Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils

References

• AASHTO., 2021. T307-99: Standard Method of Test for Determining the Resilient Modulus of Soils and Aggregate Materials. In American Association of State Highway and Transportation Officials (pp. 1–41).
   Google Scholar
• Abbasi, A., et al., 2010. Detecting fake websites: the contribution of statistical learning theory. Management Information Systems (MIS) Quarterly, 34 (3), 435–461. doi:10.2307/25750686.
   Web of Science ®Google Scholar
• Abdeljaber, O., et al., 2017. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170. doi:10.1016/j.jsv.2016.10.043.
   Web of Science ®Google Scholar
• Aboelela, A. E., et al., 2022. Characterisation and modelling of subgrade resilient modulus for pavement structural design in Egypt. Road Materials and Pavement Design, 1–24. doi:10.1080/14680629.2022.2152725.
   Web of Science ®Google Scholar
• Adem, K, 2022. Impact of activation functions and number of layers on detection of exudates using circular hough transform and convolutional neural networks. Expert Systems with Applications, 203 (117583), 1–7. doi:10.1016/j.eswa.2022.117583.
   Google Scholar
• Aitkenhead, M. J., and Coull, M. C, 2019. Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling. part 2: mapping of soil ecosystem services. Soil Use and Management, 35 (2), 217–231. doi:10.1111/sum.12491.
   Web of Science ®Google Scholar
• Al-Dulaimi, Y. F., et al., 2022. Predicting resilient modulus of unbound granular base/subbase material. Mansoura Engineering Journal, 47 (2), 1–10. doi:10.21608/bfemu.2022.223409.
   Google Scholar
• AlAfandy, K. A., Omara, H., Lazaar, M., and Al Achhab, M. (2022). Chapter 5: machine learning. In: S. S. Kshatri, K. Thakur, M. H. M. Khan, D. Singh, and G. R. Sinha, eds. Computational intelligence and applications for pandemics and healthcare (pp. 83–113). Pennsylvania: IGI Global
   Google Scholar
• Albaeni, A., et al., 2017. Regional variation in outcomes of hospitalized patients having Out-of-hospital cardiac arrest. The American Journal of Cardiology, 120 (3), 421–427. doi:10.1016/j.amjcard.2017.04.045.
   PubMed Web of Science ®Google Scholar
• Alnedawi, A., et al., 2022. Integrated and holistic knowledge map of resilient modulus studies for pavement materials: a scientometric analysis and bibliometric review of research frontiers and prospects. Transportation Geotechnics, 33 (100711), 1–22. doi:10.1016/j.trgeo.2021.100711.
   Google Scholar
• Alyasseri, Z. A. A., et al., 2021. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Systems, 39 (e12759), 1–32. doi:10.1111/exsy.12759.
   Google Scholar
• Apicella, A., et al., 2021. A survey on modern trainable activation functions. Neural Networks, 138, 14–32. doi:10.1016/j.neunet.2021.01.026.
   PubMed Web of Science ®Google Scholar
• Arshad, M, 2019. Development of a correlation between the resilient modulus and CBR value for granular blends containing natural aggregates and RAP/RCA materials. Advances in Materials Science and Engineering, 2019), doi:10.1155/2019/8238904.
   Web of Science ®Google Scholar
• Atreya, A. R., et al., 2022. Geographic variation and temporal trends in management and outcomes of cardiac arrest complicating acute myocardial infarction in the United States. Resuscitation, 170, 339–348. doi:10.1016/j.resuscitation.2021.11.002.
   PubMed Web of Science ®Google Scholar
• Bahrami, M., et al., 2019. Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: measure MSEs between targets & ANN for Fe–CuO/Eg–water nanofluid. Physica A: Statistical Mechanics and its Applications, 519, 159–168. doi:10.1016/j.physa.2018.12.031.
   Web of Science ®Google Scholar
• Banerjee, A., et al., 2020. Variation of resilient modulus of subgrade soils over a wide range of suction states. Journal of Geotechnical and Geoenvironmental Engineering, 146 (9), 1–18. doi:10.1061/(ASCE)GT.1943-5606.0002332.
   Web of Science ®Google Scholar
• Bargagli Stoffi, F. J., Cevolani, G., and Gnecco, G., 2022. Simple models in complex worlds: occam’s razor and statistical learning theory. Minds and Machines, 32 (1), 13–42. doi:10.1007/s11023-022-09592-z.
   Web of Science ®Google Scholar
• Bastola, N. R., et al., 2022. Artificial neural network prediction model for in situ resilient modulus of subgrade soils for pavement design applications. Innovative Infrastructure Solutions, 7 (54), 1–12. doi:10.1007/s41062-021-00659-x.
   Google Scholar
• Basu, S., et al., 2018. Deep neural networks for texture classification - A theoretical analysis. Neural Networks, 97, 173–182. doi:10.1016/j.neunet.2017.10.001.
   PubMed Web of Science ®Google Scholar
• Bengio, Y., Courville, A., and Vincent, P, 2013. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8), 1798–1828. doi:10.1109/TPAMI.2013.50.
   PubMed Web of Science ®Google Scholar
• Bingham, G., and Miikkulainen, R, 2022. Discovering parametric activation functions. Neural Networks, 148, 48–65. doi:10.1016/j.neunet.2022.01.001.
