研究生: |
阮玉梅 Ngoc-Mai Nguyen |
---|---|
論文名稱: |
自然啟發式創新人工智慧最佳化預測技術於土木工程實務 管理之系統研發與應用 Artificial Intelligence Using Novel Metaheuristic Optimization and Predictive Techniques for Civil Engineering and Management |
指導教授: |
周瑞生
Jui-Sheng Chou |
口試委員: |
鄭明淵
Min-Yuan Cheng 楊亦東 I-Tung Yang 王維志 Wei-Chih Wang 曾仁杰 Ren-Jye Dzeng 曾惠斌 Hui-Ping Tserng 陳柏翰 Po-Han Chen |
學位類別: |
博士 Doctor |
系所名稱: |
工程學院 - 營建工程系 Department of Civil and Construction Engineering |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 英文 |
論文頁數: | 426 |
中文關鍵詞: | 優化演算法 、營建管理 、人工智慧 、機器學習 、鋼筋混凝土 、桥梁冲刷深度 |
外文關鍵詞: | Metaheuristic optimization, Construction engineering and project management, Machine learning, Hybrid ensemble model, Reinforced concrete, Scour depth |
相關次數: | 點閱:266 下載:0 |
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Artificial Intelligence (AI), big data, and optimization technology have become increasingly innovative and widely used in various industries and cultures, including civil and construction engineering. Although numerous AI-based inference models have been put forward to address various problems, they simply are in the form of three following types: single models, ensemble models, and hybrid models. Along with AI-based inference techniques, metaheuristic optimization algorithms have attracted great interest in recent years for resolving engineering/management-related optimization issues but they have different disadvantages in terms of efficiency, effectiveness, and automation that users must deal with in utilization. The objectives and contributions of this research thus include: (1) developing a novel optimization algorithm, called forensic-based investigation algorithm (FBI) to help engineers/managers to tackle optimization problems with low computational effort and high accuracy. The effectiveness and efficiency of FBI was confirmed through analytical results that demonstrated FBI as being superior to all compared well-known algorithms; (2) developing a metaheuristic optimization platform to provide performance indicators clearly, logically, and graphically. Moreover, it is a reliable system to benchmark a new proposed optimization algorithm in the future; and (3) establishing a novel type of AI-inference technique, presented in two independent systems: metaheuristic-optimized ensemble system (MOES) and metaheuristic-optimized stacking system (MOSS) - for civil and construction engineering management. Both MOES and MOSS are powerful AI approaches with remarkably greater accuracy than current AI techniques because they combine the advantages of hybrid model and ensemble model. MOES was hybridized an optimizer and a homogeneous ensemble model, while MOSS was hybridized an optimizer and a heterogeneous ensemble model. The FBI algorithm was integrated in MOES and MOSS to simultaneously find the optimal values of all hyper-parameters of constituent AI models to generate the most effective ensemble systems. The MOES was applied to support structural engineers in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials with four real case studies. Meanwhile, MOSS was applied to assist civil engineers in accurately estimating scour depth at bridge piers. The efficiency of the MOSS is verified comprehensively using three case studies of both laboratory data and field data that cover various levels of complexity and types of pier foundations in reality. The performances of MOES and MOSS were compared to those of other single AI models, conventional ensemble AI models, hybrid models, and empirical methods. The analytical results of a cross-validation method revealed that MOES and MOSS were the most reliable approaches, achieving the best performance evaluation metrics with the lowest prediction errors. Additionally, both MOES and MOSS are user-friendly tools because they can run automatically with support of the FBI in setting all control hyper-parameters.
1. Chou, J.-S. and N.-M. Nguyen, FBI inspired meta-optimization. Applied Soft Computing, 2020. 93: p. 106339.
2. Chou, J.-S. and N.-M. Nguyen, Metaheuristics-optimized ensemble system for predicting mechanical strength of reinforced concrete materials. Structural Control and Health Monitoring, 2021. 28(5): p. e2706.
