A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Moayedi, Hossein; Mosallanezhad, Mansour; Rashid, Ahmad Safuan A.; Jusoh, Wan Amizah Wan; Abdullahi, Mu’azu Mohammed

doi:10.1007/s00521-019-04109-9

Cited by 144 publications

(49 citation statements)

References 171 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Artificial neural networks (ANNs) are capable of predictive tools that were introduced by [32], mimicking the biological neural network. multi-layer perceptron (MLP) is a common type of ANNs that have shown a satisfying performance dealing with many engineering simulations [33][34][35][36][37]. This is due to their ability in generating non-linear equations between the set of inputs and outputs [38,39].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Predicting Slope Stability Failure through Machine Learning Paradigms

Bui

Moayedi

Gör

et al. 2019

IJGI

View full text Add to dashboard Cite

In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems.

show abstract

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Predicting Slope Stability Failure through Machine Learning Paradigms

Bui

Moayedi

Gör

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…In geotechnical engineering, neural computing, i.e., artificial neural network (ANN), is widely accepted as a capable tool for modeling complex phenomena [56][57][58]. Inspired from biological neural networks, ANN simulates the information processing and knowledge generalization that happens in the human brain.…”

Section: Neural Computing Modelmentioning

confidence: 99%

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

et al. 2019

View full text Add to dashboard Cite

In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg–Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R2) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects.

show abstract

“…In the indirect analysis method, neural network fitting analysis is widely used in engineering construction with its unique advantages [20], [21]. In order to reduce the error, the method of combining neural network with other means is gradually developed.…”

Section: Introductionmentioning

confidence: 99%

Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

Gan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In mountain and ravine region of southwest China, several hydropower stations have been built in this area. It is the main national base for hydropower energy development, but under the influence of geological structure and surface erosion, the in-situ stress environment in this area is more complex. The main purpose of this paper is to establish the relationship between measured in-situ stress data and numerical calculation by means of neural network. Therefore, it can be used to analyze the distribution characteristics of large-scale in-situ stress and the failure of regional geological bodies under the action of in-situ stress in Xiluodu area. This paper analyzes the geological conditions and the measured data of in-situ stress in Xiluodu area. The results show that there is obvious surface weathering phenomenon in this area, the stress environment is complex. The depth has a positive impact on the level of in-situ stress, but the impact degree is different. The genetic algorithm-BP artificial neural networks (G-P) method is trained by the measured in-situ stress data. Based on the field measurement data, neural network algorithm and numerical simulation technology, the three-dimensional in-situ stress field distribution characteristics of the project area are carried out. The research shows that the scheme of combining the actual measurement, numerical analysis and neural network inversion is reliable; Depth is an important factor affecting the maximum horizontal stress value in Xiluodu area; Under the action of in-situ stress, the fissures of geological body are relatively developed, and three types of original water storage and flow spaces are formed before dam storage period; The flow and storage space of groundwater includes limestone fracture, basalt fracture and slope fracture aquifer; After the impoundment of the reservoir area, the reservoir water is transmitted downward through the vertical fissures, and finally the reservoir water forms a hydraulic connection with the groundwater. After impoundment, the hydrogeological conditions of the reservoir area would be changed. INDEX TERMS In-situ stress, numerical simulation, neural network, in-situ stress inversion, geological rock mass damage, hydraulic connection.

show abstract

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Cited by 144 publications

References 171 publications

Predicting Slope Stability Failure through Machine Learning Paradigms

Predicting Slope Stability Failure through Machine Learning Paradigms

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

Contact Info

Product

Resources

About