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

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.