土工合成材料的蠕变特性和试验方法

蠕变是土工合成材料应用于永久性加筋土结构中的重要特性。较详细地介绍了蠕变的机理和影响因素、试验仪器、操作步骤和试验成果的整理方法,以及为减短试验持续时间而推荐的时温叠加法和分级等温法,比较了不同规范的要求,同时还简要介绍了土工合成材料的动力蠕变试验和土工泡沫及土工合成材料粘土垫层的蠕变试验以及铺设破坏与蠕变结合的试验,给出了对蠕变研究的一些建议。     

土工合成材料在多年冻土地区路基病害处治中的应用技术研究

纵向裂缝与不均匀变形是多年冻土地区路基病害的主要类型,成因复杂,久治不愈.综合国内外现有成果,对于路基不均匀变形,根据其成因,分别提出了土工合成材料处治不均匀变形技术与土工合成材料处治路基阳坡变形技术等处治措施.对于路基纵向裂缝,根据其不同分类与不同严重程度,分别提出了土工合成材料处治纵向裂缝技术、柔性枕梁处治技术、边坡分层加筋处治技术等处治措施;根据多年冻土地区路基特点与环境条件,从土工合成材料的强度特性、构造特性、耐久性及抗施工损伤性能等方面,提出了土工合成材料选择时的原则.结合青藏公路纵向裂缝处治工程,研究了土工合成材料处治病害施工技术,解决了该地区使用土工合成材料时的工程关键技术.     

土工合成材料在沥青路面的应用及其设计

随着交通量和轴载的增加,对路面使用性能的要求越来越高,这也促进了路用土工合成材料产品的不断发展。国内外对土工合成材料应用于沥青路面进行了大量的室内与现场试验及理论分析工作。结果表明,土工合成材料能有效防止各种病害,改善路面的使用性能。对目前土工合成材料在沥青路面结构应用的几种设计方法进行了分析和评价,并根据美国地沥青协会（AI）所提出沥青路面罩面的设计程序,介绍了土工合成材料用于沥青罩面加筋,以减薄罩面厚度的设计方法。     

土工合成材料界面作用特性的拉拔试验研究

在土工合成材料加筋土工程中,土工合成材料与填料的界面作用特性是最关键的技术指标,因此利用拉拔试验研究土工合成材料与填料的界面作用特性是非常必要的。以5种不同种类的国产土工合成材料为加筋材料,以砂和石灰粉煤灰为填料,比较了各种土工合成材料与填料的界面作用特性,试验表明:（1）石灰粉煤灰自重较轻,摩擦角高,并且比砂的拉拔系数相对偏高一倍左右,是理想的土工合成材料加筋土工程的填料;（2）土工合成材料的拉拔系数从高到低排序为涤纶纤维经编土工格栅最高,塑料拉伸土工格栅次之,土工网较低,土工织物最低;（3）不同填料、不同土工合成材料的拉拔系数相差较大,具体加筋土工程采用的拉拔系数需要通过拉拔试验确定。这些结论可指导土工合成材料的优选和研究加筋机理。     

土工合成材料与土工合成材料加筋砂土的相关特性

目前土工合成材料加筋的理论研究明显落后于工程实践。为了指导土工合成材料的优选和研究加筋机理,以5种国产土工合成材料为加筋材料,它们分别是针刺无纺土工织物、涤纶纤维经编土工格栅、玻璃纤维经编土工格栅、双向塑料拉伸土工格栅和土工网,系统进行三轴压缩试验以比较各种土工合成材料对砂土的加筋效果。试验结果表明：（1）各种土工合成材料加筋砂土的抗剪强度和应力应变特性不同;（2）无纺土工织物适合用于允许大变形的加筋土工程,涤纶纤维经编土工格栅和塑料拉伸土工格栅均适合用于对变形有较严格要求的加筋土工程,玻璃纤维经编土工格栅适合用于对变形有严格限制的加筋工程,设计时需要较大的安全系数,土工网适用低等级的加筋土工程;（3）砂土对各种土工合成材料侧向收缩的约束作用差异显著。     

用非线性模型评价加筋柔性台背路堤

在考虑土的固结和土体的非线性应力应变关系的基础上,利用非线性有限元数值方法对土工织物加筋柔性台背路堤的受力性状和破坏机理进行了分析。分析结果表明,将铺有土工合成材料的桥台路堤,看作由土体、土工合成材料、土体-土工合成材料界面组成的、独立的平面应变三层连续体系,进行数值计算是行之有效的。计算结果显示,土工织物加筋可以有效减少桥台路堤50%的均匀沉降和66%的非均匀沉降,并能增加台背路堤的稳定性。现场监测表明,本文采用的数值模拟方法与实际工程具有一致性。     

粘土与筋带直剪试验与拉拔试验对比分析

在土工合成材料加筋土工程中,土工合成材料与填土的界面作用特性直接决定加筋土工程的内部稳定性,所以土工合成材料与填料的界面作用特性指标是最关键的技术指标。对两种加筋材料与高液限粘土之间分别进行了直剪试验和拉拔试验,通过分析可得筋土之间的剪应力随着垂直压力的增大而增加,但随着垂直压力的增大筋土之间剪应力增长率减小,即随着垂直压力的增加,筋土之间的剪应力收敛于某一上限值;由于加筋材料在试样制作过程中不可避免地在土中发生凹凸变形使土体对筋带产生一定的锚固作用,拉拔试验所测得的似摩擦系数及似粘聚力均大于直剪试验所测得的似摩擦系数及似粘聚力。因此,实际加筋土工程中,拉拔试验更能较好地反映其真实的工作状态。     

