Strategic Learning and Knowledge Management of Technological Innovation in Safety Evaluation Planning of Construction Projects

Bai Xiaoping and Pu Tao

SAGE Open, 2021, vol. 11, issue 4, 21582440211061536

Abstract: The safety evaluation of construction projects is a very important problem. In the current study, the new smart methods especially for the complexity and variability of construction safety evaluation are absent. Combined with the engineering examples, this paper analyzes the sensitive factors that affect the construction project safety and makes discretizating of influencing factors based on expert and BIM technique. Based on BIM and Bayesian network, this paper established a new construction project safety evaluation model and presented the hybrid methods of strategic learning and knowledge management of technological innovation in construction project safety evaluation. Combined with the example of underground traffic engineering of Zhengzhou city comprehensive transportation hub, the Bayesian network was used to predict its security level. The probability of the safety level of the project in grade 3 is 0.614. Compared to other evaluation methods, presented hybrid method is more intelligent in strategic learning and knowledge management of technological innovation. Through the Bayesian network sensitivity analysis, we can know that the sensitivity factor of the construction project safety in the project is the construction risk, the continuous operation time, the protective measures, the material risk, and the number of maintenance. Presented detailed computational methods and steps in this paper can be used to improve the level of construction project safety effectively and help construction managers to take more effective control measures to prevent or reduce the occurrence of security incidents.

Keywords: evaluation planning; technological innovation; strategic learning; knowledge management; safety; construction (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:11:y:2021:i:4:p:21582440211061536

DOI: 10.1177/21582440211061536

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