A Computationally Efficient Algorithm for Constructing Effective Vector-Valued Seismic Intensity Measures for Engineering Structures

ABSTRACT

Seismic intensity measures (IMs) quantify the severity of ground motions and their impacts on structures. They play a vital role in many aspects of earthquake engineering. This paper proposes a novel method, namely the express iteration method (EIM), for constructing effective vector-valued IMs based on dozens of existing scalar ones given a specific engineering structure or a class of them. Taking advantage of the sophisticated while efficient mapping between scalar IMs and engineering demand parameters (EDPs) via a machine learning model, EIM iteratively eliminates less important scalar IMs from a pool of candidates to find the most effective combinations for a vector-valued IM and achieves superior computational efficiency by avoiding updating the nonlinear mapping during the process. Taking a base-isolated structure and its non-isolated counterpart for a demonstrating case study, the performance of the vector-valued IMs determined by EIM is compared with those by other existing methods in the literature for the task of selecting the most unfavorable ground motions. The results show that EIM prioritizes records with the largest peak inter-story drift PIDs and thus leads to the smallest subset that imposes most severe structural damage, while its computational cost was two orders of magnitude smaller as compared to the existing methods of similar effectiveness. Such superior performance can also be expected in all tasks that involve vector-valued IMs, including but not limited to multi-dimensional fragility analysis, incremental dynamic analysis, and real-time seismic damage prediction.

KEYWORDS:

• Intensity measure
• machine learning
• back propagation neural network
• ground motion selection
• base isolation

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Author Contributions

YW: Methodology; Numerical modeling; Formal analysis; Data post-processing; Writing original draft-review & editing. ZQ: Supervision; Funding acquisition; Writing original draft-review & editing. Both authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

The study was jointly funded by the Natural Science Foundation of China [grant number 52122811].