Research Article Compressive Strength Prediction of Self-Compacting Concrete-A Bat Optimization Algorithm Based ANNs Amir Andalib , 1 Babak Aminnejad , 2 and Alireza Lork 3 1 Department of Civil Engineering, Kish International Branch Islamic Azad University, Kish Island, Iran 2 Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran 3 Department of Civil Engineering, Safadasht Branch, Islamic Azad University, Tehran, Iran Correspondence should be addressed to Babak Aminnejad; [email protected] Received 4 June 2022; Accepted 5 September 2022; Published 22 September 2022 Academic Editor: Pawel Klosowski Copyright © 2022 Amir Andalib et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is article examines the feasibility of using bat-trained artificial neural networks (ANNs) to predict the compressive strength of self-compacting concrete (SCC). e nonlinear behavior of SCC challenges traditional modeling techniques. erefore, this work takes advantage of the superior predictive performance of ANNs coupled with the bat algorithm. A database of 205 SCC samples collected from the literature is used to develop the ANN model. e correctness of the bat-based neural network model is then substantiated by contrasting its performance with that of the particle swarm optimization and teaching-learning-based opti- mization algorithms employed to train a neural network model. e statistical indices indicate the superior performance of the bat-based ANN model. In addition, a sensitivity analysis was carried out to determine the effects of various input parameters on the compressive strength of SCC. 1. Introduction Developed in Japan in 1988, self-compacting concrete (SCC) flows under its weight, solidifies inside the formwork, and is easier to produce. However, SCC can be costly, with an estimated cost of around two to three times the cost of conventional concrete. Researchers have utilized various admixtures such as fly ash, limestone filler, and ground clack brick to lower the cost of production [1]. Because of the intricate interaction of concrete mixture proportions with the mechanical and rheological properties of SCC [2], and to facilitate the time-consuming process of determining the optimum mix design, researchers have recommended sev- eral treatments in the literature; for instance, the rheological model [3], multivariate linear regression analysis [4], and numerical methods [5]. Numerous studies have been conducted on applying soft computing techniques, particularly artificial neural networks (ANNs), to examine self-compacting concrete properties. ANN models have been shown to be superior for determining the properties of fresh and hardened self- compacting concrete [1], compressive strength estimation of SCC [6], predicting the performance of self-compacting concrete mixtures [7], modeling the mechanical properties of fiber-reinforced SCC [8], approximating the strength of SCC containing polypropylene fiber and mineral additives [9], foreseeing the ingredients of SCC [10], assessment of compressive strength of self-compacting concrete with high volume fly ash [11], estimating the strength of SCC mixtures with mineral additives [12], forecasting the workability of SCC [13], approximating the strength of SCC containing bottom ash [14], estimating the initial and final setting times of SCC containing mineral admixtures [15], modeling the strength of admixture-based SCC [16], and estimating the properties of SCC containing fly ash [17]. Ghorpade and Koneru [18] used the pattern recognition neural network to predict the compressive strength grades of self-compacting concrete (SCC) effectively with experi- mental data obtained from the laboratory. Moreover, in this study, they created pattern recognition neural network Hindawi Advances in Materials Science and Engineering Volume 2022, Article ID 8404774, 12 pages https://doi.org/10.1155/2022/8404774
Compressive Strength Prediction of Self-Compacting Concrete-A Bat Optimization Algorithm Based ANNs
May 01, 2023
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