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Clay soils consist of fine particles and are generally characterized by low strength. The clays and the water interactions result to clays high-plasticity that can easily deform or crack. It should be noted that the sand addition to expansive soils can help to improve their particle size, reduce their plasticity, and consequently increase their strength. The present study aims mainly to develop prediction model of the plasticity index (PI) using the artificial neural network (ANN) method for soil treated with sand at various contents. For the purpose of predicting the plasticity index values of sand-treated soils, the experimental results were conducted on 38 soil samples, collected and thoroughly analyzed. The experimental investigation carried out on the clayey soil from the region of Medea, Algeria and treated with dune sand. It was decided to consider three inputs, namely the plastic limit, liquid limit, and sand percentage, while there was only one output, i.e. the plasticity index. The proposed model performance has been validated by the experimental test results and show that the relative error is very low (MRE = 1.06%). The proposed model could be used for predicting the plasticity index of treated clayey with sand. [ABSTRACT FROM AUTHOR]

Plasticity of clays makes Deep Soil Mixing (DSM) problematic due to the tendency of the material to congest the rotating blades, reduce mixing efficiency and remain clustered in lumps which affect the mechanical behavior of the cemented soil. The paper investigates this problem systematically with a comprehensive experimental campaign that shows the efficacy of a clay dispersant in scattering soil particles and making soil more workable. The performed laboratory investigation combines consistency, micro-structural, vane, rheological and uniaxial compression tests on two reference soils, a kaolin and a bentonite, treated with various proportions of cement, water and chemical additive to quantify the effects on workability, homogeneity and strength of the material. The variety of investigated conditions enables to understand the principles of chemical modification and infer a quantitative dependency of viscosity and uniaxial compression strength on the material composition. Observation is interpreted for the sake of generality with artificial neural networks, merging the role of the different components into a novel definition of the soil consistency index, accounting for the presence of the additive. The inferred empirical relations, expressed with charts, are proposed to optimize soil conditioning for DSM. [ABSTRACT FROM AUTHOR]

The adoption of mechanistic-empirical approach to pavement design requires the use of resilient modulus of subgrade soils as a crucial input. The determination of in the laboratory is inexpedient due to the nature of the existing test protocols. This prompted the use of estimated values, which inadvertently has gained popularity lately. However, the accuracy of estimated values is questionable due to spatial variability of soil properties. This necessitated the aggressive search for robust and thorough approaches for predictive modelling of the . In the present study, a systematic approach was adopted for the descriptive analysis and estimation of . from routine soil properties using data from Long-Term Pavement Performance (LTPP) and considering the spatial variability of the soil properties. Descriptive analysis was executed using non-parametric correlation and principal component analysis (PCA), while the estimation was done using three machine learning methods which include gradient boosting regression (GBR), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Based on the PCA, four factors which explained a total of 77.5% variance in the data had significant influence on the . These include the effect of moisture-induced changes on the soil consistency limits and physical condition, effect of the soil clay content, effect of the soil gradation and effect of the soil stress state. Various factors of the machine learning methods such as the learning rate, number of clusters and number of hidden layers had a significant effect on the prediction accuracy. The three machine learning methods were satisfactory for the prediction based on R2 values which were generally above 0.9. Also, when considering spatial variability of routine soil properties, the GBR and ANFIS have a comparative advantage over the ANN, since they exhibited a high stability in the prediction for both the training and testing dataset. [ABSTRACT FROM AUTHOR]

