Application of machine learning algorithms in tunnel engineering risk prediction model
DU Tao
PEI Chengyuan
WANG Jun
WANG Xiaonan
Abstract：To address the limitations of traditional evaluation methods in tunnel engineering risk predic-tion:such as over-reliance on empirical judgment,limited prediction accuracy,and poor adaptability to intelligent evaluation requirements in complex engineering scenarios——this study explores the appli-cation of machine learning algorithms.Initially,the study systematically reviews the core basic princi-ples of typical machine learning algorithms.Subsequently,it focuses on the full process of developing accurate risk prediction models,conducting in-depth analysis of key technical steps including data pre-processing,feature clustering and recombination,model training optimization,and quantitative evalu-ation of prediction results.The research findings not only provide practical technical support for intel-ligent risk assessment in fields like water conservancy and underground engineering but also offer new research insights for the iterative optimization of risk prediction models and the exploration of their engineering application practices in this domain.
Keywords：tunnel engineeringmachine learningrisk predictionfeature clusteringdata preprocessing
Publication Date：2026-02-28
Online Publishing Date：2026-01-31(First online date of this platform, not the publication date of the document)
Pages：7( 20-26 )