Machine Learning-Based Prediction of Seismic Failure Mode of Reinforced Concrete Structural Walls

Zeynep Tuna Deger*, Gulsen Taskin Kaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Machine learning techniques have gained significant popularity within earthquake engineering for constructing predictive models to understand how structures will behave during seismic events. These models often employ complex methodologies to attain a high level of accuracy in decision-making. However, the comprehensibility of such predictive models is just as crucial as their accuracy. Engineers require insight into the model’s decision-making process to ensure its practicality. This research strives to simultaneously achieve both transparency and precision, introducing an intelligible classification model designed to forecast the potential seismic failure mode of reinforced concrete shear walls. To accomplish this, eight distinct machine learning methods are utilized where experimental failure modes of conventional shear walls were used and designated as outputs, whereas wall design parameters such as compressive strength of concrete, axial load ratio, etc. were used as the inputs (features). The findings reveal that the Decision Tree approach emerges as the most suitable classifier, effectively delivering both high classification accuracy and interpretability.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Earthquake Engineering and Seismology - 7ICEES 2023
EditorsEren Uckan, Haluk Akgun, Elcin Gok, Cem Yenidogan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages499-507
Number of pages9
ISBN (Print)9783031576584
DOIs
Publication statusPublished - 2024
Event7th International Conference on Earthquake Engineering and Seismology, 7ICEES 2023 - Antalya, Turkey
Duration: 6 Nov 202310 Nov 2023

Publication series

NameLecture Notes in Civil Engineering
Volume488 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference7th International Conference on Earthquake Engineering and Seismology, 7ICEES 2023
Country/TerritoryTurkey
CityAntalya
Period6/11/2310/11/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Classification
  • Explainable models
  • Machine learning
  • Reinforced concrete structural walls
  • Seismic failure mode

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