   PubMed Web of Science ®Google Scholar
• Blackmore, L., et al., 2020. Saturation and its effect on the resilient modulus of a pavement formation material. Géotechnique, 70 (4), 292–302. doi:10.1680/jgeot.18.P.053.
   Web of Science ®Google Scholar
• Bohra, N. C., et al., 1999. Subgrade resilient modulus correction for saturation effects. Journal of Geotechnical and Geoenvironmental Engineering, 125 (3), 233–235. doi:10.1061/(ASCE)1090-0241(1999)125:3(233).
   Google Scholar
• Boob, D., Dey, S. S., and Lan, G, 2022. Complexity of training ReLU neural network. Discrete Optimization, 44 (100620), 1–16. doi:10.1016/j.disopt.2020.100620.
   Google Scholar
• Cai, Z., Chen, J., and Liu, M, 2022. Least-squares ReLU neural network (LSNN) method for scalar nonlinear hyperbolic conservation law. Applied Numerical Mathematics, 174, 163–176. doi:10.1016/j.apnum.2022.01.002.
   Web of Science ®Google Scholar
• Cao, W., et al., 2018. A review on neural networks with random weights. Neurocomputing, 275, 278–287. doi:10.1016/j.neucom.2017.08.040.
   Web of Science ®Google Scholar
• Cary, C. E., and Zapata, C. E, 2011. Resilient modulus for unsaturated unbound materials. Road Materials and Pavement Design, 12 (3), 615–638. doi:10.1080/14680629.2011.9695263.
   Web of Science ®Google Scholar
• Ceylan, H., Gopalakrishnan, K., and Bayrak, M. B, 2008. Neural networks based concrete airfield pavement layer moduli backcalculation. Civil Engineering and Environmental Systems, 25 (3), 185–199. doi:10.1080/10286600701838667.
   Web of Science ®Google Scholar
• Chapman, B. P., Weiss, A., and Duberstein, P. R, 2016. Statistical learning theory for high dimensional prediction: application to criterion-keyed scale development. Psychological Methods, 21 (4), 603–620. doi:10.1037/met0000088.
   PubMed Web of Science ®Google Scholar
• Chen, T., et al., 2020. Pavement crack detection and recognition using the architecture of segNet. Journal of Industrial Information Integration, 18 (100144), 1–12. doi:10.1016/j.jii.2020.100144.
   Google Scholar
• Chen, X., et al., 2016. Development of distress condition index of asphalt pavements using LTPP data through structural equation modeling. Transportation Research Part C: Emerging Technologies, 68, 58–69. doi:10.1016/j.trc.2016.03.011.
   Web of Science ®Google Scholar
• Christopher, B. R., Schwartz, C., and Boudreau, R., 2006. FHWA-NHI-05-037: geotechnical aspects of pavements. In Federal highway administration, 210–230. Washington, DC: Federal Highway Administration. https://www.fhwa.dot.gov/engineering/geotech/pubs/05037/.
   Google Scholar
• de Myttenaere, A., et al., 2016. Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48. doi:10.1016/j.neucom.2015.12.114.
   Web of Science ®Google Scholar
• Deng, L., et al., 2020. Approximation of bias and mean-squared error in two-sample Mendelian randomization analyses. Biometrics, 76 (2), 369–379. doi:10.1111/biom.13169.
   PubMed Web of Science ®Google Scholar
• Doyoro, Y. G., et al., 2022. A review of open software resources in python for electrical resistivity modelling. Geoscience Letters, 9 (3), 1–16. doi:10.1186/s40562-022-00214-1.
   Google Scholar
• Dubey, S. R., Singh, S. K., and Chaudhuri, B. B, 2021. Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing, 503, 92–108. doi:10.1016/j.neucom.2022.06.111.
   Web of Science ®Google Scholar
• Dudzik, M., and Stre¸k, A. M., 2020. ANN architecture specifications for modelling of open-cell aluminum under compression. Mathematical Problems in Engineering, 2834317, 1–26. doi:10.1155/2020/2834317.
   Google Scholar
• Dũng, D., and Nguyen, V. K, 2021. Deep ReLU neural networks in high-dimensional approximation. Neural Networks, 142, 619–635. doi:10.1016/j.neunet.2021.07.027.
   PubMed Web of Science ®Google Scholar
• El-Ashwah, A. S., et al., 2019. A new approach for developing resilient modulus master surface to characterize granular pavement materials and subgrade soils. Construction and Building Materials, 194, 372–385. doi:10.1016/j.conbuildmat.2018.10.212.
   Web of Science ®Google Scholar
• El-Ashwah, A. S., et al., 2022. Advanced characterization of unbound granular materials for pavement structural design in Egypt. International Journal of Pavement Engineering, 23 (2), 476–488. doi:10.1080/10298436.2020.1754416.
   Web of Science ®Google Scholar
• Elbagalati, O., et al., 2017. Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing. Canadian Journal of Civil Engineering, 44 (9), 700–706. doi:10.1139/cjce-2017-0132.
   Web of Science ®Google Scholar
• Farh, N. K., Awed, A. M., and El-Badawy, S. M, 2020. Artificial neural network model for predicating resilient modulus of silty subgrade soil. American Journal of Civil Engineering and Architecture, 8 (2), 52–55. doi:10.12691/ajcea-8-2-4.
   Google Scholar
• Fatima, N, 2020. Enhancing performance of a deep neural network: a comparative analysis of optimization algorithms. Advances in Distributed Computing and Artificial Intelligence Journal, 9 (2), 79–90. doi:10.14201/ADCAIJ2020927990.
