2019
DOI: 10.3390/app9142802
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Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

Abstract: It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the squa… Show more

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Cited by 57 publications
(35 citation statements)
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“…Information of the database is summarized in Table 1, including the number of data and the percentage of proportion, whereas Table 2 presents the initial statistical analysis of the corresponding database. The experimental tests were carried out considering the following steps: design, processing of steel tube, production of concrete, curing of specimens, and loading measurement [15,86]. As proposed by Sarir et al [96] and Ren et al [15] in investigating CFST columns, initial geometric imperfections as well as residual stress exhibited a negligible effect on the behavior of columns under axial loading.…”
Section: Data Used and Selection Of Variablesmentioning
confidence: 99%
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“…Information of the database is summarized in Table 1, including the number of data and the percentage of proportion, whereas Table 2 presents the initial statistical analysis of the corresponding database. The experimental tests were carried out considering the following steps: design, processing of steel tube, production of concrete, curing of specimens, and loading measurement [15,86]. As proposed by Sarir et al [96] and Ren et al [15] in investigating CFST columns, initial geometric imperfections as well as residual stress exhibited a negligible effect on the behavior of columns under axial loading.…”
Section: Data Used and Selection Of Variablesmentioning
confidence: 99%
“…The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R 2 , respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.Materials 2020, 13, 1205 2 of 25 larger energy absorption capacity [7], convenient construction [11], economy of materials [12][13][14], and excellent seismic and refractory performance [15]. In particular, this type of structure can reduce the environmental burden by removing formwork [16], reusing steel pipes, and using high quality concrete with recycled aggregate [17].…”
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confidence: 99%
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