Prediction of axial strength in circular steel tube confined concrete columns using artificial intelligence

Ngo, Ngoc-Tri; Pham, Thi-Phuong-Trang; Hoang, An Le; Nguyen, Quang-Trung; Nguyen, Thi-Thao-Nguyen; Huynh, Van-Vu

doi:10.31814/stce.nuce2021-15(2)-10

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…In the testing phase, the proposed model showed acceptable accuracy with a mean absolute percentage error of 11.10%. Ngo et al [29] used artificial neural networks (ANNs), support vector regression (SVR), linear regression (LR), and M5P techniques for the prediction of axial strength in circular steel tube confined concrete columns. The authors outlined key distinctions between the techniques and concluded that the M5P was the best artificial intelligence (AI) model for predicting experimental results when compared to others.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a New Procedural Binary Particle Swarm Optimization Algorithm to Estimate Some Properties of Local Concrete Mixtures

Alsaleh,

Hammami,

Wardeh

et al. 2023

Applied Sciences

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Artificial intelligence techniques have lately been used to estimate the mechanical properties of concrete to reduce time and financial expenses, but these techniques differ in their processing time and accuracy. This research aims to develop a new procedural binary particle swarm optimization algorithm (NPBPSO) by making some modifications to the binary particle swarm optimization algorithm (BPSO). The new software has been created based on some fresh state properties (slump, temperature, and grade of cement) obtained from several ready-mix concrete plants located in Aleppo, Syria to predict the density and compressive strength of the regional concrete mixtures. The numerical results obtained from NPBPSO have been compared with the results from BPSO and artificial neural network ANN. It has been found that BPSO and NPBPSO are both predicting the compressive strength of concrete with less number of iterations and more accuracy than ANN (0.992 and 0.998 correlation coefficient in BPSO and NPBPSO successively and 0.875 in ANN). In addition, NPBPSO is better than BPSO as it prevents the algorithm from falling into the problem of local solutions and reaches the desired optimal solution faster than BPSO. Moreover, NPBPSO improves the accuracy of obtained compressive strength values and density by 30% and 50% successively.

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Section: Literature Reviewmentioning

confidence: 99%

Developing a New Procedural Binary Particle Swarm Optimization Algorithm to Estimate Some Properties of Local Concrete Mixtures

Alsaleh,

Hammami,

Wardeh

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In this study, ANN outperforms the other three models while also showing high regression values. Ngo et al . (2021) performed the performance analysis of four models on short to intermediate circular 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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Prediction of axial strength in circular steel tube confined concrete columns using artificial intelligence

Cited by 2 publications

References 31 publications

Developing a New Procedural Binary Particle Swarm Optimization Algorithm to Estimate Some Properties of Local Concrete Mixtures

Developing a New Procedural Binary Particle Swarm Optimization Algorithm to Estimate Some Properties of Local Concrete Mixtures

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

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