Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Buckling in prestressed stayed beam–columns and intelligent evaluation
https://orcid.org/0000-0003-3225-7663
Abstract For prestressed stayed beam–columns, the buckling behaviour is studied analytically, and the intelligent models for evaluating the nonlinear behaviour are developed using machine learning. An implicit solution, which allows the direct evaluation for the buckling behaviour of both doubly-symmetric and mono-symmetric stayed members, is determined analytically for the first time, and an explicit simplified solution of the buckling load is obtained using regression. Both the implicit and explicit solutions can simplify the traditional numerical method for evaluating the buckling load of stayed members, and they are verified using numerical modelling and excellent comparisons are obtained. Based on the explicit solution of the buckling load, machine learning models are adopted to develop the intelligent methods for evaluating the nonlinear buckling behaviour of prestressed stayed beam–columns, which can overcome the difficulties in distinguishing between pure and interactive buckling modes in the traditional method for load-carrying capacity evaluation. The results in the test set show that Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) can predict the nonlinear failure mode and ultimate load of prestressed stayed beam–columns accurately and reliably.
Highlights Buckling behaviour in mono-symmetric stayed members studied analytically. Explicit solution of buckling load obtained to simplify traditional numerical evaluation method. Machine learning applied for nonlinear behaviour evaluation consider interactive buckling. Pure and interactive buckling modes distinguished using XGBoost model. Ultimate load for different modes evaluated accurately and reliably using ANN model.
Buckling in prestressed stayed beam–columns and intelligent evaluation
https://orcid.org/0000-0003-3225-7663
Abstract For prestressed stayed beam–columns, the buckling behaviour is studied analytically, and the intelligent models for evaluating the nonlinear behaviour are developed using machine learning. An implicit solution, which allows the direct evaluation for the buckling behaviour of both doubly-symmetric and mono-symmetric stayed members, is determined analytically for the first time, and an explicit simplified solution of the buckling load is obtained using regression. Both the implicit and explicit solutions can simplify the traditional numerical method for evaluating the buckling load of stayed members, and they are verified using numerical modelling and excellent comparisons are obtained. Based on the explicit solution of the buckling load, machine learning models are adopted to develop the intelligent methods for evaluating the nonlinear buckling behaviour of prestressed stayed beam–columns, which can overcome the difficulties in distinguishing between pure and interactive buckling modes in the traditional method for load-carrying capacity evaluation. The results in the test set show that Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) can predict the nonlinear failure mode and ultimate load of prestressed stayed beam–columns accurately and reliably.
Highlights Buckling behaviour in mono-symmetric stayed members studied analytically. Explicit solution of buckling load obtained to simplify traditional numerical evaluation method. Machine learning applied for nonlinear behaviour evaluation consider interactive buckling. Pure and interactive buckling modes distinguished using XGBoost model. Ultimate load for different modes evaluated accurately and reliably using ANN model.
Buckling in prestressed stayed beam–columns and intelligent evaluation
https://orcid.org/0000-0003-3225-7663
Abstract For prestressed stayed beam–columns, the buckling behaviour is studied analytically, and the intelligent models for evaluating the nonlinear behaviour are developed using machine learning. An implicit solution, which allows the direct evaluation for the buckling behaviour of both doubly-symmetric and mono-symmetric stayed members, is determined analytically for the first time, and an explicit simplified solution of the buckling load is obtained using regression. Both the implicit and explicit solutions can simplify the traditional numerical method for evaluating the buckling load of stayed members, and they are verified using numerical modelling and excellent comparisons are obtained. Based on the explicit solution of the buckling load, machine learning models are adopted to develop the intelligent methods for evaluating the nonlinear buckling behaviour of prestressed stayed beam–columns, which can overcome the difficulties in distinguishing between pure and interactive buckling modes in the traditional method for load-carrying capacity evaluation. The results in the test set show that Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) can predict the nonlinear failure mode and ultimate load of prestressed stayed beam–columns accurately and reliably.
Highlights Buckling behaviour in mono-symmetric stayed members studied analytically. Explicit solution of buckling load obtained to simplify traditional numerical evaluation method. Machine learning applied for nonlinear behaviour evaluation consider interactive buckling. Pure and interactive buckling modes distinguished using XGBoost model. Ultimate load for different modes evaluated accurately and reliably using ANN model.
Buckling in prestressed stayed beam–columns and intelligent evaluation
Wu, Kaidong (Autor:in) / Qiang, Xuhong (Autor:in) / Xing, Zhe (Autor:in) / Jiang, Xu (Autor:in)
Engineering Structures ; 255
13.01.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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