土体热传导性能及其热导率模型研究

为研究不同土体的热传导特性变化规律,利用热探针测试了南京地区典型土体在不同含水量和干密度状态下的热阻系数,分析了含水量、干密度、饱和度以及矿物成分等因素对土体热阻系数的影响,研究了不同状态下土体热导率的预测模型,提出了适用于不同地区土体热阻系数估算的修正系数.结果表明:南京地区典型土体的热阻系数随含水量和干密度增加而减小,当含水量超过一定范围后,热阻系数趋于稳定;热阻系数与饱和度之间的关系表现出与其含水量之间相似的变化规律;土颗粒的热传导特性由其矿物成分决定,石英含量对土颗粒热传导特性有着显著影响;提出了可用于非饱和土热导率估算的修正归一化模型,该模型对于粗粒土具有较高的预测精度,细粒土则需考虑区域差别进行修正.     

基于多元分布模型预测土体热阻系数

土体热阻系数是决定岩土材料热性质的重要参数。为准确预测土体热阻系数，基于多元分布模型建立土体热阻系数预测模型，对所建立的预测模型进行验证分析，并与传统经验关系模型进行对比，明确所建多元分布模型的准确性。结果表明：建立的多元分布模型能够准确预测土体热阻系数，随着输入参数增加多元分布模型预测精度显著提升，其相关系数R²从0.719 5提高到0.899 5，平均值E(ε)值从1.077 8降低到1.037，变异系数COV(ε)值从0.367 5降低到0.212 1，预测精度最好的是λ-{w，ρ_d,n,S_r,c,s_a,q_c}模型，多元分布模型预测精度明显优于传统经验关系模型；对于工程性质差异显著、沉积环境复杂的不同类型土体热阻系数预测，建议根据资料选择不同类型的多元分布模型来评价其热阻系数。     

神经网络反馈分析方法预测土体热阻系数研究

为研究不同土体的热传导特性,通过文献数据归纳整理,简要分析了土体热阻系数与主要影响因素的相关关系。利用神经网络反馈分析方法,提出土体热阻系数的预测模型,并对所提模型的有效性与优越性进行了对比验证。结果表明:反馈神经网络能够简便、有效的预测土体热阻系数,所建模型以干密度、饱和度和石英含量为输入参数,较为全面、合理地反映了影响土体热传导性质的主要因素;预测模型具有较高的精度,预测值与实测值的相关系数R~2大于0.93,均方根误差RMSE低于28 K?cm/W,方差比VAF大于94%;与传统经验关系式相比,反馈分析模型在新环境中的预测结果上具有显著的优越性。     

石油污染土体导热系数变化规律和变化机制

室内测试了不同含水量、污染强度、油水相含量的石油污染土体的导热系数。研究结果表明:干密度和污染强度一定时,其导热系数随含水量的增加而增大;干密度和含水量一定时,随污染强度的增加呈现先减小后增加最后又减小的变化趋势;温度对其导热系数的影响主要取决于各温度区间土体水相的赋存状态。内在的变化机制是:土体内部油、水的赋存位置、相状态、含量以及土体自身的导热性能共同决定了其导热系数的大小。研究成果为定量研究、评价和预测石油污染物扩散、迁移等环境问题以及石油污染土体传热、传质和热稳定性问题研究提供了重要参数;为冻土区环境恶化、冻土退化、冻害形成过程等机理性解释提供依据。     

土体导热系数温度效应及其预测模型

考虑环境温度变化对土体导热系数的影响,对于地下热工项目的优化设计和安全评价是必要的。利用热探针法测试了不同温度下红黏土、粉土、软土和膨润土的导热系数,分析了土体导热系数的温度效应及其影响因素,建立了考虑导热系数温度效应的加权几何平均模型,并与传统的预测模型进行对比分析。试验结果表明：土体导热系数随温度的增加而增大,其温度效应随干密度的增大而减小,温度对非饱和土体导热系数的影响较大,对于干燥和饱和土体导热系数的影响较微弱。温度对土体导热系数的影响可能取决于水汽潜热传输作用的变化,土中可提供潜热传输的水分和水汽运移通道越多,土体导热系数的温度效应越显著。模型计算结果表明,提出的加权几何平均模型预测性能良好,且较好预测了含水率和干密度对土体导热系数温度效应的影响,而Tarnawski模型、Gori模型、Leong模型预测精度均低于加权几何平均模型。     

