Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various machine learning techniques

•Twenty Machine Learning (ML) algorithms implemented in Python software to predict fracture load and fracture toughness in three modes.•For fracture load, the algorithms of XGBoost, BRegressor, GBM, ERTRegressor (mode I), XGBoost, GBM, ETRegressor, ERTRegressor (mode II), and BRegressor, GBM, ETRegr...

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Vydané v:Engineering fracture mechanics Ročník 276; s. 108914
Hlavní autori: Dehestani, A., Kazemi, F., Abdi, R., Nitka, M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2022
Predmet:
Data-driven techniques
Fracture load
Fracture toughness
Machine learning algorithm
Prediction model
Supervised learning
Data-driven techniques
Fracture load
Supervised learning
Machine learning algorithm
Prediction model
Fracture toughness
ISSN:0013-7944, 1873-7315
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Popis
Shrnutí:•Twenty Machine Learning (ML) algorithms implemented in Python software to predict fracture load and fracture toughness in three modes.•For fracture load, the algorithms of XGBoost, BRegressor, GBM, ERTRegressor (mode I), XGBoost, GBM, ETRegressor, ERTRegressor (mode II), and BRegressor, GBM, ETRegressor (mixed-mode) had the highest prediction accuracy.•For fracture toughness, the algorithms of BRegressor, ETRegressor, NuSVR, ANNs (mode I), ANNs (mode II), and XGBoost, RDF, BRegressor, ETRegressor, ERTRegressor, ANNs (mixed-mode) had the highest prediction accuracy.•Graphical User Interface (GUI) was developed for fracture prediction. Machine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian discs with central cracks” were used as a training and testing dataset. The main features of the experimental technique in each specimen are the fracture mode, the tensile strength of the specimen, the inclination of the crack with loading direction, the thickness of specimens and the half-length of the crack. The improved ML algorithms were implemented using Python programming language. The results of the coefficient of restitution (R2) and statistical metrics confirm that the ML algorithms are able to predict the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) with high accuracy. To validate the reliability of the proposed ML-based prediction models, three experimental tests were used. Moreover, the Graphical User Interface (GUI) of the ML-based models was created as a practical tool for estimating the fracture load and fracture toughness for engineering problems.
ISSN:0013-7944
1873-7315
DOI:10.1016/j.engfracmech.2022.108914