   Google Scholar
• Fedakar, H. I., 2022. Developing New empirical formulae for the resilient modulus of fine-grained subgrade soils using a large long-term pavement performance dataset and artificial neural network approach. Transportation Research Record, 2676 (4), 58–75. doi:10.1177/03611981211057054.
   Web of Science ®Google Scholar
• Fisher, O., et al., 2022. Data-driven modelling of bioprocesses: data volume, variability, and visualisation for an industrial bioprocess. Biochemical Engineering Journal, 185 (May), 108499. doi:10.1016/j.bej.2022.108499.
   Google Scholar
• Frías-Paredes, L., et al., 2018. Dynamic mean absolute error as new measure for assessing forecasting errors. Energy Conversion and Management, 162, 176–188. doi:10.1016/j.enconman.2018.02.030.
   Web of Science ®Google Scholar
• Gabr, A. R., et al., 2022. A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques. International Journal of Pavement Engineering, 23 (10), 3346–3356. doi:10.1080/10298436.2021.1892109.
   Web of Science ®Google Scholar
• Giannakas, F., et al., 2021. A deep learning classification framework for early prediction of team-based academic performance. Applied Soft Computing, 106 (107355), 1–17. doi:10.1016/j.asoc.2021.107355.
   Google Scholar
• Gong, H., Huang, B., and Shu, X, 2018. Field performance evaluation of asphalt mixtures containing high percentage of RAP using LTPP data. Construction and Building Materials, 176, 118–128. doi:10.1016/j.conbuildmat.2018.05.007.
   Web of Science ®Google Scholar
• Gong, H., et al., 2019. Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests. Construction and Building Materials, 204, 203–212. doi:10.1016/j.conbuildmat.2019.01.198.
   Web of Science ®Google Scholar
• Gong, H., et al., 2018. Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890–897. doi:10.1016/j.conbuildmat.2018.09.017.
   Web of Science ®Google Scholar
• Guan, Q. Z., and Yang, Z. X, 2022. Hybrid deep learning model for prediction of monotonic and cyclic responses of sand. Acta Geotechnica, 1–15. doi:10.1007/s11440-022-01656-9.
   Web of Science ®Google Scholar
• Guikema, S. D, 2009. Natural disaster risk analysis for critical infrastructure systems: an approach based on statistical learning theory. Reliability Engineering and System Safety, 94, 855–860. doi:10.1016/j.ress.2008.09.003.
   Web of Science ®Google Scholar
• Guo, Z., Ou, S., and Wang, J, 2020. Multistability of switched neural networks with sigmoidal activation functions under state-dependent switching. Neural Networks, 122, 239–252. doi:10.1016/j.neunet.2019.10.012.
   PubMed Web of Science ®Google Scholar
• Gupta, H. V., and Kling, H, 2011. On typical range, sensitivity, and normalization of mean squared error and nash-sutcliffe efficiency type metrics. Water Resources Research, 47 (10), W10601. doi:10.1029/2011WR010962.
   Web of Science ®Google Scholar
• Gutierrez, D., Tapia, A., and Rodriguez, A., 2020. Algoritmos genéticos con python: Un enfoque práctico para resolver problemas de ingeniería. Madrid: Marcombo.
   Google Scholar
• Haghighi, H., et al., 2018. A new approach for determining resilient moduli of marginal pavement base materials using the staged repeated load CBR test method. Road Materials and Pavement Design, 19 (8), 1848–1867. doi:10.1080/14680629.2017.1352532.
   Web of Science ®Google Scholar
• Han, J., Jentzen, A., and Weinan, E, 2018. Solving high-dimensional partial differential equations using deep learning. Proceedings of the National Academy of Sciences of the United States of America, 115 (34), 8505–8510. doi:10.1073/pnas.1718942115.
   PubMed Web of Science ®Google Scholar
• Hao, S., and Pabst, T, 2022. Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models. Acta Geotechnica, 17 (4), 1383–1402. doi:10.1007/s11440-022-01472-1.
   Web of Science ®Google Scholar
• Haque, M. A., et al., 2023. Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm. Materials Today Communications, 35 (105547), 1–20. doi:10.1016/j.mtcomm.2023.105547.
   Google Scholar
• Huang, Y. H, 2004. Pavement Analysis and Design, Second Edition. In Upper Saddle River, NJ:07458 Prentice Hall.
   Google Scholar
• Huyan, J., et al., 2020. CrackU-net: a novel deep convolutional neural network for pixelwise pavement crack detection. Structural Control and Health Monitoring, 27 (e2551), 1–19. doi:10.1002/stc.2551.
   Google Scholar
• Ikeagwuani, C. C., and Nwonu, D. C, 2022. Statistical analysis and prediction of spatial resilient modulus of coarse–grained soils for pavement subbase and base layers using MLR. ANN and Ensemble Techniques. Innovative Infrastructure Solutions, 7 (273), 1–19. doi:10.1007/s41062-022-00875-z.
   Google Scholar
• Ikeagwuani, C. C., Nwonu, D. C., and Nweke, C. C, 2021. Resilient modulus descriptive analysis and estimation for fine-grained soils using multivariate and machine learning methods. International Journal of Pavement Engineering, 1–16. doi:10.1080/10298436.2021.1895993.