3. Gandomi, A.H. and A.H. Alavi, Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 2012. 17(12): p. 4831-4845.
4. Wang, G.-G., Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 2018. 10(2): p. 151-164.
5. Hussain, K., et al., Metaheuristic research: a comprehensive survey. Artificial Intelligence Review, 2018: p. 1–43.
6. Abualigah, L.M., et al., A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Applied Soft Computing, 2017. 60: p. 423-435.
7. Mirjalili, S. and A. Lewis, The Whale Optimization Algorithm. Advances in Engineering Software, 2016. 95: p. 51-67.
8. Karaboga, D. and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 2007. 39(3): p. 459-471.
9. Dorigo, M., M. Birattari, and T. Stutzle, Ant colony optimization. IEEE computational intelligence magazine, 2006. 1(4): p. 28-39.
10. Mirjalili, S., The ant lion optimizer. Advances in Engineering Software, 2015. 83: p. 80-98.
11. Yang, X.-S., Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2010. 2(2): p. 78-84.
12. Yu, J.J.Q. and V.O.K. Li, A social spider algorithm for global optimization. Applied Soft Computing, 2015. 30: p. 614-627.
13. Ali, E. and S. Abd-Elazim, Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. International Journal of Electrical Power & Energy Systems, 2011. 33(3): p. 633-638.
14. Gen, M. and R. Cheng, Genetic algorithms and engineering optimization. Vol. 7. 2000: John Wiley & Sons.
15. Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11(4): p. 341-359.
16. Yang, X.-S., A New Metaheuristic Bat-Inspired Algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J.R. González, et al., Editors. 2010, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 65-74.
17. Shen, W., et al., Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Systems, 2011. 24(3): p. 378-385.
18. Zhao, R.-q. and W.-s. Tang, Monkey algorithm for global numerical optimization. Journal of Uncertain Systems, 2008. 2(3): p. 165-176.
19. Kennedy, J., Particle swarm optimization, in Encyclopedia of machine learning. 2011, Springer. p. 760-766.
20. Yang, X.-S. and S. Deb. Cuckoo search via Lévy flights. in Proceeings of World Congress on Nature & Biologically Inspired Computing ( (NaBIC 2009, India). 2009. IEEE.
21. Sahu, B.K., et al., Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system. Applied Soft Computing, 2015. 27: p. 240-249.
22. Yang, X.-S. Flower pollination algorithm for global optimization. in International conference on unconventional computing and natural computation. 2012. Springer.
23. Eskandar, H., et al., Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 2012. 110-111: p. 151-166.
24. Rayapudi, S.R., An intelligent water drop algorithm for solving economic load dispatch problem. International Journal of Electrical and Electronics Engineering, 2011. 5(2): p. 43-49.
25. Cheng, M.-Y. and D. Prayogo, Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures, 2014. 139: p. 98-112.
26. Abedinpourshotorban, H., et al., Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 2016. 26: p. 8-22.
27. Rashedi, E., H. Nezamabadi-pour, and S. Saryazdi, GSA: A Gravitational Search Algorithm. Information Sciences, 2009. 179(13): p. 2232-2248.
28. Tran, D.-H., M.-Y. Cheng, and M.-T. Cao, Solving Resource-Constrained Project Scheduling Problems Using Hybrid Artificial Bee Colony with Differential Evolution. Journal of Computing in Civil Engineering, 2016. 30(4): p. 04015065.
29. Nematollahi, A.F., A. Rahiminejad, and B. Vahidi, A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization. Applied Soft Computing, 2017. 59: p. 596-621.
30. Li, M.D., et al., A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 2016. 92: p. 65-88.
31. Jain, M., V. Singh, and A. Rani, A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 2019. 44: p. 148-175.
32. Saremi, S., S. Mirjalili, and A. Lewis, Grasshopper Optimisation Algorithm: Theory and application. Advances in Engineering Software, 2017. 105: p. 30-47.
33. Zawbaa, H.M., et al., Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach. Swarm and Evolutionary Computation, 2018. 42: p. 29-42.
34. Salehi, H., et al., Damage identification in aircraft structures with self-powered sensing technology: A machine learning approach. Structural Control and Health Monitoring, 2018. 25(12): p. e2262.
35. Cheng, M.-Y. and M.-T. Cao, Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Applied Soft Computing, 2014. 22: p. 178-188.
