Research on similarity-based engineering prediction methods using intuitionistic fuzzy sets

In the rapid quotation process of construction projects, low prediction accuracy and high computational complexity remain challenging issues. To address these problems, this study proposes a novel method that integrates the theory of intuitionistic fuzzy sets for intelligent cost prediction. Given that engineering unit prices are significantly and dynamically uncertain due to multi-source heterogeneous factors (e.g., seasonal fluctuations, regional differences, and market volatility), we construct a transformation mechanism of interval-valued intuitionistic fuzzy sets. This mechanism expands the traditional deterministic engineering feature matrix into an information matrix that includes hesitation degrees and membership degrees, thereby achieving a refined representation of uncertain parameters. On this basis, we introduce a multi-dimensional similarity matching algorithm to establish an accurate mapping relationship between the project under construction and the historical case library. A weighted correction prediction model based on similar cases is proposed to enhance the robustness of unit cost prediction results. Verification through multiple engineering examples demonstrates that this method provides a new prediction paradigm for engineering cost estimation, combining both theoretical rigor and practical applicability. The research conclusions offer important references for the development of intelligent cost estimation systems and the optimization of engineering decision-making.