Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Petros C. Lazaridis (), Ioannis E. Kavvadias, Konstantinos Demertzis, Lazaros Iliadis and Lazaros K. Vasiliadis
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Petros C. Lazaridis: Department of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, Greece
Ioannis E. Kavvadias: Department of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, Greece
Konstantinos Demertzis: Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 25520 Orestiada, Greece
Lazaros Iliadis: Department of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, Greece
Lazaros K. Vasiliadis: Department of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, Greece

Sustainability, 2023, vol. 15, issue 17, 1-31

Abstract: Recently developed Machine Learning (ML) interpretability techniques have the potential to explain how predictors influence the dependent variable in high-dimensional and non-linear problems. This study investigates the application of the above methods to damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), permutation and impurity-based techniques. Following previous investigations that examine the interdependence between predictors and the cumulative damage caused by a seismic sequence using classic statistical methods, the present study deploy ML interpretation techniques to deal with this multi-parametric and complex problem. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution with respect to the range of final damage is evaluated. Non-linear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by the Park and Ang damage index ( D I P A ) and Intensity Measures (IMs), respectively. In addition to the interpretability analysis, the study offers also a comprehensive review of ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined. Among the 17 examined predictors, the initial damage, caused by the first shock, and the IMs of the subsequent shock— I F V F and S I H —emerged as the most important ones. The novel results of this study provide useful insights in seismic design and assessment taking into account the structural performance under multiple moderate to strong earthquake events.

Keywords: seismic sequence; interpretable machine learning; successive earthquakes; seismic damage prediction; seismic damage accumulation; machine learning; explainable machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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