The analyses of this investigation were focused on conversion factors for compressive strengths of different samples. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. Comparing implemented ML algorithms in terms of Tstat, it is observed that XGB shows the best performance, followed by ANN and SVR in predicting the CS of SFRC. Southern California Eng. PubMed Central Table 3 provides the detailed information on the tuned hyperparameters of each model. The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. [1] Date:3/3/2023, Publication:Materials Journal Hameed et al.52 developed an MLR model to predict the CS of high-performance concrete (HPC) and noted that MLR had a poor correlation between the actual and predicted CS of HPC (R=0.789, RMSE=8.288). Khademi et al.51 used MLR to predict the CS of NC and found that it cannot be considered an accurate model (with R2=0.518). Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). For this purpose, 176 experimental data containing 11 features of SFRC are gathered from different journal papers. East. Mater. Review of Materials used in Construction & Maintenance Projects. Evidently, SFRC comprises a bigger number of components than NC including LISF, L/DISF, fiber type, diameter of ISF (DISF) and the tensile strength of ISFs. Caggiano, A., Folino, P., Lima, C., Martinelli, E. & Pepe, M. On the mechanical response of hybrid fiber reinforced concrete with recycled and industrial steel fibers. Mahesh et al.19 noted that after tuning the model (number of hidden layers=20, activation function=Tansin Purelin), ANN showed superior performance in predicting the CS of SFRC (R2=0.95). 232, 117266 (2020). This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. How do you convert flexural strength into compressive strength? Tree-based models performed worse than SVR in predicting the CS of SFRC. J. Adhes. These equations are shown below. Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). PDF The Strength of Chapter Concrete - ICC Constr. Skaryski, & Suchorzewski, J. Concr. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. The results of the experiment reveal that the EVA-modified mortar had a high rate of strength development early on, making the material advantageous for use in 3DAC. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. The reviewed contents include compressive strength, elastic modulus . 175, 562569 (2018). The focus of this paper is to present the data analysis used to correlate the point load test index (Is50) with the uniaxial compressive strength (UCS), and to propose appropriate Is50 to UCS conversion factors for different coal measure rocks. 38800 Country Club Dr. PDF Infrastructure Research Institute | Infrastructure Research Institute
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