A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

• Published in: Nonlinear engineering Vol. 13; no. 1; pp. 155 - 70
• Main Authors: Zain, Muhammad, Prasittisopin, Lapyote, Mehmood, Tahir, Ngamkhanong, Chayut, Keawsawasvong, Suraparb, Thongchom, Chanachai
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 01.01.2024; Walter de Gruyter GmbH
• Subjects: Algorithms; Artificial neural networks; Correlation coefficients; Deep learning; Fragility; Genetic algorithms; high-rise building; Machine learning; Parameters; seismic fragility curve; Seismic hazard; Source code
• ISSN: 2192-8029; 2192-8010; 2192-8029
• DOI: 10.1515/nleng-2022-0365

Seismic vulnerability assessments are conventionally conducted by using sophisticated nonlinear analytical models, leading to aggressive computational demands. Previous attempts were made to reduce computational efforts for establishing vulnerability assessment of structures; however, the area of super tall and tubular structures still faces considerable lack. Advent of efficient machine learning (ML) has enabled engineering practitioners to automate the processes for fragility analysis; however, its application for high-rise tubular structures is not yet exploited, and most implementations are limited to basic ML. In this work, an attempt was made to reduce computational demand for the fragility assessment process for tubular structures by employing genetic algorithms (GAs) for nonlinear structural modeling, and development of artificial neural network (ANN) using deep learning for fragility development. Consequently, a simple lumped parameter model had been developed using open-source code of ZEUS-NL, containing parameters selected by GA to acutely account for convoluted interactive behavior of structural systems and dynamic demands. Subsequently, incremental dynamic analysis (IDA) was performed on the optimized model. A new framework has been established to develop and train ANN architecture by amalgamating Weka’s capability of data preprocessing with deep learning. The established ANN model resulted in correlation coefficient of 0.9972 and of 0.95, demonstrating adequate performance.