Genetic Programming for Modelling of Geotechnical Engineering Systems

Shahin, Mohamed A.

doi:10.1007/978-3-319-20883-1_2

Cited by 19 publications

(5 citation statements)

References 59 publications

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“…The arithmetical efficacy of the models developed by GEP and MEP was evaluated across testing set. Seven statistical measures were calculated: Pearson's correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), relative squared error (RSE), Nash-Sutcliffe efficiency (NSE), and relative root mean square error (RRMSE) [57,[69][70][71]. The equations for these statistical measures are given in Eqs 2-8.…”

Section: Models' Assessment Criteriamentioning

confidence: 99%

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite

Xia

2024

PLoS ONE

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Cementitious composites’ performance degrades in extreme conditions, making it more important to enhance its resilience. To further the adaptability of eco-friendly construction, waste materials are increasingly being repurposed. Cementitious composites deteriorate in both direct and indirect ways due to the facilitation of hostile ion transport by water. The effects of using eggshell and glass powder as partial substitutes for cement and sand in mortar on the water-absorption capacity were investigated using machine learning (ML) modeling techniques such as Gene Expression Programming (GEP) and Multi Expression Programming (MEP). To further assess the importance of inputs, sensitivity analysis and interaction research were carried out. The water absorption property of cementitious composites was precisely estimated by the generated ML models. It was noted that the MEP model, with an R2 of 0.90, and the GEP model, with an R2 of 0.88, accurately predicted results. The sensitivity analysis revealed that the absorption capacity of the mortar was most affected by the presence of eggshell powder, sand, and glass powder. GEP and MEP model’s significance lies in the fact that they offer one-of-a-kind mathematical formulas that can be applied to the prediction of features in another database. The mathematical models resulting from this study can help scientists and engineers rapidly assess, enhance, and rationalize mixture proportioning. The built models can theoretically compute the water absorption of cement mortar made from eggshell powder and glass powder based on varied input parameters, resulting in cost and time savings.

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Section: Models' Assessment Criteriamentioning

confidence: 99%

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite

Xia

2024

PLoS ONE

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“…The performance evaluation of the developed models, i.e., ANN, ANFIS, and MEP, in a subset of training, testing, and validation for prediction of MS and MF was assessed using six standard analytical measuring tools which included coefficient of determination (R 2 ), correlation coefficient (R), root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE), relative square error (RSE), and Nash-Sutcliffe efficiency (NSE) [32,34,36,42,121]. Additionally, for all proposed developed models, the performance index (PI) was calculated, which is mostly dictated by RRMSE and R [29].…”

Section: Evolution Criteria and Performance Measuresmentioning

confidence: 99%

“…Data mining procedures in material, civil, and pavement engineering, in particular, have been reported widely in the previous two decades, thanks to the swift development in the approaches of ML [27]. The soft computing methods (SCMs) or artificial intelligence techniques (AITs) that are developed recently, for example, artificial neural networks (ANNs) (sub-types are; multilayer perceptron neural network (MLPNN), Bayesian neural network (BNN), general regression neural network (GRNN), backpropagation neural network (BPNN), and k-nearest neighbor (KNN)), ANNs with their hybrid form, i.e., support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme gradient boosting (XGBoost), adaptive neuro-fuzzy inference system (ANFIS), alternate decision trees), genetic algorithms (GAs), M5 model trees, evolutionary algorithms (EAs), ensemble random forest regression (ERFR), genetic expression programming (GEP), and MEP, have facilitated in the development of the various models in conjunction with conventional statistical models, e.g., regression, among many others [25,[28][29][30][31][32][33][34][35][36][37][38]. Mechanistic learning has been frequently used to evaluate the estimating models for the development of intelligent structures [39].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

et al. 2022

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The conventional method for determining the Marshall Stability (MS) and Marshall Flow (MF) of asphalt pavements entails laborious, time-consuming, and expensive laboratory procedures. In order to develop new and advanced prediction models for MS and MF of asphalt pavements the current study applied three soft computing techniques: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi Expression Programming (MEP). A comprehensive database of 343 data points was established for both MS and MF. The nine most significant and straightforwardly determinable geotechnical factors were chosen as the predictor variables. The root squared error (RSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed the rising order of input significance of MS and MF. The results of parametric analysis (PA) were also found to be consistent with previous research findings. The findings of the comparison showed that ANN, ANFIS, and MEP are all reliable and effective methods for the estimation of MS and MF. The mathematical expressions derived from MEP represent the novelty of MEP and are relatively reliable and simple. Roverall values for MS and MF were in the order of MEP > ANFIS > ANN with all values over the permissible range of 0.80 for both MS and MF. Therefore, all the techniques showed higher performance, possessed high prediction and generalization capabilities, and assessed the relative significance of input parameters in the prediction of MS and MF. In terms of training, testing, and validation data sets and their closeness to the ideal fit, i.e., the slope of 1:1, MEP models outperformed the other two models. The findings of this study will contribute to the choice of an appropriate artificial intelligence strategy to quickly and precisely estimate the Marshall Parameters. Hence, the findings of this research study would assist in safer, faster, and more sustainable predictions of MS and MF, from the standpoint of time and resources required to perform the Marshall tests.

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“…e use of ANN models has also reflected in the prediction of various mechanical and physical properties of soil such as consistency limits and other strength properties of stabilized soils [30][31][32][33]. Moreover, evolutionary predictions of the physical and mechanical properties of improved soils, such as unconfined compressive strength (UCS), internal friction, and coefficients of uniformity and gradation, with respect to the percentage of admixture contact (such as cement content) have been carried out by many emerging researchers without particular interest on the prediction of consistency limit [33][34][35][36]. Likewise, in reference [37], the authors predicted the performance characteristics of stabilized soils by utilizing a variant of genetic programming, namely, linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA.…”

Section: Evolutionary Computation Techniques For Predicting Consistency Limit: Genetic Programming and Artificial Neuralmentioning

confidence: 99%

Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks

Ebid

Nwobia

Onyelowe

et al. 2021

Applied Computational Intelligence and Soft Computing

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Unsaturated soils used as compacted subgrade, backfill, or foundation materials react unfavorably under hydraulically bound environments due to swell and shrink cycles in response to seasonal changes. To overcome these undesirable conditions, additive stabilization processes are used to improve the volume change phenomenon in soils. However, the use of supplementary binders made from solid waste base powder materials has become necessary to deal with the hazards of greenhouse due to ordinary cement use. Meanwhile, several studies are being carried out to design infrastructures even with the limitations of insufficient or lack of equipment needed for efficient design performance. Intelligent prediction techniques have been used to overcome this shortcoming as the primary purpose of this research work. Therefore, in this work, genetic programming (GP) and artificial neural network (ANN) have been used to predict the consistency limits, i.e., liquid limits, plastic limit, and plasticity index of unsaturated soil treated with a composite binder known as hybrid cement (HC) made from blending nanostructured quarry fines (NQF) and hydrated-lime-activated nanostructured rice husk ash (HANRHA). The database needed for the prediction operation was generated from several experiments corresponding with treatment dosages of HANRHA between 0 and 12% at a rate of 0.1%. The results of the stabilization exercise showed substantial development on the soil properties examined, while the prediction exercise showed that ANN outclassed GP in terms of performance evaluation, which was conducted using sum of squared error (SSE) and coefficient of determination (R2) indices. Generally, nanostructuring of the component binder material has contributed to the success achieved in both soil improvement and efficiency of the models predicted.

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Genetic Programming for Modelling of Geotechnical Engineering Systems

Cited by 19 publications

References 59 publications

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks

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