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- W4214896667 abstract "Machine learning methods are found better to manual and statistical methods in estimating compressive strength of concrete structures. However, there is need of exploring an effective and accurate predictor for this domain. This article proposes an artificial electric field algorithm-based neuro-fuzzy network (AEFA+NFN) for prediction of compressive strength of concrete structures. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using Gaussian triangular membership function with a degree of membership to different classes. The model parameters are decided by AEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the AEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes are in favour of AEFA+NFN, hence it can be suggested as a promising forecasting tool." @default.
- W4214896667 created "2022-03-05" @default.
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- W4214896667 date "2021-12-01" @default.
- W4214896667 modified "2023-09-23" @default.
- W4214896667 title "A machine learning approach for estimating compressive strength of concrete structures using an artificial electric field algorithm-based neuro-fuzzy predictor" @default.
- W4214896667 doi "https://doi.org/10.1109/ocit53463.2021.00054" @default.
- W4214896667 hasPublicationYear "2021" @default.
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