was checked according to EN 12697–22 [43].
Extracted bitumen from all asphalt mixtures was investigated according to before mentioned standard testing methods.
The siewing curve had to be adjusted for each asphalt mixture (Figure 1) because different shares of RA were added. In all figures addition of rejuvenator is designated with RA+rej. The curves overlap with each other well and bitumen from RA does not affect the siewing curve. The inhomogeneity of RA was successfully solved by presiewing RA into sub fractions (0/2 mm, 2/4 mm and 4/8 mm).
The sieving curves of laboratory produced asphalt mixtures AC 8 surf [
The densities were determined according to standard procedures and the results are given in Table 9. The difference between the densities and void content of individual asphalt mixtures was very small. Results confirm that mixtures were comparable.
The presence of water in the asphalt is expected. Moisture is one of the most important factors influencing the durability of asphalt. The combination of excess moisture and traffic load shortens the life of the asphalt. Moisture in asphalt causes two main destructive mechanisms: deterioration of adhesion and cohesion. Strong deterioration of adhesion can be observed as peeling of bitumen from the stone aggregate, and deterioration of cohesion is observed as softening of the binder, which leads to lower strength of asphalt. The water sensitivity of asphalt mixtures is expressed by the ITSR quotient, which represents the ratio of indirect tensile strength of wet and dry specimens, expressed as a percentage. Figure 2 shows the results of the water sensitivity test. For all tested asphalt mixtures indirect tensile strength increased with the addition of RA and decreased only slightly when rejuvenator was added. Asphalt mixtures were less sensitive to water after the addition of RA, as the ITSR ratio increased. No significant effect on ITSR was observed with the addition of a rejuvenator.
The water sensitivity of the asphalt mixtures [
Testing of wheel track formation was performed at an elevated temperature of T = 50°C. The differences between the sample results (Figure 3) were not significant, indicating that the selected test temperature was too low.
Results of the wheel tracking test at T = 50°C.
Because the aged bitumen from RA increases the stiffness of the binder in the asphalt mixture, resistance to fatigue cracking is weakened in asphalt mixtures with a high amount of RA. In our study, the resistance to low temperature cracking was checked only on mixtures with the highest proportion of RA, as 60% of RA represents the worst conditions for asphalt resistance at low temperatures. The average results (three specimens were tested for each mixture) for three mixes are shown in Figure 4. Compared to the mixture with 0%RA, the failure temperature increased due to the added RA, which means deterioration of the mechanical properties of the asphalt mixture. The results for 60% RA mixture with the addition of pyrolytic rejuvenator show that the failure temperature decreased, and the resistance of the asphalt mixture with RA and the rejuvenator was therefore slightly better than the resistance of the basic mixture. The breaking stresses were similar for the three tested mixtures, but the course of cryogenic stresses shows that the stresses were higher in the mixture with 60% RA than in the other two mixtures.
Temperature dependency of cryogenic stress [
The extracted bitumen of asphalt mixtures was also investigated; the results are presented in Table 10. The highest value of the softening and Fraass breaking point was determined in bitumen from reclaimed asphalt (100% RA), meaning the bitumen was the most brittle and hard, as expected. The results showed that the addition of RA to asphalt mixtures without pyrolytic rejuvenator increased the softening point and Fraass breaking point and decreased penetration. However, the rejuvenator had a beneficial effect, as mixtures with the rejuvenator exhibited a lower softening point, Fraass breaking point and higher penetration than mixtures without pyrolytic rejuvenator. As expected, the bitumen became harder as the RA content increased, but the bitumen softened with the addition of a rejuvenator.
RB | Fraass breaking point | Penetration | |
---|---|---|---|
Label | [°C] | [°C] | [1/10 mm] |
0% RA | 58.6 | −11 | 45 |
20% RA | 59.4 | −10 | 37 |
20% RA + rej. | 58.2 | −11 | 39 |
40% RA | 60.8 | −7 | 32 |
40% RA + rej. | 58.3 | −8 | 36 |
60% RA | 63.3 | −6 | 26 |
60% RA + rej. | 60.7 | −8 | 32 |
100% RA | 65.3 | −2 | 21 |
Results of standard mechanical tests of extracted bitumen [22].
In the presented research, several pyrolytic products were tested. Based on the results, the pyrolytic product 14 was selected as the most suitable for the purpose of rejuvenating. It had a homogeneous structure, was solid at room temperature and it flowed at elevated temperatures. The indicator of suitability for use as a rejuvenator was the pyrolytic product’s low softening and Fraass breaking point and high penetration value.
