AUT Journal of Civil Engineering

AUT Journal of Civil Engineering

A Machine Learning Framework for Predicting Maximum Displacement of Reinforced Masonry Shear Walls under Lateral Loading

Document Type : Research Article

Authors
Shoaib Mansouri
Seyed Hadi Rashedi
Alireza Rahai
Department of Civil Engineering, Amirkabir University of Technology, Tehran, Iran
10.22060/ajce.2025.24820.5953
Abstract
Accurate estimation of the maximum displacement capacity of masonry shear walls under lateral loading is essential for performance-based seismic design, yet conventional analytical and numerical approaches remain computationally intensive, sensitive to modeling assumptions, and highly dependent on expert interpretation. These limitations restrict their applicability for rapid assessment and design optimization. To address this challenge, this study proposes a machine learning (ML) framework that integrates predictive accuracy, interpretability, and mechanical validation. A database of 93 fully grouted masonry walls tested under cyclic displacement-controlled loading is utilized to develop a systematically optimized Multi-Layer Perceptron Artificial Neural Network (MLP-ANN). The model incorporates geometric, reinforcement, material, and axial-load parameters under the assumption of rectangular, fully grouted walls with consistent boundary conditions. Extensive architectural trials yielded an optimized ANN achieving R² values of 0.98, 0.97, and 0.90 for training, validation, and testing datasets, respectively. Complementary Random Forest (RF) analysis identified wall length, height, reinforcement ratios, masonry strength, and axial-load ratio as the most influential predictors governing displacement response. To verify the mechanical plausibility of the ML predictions, a finite element model (FEM) of a representative specimen was developed, reproducing experimental backbone curves within 5–10% deviation. The combined ANN–RF–FEM framework offers a fast, interpretable, and reliable tool for evaluating seismic displacement capacity of masonry walls. Future research should expand the dataset to include diverse wall geometries, boundary conditions, and materials, and explore hybrid ML–FEM or physics-informed models to further improve generalization and design applicability.
Keywords
Masonry Shear Walls
Maximum Displacement Prediction
Artificial Neural Network
Random Forest Algorithm
Seismic Performance Assessment
Subjects
[1] W. El-Dakhakhni, A. Ashour, Seismic response of reinforced-concrete masonry shear-wall components and systems: State of the art, Journal of Structural Engineering, 143(9) (2017) 03117001.
[2] A. Rahai, M. Alipoura, Behavior and characteristics of innovative composite plate shear walls, Procedia Engineering, 14 (2011) 3205-3212.
[3] F. Yáñez, M. Astroza, A. Holmberg, O. Ogaz, Behavior of confined masonry shear walls with large openings, in:  13th world conference on earthquake engineering, International Association for Earthquake Engineering, Tokyo, Japan, 2004.
[4] M. Priestley, Seismic design of concrete masonry shearwalls, in:  Journal Proceedings, 1986, pp. 58-68.
[5] S.H. Rashedi, A. Rahai, P. Tehrani, Seismic performance evaluation of RC bearing wall structures, Computers and Concrete, 30(2) (2022) 113-126.
[6] F. Ahmadi, M. Mavros, R.E. Klingner, B. Shing, D. McLean, Displacement-based seismic design for reinforced masonry shear-wall structures, part 1: Background and trial application, Earthquake Spectra, 31(2) (2015) 969-998.
[7] F. Ahmadi, J. Hernandez, J. Sherman, C. Kapoi, R.E. Klingner, D.I. McLean, Seismic performance of cantilever-reinforced concrete masonry shear walls, Journal of Structural Engineering, 140(9) (2014) 04014051.
[8] A. Siam, M. Ezzeldin, W. El-Dakhakhni, Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls, in:  Structures, Elsevier, 2019, pp. 252-265.
[9] A. Hayatdavoodi, A. Dehghani, F. Aslani, F. Nateghi-Alahi, The development of a novel analytical model to design composite steel plate shear walls under eccentric shear, Journal of Building Engineering, 44 (2021) 103281.
[10] S. Sabouri-Ghomi, S.R.A. Sajjadi, Experimental and theoretical studies of steel shear walls with and without stiffeners, Journal of Constructional Steel Research, 75 (2012) 152-159.
[11] A. Rahai, F. Hatami, Evaluation of composite shear wall behavior under cyclic loadings, Journal of Constructional Steel Research, 65(7) (2009) 1528-1537.
[12] A. Rahai, S.H. Rashedi, Evaluation of ductility of bearing concrete wall systems with regard to their boundary element, Amirkabir Journal of Civil Engineering, 49(1) (2017) 13-22.
[13] J.P. Gouveia, P.B. Lourenço, Masonry shear walls subjected to cyclic loading: influence of confinement and horizontal reinforcement, (2007).
[14] H. Naderpour, M. Sharei, P. Fakharian, M.A. Heravi, Shear strength prediction of reinforced concrete shear wall using ANN, GMDH-NN and GEP, Journal of Soft Computing in Civil Engineering, 6(1) (2022) 66-87.
[15] M.S. Barkhordari, M. Tehranizadeh, Response estimation of reinforced concrete shear walls using artificial neural network and simulated annealing algorithm, in:  Structures, Elsevier, 2021, pp. 1155-1168.
[16] M. Ehsani, P. Hajikarimi, M. Esfandiar, M. Rahi, B. Rasouli, Y. Yousefi, F.M. Nejad, Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens, Construction and Building Materials, 401 (2023) 132808.
