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

Liao, Zhihao; Liao, Zhiwei

doi:10.1515/geo-2020-0198

Cited by 16 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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“…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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“…According to their ndings, the ensemble learners using the extreme gradient boosting framework delivered the best performance. Liao [25] investigated the application of multivariate adaptive regression splines (MARS) to grasp the naturally occurring multidimensional and nonlinear interaction between the factors involved in the assessment of slope stability. A comparison of back-propagation neural network and MARS-based machine learning approaches to slope stability estimation was performed.…”

Section: Introductionmentioning

confidence: 99%

Straightforward slope stability prediction under seismic conditions using machine learning algorithms

Barkhordari,

Armaghani

et al. 2023

Preprint

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One of the most significant and crucial issues in geotechnical engineering works, such as earth dams, embankments, and landfills to name a few, is slope stability assessment. Better methods are required to anticipate slope collapse because of its fatal effects. The goal of this research is to create a straightforward machine learning (ML) model for examining slope stability under seismic conditions. Four ML algorithms are examined, including Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Light Gradient Boosting Machine (LGBM), and Linear Discriminant Analysis (LDA). The models are trained and tested on the database containing 700 slopes. 10-fold cross validation is utilized for parameter tuning, model training and performance estimating of machine learning models using training set. The best model is interpreted using the SHapley Additive exPlanations (SHAP) method, which is built on game theories. Among the studied models, the LGBM model is the most accurate model based on ranking technique. Most influential features for slope stability prediction under seismic conditions are detected by the SHAP method as follows: peak ground acceleration, friction angle, and angle of inclination.

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

Cited by 16 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

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

Straightforward slope stability prediction under seismic conditions using machine learning algorithms

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