In the heart of China’s Huainan mining area, a groundbreaking approach to understanding the mechanical behavior of surrounding rock is emerging, with significant implications for the energy sector. Researchers, led by Dr. Liu Xuewei from the State Key Laboratory of Geomechanics and Geotechnical Engineering, have developed a novel machine learning model that promises to refine the inversion of mechanical parameters of surrounding rock, considering zonal deterioration.
The team’s work, published in *Yantu gongcheng xuebao* (translated to English as *Rock and Soil Mechanics*), addresses a critical challenge in deep roadway stability evaluation. Traditional methods often overestimate parameter values by analyzing all strata within a model uniformly. “Existing methods treat the surrounding rock as a homogeneous entity, which can lead to inaccuracies in stability assessments,” explains Dr. Liu. “Our approach considers the zonation characteristics, providing a more nuanced understanding of the rock’s mechanical behavior.”
The researchers introduced a machine learning model that combines surrounding rock zonation methods with parameter inversion models. The model employs the Coronavirus Herd Immunity Optimization (CHIO) algorithm to optimize the Least Squares Support Vector Machine (LSSVM), significantly enhancing the precision and stability of parameter inversion. “By integrating the CHIO algorithm, we’ve been able to fine-tune the penalty factor and kernel function width of the LSSVM, resulting in more accurate and stable predictions,” says Dr. Liu.
The effectiveness of the proposed method was validated through theoretical solutions and engineering applications. Using the Zhangji Mine in the Huainan mining area as a case study, the team compared five different hybrid machine learning models regarding their prediction accuracy and generalization capabilities for surrounding rock mechanical parameters. The results were compelling: the CHIO-LSSVM method achieved higher accuracy in parameter prediction.
The implications for the energy sector are substantial. Accurate inversion of mechanical parameters is crucial for the stability evaluation of deep roadways, which are integral to mining operations. “Our method provides a more refined and accurate assessment of surrounding rock stability, which can lead to safer and more efficient mining practices,” says Dr. Liu.
Moreover, the integration of field-measured deformation data into the parameter inversion analysis further validated the model’s accuracy. The forward calculation results indicated that the model is suitable for the refined inversion of zonation parameters of surrounding rock in deep roadways.
This research not only advances the field of geomechanics but also has significant commercial impacts. By improving the accuracy of stability evaluations, the model can enhance safety and reduce costs in mining operations. As the energy sector continues to evolve, such advancements are crucial for sustainable and efficient resource extraction.
The study’s findings pave the way for future developments in the field, potentially influencing the design and implementation of deep roadways in various geological settings. As Dr. Liu notes, “This research is just the beginning. We hope to see our method adopted widely, contributing to safer and more efficient mining practices globally.”