西北工程技术学报2025，Vol.24Issue(1)：45-52,8.
含裂隙预崩解炭质泥岩的强度特性预测
Prediction of Strength Characteristics of Pre-disintegrating Carbonaceous Mudstone with Fissures
摘要
Abstract
Accurate prediction of strength of fissure-containing carbonaceous mudstone embankments is crucial for ensuring long-term stability.Seven influencing factors,including the maximum length of fissures,maximum width of fissures,number of fissures,fissure rate,water mass fraction,dry density,and vertical pressure were analyzed.The particles swarm algorithm was combined with the BP neural network,resulting in PSO-BP-based prediction models for shear strength.The results show that the average absolute error,root mean square error,and average absolute percentage error of the PSO-BP shear strength prediction model decreased by 66.17%,63.79%and 58.78%,respectively,compared with that of the traditional BP neural network.In addition,the model prediction results indicate that when the number of fissures increases,the shear strength initially increases then decreases,and finally stabilizes;the increase of the maximum length of the fissures leads to a continuous increase in shear strength which tends to stabilize;whereas the increase of the maximum width of the fissures and fissure rate results in a gradual decrease in the shear strength,which also tends to stabilize.The improved performance of the PSO-BP prediction model over the BP neural network model significantly enhances prediction accuracy,providing a reliable foundation for the formulation of scientific reinforcement and maintenance strategies to ensure the long-term stability of carbonaceous mudstone embankment slopes.关键词
炭质泥岩/裂隙/路堤/抗剪强度/PSO-BP神经网络Key words
carbonaceous mudstone/fissure/embankments/shear strength/PSO-BP neural network分类
交通运输引用本文复制引用
杨禹,章赛泽..含裂隙预崩解炭质泥岩的强度特性预测[J].西北工程技术学报,2025,24(1):45-52,8.基金项目
湖南省教育厅科学研究项目(22B0342) （22B0342）
长沙理工大学实践创新与创业项目(CLSJCX23025) （CLSJCX23025）