   Web of Science ®Google Scholar
• Jeong, J.-H., Jo, H., and Ditzler, G, 2020. Convolutional neural networks for pavement roughness assessment using calibration-free vehicle dynamics. Computer-Aided Civil and Infrastructure Engineering, 35, 1209–1229. doi:10.1111/mice.12546.
   Web of Science ®Google Scholar
• Jia, Y., et al., 2021. A comparative long-term effectiveness assessment of preventive maintenance treatments under various environmental conditions. Construction and Building Materials, 273 (121717), 1–10. doi:10.1016/j.conbuildmat.2020.121717.
   Google Scholar
• Jiang, M., et al., 2022. Fiber laser development enabled by machine learning: review and prospect. PhotoniX, 3 (16), 1–27. doi:10.1186/s43074-022-00055-3.
   Google Scholar
• Jung, S., and Kwon, S.-D, 2013. Weighted error functions in artificial neural networks for improved wind energy potential estimation. Applied Energy, 111, 778–790. doi:10.1016/j.apenergy.2013.05.060.
   Web of Science ®Google Scholar
• Karal, O, 2017. Maximum likelihood optimal and robust support vector regression with lncosh loss function. Neural Networks, 94, 1–12. doi:10.1016/j.neunet.2017.06.008.
   PubMed Web of Science ®Google Scholar
• Karlaftis, A. G., and Badr, A, 2015. Predicting asphalt pavement crack initiation following rehabilitation treatments. Transportation Research Part C: Emerging Technologies, 55, 510–517. doi:10.1016/j.trc.2015.03.031.
   Web of Science ®Google Scholar
• Karunasingha, D. S. K, 2022. Root mean square error or mean absolute error? Use their ratio as well. Information Sciences, 585, 609–629. doi:10.1016/j.ins.2021.11.036.
   Web of Science ®Google Scholar
• Kashyap, A. A., et al., 2022. Traffic flow prediction models – A review of deep learning techniques. Cogent Engineering, 9 (1), 2010510. doi:10.1080/23311916.2021.2010510.
   Web of Science ®Google Scholar
• Kazi, M.-K., Eljack, F., and Mahdi, E, 2020. Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves. Composite Structures, 254 (112885), 1–10. doi:10.1016/j.compstruct.2020.112885.
   Google Scholar
• Kearns, M. J., and Vazirani, U. V., 1994. An introduction to computational learning theory. Cambridge, MA: MIT press. ISBN: 9780262111935.
   Google Scholar
• KERAS, 2021. Keras API reference / Optimizers / RMSprop. Keras Website.
   Google Scholar
• Khan, M. E., et al., 2017. Vprop: variational Inference using RMSprop. 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA., 1–8.
   Google Scholar
• Khosrowabadi, N., Hoberg, K., and Imdahl, C, 2022. Evaluating human behaviour in response to AI recommendations for judgemental forecasting. European Journal of Operational Research, 303 (3), 1151–1167. doi:10.1016/j.ejor.2022.03.017.
   Web of Science ®Google Scholar
• Kim, D., and Kim, J. R, 2007. Resilient behavior of compacted subgrade soils under the repeated triaxial test. Construction and Building Materials, 21 (7), 1470–1479. doi:10.1016/j.conbuildmat.2006.07.006.
   Web of Science ®Google Scholar
• Kim, S., and Kim, H, 2016. A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32 (3), 669–679. doi:10.1016/j.ijforecast.2015.12.003.
   Web of Science ®Google Scholar
• Kim, Y., and Kim, Y, 2022. Explainable heat-related mortality with random forest and SHapley additive exPlanations (SHAP) models. Sustainable Cities and Society, 79 (103677), 1–15. doi:10.1016/j.scs.2022.103677.
   Google Scholar
• Kriegeskorte, N., and Golan, T, 2019. Neural network models and deep learning. Current Biology, 29, R231–R236. doi:10.1016/j.cub.2019.02.034.
   PubMed Web of Science ®Google Scholar
• Kulkarni, S. R., and Harman, G, 2011. Statistical learning theory: a tutorial. Wiley Interdisciplinary Reviews: Computational Statistics, 3 (6), 543–556. doi:10.1002/wics.179.
   Google Scholar
• Kumar, T., et al., 2022. Fuzzy logic and machine learning-enabled recommendation system to predict suitable academic program for students. Mathematical Problems in Engineering, 5298468, 1–7. doi:10.1155/2022/5298468.
   Google Scholar
• Kumari, A., et al., 2021. Application of statistical learning theory for thermodynamic modeling of natural gas hydrates. Petroleum, 7 (4), 502–508. doi:10.1016/j.petlm.2021.10.005.
   Google Scholar
• Langdon, W. B., et al., 2016. Exact mean absolute error of baseline predictor, MARP0. Information and Software Technology, 73, 16–18. doi:10.1016/j.infsof.2016.01.003.
   Web of Science ®Google Scholar
• Langer, S, 2021. Approximating smooth functions by deep neural networks with sigmoid activation function. Journal of Multivariate Analysis, 182 (104696), 1–21. doi:10.1016/j.jmva.2020.104696.
   Google Scholar
• Le, H. V., et al., 2021. A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas. Ecological Informatics, 63 (101300), 1–13. doi:10.1016/j.ecoinf.2021.101300.
   Google Scholar
• LeCun, Y., Bengio, Y., and Hinton, G, 2015. Deep learning. Nature, 521 (7553), 436–444. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Ledesma, S., et al., 2022. On removing conflicts for machine learning. Expert Systems With Applications, 206 (117835), 1–8. doi:10.1016/j.eswa.2022.117835.
