Model and Simulation of Engineering Safety Risk Control Based on Artificial Intelligence Algorithm

Abstract

The implementation of engineering projects has a profound impact on the national economy and people’s livelihood. The current engineering projects are in full swing with the vigorous development of the economy. However, due to the high complexity of the engineering project environment, a large number of participants, the high technical standards, and the high labor intensity, it is easy to induce engineering safety risk accidents. Therefore, the demand for research on safety risks in engineering construction is becoming more and more urgent. However, the traditional construction safety risk management lacks systematicness and standardization, and it has limitations in engineering safety risk early warning and control. In order to solve this problem, this paper applies artificial intelligence algorithms to engineering construction safety risk management and establishes an engineering safety risk early warning control model. The Bayesian formula, information entropy theory, and other algorithms provide the theoretical basis and feasibility analysis for the model. Four engineering safety risk factors, including human factors, physical factors, management factors, and environmental factors, are analyzed through simulation experiments. The results show that the probability of injury to construction personnel has been reduced by 51.3%, the qualified rate of production materials in construction projects has increased by 6.5%, the risk factors of management have been reduced by 7%, and the environmental risk factors have been reduced by 7.7%; the final risk early warning control effect has increased by 7.45%.