Prediction of the unconfined compressive strength of compacted granular soils by using inference systems

Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have been extensively used to predict different soil properties in geotechnical applications. In this study, it was aimed to develop ANFIS and ANN models to predict the unconfined compressive strength (UCS) of compacted soils. For this purpose, 84 soil samples with different grain-size distribution compacted at optimum water content were subjected to the unconfined compressive tests to determine their UCS values. Many of the test results (for 64 samples) were used to train the ANFIS and the ANN models, and the rest of the experimental results (for 20 samples) were used to predict the UCS of compacted samples. To train these models, the clay content, fine silt content, coarse silt content, fine sand content, middle sand content, coarse sand content, and gravel content of the total soil mass were used as input data for these models. The UCS values of compacted soils were output data in these models. The ANFIS model results were compared with those of the ANN model and it was seen that the ANFIS model results were very encouraging. Consequently, the results of this study have important findings indicating reliable and simple prediction tools for the UCS of compacted soils.     

Artificial Neural Network Prediction Models for Soil Compaction and Permeability

This paper presents Artificial Neural Network (ANN) prediction models which relate permeability, maximum dry density (MDD) and optimum moisture content with classification properties of the soils. The ANN prediction models were developed from the results of classification, compaction and permeability tests, and statistical analyses. The test soils were prepared from four soil components, namely, bentonite, limestone dust, sand and gravel. These four components were blended in different proportions to form 55 different mixes. The standard Proctor compaction tests were adopted, and both the falling and constant head test methods were used in the permeability tests. The permeability, MDD and optimum moisture content (OMC) data were trained with the soil’s classification properties by using an available ANN software package. Three sets of ANN prediction models are developed, one each for the MDD, OMC and permeability (PMC). A combined ANN model is also developed to predict the values of MDD, OMC, and PMC. A comparison with the test data indicates that predictions within 95% confidence interval can be obtained from the ANN models developed. Practical applications of these prediction models and the necessary precautions for using these models are discussed in detail in this paper.     

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

The unconfined compressive strength (UCS) of intact rocks is an important geotechnical parameter for engineering applications. Determining UCS using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The objective of study is to select the explanatory variables (predictors) from a subset of mineralogical and index properties of the samples, based on all possible regression technique, and to prepare a prediction model of UCS using artificial neural networks (ANN). As a result of all possible regression, the total porosity and P-wave velocity in the solid part of the sample were determined as the inputs for the Levenberg–Marquardt algorithm based ANN (LM-ANN). The performance of the LM-ANN model was compared with the multiple linear regression (REG) model. When training and testing results of the outputs of the LM-ANN and REG models were examined in terms of the favorite statistical criteria, which are the determination coefficient, adjusted determination coefficient, root mean square error and variance account factor, the results of LM-ANN model were more accurate. In addition to these statistical criteria, the non-parametric Mann–Whitney U test, as an alternative to the Student’s t test, was used for comparing the homogeneities of predicted values. When all the statistics had been investigated, it was seen that the LM-ANN that has been developed, was a successful tool which was capable of UCS prediction.     

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.     

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

固化铅污染土的干湿循环耐久性试验研究

在商用高岭土、膨润土与商业黄砂混合物中加入硝酸铅溶液,添加水泥和石灰两种固化剂,采用室内压实制样方法获得固化的铅污染土试样。进行干、湿循环试验,测试固化体的质量损失和无侧限抗压强度等参数随干、湿循环次数的变化规律,评价固化铅污染土的干、湿耐久性。测试结果表明,本试验8种配比的试样都满足干、湿循环的要求;黏土矿物为膨润土的试样干、湿循环耐久性比黏土矿物为高岭土的试样要差;水泥固化土的干、湿循环耐久性要略优于石灰固化土;加入 8 000 mg/kg的铅可略增大土体的抗干、湿循环耐久性。水泥和石灰固化/稳定化重金属污染土时,土体中含水率是保证加固效果的关键参数之一。土体中含水率应能满足固化剂充分水化、水解、火山灰和碳酸化反应之需要。     

Slope stability analysis: a support vector machine approach

Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.     