   Google Scholar
• Lekarp, F., Isacsson, U., and Dawson, A, 2000. State of the Art. I: resilient response of unbound aggregates. Journal of Transportation Engineering, 126 (1), 66–75. doi:10.1061/(ASCE)0733-947X(2000)126:1(66).
   Web of Science ®Google Scholar
• Li, B. D., and Selig, E. T, 1994. Resilient modulus for fine-grained. Journal of Geotechnical Engineering, 120 (6), 939–957. doi:10.1061/(ASCE)0733-9410(1994)120:6(939).
   Google Scholar
• Li, L., Cai, Y., and Zhou, Q, 2022. A survey on machine learning-based routing for VLSI physical design. Integration, the VLSI Journal, 86, 51–56. doi:10.1016/j.vlsi.2022.05.003.
   Web of Science ®Google Scholar
• Li, L., et al., 2017. A convergent smoothing algorithm for training max-min fuzzy neural networks. Neurocomputing, 260, 404–410. doi:10.1016/j.neucom.2017.04.046.
   Web of Science ®Google Scholar
• Liao, Z., et al., 2021. Echo state network activation function based on bistable stochastic resonance. Chaos, Solitons and Fractals: Nonlinear Science, and Nonequilibrium and Complex Phenomena, 153 (111503), 1–14. doi:10.1016/j.chaos.2021.111503.
   Google Scholar
• Liu, C., and Jiang, M, 2020. Robust adaptive filter with lncosh cost. Signal Processing, 168 (107348), 1–14. doi:10.1016/j.sigpro.2019.107348.
   Google Scholar
• Liu, F., Ye, Z., and Wang, L, 2022. Deep transfer learning-based vehicle classification by asphalt pavement vibration. Construction and Building Materials, 342 (127997), 1–14. doi:10.1016/j.conbuildmat.2022.127997.
   Google Scholar
• Liu, S., et al., 2016. Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils. Engineering Geology, 209, 128–142. doi:10.1016/j.enggeo.2016.05.018.
   Web of Science ®Google Scholar
• Liu, W., et al., 2017. A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. doi:10.1016/j.neucom.2016.12.038.
   Web of Science ®Google Scholar
• Liu, Y., et al., 2022. Understanding information diffusion with psychological field dynamic. Information Processing and Management, 59 (102956), 1–16. doi:10.1016/j.ipm.2022.102956.
   Google Scholar
• Liu, Y., et al., 2022. Multistability analysis of delayed recurrent neural networks with a class of piecewise nonlinear activation functions. Neural Networks, 152, 80–89. doi:10.1016/j.neunet.2022.04.015.
   PubMed Web of Science ®Google Scholar
• Lo, S. K., et al., 2021. A systematic literature review on federated machine learning: from a sofware engineering perspective. ACM Computing Surveys, 54 (5:95), 1–39. doi:10.1145/3450288.
   Web of Science ®Google Scholar
• Lozano-Diez, A., et al., 2017. An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition. PLoS ONE, 12 (8), e0182580. doi:10.1371/journal.pone.0182580.
   PubMed Web of Science ®Google Scholar
• Malla, R. B., and Joshi, S, 2007. Resilient modulus prediction models based on analysis of LTPP data for subgrade soils and experimental verification. Journal of Transportation Engineering, 133 (9), 491–504. doi:10.1061/(ASCE)0733-947X(2007)133:9(491).
   Web of Science ®Google Scholar
• Malla, R. B., and Joshi, S, 2008. Subgrade resilient modulus prediction models for coarse and fine-grained soils based on long-term pavement performance data. International Journal of Pavement Engineering, 9 (6), 431–444. doi:10.1080/10298430802279835.
   Google Scholar
• Mehdizadeh, S., et al., 2020. Implementing novel hybrid models to improve indirect measurement of the daily soil temperature: elman neural network coupled with gravitational search algorithm and ant colony optimization. Measurement, 165 (108127), 1–17. doi:10.1016/j.measurement.2020.108127.
   Google Scholar
• Menares, C., et al., 2021. Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks. Urban Climate, 38, 100906. doi:10.1016/j.uclim.2021.100906.
   Web of Science ®Google Scholar
• Minh, D., et al., 2022. Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 55 (5), 3503–3568. doi:10.1007/s10462-021-10088-y.
   Web of Science ®Google Scholar
• Mohammad, L. N., et al., 2007. Prediction of resilient modulus of cohesive subgrade soils from dynamic cone penetrometer test parameters. Journal of Materials in Civil Engineering, 19 (11), 986–992. doi:10.1061/(ASCE)0899-1561(2007)19:11(986).
   Web of Science ®Google Scholar
• Mohanraj, M., Jayaraj, S., and Muraleedharan, C, 2012. Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems - A review. Renewable and Sustainable Energy Reviews, 16, 1340–1358. doi:10.1016/j.rser.2011.10.015.
   Web of Science ®Google Scholar
• Moosavi, A. A., Nematollahi, M. A., and Rahimi, M, 2021. Predicting water sorptivity coefficient in calcareous soils using a wavelet-neural network hybrid modeling approach. Environmental Earth Sciences, 80 (226), 1–19. doi:10.1007/s12665-021-09518-5.
