Wu, W; Wen, C; Yuan, Q; Chen, Q and Cao, Y (2025) Construction and application of knowledge graph for construction accidents based on deep learning. Engineering, Construction and Architectural Management, 32(2), pp. 1097-1121. ISSN 0969-9988
Abstract
Purpose: Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services. Design/methodology/approach: The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management. Findings: The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization. Originality/value: The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction safety; knowledge management; management |
| Index terms: | dimension, construction industry, adjudication, dispute, data management, deep learning, database, methodology, decision-making, evolution, construction accident, practitioner, construction safety, safety management, visualization, knowledge management, accident prevention, manager, unstructured data, ontology, experiment, construction method |
| Subjects: | data collection methods, dispute resolution, artificial intelligence, data science, health monitoring assessment and metrics, design practice, education and knowledge transfer, professional development, occupational health and safety management, data management, decision analysis, industry analysis, environmental health, practitioner, environmental science, research methods, building construction |
| Topics: | Legal Issues, Health and Safety, Risk Management, Sustainability, Digital Applications, Design Practice, Site Management, Information Management, Research Practice, Roles and Professions |
| Descriptive scope: | 4 PCTE |
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