NLP-Based Approach for Identifying Quality Risk Factors in Steel Structure Construction

Steel structure buildings are widely favored for their environmental friendliness and exceptional performance. However, traditional methods of quality risk factor assessment are limited by subjectivity and inefficiency. To address this, our study introduces a natural language processing (NLP) model algorithm to identify a list of quality risk factors. Initially, quality acceptance and accident reports of 403 prefabricated steel structure buildings were processed and preprocessed. Using NLP algorithms, texts were successfully clustered into themes, yielding five thematic results, each containing ten effective keywords. Through in-depth analysis of these themes, labels for each theme were identified, and a list of quality risk factors was compiled. This research not only provides a new method of indexing quality risk for steel structures but also significantly enhances the sector’s digitization and intelligence. This advancement is crucial for the development of the steel structure building industry, aiding in more efficient and accurate identification and management of potential quality risks.