Buckling in prestressed stayed beam–columns and intelligent evaluation

Wu, Kaidong; Qiang, Xuhong; Xing, Zhe; Jiang, Xu

doi:10.1016/j.engstruct.2022.113902

Cited by 12 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shen et al [23] developed a health monitoring technology for prestressed strut columns, considering erosion impacts and demonstrating high accuracy. Wu et al [24] analyzed the buckling behavior of prestressed strut columns using machine learning and enabling an intelligent assessment of the structure's nonlinear behavior. Currently, evaluations predominantly concentrate on traditional prestressed columns, with the analysis and assessment of new prestressed column types warrant further exploration.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Elastoplastic Stable Bearing Capacity of Double-Steering Prestressed Plate Columns

Li,

Yin

et al. 2023

Buildings

View full text Add to dashboard Cite

This study investigates the elastoplastic stable bearing capacity of prestressed columns. The end restraint of the novel dual-steering plate brace prestressed column is enhanced, augmenting the efficiency of utilizing steel material. The elastoplastic stable bearing capacity of this new prestressed column is 5.41 times greater than that of a standard non-prestressed column. In contrast, the traditional prestressed columns with identical parameters show only a 2.49 to 3.55 times increase. In addition, this study conducts parameter optimization on the prestressed value, the position of the transverse brace, and the size of the transverse brace in the prestressed column. The buckling load escalates rapidly with an increase in the prestress value within a specific range and then diminishes gradually. The bearing capacity peaks when the transverse brace is positioned at the midpoint. As the size of the transverse brace expands, the load-bearing capacity initially rises linearly and subsequently stabilizes. The findings on the elastoplastic stable bearing capacity and parameter optimization are significantly relevant for practical engineering applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A Study on the Elastoplastic Stable Bearing Capacity of Double-Steering Prestressed Plate Columns

Li,

Yin

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

Mustafa

2024

AI Civ. Eng.

View full text Add to dashboard Cite

This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

show abstract

Stability of imperfect prestressed stayed beam-columns under combined axial load and bending

Xing

2021

Engineering Structures

View full text Add to dashboard Cite

Buckling in prestressed stayed beam–columns and intelligent evaluation

Cited by 12 publications

References 40 publications

A Study on the Elastoplastic Stable Bearing Capacity of Double-Steering Prestressed Plate Columns

A Study on the Elastoplastic Stable Bearing Capacity of Double-Steering Prestressed Plate Columns

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

Stability of imperfect prestressed stayed beam-columns under combined axial load and bending

Contact Info

Product

Resources

About