A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria

Weiye Li

doi:10.1016/j.asoc.2022.109476

A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria

2022-10-01

15

PRE

AI

DOI：10.1016/j.asoc.2022.109476

Original

Original request for help

Save

Abstract

En 中文

Physical and mechanical parameter identification could provide useful information for dam health monitoring. Traditional methods for parameter identification commonly use subjective weights to convert multi-objective problems into single-objective problems, which may result in unreasonable identification results. In recent years, multi-objective optimization algorithms have been applied to avoid introduction of subjective weights in parameter identification. Since multi-objective optimization algorithms produce a massive number of Pareto solutions, how to select acceptable parameters from the Pareto solutions is an intractable problem, and effective decision criteria for selecting acceptable solutions is required. In this paper, a methodology combining machine learning, multi-objective optimization, and multiple decision criteria is proposed to effectively improve efficiency and credibility of parameter identification for dams. A surrogate model constructed using the Support Vector Machine (SVM) based on grid search and cross-validation is employed to replace time-consuming finite element calculations in the process of parameter identification. Multiple decision criteria incorporating influencing factors of parameter identification (such as the membership degree of identified parameters with respect to designed ones, fitting error of monitoring data, and the degree of overfitting) are devised to compare Pareto solutions derived from multi-objective optimization and determine the optimal one. Parameter identification of an actual gravity dam is implemented to illustrate and verify the proposed methodology. It is shown that the results yielded with the proposed methodology are superior to those produced by traditional methods in terms of robustness of reasonable parameter identification, prediction accuracy of monitoring data, and reasonable physical meaning of material parameters. (C) 2022 Elsevier B.V. All rights reserved.

Keywords:

Dams

Parameter identification

Multi -objective optimization

Multiple decision criteria

A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria

Abstract

Journal

Organization

Cited Papers