Abstract
In this study, soft computing techniques, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Random Forest (RF), and Random Tree (RT), were used to estimate the compressive strength (CS) of jute fiber reinforced concrete (JFRC). The study establishes the best-suited model to forecast the CS of JFRC. A total of 103 experimental observations were extracted from the literature. Models were formulated using input variables, i.e., aspect ratio, percentage of fiber, and the number of curing days to predict the CS of JFRC. Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe model efficiency coefficient (NSE), and Fractional Bias (FB) were used to evaluate the performance of formulated models. The results showed that, to forecast the CS of JFRC, the RF model outperforms when compared with ANFIS, ANN, and RT models with CC (0.987, 0.924), RMSE (1.324, 2.652), MAE (1.020, 2.196), NSE (0.924, 0.894) and FB (0.006, 0.003) for the training and testing stage.
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Some data or models used during the study are available from the corresponding author by request.
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The authors gratefully acknowledge the School of Core Engineering, Shoolini University Solan for providing the necessary facilities related to the study.
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VK: writing—original draft; VK, AP: methodology; VK: data curation; VK, BK: investigation; VK, PS: formal analysis; AP, PS, BK: resources; AP, PS: visualization; VK, AP: conceptualization; VK, AP, PS: writing—review and editing; AP: supervision; VK, PS: validation; VK, PS: software.
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Kashyap, V., Poddar, A., Sihag, P. et al. Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT models. Asian J Civ Eng 25, 2033–2044 (2024). https://doi.org/10.1007/s42107-023-00892-y
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DOI: https://doi.org/10.1007/s42107-023-00892-y