Mechanics-Guided Genetic Programming Expression for Shear-Strength Prediction of Squat Reinforced Concrete Walls with Boundary Elements

Gondia, Ahmed; Ezzeldin, Mohamed; El‐Dakhakhni, Wael

doi:10.1061/(asce)st.1943-541x.0002734

Cited by 44 publications

(10 citation statements)

References 47 publications

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“…Baghi et al [25] suggested an empirical model using the PSO technique. Gondia et al [26] and Tariq et al [27] introduced expressions to calculate the shear strength of flanged squat walls using genetic programming (GP) and gene expression programming (GEP) techniques, respectively. While Feng et al [28] and Parsa & Naderpour [29] developed shear strength predictive models for flanged squat walls using the eXtreme Gradient Boosting (XGBoost) algorithm and improved support vector regression method (SVR), respectively.…”

Section: Introductionmentioning

confidence: 99%

Shear Strength of Reinforced Concrete Squat Walls

Al-Bayati

2023

Civ Eng J

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Squat shear walls are widely used in various structures to resist earthquake loads. However, the relevant design expressions found in building codes and literature do not incorporate the influence of all crucial parameters and provide inconsistent peak shear strength estimations. This study adopts the artificial neural network (ANN) to predict the peak shear strength of squat walls using an extensive database that includes the results of 487 walls with wide-ranging test parameters. The ANN models consider the effect of concrete strength, the wall aspect ratio, vertical and horizontal reinforcements, vertical reinforcement of boundary elements, and axial load ratio. These accurately predicted the available test results. They implemented it to carry out parametric and sensitivity analysis to investigate the effect of the main parameters on the peak strength and to give information about the factors that contribute most to the shear response. In addition, a softened strut and tie method is proposed, considering the variables that substantially influence the shear strength. A nonlinear regression analysis is employed to determine the coefficients of the proposed model using the available database. The performance of the proposed model is measured using the existing models, which results in the best favorable agreement with the test results. Doi: 10.28991/CEJ-2023-09-02-03 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%

Shear Strength of Reinforced Concrete Squat Walls

Al-Bayati

2023

Civ Eng J

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“…(2021) utilized neural networks for shear strength prediction of reinforced concrete squat walls. Gondia et. al.…”

Section: Introductionmentioning

confidence: 99%

Failure Mode Detection of Reinforced Concrete Shear Walls Using Ensemble Deep Neural Networks

Barkhordari

Massone

2022

Int J Concr Struct Mater

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Reinforced concrete structural walls (RCSWs) are one of the most efficient lateral force-resisting systems used in buildings, providing sufficient strength, stiffness, and deformation capacities to withstand the forces generated during earthquake ground motions. Identifying the failure mode of the RCSWs is a critical task that can assist engineers and designers in choosing appropriate retrofitting solutions. This study evaluates the efficiency of three ensemble deep neural network models, including the model averaging ensemble, weighted average ensemble, and integrated stacking ensemble for predicting the failure mode of the RCSWs. The ensemble deep neural network models are compared against previous studies that used traditional well-known ensemble models (AdaBoost, XGBoost, LightGBM, CatBoost) and traditional machine learning methods (Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest). The weighted average ensemble model is proposed as the best-suited prediction model for identifying the failure mode since it has the highest accuracy, precision, and recall among the alternative models. In addition, since complex and advanced machine learning-based models are commonly referred to as black-box, the SHapley Additive exPlanation method is also used to interpret the model workflow and illustrate the importance and contribution of the components that impact determining the failure mode of the RCSWs.

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“…On the other hand, if the selection pressure of the model is too low, computer time is spent on useless iterations because the convergence is too slow. Thus, this state has to be avoided with proper programming and specification of limit conditions [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Application of Temperature and Process Duration as a Method for Predicting the Mechanical Properties of Thermally Modified Timber

Chu

Hasanagić

Hodžić

et al. 2022

Forests

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This study aims to investigate the influence of thermal modification (TM) on the physical and mechanical properties of wood. For this purpose, the experimental part focused on selected influential parameters, namely temperature, residence time, and density, while the four-point bending strength is obtained as the output parameter. The obtained experimental data are stochastically modeled and compared with the model created by genetic programming (GP). The classical mathematical analysis obtained treatment parameters in relation to the maximum bending strength (T = 187 °C, t = 125 min = 0.780 g/cm3) and compared with the results obtained by genetic algorithm (GA) (T = 208 °C, t = 122 min, and = 0.728 g/cm3). It is possible to obtain models that describe experimental results well with stochastic modeling and evolutionary algorithms.

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Mechanics-Guided Genetic Programming Expression for Shear-Strength Prediction of Squat Reinforced Concrete Walls with Boundary Elements

Cited by 44 publications

References 47 publications

Shear Strength of Reinforced Concrete Squat Walls

Shear Strength of Reinforced Concrete Squat Walls

Failure Mode Detection of Reinforced Concrete Shear Walls Using Ensemble Deep Neural Networks

Application of Temperature and Process Duration as a Method for Predicting the Mechanical Properties of Thermally Modified Timber

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