Prediction of rheological parameters of 3D printed polypropylene fiber-reinforced concrete (3DP-PPRC) by machine learning

Uddin, Md Nasir; Mahamoudou, Faharidine; Deng, Bo-Yu; Musa, Moneef Mohamed Elobaid; Sob, Landry Wilfried Tim

doi:10.1016/j.matpr.2023.03.191

Cited by 6 publications

(1 citation statement)

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“…With the evolution of artificial intelligence (AI), machine learning (ML) algorithms are commonly used because ML can provide fast and economical solutions for civil engineering problems [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Moreover, in the field of fiber-reinforced composites, ML algorithms have been increasingly applied [ 9 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. In what follows, some of the research carried out with basalt fibers has been summarized.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

et al. 2023

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Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.

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