Multi-parameter identification of concrete dam using polynomial chaos expansion and slime mould algorithm

• Published in: Computers & structures Vol. 281; p. 107018
• Main Authors: YiFei, Li, MaoSen, Cao, H.Tran-Ngoc, Khatir, Samir, Abdel Wahab, Magd
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023
• Subjects: Concrete dams; Parameter sensitivity analysis; Parameters identification; Polynomial chaos expansion; Slime mould algorithm; Polynomial chaos expansion; Concrete dams; Parameter sensitivity analysis; Slime mould algorithm; Parameters identification
• ISSN: 0045-7949; 1879-2243
• DOI: 10.1016/j.compstruc.2023.107018

•A new methodology for multi-parameter identification of concrete dams is proposed.•For the first time, polynomial chaos expansion and slime mould algorithm are integrated.•A very high computational efficiency is achieved using the proposed methodology. This paper presents a novel methodology that combines polynomial chaos expansion and slime mould algorithm for multi-parameter identification of concrete dams. This methodology not only incorporates the merits of low computational cost in the polynomial chaos expansion and fast convergence of slime mould algorithm, but also considers the priori uncertainty in the input parameters by introducing statistical probability theory. By considering two examples with different complexity, this paper verifies the effectiveness of the proposed method with a univariate simply supported beam model, followed by a complex multivariate dam model to demonstrate its practicability in real engineering problems. In addition, parameter sensitivity analysis of the dam model is conducted at an extremely low cost by polynomial chaos expansion based on Sobol’ indices. Furthermore, the conventional parameter identification methods based on optimization methods directly combined with the finite element model are employed for comparison, highlighting two distinct advantages of the proposed method: (i) the proposed method improves the computational efficiency by nearly 52 times while ensuring a high accuracy, and (ii) the classical non-population optimization algorithm, Bayesian optimization, is used for comparison, revealing the outstanding performance of slime mould algorithm in terms of convergence speed and robustness. The application of the proposed algorithm is not only limited to dams, but also it can be extended to any structure.