Geomechanics and Engineering

  • 제32권6호
  • /
  • Pages.583-600
  • /
  • 2023
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles

  • Mahzad Esmaeili-Falak (Department of Civil Engineering, North Tehran Branch, Islamic Azad University) ;
  • Reza Sarkhani Benemaran (Department of Civil Engineering, Faculty of Geotechnical Engineering, University of Zanjan)
  • 투고 : 2022.10.21
  • 심사 : 2023.02.19
  • 발행 : 2023.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

The resilient modulus (MR) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The MR determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the MR of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviatoric stress (σd). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ3, and σd is recognized as the most suitable model, with R2 and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in MR prediction procedure.

키워드

참고문헌

  1. AASHTO T-307. (2017), Standard Method of Test for Determining the Resilient Modulus of Soil and Aggregate Materials; AASHTO: Washington, DC, USA.
  2. Addison, M.B. and Polma, F.A. (2007), "Extending durability of lime modified clay subgrades with cement stabilization", Soil Improvement, 1-10. https://doi.org/10.1061/40916(235)7.
  3. Aghayari Hir, M., Zaheri, M. and Rahimzadeh, N. (2022), "Prediction of rural travel demand by spatial regression and artificial neural network methods (Tabriz County)", J. Transport. Res., https://doi.org/10.22034/TRI.2022.312204.2970.
  4. Amadi, A.A. (2014), "Enhancing durability of quarry fines modified black cotton soil subgrade with cement kiln dust stabilization", Transport. Geotech., 1(1), 55-61. https://doi.org/10.1016/j.trgeo.2014.02.002.
  5. Babbar, A., Prakash, C., Singh, S., Gupta, M.K., Mia, M. and Pruncu, C.I. (2020), "Application of hybrid nature-inspired algorithm: Single and bi-objective constrained optimization of magnetic abrasive finishing process parameters", J. Mater. Res. Technol., 9(4), 7961-7974. https://doi.org/10.1016/j.jmrt.2020.05.003.
  6. Bandara, N., Binoy, T.H. and Aboujrad, H.S. (2015), "Freeze-thaw durability of subgrades stabilized with recycled materials", In Cold Reg. Eng., 135-145. https://doi.org/10.1061/9780784479315.013.
  7. Benemaran, R.S. and Esmaeili-Falak, M. (2020), "Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO", Comput. Concrete, 26(4), 309-316. https://doi.org/10.12989/cac.2020.26.4.309.
  8. Camarena, L. (2021), "Using artificial intelligence to estimate nonlinear resilient modulus parameters from common index properties", Transport. Res. Record, 2675(11), 1054-1061. https://doi.org/10.1177/03611981211023766.
  9. Cemiloglu, A., Zhu, L., Arslan, S., Xu, J., Yuan, X., Azarafza, M., and Derakhshani, R. (2023), "Support Vector Machine (SVM) application for Uniaxial Compression Strength (UCS) prediction: A case study for maragheh limestone", Appl. Sci., 13(4), 2217. https://doi.org/10.3390/app13042217.
  10. Chauhan, P., Akiner, M.E., Sain, K. and Kumar, A. (2022), "Forecasting of suspended sediment concentration in the Pindari-Kafni glacier valley in Central Himalayan region considering the impact of precipitation: using soft computing approach", Arabian J. Geosci., 15(8), 683. https://doi.org/10.1007/s12517-022-09773-1
  11. Chen, L., Li, Y., Zhao, G., Zhang, C. and Gao, F. (2020), "Multi-objective optimization and experimental investigation on hot extruded plate of high strength Al-Zn-Mg alloy", J. Mater. Res. Technol., 9(1), 507-519. https://doi.org/10.1016/j.jmrt.2019.10.080.
  12. Chen, T. and Guestrin, C. (2016). "Xgboost: A scalable tree boosting system", Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining.
