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A new approach to predict the compression index using artificial intelligence methods

Authors:
Benbouras Mohammed Amin at University of Science and Technology Houari Boumediene
Benbouras Mohammed Amin
Ratiba Mitiche-Kettab at National Polytechnic School of Algiers
Ratiba Mitiche-Kettab
ZEDIRA Hamma
ZEDIRA Hamma
ZEDIRA Hamma
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Alexandru-Ionut Petrisor at Ion Mincu University of Architecture and Urbanism
Alexandru-Ionut Petrisor
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Abstract and Figures

The compression index is one of the important geotechnical parameters, essential for the structural design. Since the determination of the compression index based on oedometer tests is relatively expensive and time-consuming, different authors have proposed for its estimation models using regression analysis and artificial neuron networks. However, they have ignored several parameters that could have increased the predictive capability of models. Other studies have concluded that genetic programming could have yielded better results. Unfortunately, no compression index models or effective comparisons of different methods have been published. The aim of this study is to propose a novel approach for estimating the compression index more accurately. To test the approach, a comparison study using K-fold cross-validation technique was conducted utilizing several models of multilayer neural networks, genetic programming, and multiple regression analysis. These models have been applied to 373 oedometer test samples to predict the compression index from soil physical parameters. The results indicate that the neural network with two hidden layers (7-14-4-1) provides the most appropriate prediction, compared with other models and the formulae suggested by previous studies. Based on these findings, this study‏ proposed a MATLAB script for efficiently estimating the compression index in the future studies.
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A new approach to predict the compression index using articial intelligence methods
Mohammed Amin Benbouras, Ratiba Kettab Mitiche, Hamma Zedira, Alexandru-Ionut Petrisor, Nourredine Mezouar, Fatiha
Debiche
Marine Georesources and Geotechnology, October 2018, Taylor & Francis
DOI: 10.1080/1064119x.2018.1484533
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https://goo.gl/MmWVHA
A new approach to predict the
Compression Index using Articial
Intelligence Methods
What is it about?
The aim of this study is to propose a novel approach for estimating the compression
index more accurately. In order to test the approach, a comparison study between AI
methods has been made (multilayer neural networks, genetic programming, and
multiple regression analysis). These models have been applied to samples consisting
of 373 oedometer tests to predict the compression index from physical soil
parameters. Based on the tangible ndings, this study proposed a MATLAB program
script for eciently estimating the compression index in the future studies.
Why is it important?
the best-tted model proposed in these study can easily used in the future studies
for estimating the compression index of a new site based on physical soil
parameters, in order to help geotechnical engineers and researchers. Also, for
making the use of the proposed model more easier, we proposed an algorithm
programmed by MATLAB software.
Perspectives
BM
Benbouras Mohammed Amin (Author)
Ecole Nationale Polytechnique
Since the use of oedometer tests for estimating the compression index parameter is
considered relatively expensive and time-consuming, I really hope that this article
help engineers and researchers in the future studies.
The following have contributed to this page: HAmma Zedira, Alexandru-Ionut Petrisor, and Benbouras
Mohammed Amin
PDF generated on 30-Nov-2018
Create your own PDF summaries at www.growkudos.com.
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... Utilizing an artificial neural network (ANN), a machine learning technique, researchers predicted the compression index using a dataset that included parameters like w, LL, PI, and G s [27]. Benbouras et al. [36] used ANN models, genetic programming, and multiple regression analysis to estimate the compression index on a dataset that contained LL, w, FCs (fine contents), P h (wet density), PI, e 0 , and soil types. In addition, Tsang et al. [37] employed machine learning techniques like extreme gradient boosting and random forest models on a dataset comprising e 0 , w, LL, PI, and G s to predict the compression index. ...