加筋土挡墙设计方法对比与实例分析

在工程实践中,国内外土工合成材料加筋土挡墙的设计方法存在差异。基于对国内外加筋土设计方法和工程实践的分析和认识,分别以我国相关设计规范和北欧加筋土结构设计指南为准则,以湖北省某高边坡加固工程为案例,完成了土工合成材料加筋土挡墙的详细设计方案。根据设计计算结果,对比两种设计体系在加筋土挡墙设计理论、设计参数的取值、验算方法等方面的差异。并对产生这些差异的原因进行了分析,为我国土工合成材料加筋土设计规范修订和工程实践提供借鉴。     

软粘土层上的油罐差异沉降

根据某一大型油罐软基加固处理工程方案设计和优选需要,按照离心模型相似律,开展了三组模型试验,分别模拟了天然地基、土工合成材料袋装碎石垫层和既在填土层中设置袋装碎石垫层又在淤泥质粘土层设置土工合成材料排水板三种情况,以研究这一加固布置形式对减小高压缩性软土层地基上油罐罐底的差异沉降效果反应。模型油罐地基采用原型土重塑制备,现场土工合成材料袋装碎石采用柔性机织玻璃纤维细管塞装粗砂条模拟,并在不停机运转条件下模拟了多次充放水预压加载。试验结果表明,油罐软弱地基经土工合成材料袋装碎石加固后,罐底总沉降值和差异沉降值均明显小于天然地基情形下对应的沉降值,罐底畸变得到显著减小,就本文所述的土质条件、土层厚度和预压荷载强度,地基经加固处理后,油罐罐底畸变减小了近50 %。最后就土工合成材料在加固油罐地基布置形式的合理性进行了初步探讨。     

土工织物加筋柔性台背路堤非线性计算与评价

近年来，加筋土结构的柔性桥台开始应用于工程实践，这种结构有可能在根本上防止桥头跳车的产生。为了研究这一特殊路堤结构的数值模拟计算方法，本文在考虑土的固结和土体的非线性应力应变关系的基础上，利用非线性有限元数值方法对土工织物加筋柔性台背路堤的受力性状和破坏机理进行了分析。分析结果表明，将铺有土工合成材料的桥台路堤看作由土体、土工合成材料、土体-土工合成材料界面组成的独立的平面应变三层连续体系，进行数值计算是行之有效的。计算结果显示，土工织物加筋可以有效减少桥台路堤50％的均匀沉降和66％的非均匀沉降，并能增加台背路堤的稳定性。现场监测表明，本文采用的数值模拟方法与实际工程具有一致性。     

广义回归神经网络预测加筋土支挡结构高度

土工合成材料加筋支挡结构（Geosythetics-Reinforced Retaining Wall, 简称GRW）设计方法主要是建立在似粘聚力理论基础之上的半经验设计法。由于土性及加筋机理的复杂性，常常要对它们进行人为假定，导致计算结果差强人意。神经网络方法与传统方法的不同之处在于不需要主观假定，而是模拟人脑思维，通过数据样本的学习来获得预测结果。引入神经网络技术来预测加筋土支挡结构的设计高度是一种新尝试。由于本问题具有样本容量非常有限、影响因素复杂多样的特点。因此，采用适用于稀土样本数据的广义回归网络（General Regression Neural Network)来预测加筋土支挡结构设计高度。基于MATLAB神经网络工具箱及文献[1]的挡墙离心模型试验结果，建立了一个可用于加筋支挡结构设计高度预测的GRNN网络。通过对足尺试验，实际工程及模型试验结果的检验，表明网络的学习是成功的，具有一定指导意义。     

Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

Modeling of tensile strength of rocks materials based on support vector machines approaches

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

Predicting the Shear Behavior of Cemented and Uncemented Carbonate Sands Using a Genetic Algorithm-Based Artificial Neural Network

Carbonate sands that are known as problematic soils, have some unusual features like particle crushability and compressibility that discriminate their behavior from other types of soil. Because of their vast diversity, they have a wide range of mechanical behavior. In recent decades, there have been many attempts to model the mechanical behavior of carbonate sands but all these efforts have been focused on experimental and case studies of some especial sands and there is still no unique way which can appraises all types of carbonate sands behavior and describes their various aspects. In this paper, a new approach is presented based on the integration of Genetic Algorithm (GA) into an Artificial Neural Network (ANN) to predict the shear behavior of carbonate sands. In the proposed approach, the GA was utilized to optimize the connection weights of the ANN. The network was trained and tested using a comprehensive set of triaxial tests that were carried out on three different carbonate sands in both grouted and ungrouted (cemented and uncemented) condition. The network prediction was then compared to the experimental results and it was concluded that the GA-based ANN has a good potential in predicting the behavior and generalizing the training data to simulate new unseen data.     

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

Application of an Expert System for the Assessment of Blast Vibration

The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.