  13. Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y. and Cho, H. (2015), "Xgboost: extreme gradient boosting", R Package Version 0.4-2, 1(4), 1-4.
  14. Chen, W., Min, S., Chala, A.T., Zhang, Y. and Liu, X. (2020), "Assessing compaction of existing railway subgrades using dynamic cone penetration testing", Proceedings of the Institution of Civil Engineers-Geotechnical Engineering.
  15. Chou, J.S., Chong, W.K. and Bui, D.K. (2016), "Nature-inspired metaheuristic regression system: programming and implementation for civil engineering applications", J. Comput. Civil Eng., 30(5), 4016007. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000561.
  16. Coban, H.S. and Cetin, B. (2022), "Suitability assessment of using lime sludge for subgrade soil stabilization", J. Mater. Civil Eng., 34(3), 4021486. https://doi.org/10.1061/(ASCE)MT.1943-5533.0004122.
  17. Dhar, A.R., Gupta, D., Roy, S.S. and Lohar, A.K. (2022), "Forward and backward modeling of direct metal deposition using metaheuristic algorithms tuned artificial neural network and extreme gradient boost", Progress Additive Manufact., 1-15. https://doi.org/10.1007/s40964-021-00251-w.
  18. Ding, G., Zhou, Y., Wu, M. and Wang, J. (2021), "Improved performance of calcareous sand subgrade reinforced by soilbags under traffic load", P. I. Civil Eng. Geotec., 174(6), 670-681. https://doi.org/10.1680/jgeen.19.00210.
  19. Ding, Z., Nguyen, H., Bui, X.N., Zhou, J. and Moayedi, H. (2020), "Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms", Nat. Resour. Res., 29(2), 751-769. https://doi.org/10.1007/s11053-019-09548-8.
  20. Dong, J., Zeng, W., Lei, G., Wu, L., Chen, H., Wu, J., Huang, J., Gaiser, T. and Srivastava, A.K. (2022), "Simulation of dew point temperature in different time scales based on grasshopper algorithm optimized extreme gradient boosting", J. Hydrol., 606, 127452. https://doi.org/10.1016/j.jhydrol.2022.127452.
  21. Dong, Y., Qiu, L., Lu, C., Song, L., Ding, Z., Yu, Y. and Chen, G. (2022), "A data-driven model for predicting initial productivity of offshore directional well based on the physical constrained eXtreme gradient boosting (XGBoost) trees", J. Petroleum Sci. Eng., 211, 110176. https://doi.org/10.1016/j.petrol.2022.110176.
  22. Dosdogru, A.T. and Ipek, A. (2022), "Hybrid boosting algorithms and artificial neural network for wind speed prediction", Int. J. Hydrogen Energ., 47(3), 1449-1460. https://doi.org/10.1016/j.ijhydene.2021.10.154.
  23. Duan, L., Wu, M. and Wang, Q. (2022), "Predicting the CPT-based pile set-up parameters using HHO-RF and WOA-RF hybrid models", Arabian J. Geosci., 15(7), 1-19. https://doi.org/10.1007/s12517-022-09843-4.
  24. Elsheikh, A.H., Abd Elaziz, M., Ramesh, B., Egiza, M. and Alqaness, M.A.A. (2021), "Modeling of drilling process of GFRP composite using a hybrid random vector functional link network/parasitism-predation algorithm", J. Mater. Res. Technol., 14, 298-311. https://doi.org/10.1016/j.jmrt.2021.06.033.
  25. Esmaeili-Falak, M, Katebi, H. and Javadi, A.A. (2020), "Effect of freezing on stress-strain characteristics of granular and cohesive soils", J. Cold Reg. Eng., 34(2), 5020001. https://doi.org/https://doi.org/10.1061/(ASCE)CR.19435495.0000205.
  26. Esmaeili-Falak, Mahzad, Katebi, H. and Javadi, A. (2018), "Experimental study of the mechanical behavior of frozen soils-A case study of tabriz subway", Periodica Polytechnica Civil Eng., 62(1), 117-125. https://doi.org/10.3311/PPci.10960.
