Session: SE-04-01 Machine Learning for Seismic Analysis of Industrial Facilities

Paper Number: 123479

123479 - Seismic Risk Assessment of Non-Structural Components in Hazardous Facilities Through a Novel Ann-Based Technique 

Seismic events pose a significant threat to industrial facilities, and the risk assessment of non-structural components (NSCs) within these structures is paramount for ensuring the safety and functionality of critical infrastructure. This paper presents a novel approach utilizing machine learning techniques to enhance the seismic risk assessment of NSCs in industrial facilities. The proposed methodology integrates data from multiple sources, including seismic records, structural characteristics, and NSC vulnerability parameters, to develop predictive models for evaluating the vulnerability and potential damage to non-structural components during seismic events.

The study starts from a dataset generated by using a numerical low-fidelity model subjected to a set of natural records. An artificial neural network machine learning algorithm is then adopted for model training and evaluation. The latter is used for predicting the likelihood of damage to NSCs based on factors such as the intensity and duration of ground motion, the NSC's location within the structure, and its inherent vulnerability characteristics.

Results are compared with traditional methods. The outcomes indicate the effectiveness of machine learning in improving the accuracy and efficiency of seismic risk assessment for NSCs in industrial plant. Moreover, the study highlights the importance of incorporating machine learning techniques to enhance the resilience of industrial facilities in seismically active regions.

The research contributes to the field of seismic risk assessment by demonstrating the potential of machine learning in providing more accurate and timely predictions for the vulnerability of non-structural components, thereby aiding in the development of targeted mitigation strategies and emergency response plans. This paper serves as a foundational step towards a data-driven approach to seismic risk assessment for non-structural industrial components, ultimately reducing the economic and human losses associated with seismic events.

Presenting Author: Gianluca Quinci Roma Tre University

Presenting Author Biography: Gianluca Quinci is an Assistant Professor of the Structures Research Group at the Department of Civil Engineering, Informatics and Aeronautical technologies of Roma Tre University, in Rome (Italy). Born in 1992, he got his MSc in 2019 and and the PhD in Civil Engineering in 2019. Gianluca’s research interests include the seismic risk assessment of critical civil infrastructure (C.I.) (i.e. bridges, industrial plant, etc.), seismic risk mitigation by means traditional techniques and innovative smart techniques, experimental test campaign to investigate the seismic behaviour of C.I., machine learning techniques applied to the Civil Engineering field.

Authors:

Gianluca Quinci Roma Tre University
Fabrizio Paolacci University of Roma Tre
Michalis Fragiadakis National Technical University of Athens