Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
Source abstract
Abstract
There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test.
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Research context
OpenAlex topics: Structural Health Monitoring Techniques · Seismic Performance and Analysis · Structural Engineering and Vibration Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Razi University, Kermanshah 67144-14971, Iran
Department of Civil Environmental and Architectural Engineering, University of Colorado, Boulder, CO 80302, USA
References (100+)
Show source-provided references
1. Bertero, V.V. (1957). The Response of Shear Walls Subjected to Dynamic Loads. [Ph.D. Thesis, Massachusetts Institute of Technology].
2. Seismic behavior of squat reinforced concrete shear walls10.1193/1.1490353 ↗
3. Simplified progressive collapse simulation of RC frame–wall structures10.1016/j.engstruct.2010.06.003 ↗
4. Practical nonlinear modeling of reinforced concrete structural walls10.1061/(ASCE)ST.1943-541X.0001492 ↗
5. Thorburn, L.J., Montgomery, C., and Kulak, G.L. (1983). Analysis of Steel Plate Shear Walls, University of Alberta. Technical Report.
6. Development of a new deteriorating hysteresis model for seismic collapse assessment of thin steel plate shear walls10.1016/j.tws.2016.05.008 ↗
7. Failure hypothesis for masonry shear walls10.1061/JSDEAG.0004294 ↗
8. A macro-element modelling approach of Infilled Frame Structures10.1016/j.compstruc.2014.07.008 ↗
9. 3D macro-element modelling approach for seismic assessment of historical masonry churches10.1016/j.advengsoft.2016.02.009 ↗
10. Experimental and numerical assessment of woodframe sheathing layer combinations for use in strength-based and performance-based design10.1061/(ASCE)ST.1943-541X.0001134 ↗
11. Stitic, A., Nguyen, A.C., Rezaei Rad, A., and Weinand, Y. (2019). Numerical Simulation of the Semi-Rigid Behaviour of Integrally Attached Timber Folded Surface Structures. Buildings, 9.10.3390/buildings9020055 ↗
12. Timler, P.A., and Kulak, G.L. (1983). Experimental Study of Steel Plate Shear Walls, University of Alberta. Technical Report.
13. Tromposch, E.W., and Kulak, G.L. (1987). Cyclic and Static Behaviour of Thin Panel Steel Plate Shear Walls, University of Alberta. Technical Report.
14. Timler, P. (1999). Economical design of steel plate shear walls from a consulting engineers perspective. 1999 North American Steel Construction Conference (NASCC), American Institute of Steel Construction.
15. Experimental cyclic test and failure modes of a full scale enhanced modular steel plate shear wall10.1016/j.engfailanal.2018.09.025 ↗
16. AISC (1999). Load and Resistance Factor Design Specifications for Structural Steel Buildings, American Institute of Steel Construction.
17. CSA-S16-01 (2001). Limit States Design of Steel Structures, Canadian Standard Association.
18. Marzban, S., Almasi, A., and Schwarz, J. (2014, January 25–29). Reinforced Concrete Structural Wall Database Development for Model Validation. Proceedings of the 2nd European Conference on Earthquake Engineering and Seismology, Istanbul, Turkey.
19. Sensitivity and uncertainty analysis of AAR affected reinforced concrete shear walls10.1016/j.engstruct.2018.05.115 ↗
20. Data-based models for the prediction of dam behaviour: A review and some methodological considerations10.1007/s11831-015-9157-9 ↗
21. Support vector machine based reliability analysis of concrete dams10.1016/j.soildyn.2017.09.016 ↗
22. Adaptive sparse polynomial chaos expansion based on least angle regression10.1016/j.jcp.2010.12.021 ↗
23. Du, K.L., and Swamy, M.N. (2006). Neural Networks in a Softcomputing Framework, Springer Science & Business Media.
24. A logical calculus of the ideas immanent in nervous activity10.1007/BF02478259 ↗
25. Hebb, D. (1949). The Organization of Behavior, Wiley.
26. Widrow, B., and Hoff, M.E. (1960). Adaptive Switching Circuits, Stanford Univ CA Stanford Electronics Labs. Technical Report.10.21236/AD0241531 ↗
27. Rosenblatt, F. (1961). Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms, Cornell Aeronautical Lab Inc.. Technical Report.10.21236/AD0256582 ↗
28. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science. Technical Report.10.21236/ADA164453 ↗
29. Fausett, L.V. (1994). Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice-Hall.
30. Approximation by superpositions of a sigmoidal function10.1007/BF02551274 ↗
31. On the approximate realization of continuous mappings by neural networks10.1016/0893-6080(89)90003-8 ↗
32. Multilayer feedforward networks are universal approximators10.1016/0893-6080(89)90020-8 ↗
33. Geometrical interpretation and architecture selection of MLP10.1109/TNN.2004.836197 ↗
34. Cybenko, G. (1988). Continuous Valued Neural Networks with Two Hidden Layers Are Sufficient, Department of Computer Science, Tufts University. Technical Report.