... Park and Lee [27] used artificial neural networks (ANNs) and achieved an R 2 value of 0.885. Benbouras et al. [36] obtained R 2 values of 0.562 with artificial neural networks, 0.360 with genetic programming, and 0.409 with multiple regression analysis. Tsang et al. [37] achieved R 2 values of 0.833 with extreme gradient boosting and 0.818 with random forest methods. ...
... Comparison of the results of the testing R 2 between this study and previous studies.Park and Lee[27] Artificial Neural Network [w, e 0 , LL, PI, G s , W sand, silt, clay ] 0.885Benbouras et al.[36] ...
Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
Article
Full-text available
  • Sep 2024
Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (Cc), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e0), and specific gravity (Gs), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the Cc values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, Gs, and e0 as the inputs, with Cc as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R2 of 0.925 and a testing R2 of 0.930 with the input combination [w, PL, LL, PI, e0, Gs]. The RFR model follows closely, with a training R2 of 0.970 and a testing R2 of 0.926 using the same input combination. The ABR model records a training R2 of 0.847 and a testing R2 of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the Cc value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing.
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... While statistical models based on geotechnical variables like natural water content (w), liquid limit (LL), plasticity index (PI), and initial void ratio (e 0 ) have been proposed [12][13][14][15], they often lack generalizability across diverse datasets. Recent studies have shifted towards machine learning models [16][17][18], particularly ensemble learning approaches, to overcome these limitations and improve predictive accuracy. For example, Zhang et al. [19] implemented Random Forest (RF), a classic ensemble learning technique, utilizing a dataset of 311 samples with three factors: LL, PI, and e 0 . ...
... A comprehensive global dataset of clay soil parameters was compiled from peerreviewed studies, representing 1080 samples from diverse geographical regions such as Algeria, Spain, Nigeria, Iran, India, Germany, Ireland, and Bangladesh, among others [18,23,[27][28][29][30][31][32][33][34] This dataset was selected to reduce regional bias and improve the generalizability of the model across a wide range of soil conditions. It encompasses four independent variables (i.e., the input features)-LL, PI (with 169 missing values), e 0 , and w-which are used to establish a relationship with the output feature, the compression index. ...
Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays
Article
Full-text available
  • Sep 2024
Accurate prediction of the compression index (𝑐𝑐 ) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining 𝑐𝑐 are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the 𝑐𝑐 of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e0), and natural water content w—were leveraged for accurate 𝑐𝑐 prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination (𝑅2) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with 𝑒0 identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments.
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... The compression index, which may be calculated from the slope of the e -log p curve using the oedometer consolidation test, is one of the parameters needed to calculate the settlement of fine-grained soil. In addition to being difficult, time-consuming, and arduous, estimating the compression index from the oedometer test also requires personal experience because it uses the graphical technique of calculation [2,20]. As a result, the idea of calculating the compression index from several factors using empirical correlations was developed. ...
... Nodes stand in for purposes when connections are employed to categorize. Each tree is made up of branches and nodes, where each branch represents a subset of the node's possible values and each node contains features in a category that needs to be classified [2,6,18]. Decision trees have a wide range of application domains and are used for a variety of tasks, including pattern recognition, image processing, and machine learning because of their straightforward analysis and accuracy on various data types [24]. ...
Machine Learning Methods for Predicting Soil Compression Index
Chapter
  • Mar 2024
The compression index is an important consideration when figuring out how fine-grained soil settles. The compression index is determined from the oedometer consolidation test which is tedious and time-consuming. As a result, numerous correlations between the compression index and the index properties were developed. As soil is a very unpredictable substance, those correlations do not hold for all types of soil. This opens the door for the development of machine learning methods to forecast compression index. In this study, the compression index of soil is predicted using a decision tree, random forest, and multiple linear regression. Index properties, like liquid limit, natural moisture content, initial void ratio, and plasticity index are used as input variables in the machine learning models that are created to forecast the output variable compression index. The dataset used contains 359 data from diverse soil types and was gathered from several published articles (CH soil—62, CI soil—186, and CL soil—111). Since the machine learning models are trained using the training dataset before being evaluated using the testing dataset, the data has been divided into a training dataset and a testing dataset. In this paper, the impact of data splitting is also examined because it affects model performance. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) are used to assess the performance of the models. The results show that when training, decision trees perform well, whereas the testing dataset favors multiple linear regression for prediction. The data partitioning that results in the optimum performance for each model is different.
