Application of ANN in predicting ACC of SCFST column

Tran, Viet‐Linh; Thai, Duc‐Kien; Kim, Seung-Eock

doi:10.1016/j.compstruct.2019.111332

Cited by 104 publications

(31 citation statements)

References 56 publications

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“…In Figure 9 than the mechanical properties of the constituent materials. Generally speaking, these observations are in close agreement with the experimental results in the literature [21,35,36]. e maps presented herein aim exclusively to demonstrate the advantage of the proposed machine learning approach.…”

Section: Pd Analysis and Surfacesupporting

confidence: 89%

“…If the length increases, the column becomes slender, and thus the ultimate load decreases. On the other hand, the ultimate load increases when increasing all other variables-especially the cross-sectional area (i.e., H, W, and t) [21,35,36].…”

Section: Pd Analysis and Surfacementioning

confidence: 99%

“…Although the design process for rectangular CFST columns is set forth in many current codes such as Eurocode 4 [18], AISC [19], and ACI [20], up until now, the axial behavior of rectangular CFSTmembers has received crucial attention from researchers/engineers. e main reason is that current codes do not necessarily have the capacity to take account of different material strengths or ranges of geometrical dimension [21][22][23][24]. As indicated in Xiong et al [22], Eurocode 4 is only applicable to CFST members with steel yield strength in the range of 235 MPa to 460 MPa, whereas concrete cylinder compressive strength varies from 20 MPa to 50 MPa.…”

Section: Introductionmentioning

confidence: 99%

“…e FE technique has also been used in many other works to numerically model the axial behavior of CFST columns [30][31][32][33]. ere are also several empirical formulae in the available literature such as Ding et al [1], Wang et al [23], Tran et al, [21] and Han et al [34] for predicting the ultimate load of rectangular CFST members. However, these equations have been derived on the basis of simple assumptions and observations.…”

Section: Introductionmentioning

confidence: 99%

“…Ren et al [35] employed support vector machine and particle swarm optimization to investigate the axial capacity of square CFST members. Tran et al [21] developed a neural network-based model to predict the load-bearing capacity of square CFST columns. erefore, more investigations are required to assess the potential applications of AI-based models for studying axial behavior of rectangular CFST columns, especially in the highly topical context of high-rise construction.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Phan

2020

Advances in Civil Engineering

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The ultimate compressive load of concrete-filled steel tubular (CFST) structural members is recognized as one of the most important engineering parameters for the design of such composite structures. Therefore, this paper deals with the prediction of ultimate load of rectangular CFST structural members using the adaptive neurofuzzy inference system (ANFIS) surrogate model. To this end, compression test data on CFST members were extracted from the available literature, including: (i) the mechanical properties of the constituent materials (i.e., steel’s yield strength and concrete’s compressive strength) and (ii) the geometric parameters (i.e., column length, width and height of cross section, and steel tube thickness). The ultimate load is the output response of the problem. The ANFIS model was trained using a hybrid of the least-squares and backpropagation gradient descent method. Quality assessment criteria such as coefficient of determination (R2), root mean square error (RMSE), and slope of linear regression were used for error measurements. A 11-fold cross-validation technique was employed to evaluate the performance of the model. Results showed that for the training process, the average performance was as follows: R2, RMSE, and slope were 0.9861, 89.83 kN, and 0.9861, respectively. For the validating process, the average performance was as follows: R2, RMSE, and slope were 0.9637, 140.242 kN, and 0.9806, respectively. Therefore, the ANFIS model may be considered valid because it performs well in predicting ultimate load using the validated data. Moreover, partial dependence (PD) analysis was employed to interpret the “black-box” ANFIS model. It is observed that PD enabled us to locally track the influence of each input variable on the output response. Besides reliable prediction of ultimate load, ANFIS can also provide maps of ultimate load. Finally, the ANFIS model developed in this study was compared with other works in the literature, showing that the ANFIS model could improve the accuracy of ultimate load prediction, in comparison to previously published results.

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Section: Pd Analysis and Surfacesupporting

confidence: 89%

Section: Pd Analysis and Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Phan

2020

Advances in Civil Engineering

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Predicting axial load capacity of CFST columns using machine learning

Faridmehr

Nehdi

2022

Structural Concrete

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Owing to their economic and structural advantages, concrete-filled steel tubular (CFST) columns have been implemented in diverse structural applications, especially in high-rise buildings, suspension bridges, and subway stations. However, there is no agreement between international standards regarding the ultimate compressive strength of CFST columns subjected to concentric axial force or combination of bending moment and axial force, especially for slender sections and high-strength materials. Considering such limitations, a comprehensive dataset on rectangular and circular (built up and hot rolled) CFST sections with different slenderness ratios was collected. The compiled experimental results were compared with corresponding values calculated by pertinent design code provisions, including AISC 360-16 and Eurocode 4. The accuracy of design codes in predicting the axial capacity of CFST sections under concentric and eccentric loading was assessed accordingly. The distribution of the experimental-to-predicted data confirmed that the prediction error was dependent on the slenderness ratio of columns. However, the effects of other parameters, including the mechanical properties of materials and eccentricity level on the model error, were negligible. In the second phase of the study, a surrogate Machine-Learning (ML) model was developed to estimate the axial capacity of circular and rectangular CFST columns under centric or eccentric loading condition. The accuracy of the proposed ML predictive model was appraised using several statistical metrics. The novel informational model attained superior accuracy and could be used to simplify generative design in future computational intelligence structural design platforms.

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Application of ANN in predicting ACC of SCFST column

Cited by 104 publications

References 56 publications

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Predicting axial load capacity of CFST columns using machine learning

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