基于能量比例系数的滚石冲击力计算方法研究

滚石冲击力是滚石防护工程设计的重要依据。为此，基于能量守恒原理，引入能量比例系数，考虑滚石冲击角度，提出滚石冲击土体的最大冲击力计算方法。通过物理模型试验，研究了滚石特征参数(质量与尺寸)、动力学参数(冲击速度与角度)及土体性质参数(密度与抗压强度)对能量比例系数的影响，构建能量比例系数无量纲经验公式，建立滚石冲击力计算模型并验证了模型的可靠性。结果表明：能量比例系数与滚石质量、冲击速度、冲击角度呈负相关关系，而与滚石尺寸、土体密度以及抗压强度正相关；提出的基于能量比例系数的滚石冲击力计算模型能够有效预测滚石最大冲击力，且与已有研究中大尺度模型试验结果吻合良好。研究成果可为滚石灾害防治提供理论依据及技术支持。     

改进一步法模型及TDR自适应方法研究

建立土体含水率和干密度与电磁参数间的经验模型是TDR技术在岩土工程中应用的关键。在原一步法模型基础上,分别建立了土体含水率、干密度与土体介电常数等电磁参数间新的经验关系模型,实现了干密度和含水率的解耦计算。分析了击实功、孔隙水电导率、温度等因素对新经验公式的影响。通过标定后,新经验公式计算的干密度相对偏差在±5%以内,含水率偏差在±0.02 g.g~(-1)以内。同时,提出了一种TDR自适应方法,可在现场无标定条件下实现土体干密度和含水率的快速高效测定。     

基于主动加热型FBG的土体干密度原位测量方法研究

针对现有干密度原位测量技术的局限性,提出了一种基于主动加热型FBG的土体干密度原位测量方法(简称H-FBG干密度法),该法通过土体的导热系数,建立温度特征值(Tt)与干密度ρd之间的关系,进而对干密度进行原位测量;在室内试验的基础上,讨论了该方法的最优加热参数,研究了土类型与含水率对测量结果的影响,证明了该方法的可行性...     

基于径向基神经网络的边坡稳定性评估方法

在现有研究的基础上,引入径向基神经网络理论,提出了边坡稳定性的径向基神经网络预测方法。以土体重度、内摩擦角、粘聚力、锚固段长度等为输入参数,边坡稳定性系数为输出参数,建立精确RBFNN神经网络模型,对边坡稳定性进行了预测,结果表明：用训练成熟的径向基神经网络进行仿真,避免了诸多人为因素的影响,提高了结果的精度,使得计算高效、结果更加准确。     

不同因素对浅表土导热系数影响的试验研究

土体导热系数是预测人工冻结温度场发展、冻结壁厚度的重要参数,主要取决于固体颗粒成分、含水率和土体密实程度。本文以南京地区浅表土为研究对象,开展了常温与-10 ℃冻结时含水率、干密度对淤泥质黏土、淤泥质粉质黏土、粉砂导热系数的影响规律研究。研究结果表明:土质、含水率、干密度对导热系数影响显著,在试验含水率与干密度范围内,导热系数随含水率、干密度增加呈近似线性规律升高;冻土导热系数大于常温土导热系数,随含水率增加冻土导热系数的增加幅度与干密度相关。     

