Model Updating in Complex Bridge Structures using Kriging Model Ensemble with Genetic Algorithm

• Published in: KSCE journal of civil engineering Vol. 22; no. 9; pp. 3567 - 3578
• Main Authors: Qin, Shiqiang, Zhou, Yun-Lai, Cao, Hongyou, Wahab, Magd Abdel
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01-09-2018; Springer Nature B.V; 대한토목학회
• Subjects: Algorithms; Bridges; Civil Engineering; Computational efficiency; Computer applications; Computing costs; Design parameters; Engineering; Finite element method; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Industrial Pollution Prevention; Kriging interpolation; Light rail transportation; Mathematical models; Model accuracy; Model updating; Objective function; Parameter sensitivity; Parameters; Railway bridges; Rivers; Sensitivity analysis; Statistical methods; Structural Engineering; Structures; 토목공학; model updating; complex structures; kriging model; genetic algorithms; structural optimization
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-017-1107-7

Computational cost reduction and the best solution seeking are frequently encountered during model updating for complex structures. In this study, a hybrid algorithm using kriging model and genetic algorithms (GAs) is proposed for updating the Finite Element (FE) model of complex bridge structures employing both static and dynamic experimental measurements. The kriging model is first established to approximate the implicit relationship between structural parameters and responses, serving as a surrogate model for complex FE model when deriving analytical responses. An objective function is later defined based on the residual between analytical response values and experimental measured ones. GAs are finally employed to find the best solution by searching on the whole design space of updating parameters selected based on a sensitivity analysis. To verify the proposed algorithm, Caiyuanba Yangtze River Bridge, a double decked of roadway and light railway bridge with a main span of 420 m is used. Both frequencies and displacements predicted by the updated model are more close to experimental measured ones. The results show that the kriging surrogate model has good accuracy in predicting response and can be used as a surrogate model to reduce computational cost, and GAs provide a higher chance to obtain global best solution.