  27. Esmaeili-Falak, M., Katebi, H., Vadiati, M. and Adamowski, J. (2019), "Predicting triaxial compressive strength and Young's modulus of frozen sand using artificial intelligence methods", J. Cold Reg. Eng., 33(3), 4019007. https://doi.org/https://doi.org/10.1061/(ASCE)CR.19435495.0000188.
  28. Esmaeili Falak, M. and Sarkhani Benemaran, R. (2022), "Investigating the stress-strain behavior of frozen clay using triaxial test", J. Struct. Constr. Engineering.
  29. Fedakar, H.I. (2021), "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", Transport. Res. Record, 2676(4), https://doi.org/10.1177/0361198121105705.
  30. Fouad, A., Hassan, R. and Mahmood, A. (2022), "Developing resilient modulus prediction models based on experimental results of crushed hornfels mixes with different gradations and plasticity", Int. J. Pavement Res. Technol., 15(1), 124-137. https://doi.org/10.1007/s42947-021-00005-5.
  31. Gabr, A.R., Roy, B., Kaloop, M.R., Kumar, D., Arisha, A., Shiha, M., Shwally, S., Hu, J.W. and El-Badawy, S.M. (2021), "A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques", Int. J. Pavement Eng., 23(10), 1-11. https://doi.org/10.1080/10298436.2021.1892109.
  32. Ge, D.M., Zhao, L.C. and Esmaeili-Falak, M. (2022), "Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models", J. Sustain. Cement-Based Mater., 1-19. https://doi.org/10.1080/21650373.2022.2093291.
  33. Ghanizadeh, A.R. and Rahrovan, M. (2016), "Application of artifitial neural network to predict the resilient modulus of stabilized base subjected to wet dry cycles", Comput. Mater. Civ. Eng., 1, 37-47.
  34. Ghanizadeh, A.R., Heidarabadizadeh, N. and Heravi, F. (2021), "Gaussian process regression (Gpr) for auto-estimation of resilient modulus of stabilized base materials", J. Soft Comput. Civil Eng., 5(1), 80-94.
  35. Ghorbani, B., Arulrajah, A., Narsilio, G., Horpibulsuk, S. and Bo, M.W. (2020), "Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils", Soils Found., 60(2), 398-412. https://doi.org/10.1016/j.sandf.2020.02.010.
  36. Gumaei, A., Al-Rakhami, M.S., Hassan, M.M., De Albuquerque, V.H.C. and Camacho, D. (2022), "An effective approach for rumor detection of Arabic tweets using extreme gradient boosting method", Transactions on Asian and Low-Resource Language Information Processing, 21(1), 1-16. https://doi.org/10.1145/3461697.
  37. Ha, N.T., Manley-Harris, M., Pham, T.D. nad Hawes, I. (2021), "The use of radar and optical satellite imagery combined with advanced machine learning and metaheuristic optimization techniques to detect and quantify above ground biomass of intertidal seagrass in a New Zealand estuary", Int. J. Remote Sens., 42(12), 4712-4738. https://doi.org/10.1080/01431161.2021.1899335.
  38. Hammerstrom, D. (1993), "Neural networks at work", IEEE Spectrum, 30(6), 26-32. https://doi.org/10.1109/6.214579.
  39. Hanittinan, W. (2007), Resilient modulus prediction using neural network algorithm, The Ohio State University.
  40. Hayyolalam, V. and Kazem, A.A.P. (2020), "Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems", Eng. Appl. Artif. Intel., 87, 103249. https://doi.org/10.1016/j.engappai.2019.103249.
  41. Heidarabadizadeh, N., Ghanizadeh, A.R. and Behnood, A. (2021), "Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm", Constr. Build. Mater., 275, 122140. https://doi.org/10.1016/j.conbuildmat.2020.122140.
