摘要
针对传统统计学习模型等方法对边坡失稳预测精度低、难度大等问题,在对国内外304个边坡案例中高度、角度、容重、黏聚力、内摩擦角、孔隙压力比和边坡状态等参数进行搜集统计的基础上,建立边坡预测数据库,采用麻雀搜索算法(SSA)优化支持向量机(SVM),构建SSA-SVM边坡失稳智能预测模型,实现对边坡失稳智能预测.采用灰狼优化算法、遗传算法、布谷鸟搜索算法、粒子群优化算法、哈里斯鹰优化算法和鲸鱼优化算法优化SVM,并与SSA-SVM模型进行对比,结果表明:SSA-SVM模型在边坡失稳预测中具有突出优势,其准确率、精确率、F1分数、平均精度分数和AUC值分别达到了90.16%,94.28%,91.43%,96.79%和0.954,高于其他优化模型的相应指标,SSA算法相比其他优化算法具有很强的竞争力.
Aiming at the problems of low accuracy and high difficulty in slope instability prediction using traditional statistical learning models and other methods,the predicted slope database was covered by 304 slope cases from domestic and international,including the parameters such as height,angle,bulk density,cohesion,internal friction angle,pore pressure ratio and slope status,and support vector machine(SVM)was optimized by sparrow search algorithm(SSA)to construct the SSA-SVM slope instability intelligent prediction model,which could intelligently predict slope instability.To compare with the SSA-SVM model,SVM was optimized respectively by gray wolf optimization algorithm,genetic algorithm,cuckoo search algorithm,particle swarm optimization algorithm,Harris hawk optimization algorithm and whale optimization algorithm.Results show that the SSA-SVM model has outstanding advantages in slope instability prediction,and its accuracy,precision,F1-score,average precision score and area under curve(AUC)reach at 90.16%,94.28%,91.43%,96.79%and 0.954,respectively,which are higher than the corresponding indicators of other optimization models.Compared with other optimization algorithms,the SSA algorithm has strong competitiveness.
作者
金爱兵
张静辉
孙浩
王本鑫
JIN Aibing;ZHANG Jinghui;SUN Hao;WANG Benxin(Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine,University of Science and Technology Beijing,Beijing 100083,China;School of Civil and Resources Engineering,University of Science and Technology Beijing,Beijing 100083,China)
出处
《华中科技大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2022年第11期142-148,共7页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(52174106,52004017)
中央高校基本科研业务费专项资金资助项目(FRF-IDRY-20-021)。
关键词
边坡失稳
边坡预测
边坡预警
麻雀搜索算法
支持向量机
slope instability
slope prediction
slope alert
sparrow search algorithm
support vector machine