A Fuzzy Adaptive Resonance Theory‐Based Model for Mix Proportion Estimation of High‐Performance Concrete

• Published in: Computer-aided civil and infrastructure engineering 2017-09, Vol.32 (9), p.772-786
• Main Authors: Chiew, Fei Ha, Ng, Chee Khoon, Chai, Kok Chin, Tay, Kai Meng
• Format: Article
• Language: English
• Subjects: Clusters; Compressive strength; Concrete; Estimation; Neural networks
• ISSN: 1093-9687; 1467-8667
• DOI: 10.1111/mice.12288

A new approach that adopts the use of fuzzy adaptive resonance theory (ART) neural network in estimating high‐performance concrete (HPC) mix proportion from experimental data is devised. The proposed model receives a set of desired concrete performances, searches for a set of mix proportions that is near to the desired concrete performances, classifies the mix proportions into clusters, measures the similarity between performances of deduced clusters with desired performances, and deduces a mix proportion. The proposed model was used to estimate the mix proportions of five batches of concrete based on the performance criteria of 7th and 28th day compressive strengths. The generated mix proportions were used in an experimental work and the errors were within 13% for 7th compressive strength; and 7% for the 28th day compressive strength, signifying the reliability of the fuzzy ART‐based model in estimating the mix proportion of HPC. This article contributes to an alternative method of mix proportion estimation of HPC by avoiding the use of complicated function approximation techniques.