  42. Heidaripanah, A., Nazemi, M. and Soltani, F. (2017), "Prediction of resilient modulus of lime-treated subgrade soil using different kernels of support vector machine", Int. J. Geomech., 17(2), 6016020. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000723.
  43. Ibrahim, B., Majeed, F., Ewusi, A. and Ahenkorah, I. (2022), "Residual geochemical gold grade prediction using extreme gradient boosting", Environ. Challenges, 6, 100421. https://doi.org/10.1016/j.envc.2021.100421.
  44. Ifediniru, C. and Ekeocha, N.E. (2022), "Performance of cement-stabilized weak subgrade for highway embankment construction in Southeast Nigeria", Int. J. Geo-Eng., 13(1), 1-16. https://doi.org/10.1186/s40703-021-00166-z.
  45. 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", Int. J. Pavement Eng., 23(10), 1-16. https://doi.org/10.1080/10298436.2021.1895993.
  46. Inan, M.S.K. and Rahman, I. (2022), "Integration of explainable artificial intelligence to identify significant landslide causal factors for extreme gradient coosting based landslide susceptibility mapping with improved feature selection", ArXiv Preprint ArXiv:2201.03225. https://doi.org/10.48550/arXiv.2201.03225.
  47. Ismail, M. and Islam Mondal, M. (2022), "Extreme gradient boost with CNN: A deep learning-based approach for predicting protein subcellular localization", Proceedings of the International Conference on Big Data, IoT, and Machine Learning.
  48. Jaber, A.K. (2022), "Genetic algorithm to optimize miscible water alternate CO2 flooding in heterogeneous clastic reservoir", Arabian J. Geosci., 15(8), 714. https://doi.org/10.1007/s12517-022-09958-8.
  49. Kalantari, B., Prasad, A. and Huat, B.B.K. (2011), "Stabilising peat soil with cement and silica fume", P. I. Civil Eng.-Geotec., 164(1), 33-39. https://doi.org/10.1680/geng.900044.
  50. Kaloop, M.R., Kumar, D., Samui, P., Gabr, A.R., Hu, J.W., Jin, X., and Roy, B. (2019), "Particle Swarm Optimization algorithmExtreme Learning Machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases", Appl. Sci., 9(16), 3221. https://doi.org/10.3390/app9163221.
  51. Kardani, N., Zhou, A., Nazem, M. and Shen, S.L. (2020), "Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches", Geotech. Geol. Eng., 38(2), 2271-2291. https://doi.org/10.1007/s10706-019-01085-8
  52. Kaveh, A., Javadi, S.M. and Moghani, R.M. (2022), "Shear strength prediction of FRP-reinforced concrete beams using an extreme gradient boosting framework", Periodica Polytechnica Civil Eng., 66(1), 18-29. https://doi.org/10.3311/PPci.18901.
  53. Kavzoglu, T. and Teke, A. (2022), "Predictive performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and Natural Gradient Boosting (NGBoost)", Arabian J. Sci. Eng., 47, 7367-7385 https://doi.org/10.1007/s13369-022-06560-8.
  54. Kayadelen, C., Altay, G. and Onal, Y. (2021), "Numerical simulation and novel methodology on resilient modulus for traffic loading on road embankment", Int. J. Pavement Eng., 23(9), 3212-3221. https://doi.org/10.1080/10298436.2021.1886296.
  55. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95-International Conference on Neural Networks, 4, 1942-1948. https://doi.org/10.1109/ICNN.1995.488968.
  56. Khalife, R., Solanki, P. and Zaman, M.M. (2012), "Evaluation of durability of stabilized clay specimens using different laboratory procedures", J. Test. Eval., 40(3), 363-375. https://doi.org/10.1520/JTE104194
  57. Khoshaim, A.B., Elsheikh, A.H., Moustafa, E.B., Basha, M. and Mosleh, A.O. (2021), "Prediction of residual stresses in turning of pure iron using artificial intelligence-based methods", J. Mater. Res. Technol., 11, 2181-2194. https://doi.org/10.1016/j.jmrt.2021.02.042.
