Predictive modeling of static and seismic stability of small homogeneous earth dams using artificial neural network

Zeroual, Abdelatif; Fourar, Ali; Djeddou, Messaoud

doi:10.1007/s12517-018-4162-6

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…Artificial neural networks are often divided into two categories: one is a recursive network that generates loops through feedback connections, and the other is a feedforward neural network [22] in which the network structure has no loops. The typical single hidden layer feedforward neural network structure is shown in Figure 1.…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

Wang

2019

Mathematical Problems in Engineering

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Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

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Section: Backpropagation Neural Networkmentioning

confidence: 99%

An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

Wang

2019

Mathematical Problems in Engineering

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“…Fattahi [13] utilized different ANFIS models (grid partitioning, subtractive clustering, and fuzzy cmeans clustering) to assess their ability for the estimation of FOS. Zeroual et al [14] used the ANN to develop a prediction model for the FOS estimation of earth dams. Haghshenas et al [15] developed a hybrid algorithm using Harmony search and the K-means algorithm to analyze the slope stability based on a limited database made up of 19 case studies.…”

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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“…Arti cial neural networks (ANN) for structural reliability analysis have been used since 1989 when Hornik et al [16][17][18] proved that multilayer networks can approximate any function with a high degree of accuracy. Some other applications are as follows: Chen [19][20][21] uses genetic algorithms to calculate the reliability index of bridges, trusses, and at frames; Bojorquez et al [22] propose a methodology based on optimization to nd live, dead, and earthquake load factors for design of buildings located on soft soil; Dai and Cao [23] use hybrid methods (ANN and wavelet neural network) to obtain the probability of failure of various structures; Santana Gomes [18] calculates the probability of failure of several structures using adaptive ANN and compares its results obtained with these networks with those from Monte Carlo simulations; Wen et al [24] optimize ANN to obtain the reliability of gas lines and compare their results with an unoptimized network and with Monte Carlo simulations; however, there are very few studies focused on the use of artificial intelligence for the calculation of structural safety factors; for example, Koopialipoor [25] uses the ant colony optimization algorithm to maximize the safety factor of retaining walls; Zeroual [26] calculates safety factors for dams, using a feedforward backpropagation network; Gordan et al [27] obtain safety factors for retaining walls using ANN and "artificial bee colony"; and Zhang et al [28] propose a Bayesian network for calculating pile resistance factors.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Neural Networks and Response Surface Methodology for Evaluating the Reliability Index of Steel Wind Towers

Inzunza-Aragón

Ruiz

Cruz-Reyes

2022

Advances in Civil Engineering

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The estimation of structural reliability is a process that requires a large number of computational hours when statistical data are not available since it is necessary to perform a large amount of analysis or numerical simulations to estimate parameters related to the reliability. A methodology is proposed for estimating the structural reliability index, as well as the demand and structural capacity factors inherent to the structure, given the fundamental vibration period and the height of the structure, by using artificial neural networks (ANN) and, alternatively, the response surface method (RSM). Both approaches are applied to steel wind turbine towers. For the cases studied, ANN allow evaluating the reliability index and both the demand and structural capacity factors with greater accuracy than when using RSM.

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Predictive modeling of static and seismic stability of small homogeneous earth dams using artificial neural network

Cited by 12 publications

References 36 publications

An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

Straightforward slope stability prediction under seismic conditions using machine learning algorithms

Use of Artificial Neural Networks and Response Surface Methodology for Evaluating the Reliability Index of Steel Wind Towers

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