36. Chou, J.-S., et al., Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Computing, 2020. 24(5): p. 3393-3411.
37. Rafajłowicz, W., et al., Iterative learning from suppressing vibrations in construction machinery using magnetorheological dampers. Automation in Construction, 2020. 119: p. 103326.
38. Cao, M.-T., M.-Y. Cheng, and Y.-W. Wu, Hybrid Computational Model for Forecasting Taiwan Construction Cost Index. Journal of Construction Engineering and Management, 2015. 141(4): p. 04014089.
39. Chou, J.-S. and A.-D. Pham, Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Construction and Building Materials, 2013. 49: p. 554-563.
40. Erdal, H.I., O. Karakurt, and E. Namli, High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence, 2013. 26(4): p. 1246-1254.
41. Cao, M.S., et al., Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Computing and Applications, 2017. 28(7): p. 1583-1590.
42. Tang, L., et al., A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Annals of Operations Research, 2015. 234(1): p. 111-132.
43. Xiao, J., et al., A hybrid model based on selective ensemble for energy consumption forecasting in China. Energy, 2018. 159: p. 534-546.
44. Jovanović, R.Ž., A.A. Sretenović, and B.D. Živković, Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings, 2015. 94: p. 189-199.
45. Rofooei, F.R., A. Kaveh, and F.M. Farahani, Estimating the vulnerability of concrete moment resisting frame structures using artificial neural networks. IUST, 2011. 1(3): p. 433-448.
46. Darain, K.M.u., et al., Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams. Construction and Building Materials, 2015. 98: p. 276-285.
47. Hoang, N.-D., X.-L. Tran, and H. Nguyen, Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Computing and Applications, 2019.
48. Chou, J.-S., et al., Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Computing, 2019.
49. Kaya, A., Artificial neural network study of observed pattern of scour depth around bridge piers. Computers and Geotechnics, 2010. 37(3): p. 413-418.
50. Samadi, M., E. Jabbari, and H.M. Azamathulla, Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways. Neural Computing and Applications, 2014. 24(2): p. 357-366.
51. Pal, M., N.K. Singh, and N.K. Tiwari, Support vector regression based modeling of pier scour using field data. Engineering Applications of Artificial Intelligence, 2011. 24(5): p. 911-916.
52. Pal, M., N.K. Singh, and N.K. Tiwari, M5 model tree for pier scour prediction using field dataset. KSCE Journal of Civil Engineering, 2012. 16(6): p. 1079-1084.
53. Chou, J.-S. and A.-D. Pham, Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information. Information Sciences, 2017. 399: p. 64-80.
54. Sreedhara, B.M., M. Rao, and S. Mandal, Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers. Neural Computing and Applications, 2019. 31(11): p. 7335-7349.
55. Hosseini, R., et al., Bagged neural network for estimating the scour depth around pile groups. International Journal of River Basin Management, 2018. 16(4): p. 401-412.
56. Khan, M., et al., Genetic functions-based modelling for pier scour depth prediction in coarse bed streams. Proceedings of the Institution of Civil Engineers - Water Management, 2018. 171(5): p. 225-240.
57. Ebtehaj, I., et al., Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. Journal of Hydroinformatics, 2016. 19(2): p. 207-224.
58. College of Policing, Investigation process. 2013: https://www.app.college.police.uk/app-content/investigations/investigation-process/.
59. Salet, R., Framing in criminal investigation: How police officers (re) construct a crime. The police journal, 2017. 90(2): p. 128-142.
60. Horse Sanctuary UK Limited, S.W.A.T. 2018: http://www.bushywood.com/SWAT.htm.
61. Droog, T., International Campaign to free Jose Maria Sison launched. 2007: http://www.statewatch.org/news/2007/sep/sison-campaign.pdf.
62. VnExpress, Drug lords killed in massive raid in northern Vietnam. 2018: https://e.vnexpress.net/news/news/drug-lords-killed-in-massive-raid-in-northern-vietnam-3770389.html.
63. Gehl, R. and D. Plecas, Introduction to Criminal Investigation: Processes, Practices and Thinking. 2016, New Westminster, BC: Justice Institute of British Columbia.
