Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm

Seghier, Mohamed El Amine Ben; Gao, Xiao‐Zhi; Jafari‐Asl, Jafar; Thai, Duc‐Kien; Ohadi, Sima; Nguyen-Thoi, T.

doi:10.1016/j.istruc.2021.01.036

Cited by 30 publications

(10 citation statements)

References 32 publications

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“…The best performing machine learning model is then selected based on the evaluation results from both training and testing phases. The formula for the evaluation metrics is represented by the expressions below [30,31].…”

Section: Methodsmentioning

confidence: 99%

Application of machine learning approaches for modelling crack growth rates

Ben Seghier,

Plevris

2023

ce papers

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The scientific community has widely accepted the use of machine learning techniques to tackle complex engineering problems. Among the most intriguing problems is finding the correlation between alloy steel properties and cyclic fatigue and crack growth rate. Employing machine‐learning models can provide more robust and accurate predictive models to address such challenges. This paper presents the application of four machine learning models, namely decision trees (DT), random forest (RF), adoptive boosting (AdaBoost), and gradient boosting regression tree (GBRT) to predict the crack growth rate of steel/alloys. The study utilizes a large database gathered from literature to construct the predictive models and compares the results using various statistical metrics and graphical representation. The study's findings demonstrate the effectiveness and suitability of machine learning techniques to handle complex databases related to fatigue problems.

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Section: Methodsmentioning

confidence: 99%

Application of machine learning approaches for modelling crack growth rates

Ben Seghier,

Plevris

2023

ce papers

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“…To evaluate the accuracy of the proposed models' several statistical evaluation indices are used. These include the root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R 2 ) which are defined by equation (26), respectively [34][35][36][37]. A MATLAB code was developed to obtain the optimal tilt angle for a solar module facing due south.…”

Section: Endmentioning

confidence: 99%

Development of Optimal Tilt Angle Models of a Photovoltaic Module for Maximum Power Production: Ethiopia

Ashetehe

Gessesse

Shewarega

2022

International Journal of Photoenergy

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The power generated from the photovoltaic module is directly related to the magnitude of total incident solar radiation on the surface of the solar module. The total incident solar radiation depends on the location, tilt angle, and orientation of the solar module. In this paper, generic models were developed that determine the seasonal and annual optimal tilt angle of the Photovoltaic module at any location in Ethiopia without using meteorological data. Both isotropic and anisotropic diffuse solar radiation models were used to estimate monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 cities -32 for training and 12 for testing were obtained from the National Aeronautical and Space Administration database, and algorithms were developed and implemented using MATLAB and R programming software to obtain optimum tilt angle and regression models. The study showed that the developed model accurately estimates the optimal tilt angle with the minimum statistical validation errors. It is also found that 5.11% to 6.275% (isotropic) and 5.72% to 6.346% (anisotropic models) solar radiation energy is lost when using the yearly average fixed optimal tilt angle as compared with the monthly optimal tilt angle. The result of this study was also validated by comparing it with the previously published works, PVGIS and PVWatt online software. The graphical abstract is included in the supplementary file.

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“…The predictions were also compared with EC4, AISC and AS. Seghier et al (2021) used six GEP models with combinations of input parameters to predict the ACC for square CFST columns. GEP model with input parameters given in Table 8 proved to be the best among the six models.…”

Section: Gene Expression Programming (Gep) For Acc Prediction Of Cfst...mentioning

confidence: 99%

“…Seghier et al . (2021) used six GEP models with combinations of input parameters to predict the ACC for square CFST columns.…”

Section: Techniques Used To Predict the Acc Of Cfst Columnsmentioning

confidence: 99%

Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review

Narang

Kumar

Dhiman

2022

MMMS

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PurposeThis study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).Design/methodology/approachConcrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.FindingsThe implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.Originality/valueThis study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

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Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm

Cited by 30 publications

References 32 publications

Application of machine learning approaches for modelling crack growth rates

Application of machine learning approaches for modelling crack growth rates

Development of Optimal Tilt Angle Models of a Photovoltaic Module for Maximum Power Production: Ethiopia

Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review

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