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... They considered as input variables, w, LL, PI, e 0 , specific gravity of soil particles (G s ), and weight percentage of sand, silt and clay. Benbouras et al. (2019) developed an ANN model with 373 oedometer test samples to correlate c c with wet density, w, e 0 , fine content, LL, PI, and soil type. The dataset utilized in this research comprised samples gathered from various projects executed in the city of Algiers (Algeria). ...
... The collection of the dataset is the first step in the building of a machine learning model. In the present work, the experimental database of c c (1008 samples) comprised 913 samples from research papers (Alhaji et al., 2017;Benbouras et al., 2019;Kalantary and Kordnaeij, 2012;LCPC, 1973;McCabe et al., 2014;Mitachi and Ono, 1985;Widodo and Ibrahim, 2012), and 95 samples from authors' own data. This database has samples of different countries as Nigeria, Ireland, Spain, Iran, Indonesia, France, Algeria, Bangladesh, among others. ...
A super-learner machine learning model for a global prediction of compression index in clays
Article
  • Jan 2024
  • APPL CLAY SCI
Settlement of structures is determined by the stiffness of the soil where they are built. Compression index ( ) quantifies the compressibility of the soil and is a key parameter in the design of geotechnical structures. To predict the value of in clay soils, a global database of more than 1000 data points was collected and analysed. Liquid limit, plasticity index, natural water content, and initial void ratio were considered as predictor variables. A super-learner machine learning model was developed to predict from these variables. The model demonstrated a reasonable predictive performance and was subsequently integrated into an online tool. Additionally, four symbolic regression expressions were obtained to relate with some of the input variables when not all data are available, providing simple and practical alternatives for , estimation. This study provided two major contributions: (1) the non-local nature of the data expands the scope and generalizability of the findings, and (2) the availability of the proposed algorithm through an online application ensures its accessibility for geotechnical engineers, enhancing the work's practical applicability and intrinsic value.
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... Using the above settings, the models were developed and, their effectiveness and generalizability were assessed using different performance indices. The results of the performance indexes stated above (MAE, RMSE, R 2 , NSE, VAF, PI, WI, and MBE) were used to assess the predictive capability of the proposed models, as shown in Tables 3 and 4 [33][34][35][36][37][38][39][40]. The scatter plots for the models CNN, DNN, and RNN were illustrated in figure 3a-c for training datasets, and in Figure 4a, b, and c for testing datasets. ...
Estimation of water quality index using modern-day machine learning algorithms
Article
Full-text available
  • Jun 2024
  • SADHANA-ACAD P ENG S
Many human-made activities currently pollute groundwater supplies, with mining operations playing a substantial role in this degradation. In this study, water quality index (WQI) was calculated and forecasted for groundwater in gold mining sites of Kolar Gold Fields, Karnataka, using several water quality criteria and modern-day soft computing approaches. Specifically, three sophisticated deep learning models: convolution neural network (CNN), deep neural network (DNN), and recurrent neural network were used to estimate the WQI using various water quality metrics. The outcomes of these models were also compared with three widely used soft computing models namely support vector machine (SVM), least-square support vector machine (LS-SVM), and artificial neural network. Experimental results reveals that the developed CNN model outperform other two models with R2 values of 0.9998 and 0.9996 in the training and testing phases, respectively. The RMSE values of the CNN model were determined to be 0.0034 and 0.0038 in the training and testing phases, respectively. As per the results, the developed CNN model can be used as alternate tool for rapid water quality monitoring.