64. Karaboga, D. and B. Akay, A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 2009. 214(1): p. 108-132.
65. Chou, J.-S. and N.-T. Ngo, Modified firefly algorithm for multidimensional optimization in structural design problems. Structural and Multidisciplinary Optimization, 2017. 55(6): p. 2013-2028.
66. Rao, R.V., V.J. Savsani, and D. Vakharia, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 2011. 43(3): p. 303-315.
67. N. H. Awad, et al. Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization. in 2017 IEEE congress on evolutionary computation (CEC). 2017. IEEE.
68. Brest, J., M.S. Maučec, and B. Bošković. Single objective real-parameter optimization: Algorithm jSO. in 2017 IEEE Congress on Evolutionary Computation (CEC). 2017.
69. Kumar, A., R.K. Misra, and D. Singh. Improving the local search capability of Effective Butterfly Optimizer using Covariance Matrix Adapted Retreat Phase. in 2017 IEEE Congress on Evolutionary Computation (CEC). 2017.
70. Tanabe, R. and A.S. Fukunaga. Improving the search performance of SHADE using linear population size reduction. in 2014 IEEE Congress on Evolutionary Computation (CEC). 2014.
71. Awad, N.H., et al., Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization, in Technical Report. 2016: Nanyang Technological University, Singapore, Jordan University of Science and Technology, Jordan, And Zhengzhou University, Zhengzhou China
72. Hosni Elhewy, A., E. Mesbahi, and Y. Pu, Reliability analysis of structures using neural network method. Probabilistic Engineering Mechanics, 2006. 21(1): p. 44-53.
73. Broomhead, D.S. and D. Lowe, Radial basis functions, multi-variable functional interpolation and adaptive networks. 1988, Royal Signals and Radar Establishment Malvern (United Kingdom).
74. Jain, T., S.N. Singh, and S.C. Srivastava, Fast static available transfer capability determination using radial basis function neural network. Applied Soft Computing, 2011. 11(2): p. 2756-2764.
75. Sudheer, K. and S. Jain, Radial Basis Function Neural Network for Modeling Rating Curves. Journal of Hydrologic Engineering, 2003. 8(3): p. 161-164.
76. Mateo, F., et al., Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum. Food Control, 2011. 22(1): p. 88-95.
77. Singh, A., et al., Comparison of Artificial Neural Network Models for Sediment Yield Prediction at Single Gauging Station of Watershed in Eastern India. Journal of Hydrologic Engineering, 2013. 18(1): p. 115-120.
78. Bateni, S.M., S.M. Borghei, and D.S. Jeng, Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, 2007. 20(3): p. 401-414.
79. Han, H., Q. Chen, and J. Qiao, Research on an online self-organizing radial basis function neural network. Neural Computing and Applications, 2010. 19(5): p. 667-676.
80. Yang, Y.-K., et al., A novel self-constructing Radial Basis Function Neural-Fuzzy System. Applied Soft Computing, 2013. 13(5): p. 2390-2404.
81. Cortes, C. and V. Vapnik, Support-vector networks. Machine learning, 1995. 20(3): p. 273-297.
82. Roy, A., R. Manna, and S. Chakraborty, Support vector regression based metamodeling for structural reliability analysis. Probabilistic Engineering Mechanics, 2019. 55: p. 78-89.
83. Alibrandi, U., A.M. Alani, and G. Ricciardi, A new sampling strategy for SVM-based response surface for structural reliability analysis. Probabilistic Engineering Mechanics, 2015. 41: p. 1-12.
84. Wang, H. and D. Hu, Comparison of SVM and LS-SVM for regression. Vol. 1. 2005. 279-283.
85. Chen, X., et al., Ensemble Learning Multiple LSSVR With Improved Harmony Search Algorithm for Short-Term Traffic Flow Forecasting. IEEE Access, 2018. 6: p. 9347-9357.
86. Li, M.-W., et al., Periodogram estimation based on LSSVR-CCPSO compensation for forecasting ship motion. Nonlinear Dynamics, 2019. 97(4): p. 2579-2594.
