Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model

•A novel method for parameter inverse analysis of concrete dams based on an intelligent response surface model is presented.•Sample data of the proposed model are generated by Latin hypercube sampling.•The optimization algorithms are utilized to minimize the objective function for material parameter...

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Vydané v:Engineering structures Ročník 256; s. 113999
Hlavní autori: Kang, Fei, Liu, Xi, Li, Junjie, Li, Hongjun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Ltd 01.04.2022
Elsevier BV
Predmet:
Algorithms
Artificial neural networks
Comparative studies
Computation
Concrete dams
Cost analysis
Dams
Displacements
Economic analysis
Finite element method
Gravity dams
Hypercubes
Inverse/back analysis
Jaya optimization algorithm
Kernel extreme learning machines
Kernels
Latin hypercube sampling
Machine learning
Mathematical models
Objective function
Optimization
Parameter identification
Response surface methodology
Sampling
Sampling methods
Jaya optimization algorithm
Inverse/back analysis
Concrete dams
Kernel extreme learning machines
Displacements
ISSN:0141-0296, 1873-7323
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Shrnutí:•A novel method for parameter inverse analysis of concrete dams based on an intelligent response surface model is presented.•Sample data of the proposed model are generated by Latin hypercube sampling.•The optimization algorithms are utilized to minimize the objective function for material parameter identification.•Two cases and two real-world concrete dams are utilized to verify the engineering applicability of the proposed approach.•The proposed model combined with the Jaya algorithm shows the most promising results. Inverse analysis by finite element model (FEM) based on measured displacement data is a popular approach for parameter identification of concrete dams. FEM-based inverse analysis method is economical and effective, but with some limitations such as complex computation and time-consuming. This paper presents a kernel extreme learning machine (KELM)-based response surface model (RSM) for parameter inverse analysis of concrete dams. The FEM is replaced by KELM-based RSM to explore the relationships between material parameters and displacement response of dam-foundation systems. Sample data of KELM-based RSM are generated by the efficient sampling technique Latin hypercube sampling. Subsequently, a novel optimization algorithm Jaya is adopted to minimize the objective function for material parameters identification. The effectiveness and practicability of the proposed approach are verified by two cases and two real concrete gravity dams in operation with sufficient monitoring data. Comparative studies with direct finite element method and several optimization algorithms were also performed to demonstrate the superiority of the proposed approach over commonly used techniques. Results show that the proposed KELM-based RSM is a simple and efficient way to achieve high accuracy in parameter inverse analysis of concrete dams at a low computation cost.
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ISSN:0141-0296
1873-7323
DOI:10.1016/j.engstruct.2022.113999