Evaluation of lateral spreading using artificial neural networks

Baziar, Mohammad Hassan; Ghorbani, Ali

doi:10.1016/j.soildyn.2004.09.001

Cited by 87 publications

(45 citation statements)

References 16 publications

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“…In spite of classical statistical methods, neural networks do not need any previous knowledge about the quality and mechanics of the problem and their concerning parameters. One of the most in-demand kinds of neural networks is multilayer perceptron (MLP) networks, which is formed using correct definition of its constructing layers, input, and hidden and output layers [23]. Regarding the type of problem complexity and its nonlinearity, the number of MLP layers is defined [24].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Hence, the application of neural network modeling and evolutionary polynomial regressions (EPRs), which are believed to be common ways to accurately and timely predict engineering complicated functions, can be examined. Different attempts to apply neural networks and EPRs to model different civil and geotechnical problems are presented in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. They are well-applied in a wide range of problems from deep soil stabilizations, concrete, and their related structures, compressive strength of soils, rocks, and stabilized samples, bearing capacity of shallow and deep foundations, lateral spreading, rock mechanics, rock engineering, and soil mechanics [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using threedimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile's configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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“…ANNs are broadly applied in engineering [22][23][24][25][26][27][28][29]. Also, over the last decades, ANNs have appeared as efficient meta-modelling methods applicable to a wide range of sciences, including material science and structural engineering [30][31][32].…”

Section: Methodsmentioning

confidence: 99%

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

2018

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Featured Application: This work can be conjunctly used with the support characteristic curve of circular tunnels to find the optimum time of the installation of the support system in a way to restrict the displacements to a specific value. The approach described in this manuscript facilitates the design of circular tunnels for elasto-plastic, strain-softening rock masses obeying both Mohr-Coulomb and Hoek-Brown strength criteria. Abstract:The prediction of the support pressure (P i ) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the P i of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the P i . On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the P i .

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“…The neural network is a powerful prediction tool and is more accurate than other conventional methods for complex problems such as liquefaction, where the relationship between variables is not clear [27]. Arti cial neural networks are used in various geotechnical elds such as liquefaction [28][29][30], soil behavior modeling, earth-retaining structures, prediction of bearing capacity of piles, settlement of structures, slope stability, designing tunnels, and hydraulic conductivity of soil [31].…”

Section: Introductionmentioning

confidence: 99%

Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

2017

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Abstract. In recent years, controlled blasting has turned into an e cient method for evaluation of soil liquefaction on a real scale and of ground improvement techniques. Predicting blast-induced soil liquefaction using collected information can be an e ective step in the study of blast-induced liquefaction. In this study, to estimate residual pore pressure ratio, rst, a multi-layer perceptron neural network is used in which error (RMS) for the network was calculated as 0.105. Next, a neuro-fuzzy network, ANFIS, was used for modeling. Di erent ANFIS models are created using Grid Partitioning (GP), subtractive clustering (SCM), and Fuzzy C-Means clustering (FCM). Minimum error is obtained using FCM at about 0.081. Finally, Radial Basis Function (RBF) network is used. Error of this method was about 0.06. Accordingly, RBF network has better performance. Variables, including ne-content, relative density, e ective overburden pressure, and SPT value, are considered as input components, and residual pore pressure ratio, Ru, was used as the only output component for designing prediction models. In the next stage, the network output is compared with the results of a regression analysis. Finally, sensitivity analysis for RBF network is tested, and its results reveal that 0 v0 and SPT are the most e ective factors for determining Ru.

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Evaluation of lateral spreading using artificial neural networks

Cited by 87 publications

References 16 publications

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

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