87. Liu, X., A. Ouyang, and Z. Yun, Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering, 2018. 2018.
88. Suykens, J.A.K., et al., Least Squares Support Vector Machines. 2002: World Scientific Publishing Company. 308.
89. Zhou, Z.-H., Ensemble methods: foundations and algorithms. 2012: CRC press.
90. Xie, G., et al., Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study. Applied Soft Computing, 2013. 13(5): p. 2232-2241.
91. Bishop, C.M., Pattern Recognition and Machine Learning (Information Science and Statistics). 2006: Springer-Verlag New York, Inc.
92. Chou, J.-S., et al., Machine learning in concrete strength simulations: Multi-nation data analytics. Construction and Building Materials, 2014. 73: p. 771-780.
93. Lam, A.Y. and V.O. Li, Chemical-reaction-inspired metaheuristic for optimization. IEEE transactions on evolutionary computation, 2010. 14(3): p. 381-399.
94. Christodoulou, S., Scheduling resource-constrained projects with ant colony optimization artificial agents. Journal of computing in civil engineering, 2009. 24(1): p. 45-55.
95. Lamas, P. and E. Demeulemeester, A purely proactive scheduling procedure for the resource-constrained project scheduling problem with stochastic activity durations. Journal of Scheduling, 2016. 19(4): p. 409-428.
96. Cheng, M. and D. Tran, Two-Phase Differential Evolution for the Multiobjective Optimization of Time–Cost Tradeoffs in Resource-Constrained Construction Projects. IEEE Transactions on Engineering Management, 2014. 61(3): p. 450-461.
97. Möhring, R.H., et al., Solving project scheduling problems by minimum cut computations. Management Science, 2003. 49(3): p. 330-350.
98. Sears, K., G. Sears, and R. Clough, Construction Project Management: A Practical Guide to Field Construction Management. 5th Edition ed. 2008: John Wiley & Sons.
99. Kikuchi, M., 6.27 - CFRP Reinforcement Rods, in Comprehensive Composite Materials, A. Kelly and C. Zweben, Editors. 2000, Pergamon: Oxford. p. 541-547.
100. Gaetano Russo, R.V. and P. Margherita, Reinforced Concrete Deep Beams- Shear Strength Model and Design Formula. ACI Structural Journal, 2005. 102(3).
101. Boyan I. Mihaylov, E.C.B. and P.C. Michael, Two-Parameter Kinematic Theory for Shear Behavior of Deep Beams. ACI Structural Journal, 2013. 110(3).
102. Tan, K.H., L.W. Weng, and S. Teng, A Strut-And-Tie Model for Deep Beams Subjected To Combined Top-And-Bottom Loading. Structural Engineer Journal, 1997. 75(13): p. 215-225.
103. Park, J.-w. and D. Kuchma, Strut-and-Tie Model Analysis for Strength Prediction of Deep Beams. ACI Structural Journal, 2007. 104(6): p. 657-666.
104. Tang, C. and K. Tan, Interactive Mechanical Model for Shear Strength of Deep Beams. Journal of Structural Engineering, 2004. 130(10): p. 1534-1544.
105. Yaseen, Z.M., et al., Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach. Engineering Structures, 2018. 177: p. 244-255.
106. Cheng, M.-Y. and M.-T. Cao, Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Engineering Applications of Artificial Intelligence, 2014. 28: p. 86-96.
107. Yang, K.-H., H.-S. Chung, and A. Ashour, Influence of section depth on the structural behaviour of reinforced concrete continuous deep beams. Magazine of Concrete Research, 2007. 59(8): p. 575-586.
108. Yang, K.-H., H.-S. Chung, and A.F. Ashour, Influence of section depth on the structural behaviour of reinforced concrete continuous deep beams. Magazine of Concrete Research, 2007. 59(8): p. 575-586.
109. Yang, K.-H., H.-S. Chung, and A.F. Ashour, Influence of Shear Reinforcement on Reinforced Concrete Continuous Deep Beams. ACI Structural Journal, 2007. 104(4): p. 420.
