Liu, J; Jiang, S; Sun, H; Liu, W and Xiao, B (2026) Quality inspection information extraction: Intelligent parsing of construction quality inspection specifications based on natural language processing. Journal of Construction Engineering and Management, 152(4): 04026027, ISSN 0733-9364
Abstract
Construction quality inspections play a vital role in ensuring project safety, regulatory compliance, and long-term sustainability. The diverse and intricate nature of construction quality inspection specifications poses substantial challenges for manual parsing. This paper presents an innovative parsing method grounded in natural language processing (NLP), aimed at efficiently extracting inspection information from a wide range of quality inspection specifications through a generalized and adaptable approach. A structured extraction method utilizing regular expressions is introduced, accompanied by a comprehensive labeling system developed by this paper for named entity recognition (NER). Additionally, a corresponding data set is constructed to address existing data deficiencies in this domain. By leveraging the robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa) with whole word masking (WWM), combined with a bidirectional long short-term memory (BiLSTM) and a conditional random field (CRF) module, information extraction performance is significantly enhanced. Through prompt engineering, cypher statements are generated based on named entity recognition (NER) results with the assistance of a large language model (LLM), enabling the construction of a knowledge graph for inspection information. This method effectively parses quality inspection specifications and automates the extraction of inspection data, thereby reducing reliance on manual operations. It lays a solid foundation for the future automation of quality inspection tasks and contributes to the advancement of intelligent management practices.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction quality inspection; named entity recognition; natural language processing; roberta-wwm |
| Index terms: | automation, quality inspection, specification, inspection, construction quality, module, regulatory compliance, management practice, large language model |
| Subjects: | contractual condition, automation and robotics, data science, management, environmental policy, architectural elements, quality assurance |
| Topics: | Contract Administration, Business Strategy, Engineering Principles, Design Practice, Quality Management, Sustainability, Digital Applications |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here