Spilt Tensile Strength of Fiber-Reinforced Recycled Aggregate Concrete Simulation Employing Tunned Random Forests Trees
A significant quantity of waste concrete is produced each year due to the demand for concrete manufacturing, which drives the yearly need for raw materials. Recycled aggregate concrete has become a viable remedy as a result. It is vulnerable to breaking and has less strength since the hardened mort...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronic Journals for Science and Engineering - International
2025-06-01
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| Series: | Electronic Journal of Structural Engineering |
| Subjects: | |
| Online Access: | https://ejsei.com/EJSE/article/view/756 |
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| Summary: | A significant quantity of waste concrete is produced each year due to the demand for concrete manufacturing, which drives the yearly need for raw materials. Recycled aggregate concrete has become a viable remedy as a result. It is vulnerable to breaking and has less strength since the hardened mortar is affixed to natural aggregates, which presents a problem. The goal of this research is to employ random forests (RF) frameworks to project the split tensile strength (STS) of fiber-reinforced recycled aggregate concrete (RAC). The RF framework uses the Chimp optimization algorithm (CHOA) and artificial hummingbird optimization (ARHA) to tweak hyperparameters and select the best-performing combination. A data set including 257 data points and 10 input variables was taken from peer-reviewed published research and arbitrarily split into three phases: testing, validating, and training. The RF-AR approach exhibited high reliability, with R2 of 0.9942, 0.9824, and 0.9913 throughout the learning, validating, and assessment stages. RF-AR had higher results than RF-CH, with R2 of 0.9796, 0.9566, and 0.9694, respectively. Considering the values of the Theil inequality coefficient (TIC), RF-AR depicted the lowest values at 0.0128, 0.0213, and 0.0171 concerning 0.0241, 0.0333, and 0.0318 related to RF-CH for the train, validation as well as test phases, in that order. The RF-AR strategy performed better, even if the RF-CH method was dependable in forecasting the STS of fiber-reinforced RAC, according to the previously stated reasoning and the data.
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| ISSN: | 1443-9255 |