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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| Published in: | Engineering structures Vol. 256; p. 113999 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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| ISSN: | 0141-0296, 1873-7323 |
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| Abstract | •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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| AbstractList | 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. •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. |
| ArticleNumber | 113999 |
| Author | Li, Junjie Li, Hongjun Kang, Fei Liu, Xi |
| Author_xml | – sequence: 1 givenname: Fei surname: Kang fullname: Kang, Fei email: kangfei@dlut.edu.cn, kangfei2009@163.com organization: School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China – sequence: 2 givenname: Xi surname: Liu fullname: Liu, Xi email: liuxidlut@163.com organization: School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China – sequence: 3 givenname: Junjie surname: Li fullname: Li, Junjie email: lijunjie@dlut.edu.cn organization: School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China – sequence: 4 givenname: Hongjun surname: Li fullname: Li, Hongjun email: lihj@iwhr.com organization: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, PR China |
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| Keywords | Jaya optimization algorithm Inverse/back analysis Concrete dams Kernel extreme learning machines Displacements |
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| Snippet | •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... Inverse analysis by finite element model (FEM) based on measured displacement data is a popular approach for parameter identification of concrete dams.... |
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| SubjectTerms | 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 |
| Title | Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model |
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