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... Soil settlement is a key consideration in finegrained soils, and it is determined by calculating the compression index (Cc), which can be obtained from the e -log p curve of the oedometer consolidation test. The consolidation test is frequently time consuming and tiresome, since the result is read graphically [1,2]. Therefore various researches IOP Publishing doi: 10.1088/1755-1315/1326/1/012121 2 have been carried out to correlate the compression index with other soil parameters [3][4][5][6][7][8][9][10][11]. ...
Prediction of Soil Compression Index using SVM and kNN
Article
Full-text available
  • Jun 2024
Soil is used both as a building material and as the surface on which construction is carried out hence this demonstrates how important soil is as a material. Therefore, it is vital to evaluate the soil’s characteristics, such as its strength and settlement, before any construction is built on the soil. The most crucial factor that must be established in order to compute soil settlement is compression index, which can be obtained through a laboratory oedometer consolidation test. Numerous empirical correlations were created because the oedometer test is challenging and takes time for determining this parameter. With the development of technology, it has become much simpler to forecast the compression index parameter using a variety of other easy-to-find soil parameters. In this study, the prediction of the compression index has been attempted utilizing machine learning methods such as support vector machines and k-nearest neighbors. In order to forecast the output parameter, compression index, machine learning models use soil index qualities including liquid limit, plasticity index, natural moisture content, and void ratio as the input parameter. There are 359 total data used for analysis from data acquired from various studies. Typically, machine learning models divide the data into training and testing datasets in order to train the model and forecast its performance. As a result, different ratios of data splitting are also utilized when developing the machine learning model. Using measures like mean square error, mean absolute error and correlation coefficient, the model’s performance is assessed. Additionally, this paper also examines the impact of the model-creating parameters.
View
... They used 41 test results with inputs fines content (FC), LL, PI, maximum dry density (MDD), and optimum water content (OWC). Lastly, Benbouras et al. (2019) tried to propose a novel approach for an accurate estimation of Cc. To test the approach, they adopted a Kfold cross-validation technique based on several multilayer neural network models, genetic programming and multiple regression analysis. ...
Nonlinear regression methods and genetic algorithms for estimation of compression index of clays using toughness limit
Article
  • May 2024
Measurement or prediction of compression index (Cc) of soils is essential for assessment of total and differential settlement of structures. It is a well-known fact that this parameter is controlled by several index identifiers of soil including initial void ratio, Atterberg limits, overconsolidation ratio, specific gravity, etc. Many studies in the past proposed relationships for prediction of Cc based on different index properties. Therefore, this study aims to present a comparison of previously proposed equations for estimation of Cc. Data from literature was compiled, and a total of 90 and 623 test results on remolded and undisturbed specimens were used to question the validity of previously proposed equations. Nevertheless, the modeling ability of 7 and 12 equations for estimation of Cc of remolded and undisturbed soils were questioned by use of compiled data. Moreover, new empirical relationships based on initial void ratio and toughness limit for prediction of Cc was proposed by use of nonlinear multivariable regression and evolutionary based regression analyses. The results are promising-the performances of models established are quite acceptable, which are verified by statistical analyses.
View
Cohesion estimation using multiple regression analysis and artificial neural networks
Conference Paper
  • Nov 2024
In the context of estimating the intrinsic parameter of soil, the direct and triaxial shear tests are useful to define the shear stress, however, these tests are expensive and take time. An optimization study allows to minimize the cost and time of the process. The current study attempts to propose a new approach to estimate Cu Cohesion; the approaches are tested using several multiple linear regression analysis models and artificial neural network, with a database consisting of 188 samples from the city of Boumerdes. The Cohesion Cu is estimated based on the dry and humid density λd and λh, water content W, plasticity limit Wp and the liquidity limit WL, the percentage of fine particles FC and the degree of saturation Sr. The results show the effectiveness of artificial neural networks which provide an estimate with very satisfying accuracy.
