Intelligent Matching of Construction Materials: A Multi-Agent Framework Based on Large Language Models and a Multi-Scale Strategy

Construction material descriptions in engineering projects are often heterogeneous due to inconsistent naming conventions, supplier-specific terminology, and incomplete specification information, making it difficult to automatically map non-standard descriptions to standardized material dictionaries. Existing rule-based approaches rely heavily on manually constructed rules and predefined dictionaries, limiting their adaptability, whereas direct large language model (LLM)-based methods often lack domain-specific constraints, resulting in less reliable material classification and attribute decomposition. To address these challenges, this paper proposes LLM-MSMA, an LLM-based multi-agent framework for construction material classification and attribute decomposition. The framework adopts a multi-scale task decomposition strategy that integrates hybrid retrieval, logic validation, and template-guided attribute decomposition. Hybrid retrieval generates candidate material categories, which are subsequently refined through LLM-based semantic reasoning. A Logic Validation Agent further verifies classification results using engineering rules, while an Attribute Decomposition Agent extracts structured material attributes according to classification-specific templates. The proposed framework is evaluated using a real-world bill-of-materials dataset containing 2912 material records collected from a large-scale engineering project as a case study. Experimental results show that, compared with the rule-based baseline, LLM-MSMA improves material classification accuracy by 43.39 percentage points and increases the attribute decomposition F1-score by 68.89 percentage points. Compared with the direct LLM baseline, the proposed framework further improves material classification accuracy by 8.24 percentage points and the attribute decomposition F1-score by 7.31 percentage points, while achieving an Exact Match Accuracy of 82.83%. These results indicate that combining hybrid retrieval with domain-specific structured knowledge and multi-agent LLM reasoning improves the reliability of construction material classification and attribute decomposition for heterogeneous material descriptions within the evaluated engineering context.