Using Machine Learning for the Performance-Based Seismic Assessment of Slope Systems
Publication: Geo-Congress 2022
ABSTRACT
Engineers often use analytical procedures, which estimate the amount of seismically induced slope displacements (D), to evaluate the seismic performance of earth structures and natural slopes. These procedures often use as inputs slope properties, earthquake parameters, and ground motion intensity measures (IMs). In this study, we propose a new set of machine learning (ML) based models to estimate D using the NGA-West2 shallow crustal ground motion database. Our findings suggest that the most efficient features to evaluate the seismic performance of slope systems are the slope’s yield coefficient (ky), its fundamental period (Ts), the earthquake magnitude (Mw), the peak ground velocity (PGV), and the degraded spectral acceleration at 1.3 Ts. We assess the performance of the proposed models by evaluating cross-validation errors, their predictive performance in case histories, and comparisons against existing models. Based on the assessments, we recommend 6 ML-based models to estimate D in engineering practice.
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Geo-Congress 2022
Pages: 649 - 658
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Published online: Mar 17, 2022
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