  58. Khoury, N.N. (2005), Durability of cementitiously stabilized aggregate bases for pavement application, The University of Oklahoma.
  59. Khoury, N. and Zaman, M.M. (2007), "Durability of stabilized base courses subjected to wet-dry cycles", Int. J. Pavement Eng., 8(4), 265-276. https://doi.org/10.1080/10298430701342874.
  60. Le, L.T., Nguyen, H., Zhou, J., Dou, J. and Moayedi, H. (2019), "Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost", Appl. Sci., 9(13), 2714. https://doi.org/10.3390/app9132714.
  61. Linh, N.T.T., Pandey, M., Janizadeh, S., Bhunia, G.S., Norouzi, A., Ali, S., Pham, Q.B., Anh, D.T. and Ahmadi, K. (2022), "Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm", Adv. Sp. Res., 69(9), 3301-3318. https://doi.org/10.1016/j.asr.2022.02.027.
  62. Liu, W., Tian, S. and Hu, L. (2022), "Classification of pile foundation integrity based on convolutional neural network", Arabian J. Geosci., 15(8), 793. https://doi.org/10.1007/s12517-022-10057-x.
  63. Looney, C.G. (1996), "Advances in feedforward neural networks: demystifying knowledge acquiring black boxes", IEEE T. Knowledge Data Eng., 8(2), 211-226. https://doi.org/10.1109/69.494162
  64. Lu, Z., Tang, C., Xian, S., She, J. and Yao, H. (2021), "Experimental study on site filling of sandy soil for railway subgrade", P. I. Civil Eng. Geotec., 176(1), 49-57. https://doi.org/10.1680/jgeen.19.00254.
  65. Lv, W., Lv, Y., Ouyang, Q. and Ren, Y. (2022), "A bus passenger flow prediction model fused with point-of-interest data based on extreme gradient boosting", Appl. Sci., 12(3), 940. https://doi.org/10.3390/app12030940.
  66. Lv, Y., Liu, T., Ma, J., Wei, S. and Gao, C. (2020), "RETRACTED ARTICLE: Study on settlement prediction model of deep foundation pit in sand and pebble strata based on grey theory and BP neural network", Arabian J. Geosci., 13(23), 1238. https://doi.org/10.1007/s12517-020-06232-7.
  67. Ma, T., Wu, L., Zhu, S. and Zhu, H. (2022), "Multiclassification prediction of clay sensitivity using extreme gradient boosting based on imbalanced dataset", Appl. Sci., 12(3), 1143. https://doi.org/10.3390/app12031143.
  68. Ma, X., Zhang, Z., Zhang, P. and Wang, X. (2020), "Long-term dynamic stability of improved loess subgrade for high-speed railways", P. I. Civil Eng.-Geotec., 173(3), 217-227. https://doi.org/10.1680/jgeen.19.00088.
  69. Maalouf, M., Khoury, N., Laguros, J.G. and Kumin, H. (2012), "Support vector regression to predict the performance of stabilized aggregate bases subject to wet-dry cycles", Int. J. Numer. Anal. Method. Geomech., 36(6), 675-696. https://doi.org/10.1002/nag.1023.
  70. Moayed, R.Z. and Janbaz, M. (2011), "Subgrade reaction modulus of Tehran alluvium", P. I. Civil Eng. Geotec., 164(4), 283-288. https://doi.org/10.1680/geng.9.00076.
  71. Moradi, G., Hassankhani, E. and Halabian, A.M. (2022), "Experimental and numerical analyses of buried box culverts in trenches using geofoam", P. I. Civil Eng. Geotec., 175(3), 311-322. https://doi.org/10.1680/jgeen.19.00288.
