原状黄土天然孔隙比定量评价方法  

A quantitative evaluation method regarding the natural void ratio of undisturbed loess

作　　者：高智慧 左璐 Gao Zhihui;Zuo Lu(School of Human Settlements and Civil Engineering,Xi′an Jiaotong University,Xi′an 710049,China)

机构地区：[1]西安交通大学人居环境与建筑工程学院,西安710049

出　　处：《地质科技通报》2023年第6期53-62,共10页Bulletin of Geological Science and Technology

基　　金：国家自然科学基金项目(41790441,41772316)。

摘　　要：天然孔隙比是初始结构的基本表征参数,故从岩土角度对黄土天然孔隙比分布规律进行分析和预测,对于掌握原位黄土灾变力学行为并进行灾害预警工作具有重要意义。通过选取典型场地不同层位原状黄土开展了颗粒分析试验、X射线衍射(XRD)试验、天然孔隙比试验和一维固结试验,分析得到了天然孔隙比与颗粒组分、应力历史的相关规律。结果表明,天然孔隙比受应力历史和颗粒级配影响,上覆压力越大,级配越均匀,天然孔隙比越小,同时含水状态也可能是天然孔隙比变化的原因之一。在此基础上,以层位埋深、颗粒级配不均匀系数和曲率系数、天然含水量作为影响因素,基于BP神经网络对天然孔隙比进行了定量评价。引入麻雀算法(SSA)与粒子群优化算法(PSO),建立了BP、SSA-BP与PSO-BP神经网络的天然孔隙比预测模型。随机选取51组实测数据进行了模型训练,将训练后的模型对16组验证与测试数据进行了预测,并将预测结果与实测天然孔隙比进行了对比。结果表明基于PSO-BP的神经网络模型预测效果显著优于SSA-BP、BP神经网络模型,可以有效预测天然孔隙比。The natural void ratio is the most frequently used and important characterisation parameter of the initial structure at the macroscopic level.Therefore,the analysis and prediction of the distribution pattern of the natural void ratio of loess is important for understanding undisturbed loess disaster mechanics behaviour and for disaster early warning from the geotechnical point of view.In this study,particle analysis tests,XRD tests,natural void ratio tests and 1D consolidation tests were carried out on in situ soil samples from different layers of a typical loess site to analyse the correlation between the natural void ratio and particle fraction and stress history.The results show that the natural void ratio can be affected by the stress history and particle size distribution.The higher the overburden pressure is,the more uniform the grading is and the smaller the natural pore ratio is.The water content may be one of the reasons for the variation in the natural void ratio.On this basis,the burial depth of the layer,the inhomogeneous coefficient and curvature coefficient of particle gradation,and the natural water content are selected as the influencing factors,and the natural void ratio is evaluated quantitatively based on the machine learning algorithm.The SSA and PSO algorithm were introduced to optimise the weights and thresholds of the BP neural network,and natural void ratio predicted models based on the BP,SSA-BP and PSO-BP neural networks were established.The trained BP,SSA-BP and PSO-BP neural network models were then used to predict 16 sets of validation and test data,and the predicted results were compared with the measured natural void ratios.The results show that the PSO-BP-based neural network model predicts significantly better than the SSA-BP and BP neural network models,and can effectively predict the natural void ratio.

关 键 词：原状黄土 天然孔隙比 颗粒级配 应力历史 BP神经网络 

分 类 号：TU444[建筑科学—岩土工程]