In mixtures with reference bitumen, the low Fraass breakpoint value was not maintained. The penetration of bitumen was increased by adding a pyrolytic rejuvenator and the softening point was decreased. The results of standard mechanical tests confirmed that the pyrolytic product softened the bitumen, which is the basic purpose of the rejuvenator.
The next part of the research was dedicated to the determination of the optimal proportion of rejuvenator that would regain the original properties of bitumen from reclaimed asphalt. This part of the research was performed on non-aged and laboratory aged bitumen. The addition of rejuvenator was limited at the upper limit, as the prepared pyrolytic product contained polycyclic aromatic hydrocarbons (PAHs). The results of decreased softening point and increased penetration show that the bitumen softened due to the rejuvenator. The Fraass breaking point increased in aged bitumen after the addition of a rejuvenator, indicating deterioration in the properties of the bitumen.
Despite the added rejuvenator the elongation in the ductility test was maintained for non-aged bitumen and increased for aged bitumen. However, in proportion to the increase in the rejuvenator, the maximum force in the sample decreased. The elastic recovery of non-aged bitumen did not change due to the rejuvenator, while in aged bitumen it decreased after the addition of the rejuvenator. The results of BBR confirmed our expectations, as resistance at low temperatures was increased by the addition of a rejuvenator. The addition of 20% of the pyrolytic product changed most properties of the aged bitumen in order to approach the characteristics of the non-aged bitumen. Although a complete recovery of the aged bitumen was not reached, the pyrolytic product can be successfully used as a rejuvenator. After the addition of pyrolytic product, the properties of aged bitumen were restored in the direction of the properties of the non-aged bitumen.
The asphalt mixtures were less water sensitive when RA and rejuvenator were added. Results proved RA improved the adhesion and cohesion of bitumen. Rejuvenator did not deteriorate the cohesion significantly, even though it made bitumen softer.
The results of the TSRST test revealed that RA deteriorates the properties of asphalt mixtures. However, low temperature cracking resistance improved with the addition of a rejuvenator. The improvement was made to such an extent that the asphalt mixture with the highest proportion of RA and rejuvenator had slightly better resistance than the control asphalt mixture.
On the basis of all the results, it can be concluded that pyrolytic rejuvenator enables the increase of RA share in asphalt mixture.
Traditional supervised learning deals with the analysis of single-label data, which means that samples are associated with a single label. However, in many real-world data mining applications, such as text classification [1, 2], scene classification [3, 4], crowd sensing/mining [5, 6, 7, 8, 9, 10, 11], and gene functional classification [12, 13], the samples are associated with more than one label. From this description, we understand that the challenge of the multilabel classification task is its potential output.
Basically, multilabel learning algorithms can be categorized into two different groups. 1) Problem transformation method. This method takes the multilabel problem and converts it into a single-label problem that can easily be classified using any classifier using the relationship between labels. 2) Adapted algorithm method. This method directly performs multilabel classification rather than transforming the problem into different subsets of problems, and most of these methods use the Euclidean distance between samples.
The main idea of this paper is to aggregate similar samples to obtain better results. To aggregate similar samples, we use the properties of graph neural networks (GNNs) [14]. The main contributions of this study are as follows:
We propose a method that constructs a multilabel-based Laplacian graph such that each element in it represents the relationship between samples.
We use similar samples with an aggregation approach that is not used in traditional multilabel learning methods.
The rest of this paper is arranged as follows. Section 2 shows the taxonomy of multilabel learning algorithms and describes their methods. Section 3 presents the details of our proposed method. Section 4 describes the multilabel datasets, evaluation metrics and experimental results, followed by the conclusions in Section 5.
In this section, we review multilabel learning algorithms. The algorithms that have been applied to multilabel learning over the last decade are not just those mentioned in this paper. Figure 1 summarizes the algorithms detailed in the next section.
Taxonomy of multilabel learning algorithms [
Binary relevance (BR) is used to address a multilabel problem with a binary classifier, and its advantages are simplicity and efficiency, but correlation between labels is not considered. Classifier chains (CCs) are configured in a chain of binary classifiers where a classifier in the chain is based on the prediction of the previous classifier; their advantage is that they consider the relationship between labels but hence cannot be parallelized. Calibrated label ranking (CAL) performs ranking via the pairwise comparison of labels and has the advantage of considering the relationship (but only the pairwise relationship) between labels. Label powersets (LP) treat the situation when multiple labels belong to the same sample as a new label and have the advantage of considering the relationship between labels, but the time complexity grows exponentially with label sets. Random k-labelsets (RKL) are variants of LP models where each classifier is trained with a small random set of labels; their advantage is that they consider the relationship between labels, but they have a low accuracy rate if a worse label set combination is randomly selected.
