Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques

Abstract

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber–reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R2), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R2 = 0.96, as opposed to SVR bagging and support vector regression, which had R2 values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete

“…For the prediction of CS behavior of NC, Kabirvu et al 5 implemented SVR, and observed that SVR showed high accuracy (with R 2 = 0.97). Whereas, Koya et al 39 and Li et al 54 reported that SVR showed a high difference between experimental and anticipated values in predicting the CS of NC. Based on the results obtained from the implementation of SVR in predicting the CS of SFRC and outcomes from previous studies in using the SVR to predict the CS of NC and SFRC, it was concluded that in some research, SVR demonstrated acceptable performance.…”

“…Also, the characteristics of ISF (V ISF , L/D ISF ) have a minor effect on the CS of SFRC. Li et al 54 noted that the CS of SFRC increased with increasing amounts of C and silica fume, and decreased with increasing amounts of water and SP. Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable.…”

Effect of hybrid-fiber- reinforcement on the shear behavior of high-strength-concrete beams

“…The reinforced concrete (RC) evolution have been an ongoing process with so many advancements (Ahmad et al, 2022;Ahmed et al, 2022;Ali et al, 2022;Ashraf et al, 2022;Ghareeb et al, 2022;Huang et al, 2022;Khan et al, 2022;Li et al, 2022;Mohammed et al, 2022;Shen et al, 2022;Zou et al, 2023a;Zou et al, 2023b). High-strength concrete (HSC), especially that with fiber-RC (FRC) is a versatile form of a concrete mixture with superior performance compared to that of normal RC without fiber reinforcement (Deifalla, 2021a;Deifalla et al, 2021).…”

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

“…Several studies have attempted to predict concrete strength characteristics [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Machine learning methods are employed to forecast concrete strength [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] and the durability of concrete [ 50 , 51 , 52 ]. Bagging regression (BR) and gradient boosting (GB) models based on a variation of the bootstrap aggregation decision tree (DT) method have been shown in several studies to outperform other stand-alone ML models in terms of concrete strength prediction accuracy [ 53 , 54 , 55 , 56 ].…”