110. Ashour, A.F., Tests of reinforced concrete continuous deep beams. ACI Structural Journal, 1997. 94(1): p. 3-12.
111. Rogowsky, D.M., J.G. MacGregor, and S.Y. Ong, Tests of reinforced concrete deep beams. ACI Structural Journal, 1986. 83(4): p. 614-623.
112. SUBEDI, N.K. Reinforced concrete two-span continuous deep beams. Proceedings of the ICE - Structures and Buildings, 1998. 128, 12-25 DOI: 10.1680/istbu.1998.30031.
113. Asin, M., The Behaviour of Reinforced Concrete Continuous Deep Beams, in Department of Civil Engineering and Geosciences. 1999, Delft University: Delft University Press, Amsterdam, The Netherlands. p. 188.
114. Clark, A.P., Diagonal Tension in Reinforced Concrete Beams. ACI Journal, 1951. 48(10): p. 145-156.
115. Kong, F.K., P.J. Robins, and D.F. Cole, Web Reinforcement Effects on Deep Beams. ACI Journal Proceedings, 1970. 67(12): p. 1010-1018.
116. Smith, K.N. and A.S. Vantsiotis, Shear Strength of Deep Beams. ACI Journal Proceedings, 1982. 79(3): p. 201-213.
117. Anderson, N.S. and J.A. Ramirez, Detailling of Stirrup Reinforcement. ACI Structural Journal, 1989. 86(5): p. 507-515.
118. Tan, K.-H., et al., High-Strength Concrete Deep Beams with Effective Span and Shear Span Variations. ACI Structural Journal, 1995. 92(4): p. 395-405.
119. Oh, J.-K. and S.-W. Shin, Shear Strength of Reinforced High-Strength Concrete Deep Beams. ACI Structural Journal, 2001. 98(2): p. 164-173.
120. Aguilar, G., et al., Experimental Evaluation of Design Procedures for Shear Strength of Deep Reinforced Concrete Beams. ACI Structural Journal, 2002. 99(4): p. 539-548.
121. Quintero-Febres, C.G., G. Parra-Montesinos, and J.K. Wight, Strength of Struts in Deep Concrete Members Designed Using Strut-and-Tie Method. ACI Structural Journal, 2006. 103(4): p. 577-586.
122. Naderpour, H., et al., Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Composite Structures, 2019. 215: p. 69-84.
123. Zhang, T., D.J. Oehlers, and P. Visintin, Shear Strength of FRP RC Beams and One-Way Slabs without Stirrups. Journal of Composites for Construction, 2014. 18(5): p. 04014007.
124. Arslan, M.H., Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes. Advances in Engineering Software, 2010. 41(7): p. 946-955.
125. ACI-318, A.C.I.C., 318-05/318R-05: Building Code Requirements for Structural Concrete and Commentary. 2004, American Concrete Institute p. 432.
126. Ashour, A. and C. Morley, Effectiveness Factor of Concrete in Continuous Deep Beams. Journal of Structural Engineering, 1996. 122(2): p. 169-178.
127. ACI-318, A.C.I.C., 318-11: Building Code Requirements for Structural Concrete and Commentary. 2011: American Concrete Institute. 509.
128. CSA, C.S.A., Design of concrete structures: Structures (design) - a national standard of Canada. CAN-A23.3-94. 1994, Toronto.
129. Gandomi, A.H., et al., An Empirical Model for Shear Capacity of RC Deep Beams Using Genetic-Simulated Annealing. Archives of Civil and Mechanical Engineering, 2013. 13(3): p. 354-369.
130. Chou, J.-S. and A.-D. Pham, Hybrid computational model for predicting bridge scour depth near piers and abutments. Automation in Construction, 2014. 48: p. 88-96.
131. Cheng, M.-Y., M.-T. Cao, and Y.-W. Wu, Predicting Equilibrium Scour Depth at Bridge Piers Using Evolutionary Radial Basis Function Neural Network. Journal of Computing in Civil Engineering, 2015. 29(5): p. 04014070.
132. Melville, B. and A. Sutherland, Design Method for Local Scour at Bridge Piers. Journal of Hydraulic Engineering, 1988. 114(10): p. 1210-1226.
