首页|Modal Decomposition and Genetic Algorithm-Based Vulnerability Evaluation of High-Rise Structures-An Assessment of Structural Optimization Methodologies for Fragility Curves Development

Modal Decomposition and Genetic Algorithm-Based Vulnerability Evaluation of High-Rise Structures-An Assessment of Structural Optimization Methodologies for Fragility Curves Development

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  • NETL
  • NSTL
  • Wiley
The development of methodologies for vulnerability assessment of high-rise structures is still in the early stages due to the computational toll of the whole process. This paper investigates the efficacy of vulnerability information of high-rise tubular structures, developed by means of two different analytical modeling approaches. The first approach is based on the modal decomposition to simplify the convoluted nonlinear 3D analytical model, and correspondingly, four nonlinear single-degree- of- freedom (SDOF) systems have been established to conduct the nonlinear dynamic analysis, considering the modal mass participation ratio of more than 90%. In the second modeling approach, unsupervised machine learning (ML), that is, genetic algorithms (GAs), has been implemented for extracting the structural modeling parameters from a fully nonlinear CSI Perform 3D model to establish a GA-based simplified model. The incremental dynamic analysis (IDA) results from both modeling approaches were processed, and eventually, the fragility curves were established to depict the structural vulnerability of high-rise tubular structures. A rational comparison of the fragility information and the total time consumed for both of the processes has been made to infer their practical applicability. Results reveal that both methods yielded adequate results with some disparities, attributed to the differences in the modeling processes. Both structural analytical modeling methodologies can be effectively employed for predicting the seismic vulnerability information of tubular structures after comprehending their fundamental differences presented in this paper.

fragility curvesgenetic algorithmsincremental dynamic analysismachine learningtall buildings

Lapyote Prasittisopin、Muhammad Zain、Suraparb Keawsawasvong、Muhammad Zaid Iqbal

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Center of Excellence on Green Tech in Architecture, Department of Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok, Thailand||Department of Materials Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand

Center of Excellence on Green Tech in Architecture, Department of Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok, Thailand

Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Khlong Nueng, Pathum Thani, Thailand

Department of Architecture, College of Art and Design, Punjab University, Lahore, Pakistan

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2025

10.1002/tal.70032

The structural design of tall buildings

The structural design of tall buildings

ISSN：1541-7808
年,卷(期)：2025.34(7)
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