摘要
为解决基于干密度评估压实质量所存在的压实时间不定、评价指标单一等问题,提出了一种基于粗颗粒嵌锁点的高铁级配碎石(high-speed railway graded aggregate,简称HRGA)振动压实控制新方法。首先,结合力学指标动刚度Krb和修正地基系数K20完善振动压实评估体系,进一步提出了压实控制嵌锁点Tlp指标,并通过室内试验研究了Tlp前后级配碎石的力学性能及适用性;其次,通过振动压实试验建立了Tlp与HRGA的各项性能指标之间的关系,并结合灰色关联度分析(grey relation analysis,简称GRA)算法分析了Tlp的主控特征;最后,基于机器学习(machine learning,简称ML)方法提出了Tlp预测模型,结合三层次优选体系选择最佳Tlp预测模型,并利用SHapley Additive ex Planations(SHAP)可解释性方法对最佳ML模型进行了解释。结果表明:基于Tlp可确定最优振动时间,进而控制压实质量;基于GRA算法明确Tlp的主控特征为填料最大粒径dmax、级配参数b、级配参数m、扁平细长颗粒Qe和洛杉矶磨耗LAA;计算各Tlp预测模型的综合评价指标(comprehensive evaluation index,简称CEI)依次为改进粒子群优化的人工神经网络(artificial neural networks for improved particle swarm optimization,简称IPSO-ANN)模型>改进粒子群优化的支持向量回归(support vector regression for improved particle swarm optimization,简称IPSO-SVR)模型>改进粒子群优化的随机森林(random forest for improved particle swarm optimization,简称IPSO-RF)模型,IPSO-ANN模型最佳;基于SHAP方法得到总体重要性值φ排序为dmax(17.31)>b(13.93)>m(6.59)>Qe(2.17)>LAA(1.54),该结果与GRA算法的结果相印证,表明SHAP方法可提升ML模型的可理解性。该研究成果可为振动压实的质量评估提供新思路,也为振动压实智能化控制提供强有力的理论支撑。
To address the issues of variable compaction time and single evaluation index based on dry density assessment of compaction quality,a new method of vibratory compaction control for high-speed railway graded aggregate(HRGA)based on coarse particles embedding point is proposed.Firstly,the vibration compaction evaluation system is improved by combining the mechanical indexes of dynamic stiffness Krb and modified foundation coefficient K20.The index of compaction control“embedded locking point”Tlp is then proposed,and the mechanical properties and applicability of graded aggregates before and after Tlp are investigated through indoor tests.Secondly,the relationship between Tlp and various performance indexes of HRGA is established through vibratory compaction test,and the main controlling features of Tlp are analyzed using grey relation analysis(GRA)algorithm.Finally,the Tlp prediction model is proposed based on the machine learning(ML)method,the best Tlp prediction model is selected using the three-level preference system,and the best ML model is interpreted using SHapley Additive exPlanations(SHAP)interpretable method.The results show that the optimal vibration time can be determined based on Tlp,thereby controlling the compaction quality.The main controlling features of the Tlp are maximum particle size of filler dmax,grading parameter b,grading parameter m,flat elongated particles Qe and Los Angeles abrasion LAA based on the GRA algorithm.The comprehensive evaluation index(CEI)of each Tlp prediction model is calculated as follows:artificial neural networks for improved particle swarm optimization(IPSO-ANN)model>support vector regression for improved particle swarm optimization(IPSO-SVR)model>random forests for improved particle swarm optimization(IPSO-RF)model,with the IPSO-ANN model being optimal.The overall importance values φ based on SHAP method are ranked as follows:dmax(17.31)>b(13.93)>m(6.59)>Qe(2.17)>LAA(1.54),which corroborates with the results obtained from the GRA algorithm,indicating that the SHAP method can improve the comprehensibility of the ML model.The research results can provide new ideas for quality assessment of vibratory compaction,and also provide strong theoretical support for intelligent control of vibratory compaction.
作者
邓志兴
谢康
李泰灃
王武斌
郝哲睿
李佳珅
DENG Zhi-xing;XIE Kang;LI Tai-feng;WANG Wu-bin;HAO Zhe-rui;LI Jia-shen(School of Civil Engineering,Central South University,Changsha,Hunan 410083,China;Railway Engineering Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation,Southwest Jiaotong University,Chengdu,Sichuan 611731,China)
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2024年第6期1835-1849,共15页
Rock and Soil Mechanics
基金
国家重点研发计划“交通基础设施”专项(No.2022YFB2603400)。
关键词
振动压实
高铁级配碎石
最优振动时间
主控特征
机器学习
可解释性
vibratory compaction
high-speed railway graded aggregate
optimal vibration time
main controlling features
machine learning
interpretable
