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- W4309152537 abstract "Seismically induced slope displacements (D) are often used as a performance index in the seismic design of slope systems. Although previous studies have developed predictive relationships for estimating D, a comprehensive selection of the most efficient parameters to explain D, considering the linearity and nonlinearity in D, has not been thoroughly performed. This study uses modern machine learning-based techniques including Stepwise Selection, LASSO, and Random Forest to identify the influential features in the estimation of D in shallow crustal tectonic settings. The detected influential features are the system’s yield coefficient (ky), initial fundamental period (Ts), earthquake moment magnitude (Mw), peak ground velocity (PGV), and the degraded spectral acceleration at 1.3Ts [Sa(1.3Ts)]. Moreover, the results indicate that there is no significant gain in accuracy beyond five features. The detected significant parameters provide insight and a basis for developing more efficient prediction models." @default.
- W4309152537 created "2022-11-24" @default.
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- W4309152537 date "2022-11-16" @default.
- W4309152537 modified "2023-09-27" @default.
- W4309152537 title "Machine Learning-Based Selection of Efficient Parameters for the Evaluation of Seismically Induced Slope Displacements" @default.
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- W4309152537 doi "https://doi.org/10.1061/9780784484449.017" @default.
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