A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil

Johari, A.; Najafi, H. K.

doi:10.24200/sci.2016.2097

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Authors who used finite element models were employed as database analysts to develop the neural network models. The result of this study shows that density has a strong influence on the lateral displacement [12]. Tohidi et al investigated the buckling capacity of corroded web opening steel beams and used neural networks to estimate the ultimate capacities of steel I-beams.…”

Section: Introductionmentioning

confidence: 87%

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

Badarloo

Jafari

2018

Buildings

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Use of composite steel shear walls (CSSW) in earthquake-resistant structures has grown in recent years. However, no thorough information exists on their performance, especially in cases where openings are present. In the present study, in order to first validate the analysis method, ABAQUS was used to model the studied composite shear wall with gap at UC-Berkeley, according to the results of which, a good agreement between the experimental and analytical models was observed. Then, the effect of the position and number of the openings on the performance of the walls was addressed. To this end, models with various openings, including openings close to the beam/column, horizontal/vertical openings and distributing opening, were prepared and analyzed. The results indicate that the maximum reduction in stiffness and strength occurred in walls with single openings. The size of opening and the opening’s area significantly affect shear wall performance. Ultimately, artificial neural network and fitness function tools were employed to obtain predictive models for shear wall performance. A neural network has proven an appropriate alternative method for predicting the displacement, stress, and strength of the composite shear wall.

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Section: Introductionmentioning

confidence: 87%

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

Badarloo

Jafari

2018

Buildings

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“…To validate the reliability and superiority of the IRMO-BP-NN prediction model, we established neural network models optimized by various algorithms using the same training samples, including BP-NN, GA-BP-NN [33,34], Support Vector Machine Regression (SVR) [35], and Bayesian surrogate model (Gaussian Processes (GP)) optimization algorithm [36,37]. SVR, a regression method based on the principles of Support Vector Machines (SVM), is widely used in regression prediction within machine learning.…”

Section: Irmo-bp-nn Model Trainingmentioning

confidence: 99%

Height Prediction of Water-Conducting Fracture Zone in Jurassic Coalfield of Ordos Basin Based on Improved Radial Movement Optimization Algorithm Back-Propagation Neural Network

Gao,

Jin,

Liu

et al. 2024

Mathematics

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The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.

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“…Keshavarz and Mehramiri [40] utilized GEP to model the normalized shear modulus and damping ratio of sands. This method has also been used to predict the maximum lateral displacement of retaining wall [41] and soil-water characteristic curve [42].…”

Section: Due To Ability Of Finding Complex Relationships In Multivarimentioning

confidence: 99%

Gene expression programming models for liquefaction-induced lateral spreading

Keshavarz

Tofighi

2018

Scientia Iranica

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Lateral spreading is one of the most significant destructive and catastrophic phenomena associated with liquefaction caused by earthquake and can impose very serious damages to structures and engineering facilities. The aim of this study is to evaluate liquefaction induced lateral spreading and finding new relations using gene expression programming (GEP) that is a new and developed generation of genetic algorithms approaches. Since there are complicated, nonlinear and higher order relationships between many factors affecting the lateral spreading, GEP is assumed to be capable of finding complex and accurate relationships between these factors. This study includes three main stages: (i) compilation of available database (484 data), (ii) dividing data into training and testing categories, and (iii) building new models and propose new relationships to predict ground displacement in free face, gentle slope and general ground conditions. The results of modeling each of the different ground conditions are presented in the form of mathematical equations. At the end, the final GEP models for 3 different cases of ground conditions are compared with multiple linear regression (MLR) and other published models. The statistical parameters indicate the higher accuracy of the GEP models over other relations.

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A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil

Cited by 11 publications

References 17 publications

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

Height Prediction of Water-Conducting Fracture Zone in Jurassic Coalfield of Ordos Basin Based on Improved Radial Movement Optimization Algorithm Back-Propagation Neural Network

Gene expression programming models for liquefaction-induced lateral spreading

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