The multilabel
GNNs were mentioned for the first time and further elaborated by [16]. The goal of a GNN is to learn a node’s representation of the acquisition of its information by propagation. Currently, there are many deep learning tasks that need to process data with graph structures. Convolutional neural networks (CNNs) [17] have been successfully developed in the field of computer vision [18, 19] but are unable to process graph structured data [20]. The method used in this paper is called a graph convolutional network (GCN). A GCN can aggregate similar samples by propagating neighbor information, giving it the ability to infer, and there is no need to consider the sequence. GCNs have appeared in many top machine learning conferences and many applications across different tasks and domains, such as manifold learning [21, 22], computer vision [23, 24, 25], text classification [26, 27], hashing [28, 29], and hyperspectral image classification [30, 31].
This section presents the overall flow of our proposed method, as shown in Figure 2. The multilabel data matrix is first converted into a similarity matrix generated from a Laplacian graph. We call this a multilabel-based Laplacian graph and use this graph as inputs to the GCN model. Each node in the output layer predicts the probability of class membership for the label.
An illustration of the work flow of the proposed method. Fully green color represents the training model; fully blue color represents the test model.
This section presents the proposed method. Before this, let us describe some notational conventions. Matrices are written in boldface capital letters (e.g.,
Based on [32], we formally present our multilabel-based Laplacian graph. For a multilabel dataset, let
We normalize
Based on [34], we fit the GCN used for single-label classification to multilabel classification. The GCN has been modified from a first-order Chebyshev approximation [35]. In order to create a multidimensional input, ChevNet convolution with an input vector
where
Repeated use of this graph convolution operation may cause serious problems such as vanishing gradients. Therefore,
Here,
This paper considers only a two-layer GCN model as the proposed method, and we modify Eq. (4) by placing the adjacent matrix into a multilabel-based Laplacian graph to obtain the formula of the two-layer GCN method proposed in this paper as follows:
where
where
The multilabel datasets used in this paper and their associated statistics are shown in Table 1.
Datasets | Domain | # of features | # of samples | # of training data | # of test data | # of classes |
---|---|---|---|---|---|---|
Emotions* | Audio | 72 | 593 | 474 | 119 | 6 |
Water Quality* | Chemistry | 16 | 1060 | 848 | 212 | 14 |
CIE Image* | Image | 294 | 2000 | 1600 | 400 | 5 |
Natural Scenes* | Image | 294 | 2407 | 1925 | 482 | 6 |
Yeast* | Biology | 103 | 2417 | 1933 | 484 | 14 |
AR Face** | Image | 1024 | 30303 | 24242 | 6061 | 6 |
Statistics of the multilabel datasets.
Multilabel datasets are available at http://mulan.sourceforge.net/datasets-mlc.html
AR Face dataset is available at http://www2.ece.ohio-state.edu/_aleix/ARdatabase.html
In this study, we have added probabilistic classifier chains [36], CSMLC [37] and RethinkNet [38] as baselines for comparison. The experimental settings are as follows: First, multilabel datasets are preprocessed to [0,1] as inputs, 80% of the samples are used for model (both multilabel learning and proposed method) training, and the last 20% of the samples are used as test sets. We also add Gaussian noise ranging from 6% to 12% of each test sample to test the robustness of the model. The overall framework is shown in Figure 2.
For deep learning, we train all models for 200 epochs using Adam [39] with a learning rate of 0.01 and the mean square error as the loss function.
In multilabel learning, the evaluation metrics must be more rigorous than traditional single-label learning because one sample may be associated with multiple labels. These evaluation metrics [15] are divided into three groups, as shown in Figure 3. The higher the values of the F1 score, precision, mean average precision and recall, the better the performance is. The lower the values of the Hamming loss, one-error, coverage and ranking loss, the better the performance is. We consider the Hamming loss, one-error and mean average precision as three major metrics.
Taxonomy of evaluation metrics.
All experiments use different combinations of training and test data to verify the trained model and average the results after repeating the training ten times. According to the observations in Figures 4–6, the following conclusions are reached:
Regardless of whether the Gaussian noise is added to the data set, the classification results of the problem transformation methods (BR, CCs, CAL, LP and RKL) are almost worse than the adaptive algorithms (MLkNN, ML-SVM and ML-DT)
Deep learning may not obtain the best performance.
We found that our method was raised on average by 1.8% and 8% higher in Hamming loss and mean average precision, respectively. And also has excellent performance even if the dataset were contaminated by noise.
Regardless of whether noise is added to the data, our method in one-error evaluation is not as good as other baselines.
Results of the proposed method compared with multilabel learning algorithms on the used multilabel datasets. (a)–(c) show the results without adding Gaussian noise.
Results of the proposed method compared with multilabel learning algorithms on the used multilabel datasets. (a)–(c) show the results of adding 6% Gaussian noise.
