Enhanced TFNN-GRA Technique for Multiple-Attribute Decision-Making with Triangular Fuzzy Neutrosophic Information and Applications to Performance Evaluation of Internally Cured High-Strength Concrete

Abstract

Internal curing technology involves the integration of internal curing materials into 
concrete, typically under pre-water storage conditions. This method works by gradually releasing 
water to increase the internal humidity of the concrete, which enhances the hydration of 
cementitious materials, reduces self-shrinkage in high-strength concrete (HSC), and improves its 
overall performance. Commonly used internal curing materials include absorbent resin and 
lightweight aggregates. However, using these materials in excessive amounts can compromise the 
strength or durability of the concrete, making it essential to determine the optimal dosage for 
effective application. When applied to HSC, internal curing helps mitigate shrinkage and enhances 
frost resistance. As noted, accurately determining the appropriate dosage of internal curing 
materials is critical to maximizing these benefits. The performance evaluation of internally cured 
HSC is a multiple-attribute decision-making (MADM) problem, and to address the uncertainties 
involved in this process, triangular fuzzy neutrosophic sets (TFNSs) are particularly suitable. TFNSs 
offer a more precise way to represent uncertain information. Grey relational analysis (GRA), a 
widely used technique in grey system theory, assesses the similarity between sequence curves based 
on their geometric shapes. In this paper, we introduce a triangular fuzzy neutrosophic number GRA 
(TFNN-GRA) technique under TFNSs, where no prior weight information is available. Weight 
values under TFNSs are determined using information entropy. By combining GRA with TFNSs, 
we propose the TFNN-GRA method and outline the steps for solving MADM problems. Finally, a 
numerical example is presented to demonstrate the performance evaluation of internally cured HSC, 
along with comparative studies that highlight the advantages of the TFNN-GRA technique

DOI: 10.5281/zenodo.13936966