用固化剂GX08加固杭州海湖相软土的强度特性研究

为了验证固化剂GX08加固杭州海湖相软土的效果及考察有机质对水泥固化的不利影响,对固化土的强度特性进行了试验研究。结果表明,有机质的添加会显著阻碍固化土强度的增长,而固化剂GX08能有效增强固化土的强度;固化土强度与有机质含量存在二次函数关系,与水泥掺量呈线性关系,与固化剂GX08掺量和龄期都以对数函数的形式相关;将总灰水比C/W用于固化土强度模型的建立,通过对试验数据的分析与整理,建立了同时考虑固化土中有机质含量、水泥掺量、固化剂掺量和龄期影响的固化土综合强度预测模型。最后对模型进行了推广使用,验证了模型的适用性。     

Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

This paper presents artificial neural network (ANN) prediction models for estimating the compaction parameters of both coarse- and fine-grained soils. A total number of 200 soil mixtures were prepared and compacted at standard Proctor energy. The compaction parameters were predicted by means of ANN models using different input data sets. The ANN prediction models were developed to find out which of the index properties correlate well with compaction parameters. In this respect, the transition fine content ratio (TFR) was defined as a new input parameter in addition to traditional soil index parameters (i.e. liquid limit, plastic limit, passing No. 4 sieve and passing No. 200 sieve). Highly nonlinear nature of the compaction data dictated development of two separate ANN models for maximum dry unit weight (γ_dmax) and optimum water content (ω_opt). It was found that generalization capability and prediction accuracy of ANN models could be further enhanced by sub-clustered data division techniques.     

Prediction of Unconfined Compressive Strength of Soil–Cement at 7?Days

Unconfined compressive strength (UCS) of cement stabilized bases was collected from a number of highway construction projects in Thailand. Results from the statistical analysis indicated that the most important factors affecting the UCS were the CBR and the water to cement ratio. The UCS was however independent on the dry density. A statistical model was developed in the study to predict the UCS of cement stabilized bases. A model was developed based on the following criteria: (1) the dry density of the sample shall be greater than 95 percent of the maximum dry density based on the modified Proctor compaction, (2) samples shall be soaked for at least 2 h prior to testing, and (3) the CBR shall be measured at 0.1 inch (2.5 mm) penetration.     

A study of soft computing models for prediction of longitudinal wave velocity

Genetic algorithm (GA) and support vector machine (SVM) optimization techniques are applied widely in the area of geophysics, civil, biology, mining, and geo-mechanics. Due to its versatility, it is being applied widely in almost every field of engineering. In this paper, the important features of GA and SVM are discussed as well as prediction of longitudinal wave velocity and its advantages over other conventional prediction methods. Longitudinal wave measurement is an indicator of peak particle velocity (PPV) during blasting and is an important parameter to be determined to minimize the damage caused by ground vibrations. The dynamic wave velocity and physico-mechanical properties of rock significantly affect the fracture propagation in rock. GA and SVM models are designed to predict the longitudinal wave velocity induced by ground vibrations. Chaos optimization algorithm has been used in SVM to find the optimal parameters of the model to increase the learning and prediction efficiency. GA model also has been developed and has used an objective function to be minimized. A parametric study for selecting the optimized parameters of GA model was done to select the best value. The mean absolute percentage error for the predicted wave velocity (V) value has been found to be the least (0.258 %) for GA as compared to values obtained by multivariate regression analysis (MVRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and SVM.     

