Computationally efficient framework for probabilistic collapse analysis of structures under extreme actions

• Published in: Engineering structures Vol. 172; pp. 440 - 452
• Main Authors: Javidan, Mohammad Mahdi, Kang, Hyungoo, Isobe, Daigoro, Kim, Jinkoo
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2018; Elsevier BV
• Subjects: Building failures; Collapse; Computational efficiency; Computer applications; Computing time; Finite element analysis; Finite element method; Fragility; Heat conductivity; Impact analysis; Impact load; Impact loads; Impact strength; Load; Mathematical models; Metamodels; Model accuracy; Neural network; Neural networks; Nonlinear analysis; Reliability; Reliability analysis; Reliability engineering; Sensitivity; Sensitivity analysis; Steel frames; Steel structures; Structural steels; Finite element method; Sensitivity; Neural network; Reliability; Impact load; Fragility
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2018.06.022

•Efficient probabilistic collapse assessment under extreme actions.•Utilizing ANN with the ASI-Gauss technique for reliability analysis.•ANN-based Global sensitivity analysis under vehicle impact loads.•Comparison between Tornado diagram analysis and Global sensitivity analysis.•Fragility analysis under vehicle impact loads. Currently there is a growing need for a versatile framework consisting of analytical and surrogate models to ensure both accuracy and computational efficiency of collapse analysis under extreme actions. However training metamodels for highly nonlinear structural responses requires large number of samples to achieve enough accuracy. In this research a method is developed to achieve computational efficiency by implementing the adaptively shifted integration-Gauss technique in conjunction with a core neural network metamodel. The analytical model is validated by experimental data and its accuracy is further evaluated by detailed finite-element analysis. The applicability and efficiency of the provided tool for highly nonlinear analyses are investigated using collapse assessment of a steel framed structure subjected to code-stipulated vehicle impact loads. Thorough probabilistic analyses are carried out including reliability assessment, fragility analysis, and two different sensitivity tests. The analysis results show the superiority and precision of this framework compared to detailed finite-element analysis.