Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

Article Chemistry, Multidisciplinary

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Maaz Amjad et al.

Summary: In this study, a new model for predicting pile bearing capacity is developed using XGBoost algorithm, which outperforms other commonly used algorithms in terms of accuracy.

APPLIED SCIENCES-BASEL (2022)

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Article Chemistry, Multidisciplinary

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

Mahmood Ahmad et al.

Summary: During earthquakes, liquefaction-induced lateral displacement causes significant damage to structures. This research proposes a Gaussian Process Regression (GPR) model to accurately estimate lateral displacement in liquefaction-prone areas and assesses its performance.

APPLIED SCIENCES-BASEL (2022)

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Article Engineering, Multidisciplinary

A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Binh Thai Pham et al.

Summary: In this study, three cost-effective algorithms - artificial neural network (ANN), support vector machine (SVM), and random forest (RF) - were used to predict the permeability coefficient of soil. Based on 84 soil samples, it was found that the random forest model performed more efficiently in estimating the permeability coefficient compared to the other two models.

MATHEMATICAL PROBLEMS IN ENGINEERING (2021)

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Article Computer Science, Software Engineering

A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient

Binh Thai Pham et al.

Summary: In this study, M5P and Gaussian process (GP) soft computing methods were utilized to estimate the soil permeability coefficient, with the M5P model showing higher determination coefficient and more reliable predictions compared to the GP model. This demonstrates that applying machine learning techniques can serve as alternative methods for predicting soil parameters.

SCIENTIFIC PROGRAMMING (2021)

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Article Chemistry, Physical

Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

Mahmood Ahmad et al.

Summary: This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, and evaluates their performance using statistical indices.

MATERIALS (2021)

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Article Agronomy

Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt

Ahmed Elbeltagi et al.

Summary: This study aims to model the Water Footprint (WF) using Gaussian processes models and select the best kernel with best climate scenario for predicting blue WF of maize in the Egyptian Nile Delta. The results show that the PUK kernel performs better than the other three kernels, with model 7 being the most accurate for predicting blue WF values. The error rate is higher in small WF values and increases again at large WF values for all four kernels.

AGRICULTURAL WATER MANAGEMENT (2021)

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Article Engineering, Civil

Evaluation and prediction of slope stability using machine learning approaches

Shan Lin et al.

Summary: In this study, a machine learning model is built for slope stability evaluation, exploring various ML methods for factor of safety prediction. Through data preprocessing and model evaluation, SVM, GBR, and Bagging are identified as the best regression methods based on evaluation indicators R-2, MAE, and MSE.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2021)

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Article Computer Science, Interdisciplinary Applications

A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

Wusi Chen et al.

Summary: This study examines the suitability of hybridizing different optimization algorithms with data-driven models to predict ground vibration, and compares the performance of various models through a case study, with the MFA-SVR model being the most accurate.

ENGINEERING WITH COMPUTERS (2021)

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Article Geosciences, Multidisciplinary