Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Liao, Zhihao; Liao, Zhiwei

doi:10.1515/geo-2020-0198

Cited by 21 publications

(7 citation statements)

References 39 publications

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“…Liao and Liao [41] used backpropagation neural networks (BPNN) and multivariate adaptive regression splines (MARS) to capture the intrinsic nonlinear and multidimensional relationship among the parameters associated with the evaluation of slope stability. The authors based the analysis on data collected from several history cases (153 in total).…”

Section: Optimization Methods For Slope Stability Analysismentioning

confidence: 99%

Recent Tendencies in the Use of Optimization Techniques in Geotechnics: A Review

Boumezerane

2022

Geotechnics

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The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics.

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Section: Optimization Methods For Slope Stability Analysismentioning

confidence: 99%

Recent Tendencies in the Use of Optimization Techniques in Geotechnics: A Review

Boumezerane

2022

Geotechnics

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“…Other researchers used a variety of novel soft computing techniques to predict FOS, including Multiple Regression (MR), Genetic Algorithm (GA), Support Vector Machine (SVM), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme gradient boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and hybrid models, Gradient Boosting Decision Tree was used in several applications, and the results were found to be noticeably superior to those attained by employing traditional techniques. (Marrapu and Jakka, 2017;Lin et al, 2018a;Bui et al, 2020a;Huang et al, 2020;Deris et al, 2021;Jingjing et al, 2021;Kardhani et al, 2021;Sina et al, 2021;Christoph et al, 2022;Feezan et al, 2022;Gagan et al, 2022;Gexue et al, 2022;Zhihao and Zhiwei, 2022;Mahmoodzadeh and Mohammadi, 2023;Xu et al, 2023). Arunav Chakraborty and Diganta Goswami (Arunav and Diganta, 2017) carried out their work on slope stability prediction utilizing artificial neural networks, very advanced modeling methods that can be suitable for modeling highly complicated functions.…”

Section: Hybrid Approachmentioning

confidence: 99%

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Ragam,

Kushal Kumar,

Ajith

et al. 2024

Front. Mater.

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Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R2 of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098.

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“…Teachers can base their instructional design on these four basic elements and design more relevant and effective motivational strategies based on students' motivational profiles and the characteristics of the required content, when appropriate. The four elements represent the four main types of motivation strategies, and only a flexible and appropriate instructional design based on the four basic elements can effectively motivate students to learn and optimize teaching efficiency [ 17 ].…”

Section: Related Researchmentioning

confidence: 99%

Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

Zhang

Wang²

2022

Computational Intelligence and Neuroscience

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This article draws on previous research on constructive English teaching models and uses multimodal neural network algorithm theory and constructive English teaching as the theoretical basis, experimental research method, questionnaire survey method, and evaluation method. In this article, we propose a multimodal neural network consisting of a multiscale FCN module and an LSTM module. The network focuses on both the multiscale geometric spatial features and the numerical time-dependent features of the time series curves, and with the comprehensive knowledge of their characteristics, it can better distinguish the classes to which the series belong. A large-scale perceptual field is achieved by null convolution in the model to ensure that the training pressure does not increase significantly. A series of experiments on the UCR dataset verifies the effectiveness and superiority of the model. Simulation experiments were conducted to build the proposed constructive English teaching model based on a multimodal neural network algorithm, and a test environment was built for use case testing. The experimental results showed that the algorithm can be better applied to constructive English teaching and has better adaptability and accuracy in various scenarios. At the end of the experiment, a posttest of grammar level was conducted in two classes to test whether the constructive English teaching model based on the multimodal neural network model could help students improve their English grammar skills. The results of the data analysis showed that the mean score of the experimental class was significantly higher than that of the control class, and the experimental class showed a more significant improvement, indicating that this new constructive English teaching model was beneficial to improving students’ English grammar skills. The interaction strategy proposed under the constructive English teaching model can effectively improve the interaction between teachers and students. This positive feedback effect can provide front-line teachers with corresponding teaching references.

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Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Cited by 21 publications

References 39 publications

Recent Tendencies in the Use of Optimization Techniques in Geotechnics: A Review

Recent Tendencies in the Use of Optimization Techniques in Geotechnics: A Review

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

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