超细水泥对固化软土早期抗压强度影响的试验研究

为研究超细水泥含量对水泥固化软土的早期力学性能的影响,本文通过在普通水泥中加入不同掺量的超细水泥组成复合水泥固化剂用以固化软土。具体研究不同超细水泥掺量、不同初始含水率、及不同养护围压条件下,复合水泥固化剂对固化软土早期抗压强度及刚度的影响。采用自制K₀围压养护装置(施加不同轴向压力的方式)、无侧限抗压强度仪(UCS)、X射线衍射仪(XRD)、电镜扫描仪(SEM)和低场核磁共振孔隙测试仪(NMR)等试验手段获取复合水泥固化软土不同龄期的抗压强度、刚度及微观结构的变化规律,并揭示其固化机理。研究结果表明:(1)相同轴向压力作用下,随着超细水泥掺量的增加,固化软土的抗压强度和弹性模量均有提高,其中复合固化剂中的活性颗粒发生水化反应生成大量胶凝产物用以黏聚土颗粒和填充孔隙,惰性颗粒用于填充土颗粒间的孔隙;(2)随着含水率的提高,固化软土中孔相对发育,从而使固化软土结构致密性减弱,抗压强度降低;(3)在K₀围压养护7d时,固化软土的抗压强度和弹性模量随着轴向压力的提高而增加,表明养护围压对软土颗粒的压缩作用能提高固化软土的密实性,同时围压对固化软土产生有效应力,与水化产物共同促进固化软土形成密实的土骨架,进而使其在7d内具有较高的抗压强度。基于试验结果,建立轴向压力、含水率和超细水泥掺量等多因素的固化软土强度预测公式,并提出复合水泥固化软土结构模型,为工程实践提供理论基础。     

Utilization of Silica Fume to Maximize the Filler and Pozzolanic Effects of Stabilized Soil with Cement

Silica fume is identified as a pozzolan and supplementary cementitious material that can utilize to improve the mechanical properties of stabilized soil with cement. Silica fume wherein mixes with cemented soil in a proper dosage, it is susceptible to induce pozzolanic effect in cemented soil due to its fineness and high content of SiO₂ and Al₂O₃. The pozzolanic effect is vital to ensure ongoing strength of stabilized soil with cement. Up to now, stabilization of clay with cement and silica fume is not completely explored. This paper investigates: (i) the capability of utilizing the silica fume as a supplementary material for cement to maximize the filler and pozzolanic effects of compacted and stabilized soil (ii) the mechanical properties of compacted and stabilized clay with various proportions of cement and silica fume. For this purpose, a total of 120 untreated and stabilized soil admixtures were prepared by replacing ordinary Portland cement with silica fume. The influence of partial replacement of cement with silica fume on the bearing capacity, shear and compressive strength of compacted and stabilized soil was investigated. To achieve such aims, the stabilized soil specimens were examined in laboratory under direct shear, unconfined compression and California bearing ratio tests. Based on the findings of this paper the 28-day UCS of the stabilized soil with 2% partial substitution of cement with silica fume is almost 3.5-fold greater than that of the untreated. It was found that the optimum mix design for the stabilized soil is 6% cement and 2% silica fume. In conclusion, a notable discovery is that the partial substitution of cement with 2% silica fume in the optimum mix design significantly refined the pore spaces as a result of pozzolanic activity and filler effect of silica fume.     

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.     

Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil

This study employs two statistical learning algorithms (Support Vector Machine (SVM) and Relevance Vector Machine (RVM)) for the determination of ultimate bearing capacity (q_u) of shallow foundation on cohesionless soil. SVM is firmly based on the theory of statistical learning, uses regression technique by introducing varepsilon‐insensitive loss function. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. It also gives variance of predicted data. The inputs of models are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and angle of shearing resistance (?). Equations have been developed for the determination of q_u of shallow foundation on cohesionless soil based on the SVM and RVM models. Sensitivity analysis has also been carried out to determine the effect of each input parameter. This study shows that the developed SVM and RVM are robust models for the prediction of q_u of shallow foundation on cohesionless soil. Copyright © 2010 John Wiley & Sons, Ltd.     

