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Article

Strength Prediction of Smart Cementitious Materials Using a Neural Network Optimized by Particle Swarm Algorithm

1
College of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
2
School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
3
School of Engineering, Ocean University of China, Qingdao 266100, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(7), 2033; https://doi.org/10.3390/buildings14072033
Submission received: 12 April 2024 / Revised: 22 June 2024 / Accepted: 28 June 2024 / Published: 3 July 2024

Abstract

Because of the improved physical, mechanical and crack–resistant properties, smart cementitious materials have garnered significant attention in civil engineering. However, the method of predicting performance of smart cementitious materials remains a formidable task. To address this issue, this study develops a neural network optimized by particle swarm algorithm, specifically designed for predicting the strength of smart cementitious materials. Particle swarm optimization is used to determine the initial weights and biases of the neural network in this algorithm. Two types of smart cementitious materials, namely 3D printed fiber reinforced concrete and graphene nanoparticles–reinforced cementitious composites, are studied as examples. Utilizing the PSO–BPNN method and data gathered from the existing articles, the predictive models for the mechanical properties of these materials are developed. Five commonly used statistical metrics are applied to evaluate the predictive performance. The results indicate suggest the PSO–BPNN outperforms the traditional back propagation neural network. Thus, a reliable and robust performance predictive model can be built for smart cementitious materials using the proposed approach.
Keywords: compressive strength prediction; neural network; particle swarm optimization; smart cementitious materials; statistical metrics compressive strength prediction; neural network; particle swarm optimization; smart cementitious materials; statistical metrics

Share and Cite

MDPI and ACS Style

Zhang, P.; Kong, F.; Hai, L. Strength Prediction of Smart Cementitious Materials Using a Neural Network Optimized by Particle Swarm Algorithm. Buildings 2024, 14, 2033. https://doi.org/10.3390/buildings14072033

AMA Style

Zhang P, Kong F, Hai L. Strength Prediction of Smart Cementitious Materials Using a Neural Network Optimized by Particle Swarm Algorithm. Buildings. 2024; 14(7):2033. https://doi.org/10.3390/buildings14072033

Chicago/Turabian Style

Zhang, Pengfei, Fan Kong, and Lu Hai. 2024. "Strength Prediction of Smart Cementitious Materials Using a Neural Network Optimized by Particle Swarm Algorithm" Buildings 14, no. 7: 2033. https://doi.org/10.3390/buildings14072033

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