35. Improved knowledge-based neural network (KBNN) model for predicting spring-back angles in metal sheet bending10.1142/S1793962313500268 ↗
36. Chester, D.L. (1990). Why Two Hidden Layers Are Better than One, Proc. IJCNN.
37. Counterpropagation neural networks in structural engineering10.1061/(ASCE)0733-9445(1995)121:8(1205) ↗
38. Adaptive multilayer perceptron networks for detection of cracks in anisotropic laminated plates10.1016/S0020-7683(00)00368-1 ↗
39. Neural networks assessment of beam-to-column joints10.1590/S1678-58782005000300015 ↗
40. Modeling and simulation of shear resistance of R/C beams using artificial neural network10.1016/j.jfranklin.2005.12.005 ↗
41. Vibration control of smart composite laminated spherical shell using neural network10.1177/1045389X07082940 ↗
42. Seismic damage identification in buildings using neural networks and modal data10.1016/j.compstruc.2007.02.021 ↗
43. Beam structure damage identification based on BP neural network and support vector machine
44. Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks10.1016/j.engstruct.2014.01.001 ↗
45. Anisotropic masonry failure criterion using artificial neural networks10.1007/s00521-016-2181-3 ↗
46. Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam10.1007/s10845-016-1217-y ↗
47. Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks10.1016/j.measurement.2018.05.051 ↗
48. Rezaei Rad, A., and Banazadeh, M. (2018). Probabilistic risk-based performance evaluation of seismically base-isolated steel structures subjected to far-field earthquakes. Buildings, 8.10.3390/buildings8090128 ↗
49. Ghorbani, A., Hasanzadehshooiili, H., and Sadowski, Ł. (2018). Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass. Appl. Sci., 8.10.3390/app8050841 ↗
50. Self-compacting concrete strength prediction using surrogate models10.1007/s00521-017-3007-7 ↗
51. Chen, H., Asteris, P.G., Jahed Armaghani, D., Gordan, B., and Pham, B.T. (2019). Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models. Appl. Sci., 9.10.3390/app9061042 ↗
52. Asteris, P.G., and Nikoo, M. (2019). Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput. Appl., 1–11.10.1007/s00521-018-03965-1 ↗
53. Li, H., Chen, C.P., and Huang, H.P. (2000). Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering, CRC Press.10.1201/9781420057997 ↗
54. High-order and multilayer perceptron initialization10.1109/72.557673 ↗
55. Back-propagation algorithm which varies the number of hidden units10.1016/0893-6080(91)90032-Z ↗
56. Comparison of concrete strength prediction techniques with artificial neural network approach
57. Kolmogorov’s mapping neural network existence theorem
58. Hush, D.R. (1989, January 24–26). Classification with neural networks: A performance analysis. Proceedings of the IEEE International Conference on Systems Engineering, Fairborn, OH, USA.10.1109/ICSYSE.1989.48672 ↗
59. Statistical aspects of neural networks10.1007/978-1-4899-3099-6_2 ↗
60. Gallant, S.I., and Gallant, S.I. (1993). Neural Network Learning and Expert Systems, MIT Press.10.7551/mitpress/4931.001.0001 ↗
61. Wang, C. (1994). A Theory of Generalization in Learning Machines with Neural Network Applications. [Ph.D. Thesis, University of Pennsylvania].
62. Masters, T. (1993). Practical Neural Network Recipes in C++, Morgan Kaufmann.10.1016/B978-0-08-051433-8.50017-3 ↗
63. Paola, J. (1994). Neural Network Classification of Multispectral Imagery. [Master’s Thesis, The University of Arizona].
64. Li, J.Y., Chow, T.W., and Yu, Y.L. (December, January 27). The estimation theory and optimization algorithm for the number of hidden units in the higher-order feedforward neural network. Proceedings of the ICNN’95-International Conference on Neural Networks, Perth, WA, Australia.
65. Capabilities of a four-layered feedforward neural network: four layers versus three10.1109/72.557662 ↗
66. Concrete strength prediction by means of neural network10.1016/S0950-0618(97)00007-X ↗
67. Nagendra, S. (1998). Practical Aspects of Using Neural Networks: Necessary Preliminary Specifications, GE Research and Development Center. Technical Paper.
68. Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules10.1016/S0022-1996(98)00017-8 ↗
69. Financial Time Series Forecasting By Neural Network Using Conjugate Gradient Learning Algorithm And Multiple Linear Regression Weight Initialization
70. Heaton, J. (2013). Artificial Intelligence for Humans. Volume 1: Fundamental Algorithms, CreateSpace Independent Publishing.
71. Bounds on the number of hidden neurons in three-layer binary neural networks10.1016/S0893-6080(03)00006-6 ↗
72. Learning capability and storage capacity of two-hidden-layer feedforward networks10.1109/TNN.2003.809401 ↗
73. Ke, J., and Liu, X. (2008, January 19–20). Empirical analysis of optimal hidden neurons in neural network modeling for stock prediction. Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China.10.1109/PACIIA.2008.363 ↗
74. Multilayer perceptrons: Approximation order and necessary number of hidden units10.1109/TNN.2007.912306 ↗
75. Shibata, K., and Ikeda, Y. (2009, January 18–21). Effect of number of hidden neurons on learning in large-scale layered neural networks. Proceedings of the 2009 ICCAS-SICE, Fukuoka, Japan.
76. Selection of Proper Neural Network Sizes and Architectures—A Comparative Study10.1109/TII.2012.2187914 ↗
77. Review on methods to fix number of hidden neurons in neural networks10.1155/2013/425740 ↗
78. Interpreting Neural-Network Connection Strengths
79. Back-Propagation Neural Networks for Modeling Complex Systems10.1016/0954-1810(94)00011-S ↗
80. Behbahanifard, M.R., Grondin, G.Y., and Elwi, A.E.A. (2003). Experimental and Numerical Investigation of Steel Plate Shear Walls, Department of Civil and Environmental Engineering, University of Alberta.
81. Analysis and design recommendations for diagonally stiffened steel plate shear walls10.1016/j.tws.2016.02.008 ↗
82. Behavior of semi-supported steel shear walls: Experimental and numerical simulations10.1016/j.engstruct.2017.01.004 ↗
83. Comparing hysteretic behavior of light-gauge steel plate shear walls and braced frames10.1016/j.engstruct.2004.11.007 ↗
84. Cyclic performance of cross restrained steel plate shear walls with transverse braces10.1016/j.tws.2018.08.020 ↗
85. Ductility and energy dissipation capacity of shear-dominated steel plate walls10.1061/(ASCE)0733-9445(2008)134:9(1495) ↗
86. Experimental and numerical study of unstiffened steel plate shear wall structures10.1016/j.jcsr.2015.05.002 ↗
87. Experimental and theoretical studies of steel shear walls with and without stiffeners10.1016/j.jcsr.2012.03.018 ↗
88. Experimental study of low-yield-point steel plate shear wall under in-plane load10.1016/j.jcsr.2011.01.011 ↗
89. Experimental study of steel plate shear walls with infill plates strengthened by GFRP laminates10.1016/j.jcsr.2012.07.002 ↗
90. Experimental study of thin steel-plate shear walls under cyclic load10.1061/(ASCE)0733-9445(1993)119:2(573) ↗
91. Experimental study on diagonally stiffened steel plate shear walls with central perforation10.1016/j.jcsr.2013.06.005 ↗
92. Framed steel plate wall behavior under cyclic lateral loading10.1061/(ASCE)0733-9445(2007)133:3(378) ↗
93. Unstiffened steel plate shear wall performance under cyclic loading10.1061/(ASCE)0733-9445(2000)126:4(453) ↗
94. Hysteretic characteristics of unstiffened perforated steel plate shear panels10.1016/0263-8231(92)90047-Z ↗
95. Effect of lightweight and low-strength concrete on seismic performance of thin lightly-reinforced shear walls10.1016/j.engstruct.2015.03.022 ↗
96. Experimental study on seismic behavior of concrete walls reinforced by PC strands10.1016/j.engstruct.2018.08.091 ↗
97. Shiga, T., Shibata, A., and Takahashi, J. (1973, January 25–29). Experimental study on dynamic properties of reinforced concrete shear walls. Proceedings of the Fifth World Conference on Earthquake Engineering, Rome, Italy.
98. Static cyclic tests on lightly reinforced concrete shear walls10.1016/j.engstruct.2005.06.008 ↗
99. Effects of axial load on seismic performance of reinforced concrete walls with short shear span10.1016/j.engstruct.2017.08.030 ↗
100. Experimental shear-dominated response of RC walls. Part II: Discussion of results and design implications10.1016/S0141-0296(00)00042-0 ↗