Disasters have different shapes, and one of them is sudden landslides, which can put the safety of highway users at risk and result in crucial economic damage. Along with the risk of human losses, each day a highway malfunctions causes high expenses to citizens, and repairing a failed highway is a time- and cost-consuming process. Therefore, correct highway functioning can be categorized as a high-priority reliability factor for cities. By detecting the failure factors of highway embankment slopes, monitoring them in real-time, and predicting them, managers can make preventive, preservative, and corrective operations that would lead to continuing the function of intracity and intercity highways. Expansive clay soil causes many infrastructure problems throughout the United States, and much of Mississippi's highway embankments and fill slopes are constructed of this clay soil, also known as High-Volume Change Clay Soil (HVCCS). Landslides on highway embankments are caused by recurrent volume changes due to seasonal moisture variations (wet-dry cycles), and the moisture content of the HVCCS impacts soil shear strength in a vadose zone. Soil Matric Suction (SMS) is another indication of soil shear strength, an essential element to consider. Machine learning develops high-accuracy models for predicting the SMS. The current work aims to develop hybrid intelligent models for predicting the SMS of HVCCS (known as Yazoo clay) based on field instrumentation data. To achieve this goal, six Highway Slopes (HWS) in Jackson Metroplex, Mississippi, were extensively instrumented to track changes over time, and the field data was analyzed and generated to be used in the proposed models. The Artificial Neural Network (ANN) with a Bayesian Regularization Backpropagation (BR-BP) training algorithm was used, and two intelligent systems, Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA) were developed to optimize the ANN-BR algorithm for predicting the HWS' SMS by utilizing 13,690 data points for each variable. Several performance indices, such as coefficient of determination (R2), Mean Square Error (MSE), Variance Account For (VAF), and Regression Error Characteristic (REC), were also computed to analyze the models' accuracy in prediction outcomes. Based on the analysis results, the PSO-ANN outperformed the BOA-ANN, and both had far better performance than ANN-BR. Moreover, the rainfall had the highest impact on SMS among all other variables and it should be carefully monitored for landslide prediction HWS. The proposed hybrid models can be used for SMS prediction for similar slopes. [ABSTRACT FROM AUTHOR]

Recently, incentives have been provided in many countries, including Canada and Denmark, to produce biochar for construction usage. This is done because biochar is carbon negative and can help achieve the emission reduction goal of 2030. This technical note aims to analyse the efficiency of biochar in soils with varying grain size distributions for enhancing soil–water characteristic curve (SWCC). The combinations of biochar content and grain size distributions corresponding to the maximum and minimum efficiencies were explored. Artificial neural network-based model for predicting SWCC as a function of soil suction and grain size distribution was developed. A new factor (the ratio of fine (silt + clay) and coarse (sand) content) was proposed for the interpretation of the efficiency of biochar in soils. The newly developed model is able to predict SWCC reasonably well. Biochar amendment is found to influence both dry and wet sides of soils with a clay content lower than threshold content (6–8%). Beyond threshold content, the influence of biochar appears to reduce. However, in the case of high sand content soils (90%), the normalized water content value on the drier side is generally higher as compared to soils with lower sand content. Based on the sensitivity analysis, it was found that the ratio of fine to sand content is the most influential, while biochar content is the least influential. [ABSTRACT FROM AUTHOR]

It is well known that the construction on soft clayey soil is always a great challenge to the geotechnical engineers. The soft clay poses high compressibility and low bearing capacity. It is a common practice to improve the properties of the soft clay prior to any construction on it. In this respect, ground improvement by stone columns is a usual choice of the geotechnical engineers. The stone columns increase the bearing capacity and reduce the settlement of the soft clay. Many theories are developed to determine the bearing capacity of the soft soil reinforced with stone columns. However, most of the theories are site-specific and do not show a very good match with the field observations. In this study, a large numbers of data were collected from previously reported studies from various parts of the globe and an empirical formula based on support vector regression (SVR) technique for the determination of the ultimate bearing capacity of the stone columns is achieved. Two different techniques, namely tenfold cross-validation ( q TFCV ) and non-cross-validation ( q NCV ), are presented for the construction of the SVR model. It is observed that the SVR method gives a better prediction than artificial neural network method. Laboratory experiments were conducted to validate the SVR-ERBF empirical approach. The formula is also validated with two field observations by two other investigators. [ABSTRACT FROM AUTHOR]

Viability of Artificial Neural Network (ANN) in predicting unconfined compressive strength (UCS) of geopolymer stabilized clayey soil has been investigated in this paper. Factors affecting UCS of geopolymer stabilized clayey soil have also been reported. Ground granulated blast furnace slag (GGBS), fly ash (FA) and blend of GGBS and FA (GGBS + FA) were chosen as source materials for geo-polymerization. 28 day UCS of 283 stabilized samples were generated with different combinations of the experimental variables. Based on experimental results ANN based UCS predictive model was devised. The prediction performance of ANN model was compared to that of multi-variable regression (MVR) analysis. Sensitivity analysis employing different methods to quantify the importance of different input parameters were discussed. Finally neural interpretation diagram (NID) to visualize the effect of input parameters on UCS is also presented. [ABSTRACT FROM AUTHOR]