چکیده مقاله
The stability of embankments in geotechnical engineering is of paramount importance, as their failure can lead to catastrophic incidents, resulting in both human and financial losses, and causing adverse impacts on the economy and environment of the affected region In this study, machine learning ML was implemented to address a binary classification problem stable vs unstable aimed at predicting embankment stability A comprehensive study was conducted using data from 627 embankments with diverse geometric and geotechnical characteristics Nine ML models were developed and tested, among which the Gradient Boosting method demonstrated the best performance In addition to evaluating the performance of the optimal model, an interpretability approach was employed to ensure transparency and explainability in the model’s decision making process, elucidating the influence of input parameters on the output The findings indicate that integrating geometric information and geotechnical properties of embankments plays a crucial role in accurately predicting their stability, thereby assisting engineers in designing safe and optimized embankments These insights can enhance safety and durability in dam construction projects and mitigate the risk of unforeseen failures
کلیدواژهها
نویسندگان
Ghazal Hashemipour
Department of Geotechnic, Faculty of Civil Engineering, Babol Noshirvani University of Technology, ۴۷۱۴۸ ۷۳۱۳, Babol, Iran
Mohsen Taghavijeloudar
Department of Civil and Environmental Engineering, Seoul National University, ۱۵۱ ۷۴۴, Seoul, South Korea
شیوه ارجاع
Hashemipour, Ghazal and Taghavijeloudar, Mohsen,1404,Application of Machine Learning on Slope Stability Prediction: From Data to Reliable Decisions Using Interpretable Models,Third International Conference on applied researches in civil engineering, architecture and urban planning
ارائهشده در
مجموعه مقالات سومین کنفرانس بین المللی پژوهش های کاربردی در مهندسی عمران، معماری و شهرسازی30 آبان 1404