木质素改良粉土热学与力学特性相关性试验研究

为揭示木质素改良粉土热学与力学特性随养护龄期的演化规律,通过击实试验、热阻系数测试、无侧限抗压强度试验、回弹模量试验、压汞试验和扫描电镜分析试验,探讨改良土热阻系数、强度和刚度与木质素掺量、含水率和养护龄期的变化规律,同时定性/定量评价改良土微观结构变化,分析改良土热学特性与力学特性间的相互关系。结果表明:改良土最大干密度较素土增加,最优含水率减小,干密度对含水率变化的敏感性增加;热阻系数随掺量和养护龄期增加而增加,60 d养护龄期后热阻系数趋于相同,热阻系数与土体密实度和组成成分的热传导特性密切相关;改良土强度随掺量和龄期增长而增加,28 d龄期12%掺量改良土强度约为素土强度6倍;回弹模量的变化特征与无侧限抗压强度类似,对于改良粉土,木质素最优掺量约为12%;改良土孔隙总体积和平均孔径显著减小,木质素包裹、连结土颗粒并填充孔隙,形成更致密土体结构。     

Training Artificial Neural Networks with the Aid of Fuzzy Sets

This article presents a technique of training artificial neural networks (ANNs) with the aid of fuzzy sets theory. The proposed ANN model is trained with field observation data for predicting the collapse potential of soils. This ANN model uses seven soil parameters as input variables. The output variable is the collapsibility (whether the soil is collapsible) or the collapse potential (if the soil is judged collapsible). The proposed technique involves a module for preprocessing input soil parameters and a module for postprocessing network output. The preprocessing module screens the input data through a group of predefined fuzzy sets, and the postprocessing module, on the other hand, "defuzzifies" the output from the network into a "nonfuzzy" collapse potential, a single value. The ANN with the proposed preprocessing and post-process techniques is shown to be superior to the conventional ANN model in the present study.     

Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts

A reliable estimation of the groutability of the target geomaterial is an essential part of any grouting project. An artificial neural network (ANN) model has been developed for the estimation of groutability of granular soils by cement-based grouts, using a database of 87 laboratory results. The proposed model used the water:cement ratio of the grout, relative density of the soil, grouting pressure, and diameter of the sieves through which 15% of the soil particles and 85% of the grout pass. A very good correlation was obtained between the ANN predictions and the laboratory experiments. Comparison of these results with those obtained using traditional methods for groutability prediction confirmed the viability of using ANN to estimate groutability.     

Prediction of rainfall runoff‐induced sediment load from bare land surfaces by generalized regression neural network and empirical model

Based on three rainfall run‐off‐induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. The GRNN model was trained and tested. The GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. In addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. The empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. The validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. The results indicated the success of the empirical models in predicting sediment load from bare land surfaces.     

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.     

人工冻融黏土导热系数试验研究

基于TC3000型热线法导热系数测试原理,研制了人工冻土导热系数测试系统,该系统具有温度精度高、测试速度快、测试灵活等优点。对冻结地下工程岩土进行了导热系数试验研究,获得人工冻结黏土随干密度、含水率、外界环境温度变化对冻土导热系数的影响规律。试验结果表明:冻融土导热系数随干密度呈线性增加,平均增长速率为0.20~0.25 W/m·K之间;冻融土导热系数随含水率变化平均增长速率相差不超过0.03 W/m·K;温度每降低1℃,冻融土导热系数增加0.01~0.014 W/m·K。研究成果可为类似冻结工程中测试土体导热系数提出一种新的有效方法,对人工冻结温度场计算具有重要指导意义。     

Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods

In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared. The nonlinear relationship between maximum ground surface settlements and geometry, geological conditions, and shield operation parameters were considered in the ANN models. A total number of 200 data sets obtained from the Changsha metro line 4 project were used to train and validate the ANN models. A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models. Based on the analysis, the GRNN model was found to outperform the BP and RBF neural networks in terms of accuracy and computational time. Analysis results also indicated that strong correlations were established between the predicted and measured settlements in GRNN model with MAE = 1.10, and RMSE = 1.35, respectively. Error analysis revealed that it is necessary to update datasets during EPB shield tunneling, though the database is huge.