Slope reliability evaluation based on multi-objective grey wolf optimization-multi-kernel-based extreme learning machine agent model

Ling, Qing; Zhang, Qin; Wei, Yanbo; Kong, Ling‐Bin; Zhu, Li

doi:10.1007/s10064-020-02090-5

Cited by 22 publications

(9 citation statements)

References 35 publications

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“…Many scholars have tried to integrate machine learning (ML) and geotechnical reliability analysis because of the complexity of expansive soils to improve computational precision and efficacy. This endeavor has resulted in a number of successful applications [34][35][36][37][38]. Within geotechnical reliability research, the major goal of machine learning (ML) is to recreate complicated, highdimensional implicit performance functions by exploiting insights from carefully selected data.…”

Section: Artificial Intelligence As a Predictive Tool In Geotechnical...mentioning

confidence: 99%

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Alnmr,

Hosamo,

Lyu

et al. 2024

Applied Sciences

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This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.

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Section: Artificial Intelligence As a Predictive Tool In Geotechnical...mentioning

confidence: 99%

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Alnmr,

Hosamo,

Lyu

et al. 2024

Applied Sciences

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“…This strategy can improve the diversity and distribution uniformity of the particles. The repel velocity increment for each particle is shown in Equations ( 11) and (12).…”

Section: Dynamic Particle Repulsion Strategymentioning

confidence: 99%

“…In the early stage of DOEs research, a sampling method called one-shot design (OSD) was often used. In OSD, to improve the convenience of sample acquisition, a large number of training samples were obtained at once in the design space according to a certain sampling method (such as Latin hypercube sampling (LHS)) [10][11][12]. New random samples may be added depending on the MCS calculation results.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Yuan

et al. 2023

Applied Sciences

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In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (Pf). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

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“…Foong and Moayedi suggested the use of equilibrium optimization (EO) and a vortex search algorithm (VSA) for optimizing a multilayer perceptron neural network (MLPNN) employed to anticipate the factor influencing the safety of a single-layer soil slope [16]. Ling et al researched slope reliability evaluation based on a multiobjective gray wolf optimization-based extreme learning machine agent model [17]. Pham et al applied parallel learning and sequential learning to implement ensemble classifier models for slope stability analysis [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Slope Stability Based on IPSO-SVM Machine Learning Model

Wang

Yang

et al. 2022

Geofluids

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In the evaluation and prediction of slope stability, the traditional numerical analysis method, which is over reliant on experience, takes a large amount of computing time and lacks the ability to reflect the fuzzy and nonlinear characteristics of slope parameters well. Considering the above characteristics, this study proposes an improved particle swarm optimization of support vector machine (IPSO-SVM) algorithm model, which combines optimized particle swarm optimization (IPSO) and support vector machine (SVM) and applies it to slope stability prediction. Based on 28 groups of slope engineering data, the stability prediction results of IPSO-SVM, PSO-SVM, and SVM models were compared with real values for analysis. The results show that the maximum relative error of the IPSO-SVM model is only 1.3%, and the average relative error is 1.1%, which is far lower than the prediction error of the PSO-SVM model and SVM model; therefore, the prediction result of IPSO-SVM is the closest to the real value. This method can accurately predict the slope safety factor under the influence of different indexes, and the research results can provide guidance for practical engineering.

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Slope reliability evaluation based on multi-objective grey wolf optimization-multi-kernel-based extreme learning machine agent model

Cited by 22 publications

References 35 publications

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Prediction and Analysis of Slope Stability Based on IPSO-SVM Machine Learning Model

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