  72. Mukiza, E., Zhang, L., Liu, X. and Zhang, N. (2019), "Utilization of red mud in road base and subgrade materials: A review", Resour. Conserv. Recy., 141, 187-199. https://doi.org/10.1016/j.resconrec.2018.10.031.
  73. Muneer, A.S., Afan, H.A., Kamel, A.H. and Sayl, K.N. (2022), "Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq", Arabian J. Geosci., 15(7), 666. https://doi.org/10.1007/s12517-022-09954-y.
  74. Nanehkaran, Y.A., Licai, Z., Chengyong, J., Chen, J., Anwar, S., Azarafza, M. and Derakhshani, R. (2023), "Comparative analysis for slope stability by using machine learning methods", Appl. Sci., 13(3), 1555. https://doi.org/10.3390/app13031555.
  75. Nazzal, M.D. and Tatari, O. (2013), "Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus", Int. J. Pavement Eng., 14(4), 364-373. https://doi.org/10.1080/10298436.2012.671944.
  76. NCHRP. (2004), Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures. Washington, DC United States.
  77. Nguyen, H., Nguyen, N.M., Cao, M.T., Hoang, N.D. and Tran, X.-L. (2021), "Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine", Eng. Comput., 38, 1255-1267. https://doi.org/10.1007/s00366-020-01260-z.
  78. Nikpeyman, Y., Nikpeyman, V., Derakhshani, R. and Raoof, A. (2022), "Assessment of a multi-layer aquifer vulnerability using a multi-parameter decision-making method in mosha plain, Iran", Water, 14(21), 3397. https://doi.org/10.3390/w14213397.
  79. Nwonu, D.C. and Ikeagwuani, C.C. (2021), "Evaluating the effect of agro-based admixture on lime-treated expansive soil for subgrade material", Int. J. Pavement Eng., 22(12), 1541-1555. https://doi.org/10.1080/10298436.2019.1703979
  80. Onyelowe, K.C. and Duc, B.V. (2020), "Durability of nanostructured biomasses ash (NBA) stabilized expansive soils for pavement foundation", Int. J. Geotech. Eng., 14(3), 254-263. https://doi.org/10.1080/19386362.2017.1422909.
  81. Pal, M. and Deswal, S. (2014), "Extreme learning machine based modeling of resilient modulus of subgrade soils", Geotech. Geol. Eng., 32(2), 287-296. https://doi.org/10.1007/s10706-013-9710-y.
  82. Park, H.I., Kweon, G.C. and Lee, S.R. (2009), "Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network", Road Mater. Pavement Design, 10(3), 647-665. https://doi.org/10.1080/14680629.2009.9690218.
  83. Sadrossadat, E., Heidaripanah, A. and Ghorbani, B. (2018), "Towards application of linear genetic programming for indirect estimation of the resilient modulus of pavements subgrade soils", Road Mater. Pavement Design, 19(1), 139-153. https://doi.org/10.1080/14680629.2016.1250665.
  84. Sadrossadat, E., Heidaripanah, A. and Osouli, S. (2016). "Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems", Constr. Build. Mater., 123, 235-247. https://doi.org/10.1016/j.conbuildmat.2016.07.008.
  85. Samantaray, S., Sahoo, A. and Satapathy, D.P. (2022). "Prediction of groundwater-level using novel SVM-ALO, SVM-FOA, and SVM-FFA algorithms at Purba-Medinipur, India", Arabian J. Geosci., 15(8), 723. https://doi.org/10.1007/s12517-022-09900-y.
  86. Sarkhani Benemaran, R., Esmaeili-Falak, M. and Katebi, H. (2022), "Physical and numerical modelling of pile-stabilised saturated layered slopes", P. I. Civil Eng. Geotec., 175(5), 523-538. https://doi.org/10.1680/jgeen.20.00152.
  87. Sarkhani Benemaran, R., Esmaeili-Falak, M. and Javadi, A. (2022), "Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimized models", Int. J. Pavement Eng., https://doi.org/10.1080/10298436.2022.2095385.