Stabilization of Tropical Peat Soil from Sarawak with Different Stabilizing Agents

This paper describes a study on tropical peat soil stabilization to improve its physical properties by using different stabilizing agents. The samples were collected from six different locations of Sarawak, Malaysia, to evaluate their physical or index properties. Out of them, sample having the highest percentage of organic content has been selected for stabilization purposes. In this study, ordinary portland cement (OPC), quick lime (QL), and class F fly ash (FA) were used as stabilizer. The amount of OPC, QL, and FA added to the peat soil sample, as percentage of dry soil mass, were in the range of 5–20%; 5–20% and 2–8%, respectively for the curing periods of 7, 14, and 28 days. The Unconfined Compressive Strength (UCS) test was carried out on treated/stabilized samples with the above mentioned percentages of the stabilizer and the result shows that the UCS value increases significantly with the increase of all stabilizing agent used and also with curing periods. However, in case of FA and QL, the UCS value increases up to 15 and 6%, respectively with a curing period of 28 days but decreases rather steady beyond this percentage. Some UCS tests have been conducted with a mixture of FA and QL to study the combined effect of the stabilizer. In addition, Scanning Electron Microscope (SEM) study was carried out on original peat soil and FA, as well as some treated samples in order to study their microstructures.     

水泥基渗透结晶型防水材料对硫铝酸盐水泥固化土性能影响及机制分析

水泥土固化过程中Ca2+浓度会随水化反应的进行而逐步降低,导致水泥颗粒未完全水化,固化土强度增长受限,而水泥基渗透结晶型防水材料（CCCW）中活性物质能催化未水化水泥颗粒反应。选择硫铝酸盐水泥（SAC）为胶凝材料、CCCW为添加剂,通过单掺与复掺的方式,结合X射线衍射（XRD）、电镜扫描（SEM）表征,分析了固化土的无侧限抗压强度、水稳定性、耐干湿循环性能及微观结构。结果表明,复掺16%混合料（4%CCCW+12%SAC）的固化土强度是同掺量下单掺SAC固化土强度的1.5倍,且比单掺20%SAC的固化土强度高1.41 MPa;复掺16%混合料（4%CCCW+12%SAC）的固化土泡水2～8 d软化系数平均达0.97,而同掺量下SAC固化土平均仅为0.73;单掺的固化土强度随干湿循环次数增加逐级降低,而复掺混合料的固化土强度呈波浪式发展;CCCW中活性物质能增加固化土中钙矾石生成量并修复微裂缝,钙矾石长径比显著增大,可直接连接两个甚至多个土颗粒,形成三维网状结构,显著提高结晶体的微观加筋、骨架及填充作用,改善SAC固化土强度、水稳定性及耐干湿循环性能。     

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.     

Mechanical behavior and micro-structure of cement kiln dust-stabilized expensive soil

In this study, the effects of cement kiln dust (CKD) on the swelling properties, strength properties, and microstructures of CKD-stabilized expansive soil were investigated. Samples were prepared and stabilized with different CKD content ratios, ranging from 0 to 18% by dry mass. The results obtained show that the maximum swelling pressures decrease exponentially with increases in CKD content. Both the cohesion and unconfined compressive strength (UCS) increase at ratios below 10% CKD and then decrease slightly, above that ratio. CKD can also improve the strength of saturated, expansive soil. There is no visible change of UCS for soil without CKD when cured, while the UCS of a sample with 10% CKD content after curing for 90 days is higher than that after curing for only 1 day. This indicates that CKD can improve the long-term strength of expansive soil. Finally, microstructure analysis reveals that the addition of CKD reduces the montmorillonite content of expansive soil and decreases its swelling properties. The addition of CKD also changes the pore volume distribution, both the size and amount of macro-pores and micro-pores decrease with increase in CKD content. For saturated samples, the size of macro-pores is obviously reduced, while that of micro-pores is slightly increased for both treated and untreated soils. Hydration and saturation processes make the soil structure become dispersive which results in a lower strength, and adding CKD can restrain this process. The suggested optimal CKD content is between 10 and 14% and with a curing time of more than 27 days.