   Google Scholar
• Mukkamala, M. C., and Hein, M, 2017. Variants of RMSProp and Adagrad with logarithmic regret bounds. 34th International Conference on Machine Learning, Sydney, Australia, 70, 2545–2553.
   Google Scholar
• Müller, A., and Taras, A, 2022. Prediction of the local buckling strength and load-displacement behaviour of SHS and RHS members using deep neural networks (DNN) – introduction to the deep neural network direct stiffness method (DNN-DSM). Steel Construction, 15, 78–90. doi:10.1002/stco.202100047.
   Google Scholar
• Murray, M., Abrol, V., and Tanner, J, 2022. Activation function design for deep networks: linearity and effective initialisation. Applied and Computational Harmonic Analysis, 59, 117–154. doi:10.1016/j.acha.2021.12.010.
   Web of Science ®Google Scholar
• Reddy, Naveen, et al., 2018. Comparison of programming languages: review. International Journal of Computer Science & Communication, 9 (2), 113–122.
   Google Scholar
• Ng, C. W. W., et al., 2013. Resilient modulus of unsaturated subgrade soil: experimental and theoretical investigations. Canadian Geotechnical Journal, 50 (2), 223–232. doi:10.1139/cgj-2012-0052.
   Web of Science ®Google Scholar
• Njima, W., et al., 2019. Beyond stochastic gradient descent for matrix completion based indoor localization. Applied Sciences, 9 (2414), 1–21. doi:10.3390/app9122414.
   Google Scholar
• Okoroafor, E. R., et al., 2022. Machine learning in subsurface geothermal energy: two decades in review. Geothermics, 102 (102401), 1–16. doi:10.1016/j.geothermics.2022.102401.
   Google Scholar
• Oneto, L., et al., 2017. Measuring the expressivity of graph kernels through statistical learning theory. Neurocomputing, 268, 4–16. doi:10.1016/j.neucom.2017.02.088.
   Web of Science ®Google Scholar
• Oostwal, E., Straat, M., and Biehl, M, 2021. Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation. Physica A: Statistical Mechanics and Its Applications, 564 (125517), 1–14. doi:10.1016/j.physa.2020.125517.
   Google Scholar
• Örnek, B. N., et al., 2022. Some remarks on activation function design in complex extreme learning using schwarz lemma. Neurocomputing, 492, 23–33. doi:10.1016/j.neucom.2022.04.010.
   Web of Science ®Google Scholar
• Pan, X., et al., 2021. A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators, B: Chemical, 342, 129982. doi:10.1016/j.snb.2021.129982.
   Web of Science ®Google Scholar
• Pan, Y., and Froese, F. J, 2022. An interdisciplinary review of AI and HRM: challenges and future directions. Human Resource Management Review, 100924, 1–22. doi:10.1016/j.hrmr.2022.100924.
   Google Scholar
• Papagiannakis, A., and Masad, E, 2012. Pavement Design and Materials. https://doi.org/10.1002/9780470259924.
   Google Scholar
• Piotrowski, A. P., and Napiorkowski, J. J, 2013. A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. Journal of Hydrology, 476, 97–111. doi:10.1016/j.jhydrol.2012.10.019.
   Web of Science ®Google Scholar
• Polo-Mendoza, R., Martinez-Arguelles, G., and Peñabaena-Niebles, R, 2022. A multi-objective optimization based on genetic algorithms for the sustainable design of warm Mix asphalt (WMA). International Journal of Pavement Engineering, 1–21. doi:10.1080/10298436.2022.2074417.
   Web of Science ®Google Scholar
• Polo-Mendoza, R., Martinez-Arguelles, G., and Peñabaena-Niebles, R, 2023. Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques. Results in Engineering, 17 (100984), 1–15. doi:10.1016/j.rineng.2023.100984.
   Google Scholar
• Pouyanfar, S., et al., 2018. A survey on deep learning: algorithms, techniques, and applications. ACM Computing Surveys, 51 (5), 1–36. doi:10.1145/3234150.
   Web of Science ®Google Scholar
• Pudjihartono, N., et al., 2022. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics, 2 (927312), 1–17. doi:10.3389/fbinf.2022.927312.
   Google Scholar
• Puntoni, S., et al., 2021. Consumers and artificial intelligence: an experiential perspective. Journal of Marketing, 85 (1), 131–151. doi:10.1177/0022242920953847.
   Web of Science ®Google Scholar
• Qiu, B., et al., 2021. Automatic segmentation of mandible from conventional methods to deep learning - A review. Journal of Personalized Medicine, 11 (629), 1–26. doi:10.3390/jpm11070629.
   Google Scholar
• Quiroga, L. A., and Rabelo, L. C, 1995. Learning from examples: a review of machine learning, neural networks and fuzzy logic paradigms. Computers and Industrial Engineering, 29 (1–4), 561–565. doi:10.1016/0360-8352(95)00134-M.
   Google Scholar
• Rahim, A. M, 2005. Subgrade soil index properties to estimate resilient modulus for pavement design. International Journal of Pavement Engineering, 6 (3), 163–169. doi:10.1080/10298430500140891.
   Google Scholar
• Rahim, A. M., and George, K. P, 2005. Models to estimate subgrade resilient modulus for pavement design. International Journal of Pavement Engineering, 6 (2), 89–96. doi:10.1080/10298430500131973.
   Google Scholar
• Rizk, H., Abbas, M., and Youssef, M, 2021. Device-independent cellular-based indoor location tracking using deep learning. Pervasive and Mobile Computing, 75, 101420. doi:10.1016/j.pmcj.2021.101420.