  88. Sentenska, L., Uhl, G. and Lubin, Y. (2020), "Alternative mating tactics in a cannibalistic widow spider: do males prefer the safer option?", Animal Behaviour, 160, 53-59. https://doi.org/10.1016/j.anbehav.2019.11.021.
  89. Shi, X., Yu, X. and Esmaeili-Falak, M. (2023), "Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation", Compos. Struct., 306, 116599. https://doi.org/10.1016/j.compstruct.2022.116599.
  90. Smith, M. and Alvarez, F. (2022), "Predicting firm-level bankruptcy in the Spanish economy using extreme gradient boosting", Comput. Economics, 59(1), 263-295. https://doi.org/10.1007/s10614-020-10078-2.
  91. Solanki, P., Zaman, M. and Ebrahimi, A. (2009), "Regression and artificial neural network modeling of resilient modulus of subgrade soils for pavement design applications", Intelligent and Soft Computing in Infrastructure Systems Engineering, 269-304.
  92. Solanki, P., Zaman, M. and Khalife, R. (2013), "Effect of freeze-thaw cycles on performance of stabilized subgrade", Sound Geotechnical Research to Practice: Honoring Robert D. Holtz II, 566-580.
  93. Stone, M. (1974), "Cross-Validatory choice and assessment of statistical predictions", J. Roy. Stat. Soc. Series B (Methodological), 36(2), 111-147. http://www.jstor.org/stable/2984809. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
  94. Tahsin, T., Mumenin, K.M., Pinki, F.T., Tuli, A.B., Sikder, S., Rahman, M.A., Bulbul, A.A.M. and Awal, M.A. (2021), "GWO-XGB: Grey Wolf Optimization-based eXtreme gradient boosting for hypertension prediction in Bangladesh", Proceedings of the 2021 International Conference on Electronics, Communications and Information Technology (ICECIT).
  95. Tao, H., Awadh, S.M., Salih, S.Q., Shafik, S.S. and Yaseen, Z.M. (2022), "Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction", Neural Comput. Appl., 34(1), 515-533. https://doi.org/10.1007/s00521-021-06362-3.
  96. Tao, H., Habib, M., Aljarah, I., Faris, H., Afan, H.A. and Yaseen, Z.M. (2021), "An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir", Inform. Sci., 570, 172-184. https://doi.org/10.1016/j.ins.2021.04.063.
  97. Tarawneh, B. and Nazzal, M.D. (2014), "Optimization of resilient modulus prediction from FWD results using artificial neural network", Periodica Polytechnica Civil Eng., 58(2), 143-154. https://doi.org/10.3311/PPci.2201.
  98. Thenmozhi, T. and Helen, R. (2022), "Feature selection using extreme gradient boosting bayesian optimization to upgrade the classification performance of motor imagery signals for BCI", J. Neurosci. Method., 366, 109425. https://doi.org/10.1016/j.jneumeth.2021.109425.
  99. Thomas, R. and Vimina, E.R. (2022), "Enhancing the classification accuracy of credit default using extreme gradient boosting with recursive feature selection", In ICDSMLA 2020, 585-591.
  100. Tiwari, N. and Satyam, N. (2021), "Coupling effect of pond ash and polypropylene fiber on strength and durability of expansive soil subgrades: An integrated experimental and machine learning approach", J. Rock Mech. Geotech. Eng., 13(5), 1101-1112. https://doi.org/10.1016/j.jrmge.2021.03.010.
  101. Tiwari, N., Satyam, N. and Puppala, A.J. (2021), "Strength and durability assessment of expansive soil stabilized with recycled ash and natural fibers", Transport. Geotech., 29, 100556. https://doi.org/10.1016/j.trgeo.2021.100556.
  102. Wang, F., Pang, W., Qin, X., Han, L. and Jiang, Y. (2021), "Durability-aimed design criteria of cement-stabilized loess subgrade for railway", Appl. Sci., 11(11), 5061. https://doi.org/10.3390/app11115061.