133. Arneson, L.A., et al., Evaluating Scour at Bridges, in FHWA-HIF-12-003. 2012, Hydraulic Engineering Circular No. 18: Washington, D.C.
134. Laursen, E.M. and A. Toch, Scour Around Bridge Piers and Abutments. 1956: Iowa Highway Research Board.
135. Breusers, H.N.C., G. Nicollet, and H.W. Shen, Local Scour Around Cylindrical Piers. Journal of Hydraulic Research, 1977. 15(3): p. 211-252.
136. DOT, U., Evaluating scour at bridges. 2nd ed. Hydraulic engineering circular ; no. 18. 1993: Federal Highway Administration, US Department of Transportation, McLean, VA. 234.
137. Van Wilson, K., M.D.o. Transportation, and G. Survey, Scour at selected bridge sites in Mississippi. 1995: U.S. Dept. of the Interior, U.S. Geological Survey.
138. Melville, B. and Y. Chiew, Time Scale for Local Scour at Bridge Piers. Journal of Hydraulic Engineering, 1999. 125(1): p. 59-65.
139. Mueller, D.S. and C.R. Wagner, Field Observations and Evaluations of Streambed Scour at Bridges. 2005: The United State. p. 134.
140. Nabian, M.A. and H. Meidani, A deep learning solution approach for high-dimensional random differential equations. Probabilistic Engineering Mechanics, 2019. 57: p. 14-25.
141. Sedehi, O., C. Papadimitriou, and L.S. Katafygiotis, Data-driven uncertainty quantification and propagation in structural dynamics through a hierarchical Bayesian framework. Probabilistic Engineering Mechanics, 2020. 60: p. 103047.
142. Abd El-Hady Rady, R., Prediction of local scour around bridge piers: artificial-intelligence-based modeling versus conventional regression methods. Applied Water Science, 2020. 10(2): p. 57.
143. Najafzadeh, M., G.-A. Barani, and H.M. Azamathulla, Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Computing and Applications, 2014. 24(3): p. 629-635.
144. Najafzadeh, M., G.-A. Barani, and M.-R. Hessami-Kermani, Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds. Ocean Engineering, 2015. 104: p. 387-396.
145. Muzzammil, M., J. Alama, and M. Danish, Scour Prediction at Bridge Piers in Cohesive Bed Using Gene Expression Programming. Aquatic Procedia, 2015. 4: p. 789-796.
146. Sharafati, A., et al., Simulation of the depth scouring downstream sluice gate: The validation of newly developed data-intelligent models. Journal of Hydro-environment Research, 2019.
147. Hoang, N.-D., K.-W. Liao, and X.-L. Tran, Estimation of scour depth at bridges with complex pier foundations using support vector regression integrated with feature selection. Journal of Civil Structural Health Monitoring, 2018. 8(3): p. 431-442.
148. Lauchlan, C. and B. Melville, Riprap Protection at Bridge Piers. Journal of Hydraulic Engineering, 2001. 127(5): p. 412-418.
149. Mia, M. and H. Nago, Design Method of Time-Dependent Local Scour at Circular Bridge Pier. Journal of Hydraulic Engineering, 2003. 129(6): p. 420-427.
150. Liao, K.-W., Y. Muto, and J.-Y. Lin, Scour Depth Evaluation of a Bridge with a Complex Pier Foundation. KSCE Journal of Civil Engineering, 2018. 22(7): p. 2241-2255.
151. Mueller, D.S. and C.R. Wagner, Field observations and evaluations of streambed scour at bridges, in No. FHWA-RD-03-052. 2005, United States. Federal Highway Administration. Office of Research, Development, and Technology.
152. Modeler, I., IBM SPSS Clementine 12.0 [Computer software] and Algorithm Guide. 2010: IBM, Chicago, USA.
153. Saxena, A., R. Kumar, and S. Das, β-Chaotic map enabled Grey Wolf Optimizer. Applied Soft Computing, 2019. 75: p. 84-105.
154. Dinkar, S.K. and K. Deep, Opposition based Laplacian Ant Lion Optimizer. Journal of Computational Science, 2017. 23: p. 71-90.