Improved predictive model to the cross-sectional resistance of CFT

Mansouri, Iman; Chacón, Rolando; Hu, Jong Wan

doi:10.1007/s12206-017-0733-9

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…These stages are repeated until pre-defined generations number or until an appropriate model has been determined. Figure 2 illustrates the flowchart of Gene expression programming [36].…”

Section: Gep Methodologymentioning

confidence: 99%

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Hasibi

Mahmoudian

Khayati

2020

IJE

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This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 ᵒC for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R 2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by R 2 = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

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“…These stages are repeated until pre-defined generations number or until an appropriate model has been determined. Figure 2 illustrates the flowchart of Gene expression programming [36].…”

Section: Gep Methodologymentioning

confidence: 99%

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Hasibi

Mahmoudian

Khayati

2020

IJE

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“…The predicted results are compared with the existing design practice codes ACI/AS, AISC, EC4, AIJ, CISC and DL/T. Mansouri et al (2017) forecasted ACC for two GEP models for CFST columns, and the database was collected so that any global or local buckling effect was not involved with the specimens. The predictions were compared with EN1994 with and without taking confinement of the concrete accounted for the columns.…”

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

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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“…The GEP method is a feature-driven technique by transforming a set of mathematical objects. In a program space, the GEP method could search for the most optimal function (Ebrahimzade et al, 2018; Mahdavi and Khayati, 2018; Mansouri and Farzampour, 2018; Mansouri et al, 2017, 2018a, 2018b). Compared to the empirical regression analysis, the GEP method is superior in finding the most optimal functional expression.…”

Section: Introductionmentioning

confidence: 99%

Peak strength prediction of reinforced concrete columns in different failure modes based on gene expression programming

Mansouri

İpek

Güneyisi

et al. 2021

Advances in Structural Engineering

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The peak strength of reinforced concrete (RC) columns plays an important role in the appraisal of inelastic seismic performance. It depends on various parameters related to the geometry, reinforcement detail, material property, confinement effect, and loading condition. In applications, it is usually a prior condition to classify the failure modes of RC columns for predicting the peak strength accurately. Yet, classifying the failure modes of RC columns in an accurate way is a difficult task due to the complexity of the shear transfer mechanism. Thus, there is a need to develop a peak strength prediction model for RC columns failing in different modes directly. In this study, an attempt has been made by implementing the gene expression programming (GEP) method to realize this purpose. The experimental data required for the implementation of the GEP method are based on extensive results of RC columns tested in quasi-static cyclic loading. To validate the efficiency of the developed model, a detailed comparison against existing equations is conducted. The comparative results indicate that the developed model produces a rational prediction for the peak strength of RC columns in various failure modes. Based on the developed model, the peak strength can be predicted in a unified way for both ductile and non-ductile RC columns, which is beneficial for the seismic evaluation of existing structures.

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Improved predictive model to the cross-sectional resistance of CFT

Cited by 13 publications

References 36 publications

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

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

Peak strength prediction of reinforced concrete columns in different failure modes based on gene expression programming

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