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Prediction on compression indicators of clay soils using XGBoost with Bayesian optimization
Article
  • Jun 2024
The determination of the compressibility of clay soils is a major concern during the design and construction of geotechnical engineering projects. Directly acquiring precise values of compression indicators from consolidation tests are cumbersome and time-consuming. Based on experimental results from a series of index tests, this study presents a hybrid method that combines the XGBoost model with the Bayesian optimization strategy to show the potential for achieving higher accuracy in predicting the compressibility indicators of clay soils. The results show that the proposed XGBoost model selected by Bayesian optimization can predict compression indicators more accurately and reliably than the artificial neural network (ANN) and support vector machine (SVM) models. In addition to the lowest prediction error, the proposed XGBoost-based method enhances the interpretability by feature importance analysis, which indicates that the void ratio is the most important factor when predicting the compressibility of clay soils. This paper highlights the promising prospect of the XGBoost model with Bayesian optimization for predicting unknown property parameters of clay soils and its capability to benefit the entire life cycle of engineering projects.
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Dry density in relation to other geotechnical proprieties of Algiers clay
Article
Full-text available
  • Dec 2017
Geotechnical study is one of the most important stages of the project study; it is based on an objective study of the soil to determine the various characteristics, whether mechanical or physical. On the other hand, in the majority of projects the number of completed tests does not allow to get reliable statistical results, but here it features the role of personal or collective experiences to use different correlations performed by researchers and statistical studies carried out for the different soil characteristics. This article shows the results of a statistical study to relate dry density to other geotechnical parameters like wet density (Dh), plasticity index (Ip), water content (w), void ratio (e), plastic limit (Wp), liquid limit (Wl), consistency index (Ic), Preconsolidation pressure (σ'p), Compression Index (Cc), swelling index (Cg), cohesion (cu), friction angle (φ) and liquidity index (Il); in Algiers region, and that in order to help geotechnical engineer to predict the values of this parameters from dry density considering this latter an account of the easiest calculated properties in the laboratory. More than 700 samples have been collected; over 120 geotechnical projects have been done in the Algiers area to be used in this study.
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Comparing nonlinear regression analysis and ‎artificial neural networks to predict geotechnical ‎parameters from standard penetration test
Article
Full-text available
  • Jan 2018
At the beginning the twenty-first century, a lot of high-level methods have become ‎available in geotechnical engineering in order to deal with the complexity and ‎heterogeneity encountered in soil, Statistical modeling (i.e. regression analysis method) ‎was used to estimate the relationships among two or more variables, however in the early ‎nineties an application of a new system emerged which gave excellent results in solving a ‎lot of problems by learning from the available data so-called ”artificial neural network”.‎ The aim of this study is to apply both methods, nonlinear regression analysis and ‎artificial neural networks in order to predict geotechnical parameters from standard ‎penetration test in all soil’s types; Comparison of the results using correlation’s ‎coefficient (R) and Root Mean Squared Error (RMSE) is done between both methods; ‎About 400 samples, over 65 boreholes in the Algiers area have been collected and were ‎used in this study, The results show the superiority of ANN‎ method in predicting data ‎that seems closer to experimental values compared to NRA method.
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Physical properties of the soils of Georgia
Article
Full-text available
  • May 2017
In article is generalized such indexes as water permeability, bulk density, particle density, total porosity, capillary porosity, non-capillary porosity, capillary water capacity, saturation water content, field capacity, permanent wilting point, hydroscopic water content, productive water, pores with air. This indexes were determined in main soils of Georgia: Red (Ferralic Nitisols, Haplic Nitisols), Yellow (Ferric Luvisols), Bog (Dystric Gleysols, Eutric Gleysols, Histosols), Yellow Yellow podzolicic (Stagnic Acrisols, Ferric Acrisols), Yellow brown forest (Stagnic Luvisols, Mollic Luvisols, Humic Luvisols, Ferric Luvisols), Brown forest (Humic Cambisols, Ferric Cambisols, Eutric Cambisols, Dystric Cambisols), Raw carbonate (Rendzic Leptosols), Grey cinnamonic (Calcic Kastanozems, Vertic Kastanozems), Meadow grey cinnamonic (Haplic Kastanozems, Gleyic Kastanozems, Vertic Kastanozems), Cinnamonic (Chromic Cambisols, Calcaric Cambisols, Humic Cambisols, Eutric Cambisols), Meadow cinnamonic (Chromic Cambisols, Calcaric Cambisols, Gleyic Cambisols, Eutric Cambisols), Black (Haplic Vertisols), Chernozems (Voronic Chernozems, Calcaric Chernozems), Mountain meadow (Hyperdistric Umbrisols), Saline soils (Vetric Solonchaks, Mollic Solonetz), Alluvial (Gleyic Fluvisols, Eurtic Fluvisos, Dystric Fluvisols).
