Intelligent optimization of slope step parameters in open pit mines containing weak interbedded layers based on machine learning and multi-objective optimization methods

The stability of open-pit slopes with weak interlayers is a critical issue in mining, as the design of bench parameters affects both slope safely and economic outcomes. Traditional optimization methods are often time-consuming and may not find global optimal solutions. This study presents an intelligent optimization approach combining machine learning (ML) and multi-objective optimization techniques. A numerical model simulating slopes with weak interlayers was developed and a dataset linking bench control parameters with slope stability and economic performance was created. Machine learning algorithms, including Support Vector Machines (SVM), were used to build predictive models to assess the impact of various parameter combinations on stability and economic performance. Optimization was carried out using the SVM-NSGAII and SVM-O algorithms. Results showed that SVM-NSGAII out performed SVM-BO, predicting a safety factor of 1.258 with a 3.8% error compared to 5.69%6 for SVM-BO, and achieving a relative economic error of 1.296, significantly lower than the 9.0%6 error of SVM-BO. A software system for bench parameter optimization was developed on the Python platform with an intuitive graphical user interface (GUI), significantly improving slope stability and mining profitability, offering scientific support for mine design in complex environments, and demonstrating both theoretical and practical applications.