Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network
This paper profoundly studies the construction engineering safety risk assessment model based on attention mechanism and graph neural network, aiming at improving the accuracy and timeliness of construction site safety risk early warning. The comprehensive evaluation of multi-dimensional and multi-l...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925000894 |
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| _version_ | 1849716594937167872 |
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| author | Lanfei He Ran Chen Jia Hu Zhenxi Huang Li Zhou Hong Zhang |
| author_facet | Lanfei He Ran Chen Jia Hu Zhenxi Huang Li Zhou Hong Zhang |
| author_sort | Lanfei He |
| collection | DOAJ |
| description | This paper profoundly studies the construction engineering safety risk assessment model based on attention mechanism and graph neural network, aiming at improving the accuracy and timeliness of construction site safety risk early warning. The comprehensive evaluation of multi-dimensional and multi-level risks of construction projects is realized by constructing an evaluation model that combines attention mechanism and graph neural network. In terms of data analysis, this paper uses the historical data of several actual construction projects as training and test samples, covering many key risk areas such as construction period, quality, and capital. The experimental results show that the average accuracy rate of the model on the test set reaches 92.3 %, which is about ten percentage points higher than the traditional risk assessment method, showing excellent performance advantages. Through experiments, we prove that the average accuracy of the model in the test set is 92.3 %, which is about 10 percentage points higher than the traditional risk assessment method. When predicting high-risk areas, the accuracy rate is as high as 95.6 %, which can provide more accurate risk warning information for project managers, show excellent performance advantages, and help to more effectively prevent risks in advance. To sum up, the construction safety risk assessment model based on the attention mechanism and graph neural network proposed in this study not only enriches the theoretical system of construction safety risk assessment but also provides a scientific and efficient risk management tool for practical engineering projects, which has important theoretical significance and application value. |
| format | Article |
| id | doaj-art-06fb95cf32794b2cb06a4f21d2cf5ceb |
| institution | DOAJ |
| issn | 2772-9419 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-06fb95cf32794b2cb06a4f21d2cf5ceb2025-08-20T03:12:57ZengElsevierSystems and Soft Computing2772-94192025-12-01720027110.1016/j.sasc.2025.200271Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural networkLanfei He0Ran Chen1Jia Hu2Zhenxi Huang3Li Zhou4Hong Zhang5Economic and Technical Research Institute, State Grid of Hubei Electric Power Co., Ltd, Wuhan, Hubei, China; Corresponding author.Economic and Technical Research Institute, State Grid of Hubei Electric Power Co., Ltd, Wuhan, Hubei, ChinaEconomic and Technical Research Institute, State Grid of Hubei Electric Power Co., Ltd, Wuhan, Hubei, ChinaState Grid of Hubei Electric Power Co., Ltd, Wuhan, Hubei, ChinaState Grid of Hubei Electric Power Co., Ltd, Wuhan, Hubei, ChinaEconomic and Technical Research Institute, State Grid of Hubei Electric Power Co., Ltd, Wuhan, Hubei, ChinaThis paper profoundly studies the construction engineering safety risk assessment model based on attention mechanism and graph neural network, aiming at improving the accuracy and timeliness of construction site safety risk early warning. The comprehensive evaluation of multi-dimensional and multi-level risks of construction projects is realized by constructing an evaluation model that combines attention mechanism and graph neural network. In terms of data analysis, this paper uses the historical data of several actual construction projects as training and test samples, covering many key risk areas such as construction period, quality, and capital. The experimental results show that the average accuracy rate of the model on the test set reaches 92.3 %, which is about ten percentage points higher than the traditional risk assessment method, showing excellent performance advantages. Through experiments, we prove that the average accuracy of the model in the test set is 92.3 %, which is about 10 percentage points higher than the traditional risk assessment method. When predicting high-risk areas, the accuracy rate is as high as 95.6 %, which can provide more accurate risk warning information for project managers, show excellent performance advantages, and help to more effectively prevent risks in advance. To sum up, the construction safety risk assessment model based on the attention mechanism and graph neural network proposed in this study not only enriches the theoretical system of construction safety risk assessment but also provides a scientific and efficient risk management tool for practical engineering projects, which has important theoretical significance and application value.http://www.sciencedirect.com/science/article/pii/S2772941925000894Security risk assessmentGraph neural networkAttention mechanismConstruction engineering |
| spellingShingle | Lanfei He Ran Chen Jia Hu Zhenxi Huang Li Zhou Hong Zhang Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network Systems and Soft Computing Security risk assessment Graph neural network Attention mechanism Construction engineering |
| title | Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network |
| title_full | Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network |
| title_fullStr | Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network |
| title_full_unstemmed | Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network |
| title_short | Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network |
| title_sort | research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network |
| topic | Security risk assessment Graph neural network Attention mechanism Construction engineering |
| url | http://www.sciencedirect.com/science/article/pii/S2772941925000894 |
| work_keys_str_mv | AT lanfeihe researchonsafetyriskassessmentmodelofconstructionengineeringbasedonattentionmechanismandgraphneuralnetwork AT ranchen researchonsafetyriskassessmentmodelofconstructionengineeringbasedonattentionmechanismandgraphneuralnetwork AT jiahu researchonsafetyriskassessmentmodelofconstructionengineeringbasedonattentionmechanismandgraphneuralnetwork AT zhenxihuang researchonsafetyriskassessmentmodelofconstructionengineeringbasedonattentionmechanismandgraphneuralnetwork AT lizhou researchonsafetyriskassessmentmodelofconstructionengineeringbasedonattentionmechanismandgraphneuralnetwork AT hongzhang researchonsafetyriskassessmentmodelofconstructionengineeringbasedonattentionmechanismandgraphneuralnetwork |