Integrating Sensor Technologies and Machine Learning for Detection and Mitigation of Structural Deformity and Slope Failure in Opencast Mines

Abstract

With furtherance in the mining industry, accidents due to slope failure are frequent in mining sites. Slope instability, a complex process, seriously threatens the miner’s life and properties. The damage inflicted by slope failures in the recent past has pulled the attention of authorities toward implementing disaster risk reduction measures. This research aims to develop an innovative approach that combines sensor technologies and machine learning techniques to detect and mitigate structural deformity and slope failure risks in opencast mines. By deploying a network of sensors, such as inclinometers, strain gauges, and accelerometers, data on various parameters related to slope stability and structural integrity can be collected in real-time. This data will be processed and analysed using advanced machine learning algorithms to identify early warning signs of potential deformities or failure. The proposed research seeks to enhance safety measures, minimize operational disruptions, and optimize maintenance strategies in opencast mines by providing timely alerts and actionable insights for risk mitigation.The approach or method used in slope failure is considered legitimate when it predicts the slope’s failure time before its actual failure. This research is paramount in mitigating the risk associated with slope failure. This study seeks to showcase the practical implementation of Machine Learning techniques for accurately forecasting slope structure failures. ML classification approaches havebeen produced in this study to forecast the factor of safety of slopes using various geometrical parameters. The performance of ML models is examined and compared through quality metric parameters.