Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Structural Damage Prediction of a Reinforced Concrete Frame Under Single and Multiple Seismic Events Using Machine Learning Algorithms

Petros C. Lazaridis
ORCID logo ,
Ioannis E. Kavvadias
ORCID logo ,
Konstantinos Demertzis
* ORCID logo ,
Lazaros Iliadis
ORCID logo ,
Lazaros K. Vasiliadis
Version 1 : Received: 12 March 2022 / Approved: 14 March 2022 / Online: 14 March 2022 (11:57:51 CET)

A peer-reviewed article of this Preprint also exists.

Lazaridis, P.C.; Kavvadias, I.E.; Demertzis, K.; Iliadis, L.; Vasiliadis, L.K. Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms. Appl. Sci. 2022, 12, 3845. Lazaridis, P.C.; Kavvadias, I.E.; Demertzis, K.; Iliadis, L.; Vasiliadis, L.K. Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms. Appl. Sci. 2022, 12, 3845.

Abstract

Advanced machine learning algorithms, have the potential to be successfully applied to many areas of system modelling. In the present study the capability of ten machine learning algorithms in predicting the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well known ground motion intensity measures are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial mainshock–aftershock sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology deployed in a user-friendly web-application.

Keywords

Seismic Sequence; Machine Learning Algorithms; Repeated Earthquakes; Structural Damage Prediction; Intensity Measures; Damage Accumulation; Machine Learning; Artificial Neural Network

Subject

Engineering, Civil Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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