Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks

The objective of the research is to estimate the value of the California bearing ratio (CBR) through the application of ANN. The methodology consists of creating a database with soil index and CBR variables of the subgrades and granular base of pavements in Jaen, Peru, carried out in the soil mechanics laboratories of the city and the National University of Jaen. In addition, the Python library Seaborn is for variable selection and relevance, and the scikit-learn and Keras libraries were used for the learning, training, and validation stage. Five ANN are proposed to estimate the CBR value, obtaining an error of 4.47% in the validation stage. It can be concluded that this method is effective and valid to determine the CBR value in subgrades and granular bases of any pavement for its evaluation or design

Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

This study evaluates the applicability of using a robust, novel, data-driven method in proposing surrogate models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio of coarse-grained soils using only the results of the grain size distribution analysis. The data-driven analysis has been conducted using evolutionary polynomial regression analysis (MOGA-EPR), employing a comprehensive database. The database included the particle diameter corresponding to a percentage of the passing of 10%, 30%, 50%, and 60%, coefficient of uniformity, coefficient of curvature, dry unit weight, optimum moisture content, and California bearing ratio. The statistical assessment results illustrated that the MOGA-EPR provides robust models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio. The new models’ performance has also been compared with the empirical models proposed by different researchers. It was found from the comparisons that the new models provide enhanced accuracy in predictions as these models scored lower mean absolute error and root mean square error, mean values closer to one, and higher a20−index and coefficient of correlation. Therefore, the new models can be used to ensure more optimised and robust design calculations

The Preliminary Study on the Effect of Coarse Particles Content on OMC and Maximum Dry Unit Weight: a Case of Aceh\u27s Fill Materials

Empirical evidence suggests that the percentage of coarse fraction content on soil has an influence on the soil optimum moisture content (OMC) and soil maximum dry density (MDD). This phenomenon is used as a basis to examine the characteristics of Aceh\u27s fill materials. The major objective of the present study is to determine the relationship between coarse-grained content and OMC and MDD of the Aceh\u27s fill materials. This relationship is important in the justification of soil suitability as materials for engineering construction. Thirty (30) soil samples from various locations in the Province of Aceh have been collected and tested. The tests carried out include soil physical properties tests and standard compaction test. In the case of a relationship between soil coarse fraction content and soil OMC or MDD, two general findings have been deduced. The first finding of the present study shows that the soil OMC decreases when the content of coarse-grained particles in the soil increases. The later finding shows a positive correlation between coarse particles content and the soil MDD which the increase of the content of coarse-grained particles in the soil will increase the value of the soil\u27s MDD. In conclusion, the coarse particles content affects the OMC and MDD of Aceh\u27s fill material

Modelling of a generalized thermal conductivity for granular multiphase geomaterial design purposes

Soil thermal conductivity has an important role in geo-energy applications such as high voltage buried power cables, oil and gas pipelines, shallow geo-energy storage systems and heat transfer modelling. Hence, improvement of thermal conductivity of geomaterials is important in many engineering applications. In this thesis, an extensive experimental investigation was performed to enhance the thermal conductivity of geomaterials by modifying particle size distribution into fuller curve gradation, and by adding fine particles in an appropriate ratio as fillers. A significant improvement in the thermal conductivity was achieved with the newly developed geomaterials. An adaptive model based on artificial neural networks (ANNs) was developed to generalize the different conditions and soil types for estimating the thermal conductivity of geomaterials. After a corresponding training phase of the model based on the experimental data, the ANN model was able to predict the thermal conductivity of the independent experimental data very well. In perspective, the model can be supplemented with data of further soil types and conditions, so that a comprehensive representation of the saturation-dependent thermal conductivity of any materials can be prepared. The numerical 'black box' model developed in this way can generalize the relationships between different materials for later added amounts of data and soil types. In addition to the model development, a detailed validation was carried out using different geomaterials and boundary conditions to reinforce the applicability and superiority of the prediction models

A decision support system for ground improvement method selection

Abstract unavailable please refer to PD

An ANN Based Sensitivity Analysis of Factors Affecting Stability of Gravity Hunched Back Quay Walls

This paper presents Artificial Neural Network (ANN) prediction models that relate the safety factors of a quay wall against sliding, overturning and bearing capacity failure to the soil geotechnical properties, the geometry of the gravity hunched back quay walls and the loading conditions. In this study, a database of around 80000 hypothetical data sets was created using a conceptual model of a gravity hunched back quay wall with different geometries, loading conditions and geotechnical properties of the soil backfill and the wall foundation. To create this database a MATLAB aided program was written based on one of the most common manuals, OCDI (2002). Comparison between the results of the developed models and cases in the data bank indicates that the predictions are within a confidence interval of 95%. To evaluate the effect of each factor on these values of factor of safety, sensitivity analysis were performed and discussed. According to the performed sensitivity analysis, shear strength parameters of the soil behind and beneath the walls are the most important variables in predicting the safety factors

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model

Appraisal of Bearing Capacity and Modulus of Subgrade Reaction of Refilled Soils

Soil is remoulded, replaced, or improved in place to meet the required engineering properties. Relative compaction is the measure of the resulting engineering improvement. But design engineers need the allowable bearing capacity while the modulus of subgrade reaction is the primary input of modern foundation design software. The current research appraised a correlation between Relative Compaction ( ), Moisture Content ( ), and allowable bearing capacity ( ) and another correlation between , RC, MC, and modulus of subgrade reaction ( ). The test samples were extracted from each trial of the standard proctor test using purpose-built extraction tubes. Allowable bearing capacity has been determined by performing unconfined compression tests on the extracted tubes. The relationships have been established employing statistical analysis. It was noticed that soil samples at the lower moisture content (6-9%) show brittle failure before reaching the allowable strain. The soil samples having a moisture content of 10-14% exhibited shear failure, nearly simultaneous to the allowable strain. The soil samples having higher moisture content undergone a strain of 15% without showing the shear failure. A simple equation has also been appraised to determined Ks involving the three-input variable, i.e., , , and . Moderate correlations have been found to exist between the studied parameters, owing to some other variables' influence. Recommendations for future studies have been drawn to quantify the effect of identified parameters. Doi: 10.28991/cej-2020-03091606 Full Text: PD

Modelling of geotechnical problems using soft computing

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks (ANN), support vector machine (SVM), multivariate adaptive regression splines (MARS) has been employed for developing the predictive models to estimate the needed parameters. In this report, four geotechnical problems like compaction parameters of sandy soil, compression index of clay, relative density of clean sand and side resistance of drilled shaft have been modeled. Various error criteria such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R) have been considered for the comparison of different models. Finally different sensitivity analysis has been shown to identify the significance of different input parameters that affects the developed models. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing may provide new approaches and methodologies to minimize the potential inconsistency of correlations