Dexin Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Dexin Yang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Guozong Zhang
School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
1 shared publication - Yuan Sun
Value Engineering Society of China, Beijing 100191, China
1 shared publication
Latest Publications
- Open Access
Construction Safety Risk Identification and Coupling Analysis Based on Data Mining
Frequent accidents in the construction sector arise from the dynamic coupling of multiple risk factors, while conventional single-factor approaches fail to capture the underlying complexity. Drawing on 702 accident investigation reports, this study develops an intelligent, data-driven framework that integrates large language model–based risk identification with association rule mining to systematically uncover risk factors and their coupling patterns. A DeepSeek-based model is employed to extract risk factors from unstructured text, followed by cosine similarity–based optimization to refine factor representations. The FP-Growth algorithm is then applied to identify strong association rules among risk factors. The results reveal that deficiencies in the management dimension account for 68.30% of all identified risks, with inadequate safety education and training emerging as the central hub in the risk coupling network, which is further corroborated by complex network analysis. Moreover, a cascading transmission pathway is identified, whereby environmental deficiencies induce weakened safety awareness, which in turn leads to unsafe behaviors. These findings further demonstrate the nonlinear amplification effects arising from concurrent management failures. By establishing a transformation pathway from unstructured textual data to structured risk knowledge, this study provides a robust, data-driven foundation for precise risk identification and systematic prevention in construction safety management.
Open AccessConstruction Safety Risk Identification and Coupling Analysis Based on Data Mining
Frequent accidents in the construction sector arise from the dynamic coupling of multiple risk factors, while conventional single-factor approaches fail to capture the underlying complexity. Drawing on 702 accident investigation reports, this study develops an intelligent, data-driven framework that integrates large language model–based risk identification with association rule mining to systematically uncover risk factors and their coupling patterns. A DeepSeek-based model is employed to extract risk factors from unstructured text, followed by cosine similarity–based optimization to refine factor representations. The FP-Growth algorithm is then applied to identify strong association rules among risk factors. The results reveal that deficiencies in the management dimension account for 68.30% of all identified risks, with inadequate safety education and training emerging as the central hub in the risk coupling network, which is further corroborated by complex network analysis. Moreover, a cascading transmission pathway is identified, whereby environmental deficiencies induce weakened safety awareness, which in turn leads to unsafe behaviors. These findings further demonstrate the nonlinear amplification effects arising from concurrent management failures. By establishing a transformation pathway from unstructured textual data to structured risk knowledge, this study provides a robust, data-driven foundation for precise risk identification and systematic prevention in construction safety management.