Predicting the patch load resistance of stiffened plate girders using machine learning algorithms

In the incremental launching method employed for steel bridge construction, the girder is subjected to patch loading which occurs at the piers’ position. This loading significantly affects the girder resistance in the construction stage. Therefore, prediction of the girder resistance under this load...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ocean engineering Jg. 240; S. 109886
Hauptverfasser: Truong, Viet-Hung, Papazafeiropoulos, George, Vu, Quang-Viet, Pham, Van-Trung, Kong, Zhengyi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.11.2021
Schlagworte:
Extreme gradient boosting algorithm
Incremental launching
Machine learning
Patch loading
Steel plate girders
Patch loading
Incremental launching
Steel plate girders
Extreme gradient boosting algorithm
Machine learning
ISSN:0029-8018, 1873-5258
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the incremental launching method employed for steel bridge construction, the girder is subjected to patch loading which occurs at the piers’ position. This loading significantly affects the girder resistance in the construction stage. Therefore, prediction of the girder resistance under this loading is important. This paper proposes a new approach for predicting the patch load resistance of stiffened plate girders using an extreme gradient boosting algorithm (XGBoost). A total of 170 experimental data on stiffened plate girders under patch loading collected from the literature serves as the training and testing data to build the predictive model. To demonstrate the efficiency of the proposed model, its predictions were compared with those obtained from other Machine Learning (ML) methods such as support vector machines (SVM), decision tree (DT), random forest (RF), adaptive boost (Adaboost), and deep learning (DL). The accuracy of the proposed model was validated against the existing equations taken from the design standards (EN-1993-1-5 and BS 5400) as well as existing formulae in the literature. The comparative results reveal that the proposed model provides better and more accurate predictions than the existing formulae. •A new approach for predicting the patch load resistance of stiffened plate girders using an XGBoost algorithm is proposed.•The efficiency of the proposed method is demonstrated by comparing its performance with that obtained from other ML methods.•The accuracy of the developed method is verified with the design equations from modern codes as well as existing formulae.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2021.109886