Results of the proposed method compared with multilabel learning algorithms on the used multilabel datasets. (a)–(c) show the results of adding 12% Gaussian noise.
In this paper, we proposed a method of constructing a relation matrix by considering the correlation and sparsity of paired samples. We then added the characteristics of a GCN, which aggregates similar samples, to finally obtain the probability of occurrence of each label. Experimental results on six datasets showed that our proposed method can deliver superior performance in comparison with eleven baselines. Our future work will include designing a general framework that can reduce the use of memory and increase the efficiency of a GCN and extending this framework to unsupervised learning.
This work is supported in part by the Data Science Lab, NSYSU, and in part by the Pervasive Artificial Intelligence Research (PAIR) Lab, Taiwan, under the grant Nos. 110-2634-F-008-004 and 110-2221-E-110-046.
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He received his Ph.D. in Molecular Biology with his thesis “Genetic variability of the tick-borne encephalitis virus in natural foci of Novosibirsk city and its suburbs.” His primary field is molecular virology with research emphasis on vector-borne viruses, especially tick-borne encephalitis virus, Kemerovo virus and Omsk hemorrhagic fever virus, rabies virus, molecular genetics, biology, and epidemiology of virus pathogens.",institutionString:"Russian Academy of Sciences",institution:{name:"Russian Academy of Sciences",country:{name:"Russia"}}},{id:"310962",title:"Dr.",name:"Amlan",middleName:"Kumar",surname:"Patra",slug:"amlan-patra",fullName:"Amlan Patra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/310962/images/system/310962.jpg",biography:"Amlan K. Patra, FRSB, obtained a Ph.D. in Animal Nutrition from Indian Veterinary Research Institute, India, in 2002. He is currently an associate professor at West Bengal University of Animal and Fishery Sciences. He has more than twenty years of research and teaching experience. He held previous positions at the American Institute for Goat Research, The Ohio State University, Columbus, USA, and Free University of Berlin, Germany. His research focuses on animal nutrition, particularly ruminants and poultry nutrition, gastrointestinal electrophysiology, meta-analysis and modeling in nutrition, and livestock–environment interaction. He has authored around 175 articles in journals, book chapters, and proceedings. Dr. Patra serves on the editorial boards of several reputed journals.",institutionString:null,institution:{name:"West Bengal University of Animal and Fishery Sciences",country:{name:"India"}}},{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.png",biography:"László Babinszky is Professor Emeritus, Department of Animal Nutrition Physiology, University of Debrecen, Hungary. He has also worked in the Department of Animal Nutrition, University of Wageningen, Netherlands; the Institute for Livestock Feeding and Nutrition (IVVO), Lelystad, Netherlands; the Agricultural University of Vienna (BOKU); the Institute for Animal Breeding and Nutrition, Austria; and the Oscar Kellner Research Institute for Animal Nutrition, Rostock, Germany. In 1992, Dr. Babinszky obtained a Ph.D. in Animal Nutrition from the University of Wageningen. His main research areas are swine and poultry nutrition. He has authored more than 300 publications (papers, book chapters) and edited four books and fourteen international conference proceedings.",institutionString:"University of Debrecen",institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"201830",title:"Dr.",name:"Fernando",middleName:"Sanchez",surname:"Davila",slug:"fernando-davila",fullName:"Fernando Davila",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201830/images/5017_n.jpg",biography:"I am a professor at UANL since 1988. My research lines are the development of reproductive techniques in small ruminants. We also conducted research on sexual and social behavior in males.\nI am Mexican and study my professional career as an engineer in agriculture and animal science at UANL. Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain. She is a Full Professor at the Department of Medicine and Animal Surgery at the same University. She developed her research activity in the field of Endocrinology, Hematology, Biochemistry and Immunology of horses. She is a scientific reviewer of several international journals : American Journal of Obstetrics and Gynecology, Comparative Clinical Pathology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology. Since 2014, she has been the Head of the Clinical Analysis Laboratory of the Hospital Clínico Veterinario from the Faculty of Veterinary, CEU-Cardenal Herrera University.",institutionString:"CEU-Cardenal Herrera University",institution:{name:"CEU Cardinal Herrera University",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",country:{name:"United Kingdom"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",biography:"Samir El-Gendy is a Professor of anatomy and embryology at the faculty of veterinary medicine, Alexandria University, Egypt. Samir obtained his PhD in veterinary science in 2007 from the faculty of veterinary medicine, Alexandria University and has been a professor since 2017. Samir is an author on 24 articles at Scopus and 12 articles within local journals and 2 books/book chapters. His research focuses on applied anatomy, imaging techniques and computed tomography. 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