Conflict and Overlap Classification in Construction Standards Using a Large Language Model (2025)

Abstract: Construction standards across different countries provide technical guidelines to ensure the quality and safety of buildings and facilities, with periodic revisions to accommodate advances in construction technology. However, these standards often contain overlapping or conflicting content owing to their broad scope and interdependence, complicating the revision process and creating public inconvenience. Although current expert-driven manual approaches aim to mitigate these issues, they are time-consuming, costly, and error-prone. To address these challenges, we propose conflict and overlap classification in construction standards using a large language model (COSLLM), a framework that leverages a construction domain-adapted large language model for the semantic comparison of sentences in construction standards. COSLLM utilizes a two-step reasoning process that adaptively employs chain-of-thought reasoning for the in-depth analysis of sentences suspected of overlaps or conflicts, ensuring computational and temporal efficiency while maintaining high classification accuracy. The framework achieved an accuracy of 97.9% and a macro F1-score of 0.907 in classifying real-world sentence pairs derived from Korean construction standards as overlapping, conflicting, or neutral. Furthermore, we develop and deploy a real-time web-based system powered by COSLLM to facilitate the efficient establishment and revision of construction standards.

## introduction national construction standards provide technical guidelines for engineers, contractors, and other construction professionals to ensure the quality and safety of buildings and facilities @xcite . while the establishment and management of these standards vary by country, figure 1 : an overview of the manual and llm-based approaches for analyzing overlapping and conflicting content in construction standards is provided. using the proposed cosllm, managers can review potential overlaps and conflicts identified by the llm, along with detailed rationales, which significantly reduces manual effort. in the process of establishing or revising standards, members of the construction standards revision committee focus on preventing overlaps and conflicts between new and existing standards @xcite . when overlapping content exists between construction standards, the revision of one stan-dard may lead to conflicts in interpretation, causing confusion among construction professionals. such conflicts can disrupt the assurance of quality and safety during the construction of buildings and facilities. to address these challenges, some countries have adopted methods such as explicitly referencing existing standards when citing content already covered by regulations, while also conducting routine reviews to resolve overlaps and conflicts @xcite (figure 1 ). however, this expert-driven approach is both time-consuming and costly. furthermore, excessive reliance on expert interpretations may result in inconsistent judgments among experts @xcite . in south korea, where revisions occur more frequently than in other countries, effectively resolving issues of overlap and conflict in construction standards is critical @xcite . recently, deep learning-based methods have been employed to classify overlaps and conflicts across various domains @xcite . previous study @xcite has defined sentence relationship analysis as being closely aligned with natural language inference (nli) tasks @xcite , providing a foundation for analyzing sentences in construction standards. recent studies @xcite @xcite have demonstrated that large language models (llms), equipped with human-level reasoning capabilities, excel at nli tasks. in addition, the use of chainof-thought reasoning (cot) @xcite in llms enables reliable explanations of the reasoning process @xcite , with the potential to assist construction standard managers in analyzing overlapping or conflicting sentences more effectively. accordingly, we reframe the classification of overlaps and conflicts in construction standards as a 3-class nli problem (including neutrality) that can be solved effectively using llms. in this paper, we propose a novel framework for automatically classifying overlaps and conflicts in construction standards, referred to as conflict and overlap classification in construction standards using a large language model (cosllm). cosllm, built on the latest opensource llm, is enhanced through two additional training stages. in the first stage, we adapt the llm to the construction domain using a corpus comprising construction standards, research publications, and news articles. in the second stage, we fine-tune the model to classify sentences into overlap, conflict, or neutral categories using expertannotated, high-quality sentence pairs from construction standards. we incorporate cot to handle subtle semantic differences, applying it selectively through task prefixes @xcite . this strategy optimizes computational efficiency while maintaining high accuracy. experiments on real-world construction standard data demonstrated the efficacy of cosllm. in addition, to support the establishment and revision of construction standards using cosllm, we develop a real-time construction standards analysis system, which has been deployed. our main contributions are as follows: large language model llms, built on the transformer @xcite decoder-only architecture and trained with billions of parameters, excel at capturing linguistic patterns and demonstrate advanced reasoning and generation capabilities across diverse tasks @xcite . models such as @xcite exhibit capabilities such as long-context understanding @xcite , showcasing abilities in in-context reasoning with few-shot @xcite and zero-shot @xcite learning. however, llms trained on general-purpose datasets often lack the domainspecific vocabulary and contextual understanding necessary for specialized applications @xcite . previous studies @xcite @xcite have demonstrated that achieving high performance with llms in specialized domains requires training on tailored corpora. consequently, fields such as law @xcite and medicine @xcite have successfully adapted llms to fulfill their unique requirements. to further enhance llm capabilities for complex tasks, techniques such as cot @xcite and plan-and-solve prompting @xcite have been developed. building on these findings, our research aims to optimize llms for resolving overlaps and conflicts in construction standards. ## method our framework, cosllm, leverages llm to classify semantic relationships between construction standard sentences. section 3.1, describes how we adapt open-source llm for the construction domain. section 3.2 outlines the method for finetuning the llm to classify sentence pairs. finally, section 3.3 introduces a real-time web-based system powered by the cosllm to assist in establishing and revising of construction standards. ## analysis effectiveness of da and it efficacy of selective cot and itrm ## conclusion in this study, we propose cosllm, which addresses the challenges of overlapping and conflicting content in construction standards by leveraging a domain-adapted llm with cot. the cosllm achieves high accuracy and efficiency, consistently outperforming baselines. the cosllm-powered construction standards analysis framework facil-itates the effective establishment and revision of construction standards.