   Web of Science ®Google Scholar
• Scavuzzo, C. M., et al., 2022. Feature importance: opening a soil-transmitted helminth machine learning model via SHAP. Infectious Disease Modelling, 7 (1), 262–276. doi:10.1016/j.idm.2022.01.004.
   PubMedGoogle Scholar
• Schaap, M. G., and Bouten, W, 1996. Modeling water retention curves of sandy soils using neural networks. Water Resources Research, 32 (10), 3033–3040. doi:10.1029/96WR02278.
   Web of Science ®Google Scholar
• Sewdien, V. N., et al., 2020. Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting. Renewable Energy, 161, 878–892. doi:10.1016/j.renene.2020.07.117.
   Web of Science ®Google Scholar
• Sezer, O. B., Gudelek, M. U., and Ozbayoglu, A. M, 2020. Financial time series forecasting with deep learning: a systematic literature review: 2005–2019. Applied Soft Computing Journal, 90 (106181), 1–32. doi:10.1016/j.asoc.2020.106181.
   Google Scholar
• Shah, J., and Wang, W, 2022. An evolving neuro-fuzzy classifier for fault diagnosis of gear systems. ISA Transactions, 123, 372–380. doi:10.1016/j.isatra.2021.05.019.
   PubMed Web of Science ®Google Scholar
• Shao, Z., et al., 2022. Tracing the evolution of AI in the past decade and forecasting the emerging trends. Expert Systems With Applications, 209 (118221), 1–21. doi:10.1016/j.eswa.2022.118221.
   Google Scholar
• Shen, S.-L., et al., 2022. Enhancement of neural networks with an alternative activation function tanhLU. Expert Systems with Applications, 199 (117181), 1–13. doi:10.1016/j.eswa.2022.117181.
   Google Scholar
• Sollazzo, G., Fwa, T. F., and Bosurgi, G, 2017. An ANN model to correlate roughness and structural performance in asphalt pavements. Construction and Building Materials, 134, 684–693. doi:10.1016/j.conbuildmat.2016.12.186.
   Web of Science ®Google Scholar
• Stefan, M., and Hidiroglou, M. A, 2021. Estimation of design-based mean squared error of a small area mean model-based estimator under a nested error linear regression model. Canadian Journal of Statistics, 49 (4), 1338–1363. doi:10.1002/cjs.11622.
   Web of Science ®Google Scholar
• Tanaka, M, 2020. Weighted sigmoid gate unit for an activation function of deep neural network. Pattern Recognition Letters, 135, 354–359. doi:10.1016/j.patrec.2020.05.017.
   Web of Science ®Google Scholar
• Tavanaei, A., et al., 2019. Deep learning in spiking neural networks. Neural Networks, 111, 47–63. doi:10.1016/j.neunet.2018.12.002.
   PubMed Web of Science ®Google Scholar
• Titi, H. H., Elias, M. B., and Helwany, S, 2006. SPR# 0092-03-11: determination of typical resilient modulus values for selected soils in Wisconsin. Wisconsin Highway Research Program, WHRP 06-06, 32–50.
   Google Scholar
• Tran, V. Q., et al., 2021. Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS. PLoS One, 16 (12), e0260847. doi:10.1371/journal.pone.0260847.
   PubMed Web of Science ®Google Scholar
• Ullah, I., et al., 2023. Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction. Travel Behaviour and Society, 31, 78–92. doi:10.1016/j.tbs.2022.11.006.
   Web of Science ®Google Scholar
• Uzan, J, 1985. Characterization of granular materials. Transportation Research Record, 1022, 52–59.
   Google Scholar
• Vallabhajosyula, S., et al., 2020. Regional variation in the management and outcomes of acute myocardial infarction With cardiogenic shock in the United States. Circulation: Heart Failure, 13 (e006661), 1–10. doi:10.1161/CIRCHEARTFAILURE.119.006661.
   Google Scholar
• Velasco-Gallego, C., and Lazakis, I, 2020. Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study. Ocean Engineering, 218 (108261), 1–23. doi:10.1016/j.oceaneng.2020.108261.
   Google Scholar
• Walubita, L. F., et al., 2022. Comparative environmental assessment of rigid. Flexible, and Perpetual Pavement: A Case Study of Texas. Sustainability, 14 (9983), 1–22. doi:10.3390/su14169983.
   Google Scholar
• Wang, S., et al., 2021. Deep neural networks for choice analysis: a statistical learning theory perspective. Transportation Research Part B: Methodological, 148, 60–81. doi:10.1016/j.trb.2021.03.011.
   Web of Science ®Google Scholar
• Wang, Z., et al., 2022. Why KDAC? A general activation function for knowledge discovery. Neurocomputing, 501, 343–358. doi:10.1016/j.neucom.2022.06.019.
   Web of Science ®Google Scholar
• Wei, C., et al., 2020. An improved phase field method by using statistical learning theory-based optimization algorithm for simulation of martensitic transformation in NiTi alloy. Computational Materials Science, 172 (109292), 1–8. doi:10.1016/j.commatsci.2019.109292.
   Google Scholar
• Wu, Q., et al., 2021. Quantized kernel lleast lncosh algorithm. Signal Processing, 189 (108255), 1–8. doi:10.1016/j.sigpro.2021.108255.
