Homogenization of interlocking brick walls under cyclic loading

Mortarless interlocking brick structures offer great potential for modern masonry construction due to their advantages in construction efficiency, cost-effectiveness, and environmental sustainability. However, their complex geometry and highly nonlinear contact interactions present significant challenges for accurate numerical simulation. To address these challenges, this study proposes an efficient computational framework that integrates the rigid-body-spring model (RBSM) with a machine-learning-based parameter identification and optimization strategy. A homogenized model that captures the geometric characteristics of a novel interlocking brick unit is developed to analyze the dynamic response of large masonry walls subjected to in-plane cyclic loading. In this framework, the multi-linear constitutive models of the interfacial springs in the RBSM are derived and simplified from the tensile–compressive behavior of concrete, with initial control-point parameters determined based on the stiffness of contact surfaces. Using these initial parameters, sample datasets are generated via Latin hypercube sampling, followed by batch dynamic simulations employing both the RBSM and finite element (FE) models. An artificial neural network (ANN) is trained to predict the error between the two models, and the RBSM parameters are subsequently optimized. Finally, the optimized RBSM is validated against experimental results of large-scale masonry walls, demonstrating excellent agreement. The proposed numerical approach effectively overcomes key limitations in the analysis of interlocking masonry systems, providing a reliable and computationally efficient tool for predicting the dynamic performance of similar structures. • A machine-learning-assisted RBSM framework enables efficient cyclic analysis of interlocking masonry. • Machine learning automates optimization of dynamic constitutive parameters in RBSM. • The optimized model accurately predicts cyclic response of full-scale interlocking walls. • A unique three-stage failure mechanism of interlocking systems is revealed.

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Publication Details

Journal
Engineering Structures
Published
2026-05-18
DOI
https://doi.org/10.1016/j.engstruct.2026.122980
Primary Topic
Masonry and Concrete Structural Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Homogenization of interlocking brick walls under cyclic loading

Yanan Lu, Lihua Huang, Xihong Zhang
Engineering Structures
Masonry and Concrete Structural Analysis
article

Homogenization of interlocking brick walls under cyclic loading

Yanan Lu, Lihua Huang, Xihong Zhang
article en

Abstract

Mortarless interlocking brick structures offer great potential for modern masonry construction due to their advantages in construction efficiency, cost-effectiveness, and environmental sustainability. However, their complex geometry and highly nonlinear contact interactions present significant challenges for accurate numerical simulation. To address these challenges, this study proposes an efficient computational framework that integrates the rigid-body-spring model (RBSM) with a machine-learning-based parameter identification and optimization strategy. A homogenized model that captures the geometric characteristics of a novel interlocking brick unit is developed to analyze the dynamic response of large masonry walls subjected to in-plane cyclic loading. In this framework, the multi-linear constitutive models of the interfacial springs in the RBSM are derived and simplified from the tensile–compressive behavior of concrete, with initial control-point parameters determined based on the stiffness of contact surfaces. Using these initial parameters, sample datasets are generated via Latin hypercube sampling, followed by batch dynamic simulations employing both the RBSM and finite element (FE) models. An artificial neural network (ANN) is trained to predict the error between the two models, and the RBSM parameters are subsequently optimized. Finally, the optimized RBSM is validated against experimental results of large-scale masonry walls, demonstrating excellent agreement. The proposed numerical approach effectively overcomes key limitations in the analysis of interlocking masonry systems, providing a reliable and computationally efficient tool for predicting the dynamic performance of similar structures. • A machine-learning-assisted RBSM framework enables efficient cyclic analysis of interlocking masonry. • Machine learning automates optimization of dynamic constitutive parameters in RBSM. • The optimized model accurately predicts cyclic response of full-scale interlocking walls. • A unique three-stage failure mechanism of interlocking systems is revealed.

Engineering StructuresVol. 362
Curtin University (AU), Dalian University of Technology (CN)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 9%
Masonry and Concrete Structural Analysis
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