  103. Wang, T., Song, H., Yue, Z., Hu, T., Sun, T. and Zhang, H. (2019), "Freeze-thaw durability of cement-stabilized macadam subgrade and its compaction quality index", Cold Reg. Sci. Technol., 160, 13-20. https://doi.org/10.1016/j.coldregions.2019.01.005.
  104. Yan, H. and Chen, W. (2022), "Landslide susceptibility modeling based on GIS and ensemble techniques", Arabian J. Geosci., 15(8), 762. https://doi.org/10.1007/s12517-022-09974-8.
  105. Yan, K., Xu, H. and Shen, G. (2014). "Novel approach to resilient modulus using routine subgrade soil properties", Int. J. Geomech., 14(6), 4014025. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000369.
  106. Yang, C., Feng, H. and Esmaeili-Falak, M. (2022), "Predicting the compressive strength of modified recycled aggregate concrete", Struct. Concrete, 23(6), 3696-3717. https://doi.org/10.1002/suco.202100681.
  107. Yuan, J., Zhao, M. and Esmaeili-Falak, M. (2022), "A comparative study on predicting the rapid chloride permeability of self-compacting concrete using meta-heuristic algorithm and artificial intelligence techniques", Struct. Concrete, 23(2), 753-774. https://doi.org/10.1002/suco.202100682.
  108. Zaman, M., Solanki, P., Ebrahimi, A. and White, L. (2010), "Neural network modeling of resilient modulus using routine subgrade soil properties", Int. J. Geomech., 10(1), 1-12. https://doi.org/10.1061/(ASCE)1532-3641(2010)10:1(1).
  109. Zeng, H., Shao, B., Bian, G., Dai, H. and Zhou, F. (2022), "A hybrid deep learning approach by integrating extreme gradient boosting-long short-term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction", Energ. Sci. Eng., 10(7), 1998-2021. https://doi.org/10.1002/ese3.1122.
  110. Zhang, W., Wu, C., Zhong, H., Li, Y. and Wang, L. (2021), "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization", Geosci. Front., 12(1), 469-477. https://doi.org/10.1016/j.gsf.2020.03.007.
  111. Zhang, Z. and Tao, M. (2008), "Durability of cement stabilized low plasticity soils", J. Geotech. Geoenviron. Eng., 134(2), 203-213. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:2(203).
  112. Zhou, J., Li, E., Wang, M., Chen, X., Shi, X. and Jiang, L. (2019), "Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories", J. Perform. Constr. Fac., 33(3), 4019024. https://doi.org/https://doi.org/10.1061/(ASCE)CF.19435509.0001292.
  113. Zhou, J., Li, E., Yang, S., Wang, M., Shi, X., Yao, S. and Mitri, H. S. (2019), "Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories", Safety Sci., 118, 505-518. https://doi.org/10.1016/j.ssci.2019.05.046.
  114. Zhou, J., Qiu, Y., Armaghani, D.J., Zhang, W., Li, C., Zhu, S. and Tarinejad, R. (2021), "Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques", Geosci. Front., 12(3), 101091. https://doi.org/10.1016/j.gsf.2020.09.020.
  115. Zhou, J., Qiu, Y., Khandelwal, M., Zhu, S. and Zhang, X. (2021), "Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations", Int. J. Rock Mech. Min. Sci., 145, 104856. https://doi.org/10.1016/j.ijrmms.2021.104856.
  116. Zhu, W., Huang, L., Mao, L. and Esmaeili-Falak, M. (2022), "Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence-based algorithms", Struct. Concrete, 23(6), 3631-3650. https://doi.org/10.1002/suco.202100656.
  117. Zou, W., Han, Z., Ding, L. and Wang, X. (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", Transport. Geotech., 28, 100520. https://doi.org/10.1016/j.trgeo.2021.100520.