摘要
液性指数是研究土壤稳定性、土体变形、土体强度等问题的关键参数,因此对液性指数的准确预测至关重要。基于南京和合肥地区黏性土的孔压静力触探(piezocone penetration test,简称CPTU)原位测试数据集,以室内液塑限试验计算的液性指数为参考值,采用支持向量回归(support vector regression,简称SVR)、粒子群算法优化支持向量回归(particle swarm optimization based SVR,简称PSO-SVR)、遗传算法优化支持向量回归(genetic algorithm based SVR,简称GA-SVR)、模拟退火算法优化支持向量回归(simulated annealing based SVR,简称SA-SVR)对土体的液性指数进行评价,并将预测结果与室内试验结果以及CPTU经验公式对比。为更贴近工程实践,以原位测试时的孔洞为单位,进行单孔预测分析,最后,进行参数敏感性分析。结果表明,SVR模型和优化的SVR模型,都能预测黏性土的液性指数,算法优化后的3种模型在性能上表现更好。单孔分析时,SA-SVR模型以波动平滑、峰值适中等优点,预测效果更佳。工程实践中,建议采用归一化锥尖阻力、摩阻比、孔压参数比、上覆应力及有效上覆应力作为输入变量。PSO-SVR模型、GA-SVR模型、SA-SVR模型敏感性走向均与理论相同,但SA-SVR模型跨度更小,与理论结果更加一致,验证了SA-SVR模型的准确性。所提出的SA-SVR模型可以更好地预测黏性土的液性指数,并指导工程实践。
The liquidity index is a critical parameter for studying soil stability,deformation,strength,and related issues.Accurate prediction of the liquidity index is crucial.In this study,we evaluated the liquidity index using support vector regression(SVR),particle swarm optimization-based SVR(PSO-SVR),genetic algorithm-based SVR(GA-SVR),and simulated annealing-based SVR(SA-SVR)algorithms.The assessment utilized the piezocone penetration test(CPTU)dataset from Nanjing and Hefei regions,with the liquidity index derived from liquid limit and plastic limit tests as a reference.Predicted results were compared with laboratory tests and the CPTU empirical formula.Single-hole prediction analysis was conducted to align with engineering practice,and sensitivity analysis explored input parameter effects.Results showed that both the SVR model and optimized SVR models effectively predicted the liquidity index of cohesive soil,with the optimized models outperforming the original.Among these,the SA-SVR model excelled in wave smoothing and moderate peak values,enhancing prediction accuracy.For improved engineering predictions,normalized parameters(cone tip resistance,frictional resistance,pore pressure parameter ratio),overburden stress and effective overburden stress should be used as input variables.Sensitivity trends of PSO-SVR,GA-SVR,and SA-SVR models aligned with theoretical expectations,with SA-SVR exhibiting narrower span and greater consistency,confirming its accuracy.Thus,the proposed SA-SVR model offers superior prediction of cohesive soil liquidity index and guidance for engineering practice.
作者
王新龙
聂利青
蔡国军
张宁
赵泽宁
刘薛宁
宋登辉
WANG Xin-long;NIE Li-qing;CAI Guo-jun;ZHANG Ning;ZHAO Ze-ning;LIU Xue-ning;SONG Deng-hui(Anhui Provincial Key Laboratory of Intelligent Underground Detection,Anhui Jianzhu University,Hefei,Anhui 230601,China;Anhui Intelligent Underground Detection and Environmental Geotechnical Engineering Research Center,Anhui Jianzhu University,Hefei,Anhui 230601,China;Anhui Green Mine Engineering Research Center,Hefei,Anhui 230088,China;Institute of Geotechnical Engineering,Southeast University,Nanjing,Jiangsu 211189,China)
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2024年第S01期645-653,共9页
Rock and Soil Mechanics
基金
国家杰出青年科学基金(No.42225206)
安徽建筑大学科研储备库项目(No.2023XMK02)
安徽省绿色矿山工程研究中心开放基金项目(No.2022-166)
关键词
孔压静力触探
液性指数
支持向量回归
粒子群算法
遗传算法
模拟退火算法
piezocone penetration test
liquidity index
support vector regression
particle swarm optimization
genetic algorithm
simulated annealing