   Google Scholar
• Xu, D., et al., 2021. Convergence of the RMSProp deep learning method with penalty for nonconvex optimization. Neural Networks, 139, 17–23. doi:10.1016/j.neunet.2021.02.011.
   PubMed Web of Science ®Google Scholar
• Xu, K., et al., 2021. Learning viscoelasticity models from indirect data using deep neural networks. Computer Methods in Applied Mechanics and Engineering, 387, 114124. doi:10.1016/j.cma.2021.114124.
   Web of Science ®Google Scholar
• Xu, S., et al., 2021. A high-precision implementation of the sigmoid activation function for computing-in-memory architecture. Micromachines, 12 (1183), 1–12. doi:10.3390/mi12101183.
   Google Scholar
• Yaghoubi, E., et al., 2021. Improving expansive clay subgrades using recycled glass: resilient modulus characteristics and pavement performance. Construction and Building Materials, 302 (124384), 1–12. doi:10.1016/j.conbuildmat.2021.124384.
   Google Scholar
• Yang, X., et al., 2022. A review: machine learning for combinatorial optimization problems in energy areas. Algorithms, 15 (205), 1–43. doi:10.3390/a15060205.
   Google Scholar
• Yang, Z., et al., 2020. Overfitting effect of artificial neural network based nonlinear equalizer: from mathematical origin to transmission evolution. Science China Information Sciences, 63 (160305), 1–13. doi:10.1007/s11432-020-2873-x.
   Google Scholar
• Yarveicy, H., Moghaddam, A. K., and Ghiasi, M. M, 2014. Practical use of statistical learning theory for modeling freezing point depression of electrolyte solutions: LSSVM model. Journal of Natural Gas Science and Engineering, 20, 414–421. doi:10.1016/j.jngse.2014.06.020.
   Web of Science ®Google Scholar
• Yau, A., and Quintus, H. L. Von., 2002. Study of LTPP laboratory resilient modulus test data and response characteristics (Issue FHWA-RD-02-051).
   Google Scholar
• Yilmaz, A., and Poli, R, 2022. Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean. Neural Networks, 153, 87–103. doi:10.1016/j.neunet.2022.05.030.
   PubMed Web of Science ®Google Scholar
• Yousefi, E., et al., 2022. A novel long-term water absorption and thickness swelling deep learning forecast method for corn husk fiber-polypropylene composite. Case Studies in Construction Materials, 17 (e01268), 1–11. doi:10.1016/j.cscm.2022.e01268.
   Google Scholar
• Yu, R., et al., 2020. Convolutional neural networks with refined loss functions for the real-time crash risk analysis. Transportation Research Part C: Emerging Technologies, 119, 102740. doi:10.1016/j.trc.2020.102740.
   Web of Science ®Google Scholar
• Yu, Y., and Sun, L, 2018. Effect of overlay thickness, overlay material, and pre-overlay treatment on evolution of asphalt concrete overlay roughness in LTPP SPS-5 experiment: a multilevel model approach. Construction and Building Materials, 162, 192–201. doi:10.1016/j.conbuildmat.2017.12.039.
   Web of Science ®Google Scholar
• Zaman, M., et al., 2010. Neural network modeling of resilient modulus using routine subgrade soil properties. International Journal of Geomechanics, 10 (1), 1–12. doi:10.1061/(ASCE)1532-3641(2010)10:1(1).
   Web of Science ®Google Scholar
• Zhang, J., et al., 2021. Prediction of resilient modulus for subgrade soils based on ANN approach. Journal of Central South University, 28 (3), 898–910. doi:10.1007/s11771-021-4652-7.
   Web of Science ®Google Scholar
• Zhang, J., et al., 2021. Predicting resilient modulus of fine-grained subgrade soils considering relative compaction and matric suction. Road Materials and Pavement Design, 22 (3), 703–715. doi:10.1080/14680629.2019.1651756.
   Web of Science ®Google Scholar
• Zhang, L., and Suganthan, P. N, 2016. A comprehensive evaluation of random vector functional link networks. Information Sciences, 367–368, 1094–1105. doi:10.1016/j.ins.2015.09.025.
   Web of Science ®Google Scholar
• Zhang, S., et al., 2019. Deep learning based recommender system: a survey and New perspectives. ACM Computing Surveys, 52 (1), 1–38. doi:10.1145/3158369.
   Web of Science ®Google Scholar
• Zhang, X., et al., 2021. Empirical study on tangent loss function for classification with deep neural networks. Computers and Electrical Engineering, 90, 107000. doi:10.1016/j.compeleceng.2021.107000.
   Web of Science ®Google Scholar
• Zhang, X., et al., 2023. Transfer learning for predicting reorganization energy. Computational Materials Science, 228 (112361), 1–11. doi:10.1016/j.commatsci.2023.112361.
   Google Scholar
• Zhao, Z., et al., 2021. Viscosity prediction of rubberized asphalt-rejuvenated recycled asphalt pavement binders using artificial neural network approach. Journal of Materials in Civil Engineering, 33 (5:04021071), 1–12. doi:10.1061/(ASCE)MT.1943-5533.0003679.
   Web of Science ®Google Scholar
• Zou, W., et al., 2021. Predicting resilient modulus of compacted subgrade soils under influences of freeze–thaw cycles and moisture using gene expression programming and artificial neural network approaches. Transportation Geotechnics, 28 (100520), 1–17. doi:10.1016/j.trgeo.2021.100520.
   Google Scholar