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Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT models

Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT models

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References (28)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-023-00892-y
Publisher site
See Article on Publisher Site

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.

Journal

Asian Journal of Civil EngineeringSpringer Journals

Published: Feb 1, 2024

Keywords: Jute fiber; Compressive strength; Soft computing techniques; Aspect ratio; Performance evaluation

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