Enhancing accuracy in flexural strength prediction of glass fibre-reinforced concrete via TPE-XGB algorithm and explainable machine learning

This study aims to accurately predict the flexural strength (FS) of glass fiber reinforced concrete (GFRC) using advanced machine learning (ML) techniques. A novel algorithm, tree structured parzen estimator based extreme gradient boosting (TPE-XGB), is proposed by integrating Bayesian optimization...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Innovative infrastructure solutions : the official journal of the Soil-Structure Interaction Group in Egypt (SSIGE) Jg. 10; H. 9; S. 416
Hauptverfasser:	Khan, Muhammad Abdullah, Ullah, Anas Rahat, Mukhtiar, Danish, Siddique, Muhammad Shahid, Iqbal, Muhammad Hammad, Inqiad, Waleed Bin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.09.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Concrete technology Earth and Environmental Science Earth Sciences Empirical equations Environmental Science and Engineering Flexural strength Foundations Gene expression Geoengineering Geotechnical Engineering & Applied Earth Sciences Glass fiber reinforced concretes Glass fiber reinforced plastics Glass fibers Hydraulics Machine learning Mathematical models Reinforced concrete Technical Paper Water-cement ratio Glass fibre reinforced concrete Multi expression programming Explainable machine learning Flexural strength prediction SHAP analysis TPE-XGB Gene expression programming
ISSN:	2364-4176, 2364-4184
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!