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Compressive strength prediction of SLWC using RBFNN and LSSVM approaches

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Abstract

One of the important types of lightweight concrete is expanded polystyrene (EPS) concrete which can be constructed by substituting part of coarse aggregates of the concrete with EPS beads. The characteristics of EPS concrete is greatly dependent on the material which is used in its manufacturing. Present study aims to develop two soft computing approaches according to radial basis function neural network (RBFNN) and coupled simulated annealing-least square support vector machine (CSA-LSSVM) models and a regression model for approximation the compressive strength of various types of concrete with main focus of EPS concrete. The regression model was considered as the benchmark model and the outcomes of RBFNN and CSA-LSSVM models were compared with it. The results revealed that the developed CSA-LSSVM and RBFNN approaches can be efficiently utilized to estimate the compressive strength of various kinds of concrete. The outcomes of the regression model were not as accurate as those of the machine learning approaches. In addition, the CSA-LSSVM model developed by the use of radial basis kernel function was decided as a better model. The R2, AARD%, RMSE, and MAPE for the benchmark model were 0.7555, 8.87%, 25.49, and 0.00887; however, these values for CSA-LSSVM were obtained as 0.9970, 3.01%, 1.08, and 0.0301 and for the RBFNN model were obtained to be 0.9953, 4.84%, 1.23, and 0.0484, respectively.

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Authors and Affiliations

  1. Department of Civil Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Hamid Reza Holakoei & Fathollah Sajedi

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  1. Hamid Reza Holakoei
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  2. Fathollah Sajedi
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Correspondence to Fathollah Sajedi.

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Holakoei, H.R., Sajedi, F. Compressive strength prediction of SLWC using RBFNN and LSSVM approaches. Neural Comput & Applic 35, 6685–6697 (2023). https://doi.org/10.1007/s00521-022-08026-2

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