[17] M.G. Zamani, M.R. Nikoo, D. Rastad, B. Nematollahi, A comparative study of data-driven models for runoff, sediment, and nitrate forecasting, Journal of Environmental Management, 341 (2023) 118006.
[18] S. Mansouri, A. Rahai, S.H. Rashedi, F. Moghadas Nejad, Predicting Concrete Carbonation Depth and investigating the influencing factors through machine learning approaches and optimization, Amirkabir Journal of Civil Engineering, 56(12) (2025) 1583-1604.
[19] M. Ehsani, M. Ostovari, S. Mansouri, H. Naseri, H. Jahanbakhsh, F.M. Nejad, Machine learning for predicting concrete carbonation depth: a comparative analysis and a novel feature selection, Construction and Building Materials, 417 (2024) 135331.
[20] M. Ehsani, F. Moghadas Nejad, P. Hajikarimi, Developing an optimized faulting prediction model in Jointed Plain Concrete Pavement using artificial neural networks and random forest methods, International Journal of pavement engineering, 24(2) (2023) 2057975.
[21] M.G. Zamani, M.R. Nikoo, F. Niknazar, G. Al-Rawas, M. Al-Wardy, A.H. Gandomi, A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy, Journal of Cleaner Production, 416 (2023) 137885.
[22] P. Hajikarimi, M. Ehsani, M. Rahi, S. Maniei, Development of Prediction Models for Complex Shear Modulus and Phase Angle of Asphalt Mastic Modified with Styrene-Butadiene-Styrene, Journal of Transportation Research, 20(1) (2023) 241-254.
[23] B. Keshtegar, M.L. Nehdi, R. Kolahchi, N.-T. Trung, M. Bagheri, Novel hybrid machine learning model for predicting shear strength of reinforced concrete shear walls, Engineering with Computers, 38(5) (2022) 3915-3926.
[24] A.H. Gandomi, G.J. Yun, A.H. Alavi, An evolutionary approach for modeling of shear strength of RC deep beams, Materials and structures, 46(12) (2013) 2109-2119.
[25] M. Dargi, E. Khamehchi, J. Mahdavi Kalatehno, Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability, Scientific Reports, 13(1) (2023) 11851.
[26] T. Taghikhany, M.N. Nazari Ghalati, Predicting the Remaining Life of Offshore Structure Members with Random Forest Algorithm, Journal of Civil and Environmental Engineering, 54(115) (2024) 85-95.
[27] S. Ghafari, M. Ehsani, F.M. Nejad, Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach, Construction and Building Materials, 314 (2022) 125332.
[28] L. Breiman, Random forests, Machine learning, 45(1) (2001) 5-32.
[29] C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, A. Zeileis, Conditional variable importance for random forests, BMC bioinformatics, 9(1) (2008) 307.
[30] O.I. Abiodun, A. Jantan, A.E. Omolara, K.V. Dada, N.A. Mohamed, H. Arshad, State-of-the-art in artificial neural network applications: A survey, Heliyon, 4(11) (2018).
[31] M.T. Shedid, R.G. Drysdale, W.W. El-Dakhakhni, Behavior of fully grouted reinforced concrete masonry shear walls failing in flexure: Experimental results, Journal of structural engineering, 134(11) (2008) 1754-1767.
[32] S. Mansouri, A. Rahai, A. Hosseinnia, F. Aslani, Performance evaluation of FRP-strengthened self-compacting concrete-filled double-steel-plate composite shear walls: Experimental and numerical studies, in:  Structures, Elsevier, 2025, pp. 110174.
[33] A. Rahai, S. Zabetian, S.H. Rashedi, Seismic fragility assessment of steel rigid‐frame bridges exposed to marine corrosion, Shock and Vibration, 2025(1) (2025) 3706021.
[34] E. Emami Meybodi, S.K. Hussain, M. Fatehi Marji, V. Rasouli, Application of machine learning models for predicting rock fracture toughness mode-I and mode-II, Journal of Mining and Environment, 13(2) (2022) 465-480.
[35] M.R. Aghakhani Emamqeysi, M. Fatehi Marji, A. Hashemizadeh, A. Abdollahipour, M. Sanei, Prediction of elastic parameters in gas reservoirs using ensemble approach, Environmental Earth Sciences, 82(11) (2023) 269.
[36] A. Abdollahipour, H. Soltanian, Y. Pourmazaheri, E. Kazemzadeh, M. Fatehi-Marji, Sensitivity analysis of geomechanical parameters affecting a wellbore stability, Journal of Central South University, 26(3) (2019) 768-778.
[37] M. Izadinia, R. Pourjaafary, Evaluation of shear behaviour of masonry walls strengthened by FRP laminates and shotcrete, Journal of Structural and Construction Engineering, 6(3) (2019) 195-210.
[38] A.H. Karimi, M.S. Karimi, A. Kheyroddin, A. Amirshahkarami, Experimental assessment and numerical modeling of the nonlinear behavior of the masonry shear walls under in-plane cyclic loading considering the brickwork-setting effect, Journal of Structural and Construction Engineering, 4(2) (2017) 19-32.
[39] M. Mohammadi Nikoo, A.H. Akhaveissy, A. Permanoon, An investigation of performance of masonry wall reinforced with timber lumbers, Journal of Rehabilitation in Civil Engineering, 9(1) (2021) 114-138.
[40] M. Soltani Mohammadi, A. Tasnimi, Modeling and Analysis of Masonry Elements by Fixed Smeared Crack Approach, Modares Civil Engineering Journal, 10(3) (2010).
[41] D.I. Salehi, M.M. Soltani, A.A. Tasnimi, Linear homogenization of brick masonry structures. Modares Civil Engineering Journal, 10(2) (2025) e11651.
[42] M. Yazdani, S.A. Hoseini, Strengthening and Investigating the Effect of Various FRP Strip Configurations on the Behavior of Masonry Wall Subjected to Blast Loading, Modares Civil Engineering Journal, 25(3) (2025) 87-99.
AUT Journal of Civil Engineering
Volume 9, Issue 4
Autumn 2025
Pages 311-324