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Prediction of compressibility parameters of the soils using artificial neural network
Article
Full-text available
  • Dec 2016
The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.
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Genetic Programming for granular compactness modelling
Article
Full-text available
  • Mar 2016
The prediction of granular mixtures compactness is a recurring question common to many technical and scientific domains. Knowing the theoretical difficulties to predict the ideal solution, the general approach consists in seeking via an experimental approach, which is based on ideal grains distribution curves, an optimal mixtures. In this context, and faced to the empiricism of current approaches, several models have been developed. These models allow predicting granular mixture compactness to some extent. The compressible packing model which is an improved version of the solid suspension model based on the linear model of compactness is one of predictive models allowing the estimation of compactness on the basis of components characteristics and the compaction mode. However, this model in its initial form loses its predictive power because its use requires the measurement of some parameters based on the derivative of experimental curves. In this context, this study aims to present a model which allows predicting the granular mixtures compactness using the intrinsic parameters of components, easily accessible to experiment. The model is issued from the application of the genetic programming (GP) approach. This work presents a double interest: proposing a predictive model of granular mixture compactness with a new approach and demonstrating the GP reliability as a revolutionary tool which forms part of the machine learning algorithms, in complex phenomena modelling.
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A Brief Note on Compression Index of Soil
Article
  • Jul 1956
New relationship between compression index and void ratio of soils after theoretical consideration from some simple assumptions; relationship agrees well with experimental results of verification and gives new approximate method to estimate compression index of soils as linear function of void ratio.
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Correlations of Soil and Rock Properties in Geotechnical Engineering
Book
  • Jan 2016
This book presents a one-stop reference to the empirical correlations used extensively in geotechnical engineering. Empirical correlations play a key role in geotechnical engineering designs and analysis. Laboratory and in situ testing of soils can add significant cost to a civil engineering project. By using appropriate empirical correlations, it is possible to derive many design parameters, thus limiting our reliance on these soil tests. The authors have decades of experience in geotechnical engineering, as professional engineers or researchers. The objective of this book is to present a critical evaluation of a wide range of empirical correlations reported in the literature, along with typical values of soil parameters, in the light of their experience and knowledge. This book will be a one-stop-shop for the practising professionals, geotechnical researchers and academics looking for specific correlations for estimating certain geotechnical parameters. The empirical correlations in the forms of equations and charts and typical values are collated from extensive literature review, and from the authors' database.
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Chapter 2: Genetic Programming for Modelling of Geotechnical Engineering Systems
Chapter
  • Jan 2015
Over the last decade or so, artificial intelligence (AI) has proved to provide a high level of competency in solving many geotechnical engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. This chapter presents one of the most interesting AI techniques, i.e. genetic programming (GP), and its applications in geotechnical engineering. In the last few years, GP, which is inspired by natural evolution of the human being, has proved to be successful in modelling several geotechnical engineering problems and has demonstrated superior predictive ability compared to traditional methods. In this chapter, the modelling aspects and formulation of GP are described and explained in some detail and an overview of most successful GP applications in geotechnical engineering are presented and discussed.
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