Damage detection in retaining wall structures through a finite element model updating approach

被引:0
作者
Mohebian P. [1 ]
Motahari M.R. [1 ]
Rahami H. [2 ]
机构
[1] Department of Civil Engineering, Faculty of Engineering, Arak University, Arak
[2] School of Engineering Science, College of Engineering, University of Tehran, Tehran
关键词
Damage detection; Finite element model updating; Retaining walls; Surrogate model;
D O I
10.1007/s42107-023-00738-7
中图分类号
学科分类号
摘要
Damage detection is a crucial task to ensure the safety and integrity of civil engineering structures. So far, only a limited number of studies have been conducted to detect damage in retaining wall structures, and those that exist rely on simplified analytical models that cannot fully capture the complex interaction between the wall and soil. Accordingly, this study aims to propose a new approach for detecting damage in retaining wall structures through the updating of a high-fidelity ABAQUS-based finite element model of the wall-soil system. The presented finite element model updating approach is further formulated as an inverse optimization problem, in which the damage variable is defined as a decrease in the elasticity modulus of the wall material, and the objective function is expressed as the discrepancy between the actual and computed displacement data collected along the wall height. In order to mitigate the computational burden associated with the iterative calculations during the model updating procedure, a surrogate model based on radial basis functions (RBF) is first established to simulate the mapping relationship between the damage variable and the objective function value to be optimized. Subsequently, the differential evolution (DE) algorithm, as a well-known meta-heuristic method, is employed to address the surrogate-based optimization problem. The capability of the proposed method is finally investigated by presenting two numerical examples, including a cantilever retaining wall and a sheet pile wall. The obtained results demonstrate the efficiency and accuracy of the presented approach for detecting damage in retaining wall structures. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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收藏
页码:3613 / 3626
页数:13
相关论文
共 56 条
[1]  
Alkayem N.F., Cao M., Shen L., Fu R., Sumarac D., The combined social engineering particle swarm optimization for real-world engineering problems: A case study of model-based structural health monitoring, Applied Soft Computing, 123, (2022)
[2]  
Amiri H.R., Esfandiari M.J., Dehghan Manshadi S.H., Urgessa G., A novel sensitivity-based method for damage detection of a structural element, Asian Journal of Civil Engineering, 21, pp. 1079-1093, (2020)
[3]  
Aval S.B.B., Mohebian P., A novel optimization algorithm based on modal force information for structural damage identification, International Journal of Structural Stability and Dynamics, 21, 7, (2021)
[4]  
Aval S.B.B., Mohebian P., Joint damage identification in frame structures by integrating a new damage index with equilibrium optimizer algorithm, International Journal of Structural Stability and Dynamics, 22, 5, (2022)
[5]  
Beheshti Aval S.B., Mohebian P., Combined joint and member damage identification of skeletal structures by an improved biology migration algorithm, Journal of Civil Structural Health Monitoring, 10, pp. 357-375, (2020)
[6]  
Broomhead D.S., Lowe D., Radial basis functions, multi-variable functional interpolation and adaptive networks, (1988)
[7]  
Das S., Saha P., Patro S.K., Vibration-based damage detection techniques used for health monitoring of structures: a review, Journal of Civil Structural Health Monitoring, 6, pp. 477-507, (2016)
[8]  
Ding Z., Fu K., Deng W., Li J., Zhongrong L., A modified Artificial Bee Colony algorithm for structural damage identification under varying temperature based on a novel objective function, Applied Mathematical Modelling, 88, pp. 122-141, (2020)
[9]  
Dou S., Li J., Kang F., Parameter identification of concrete dams using swarm intelligence algorithm, Engineering Computations, 34, 7, pp. 2358-2378, (2017)
[10]  
Dou S.Q., Li J.J., Kang F., Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model, Water Science and Engineering, 12, 3, pp. 188-195, (2019)