Abstract
Since the reform and opening up, both domestic economy and people’s living level acquire great improvement. People also pay more strong attention to risk issues of on-going engineering projects’ construction. At present, science and technology have stepped onto high-speed development stage and the world is rapidly changing. Uncertainty of social external condition may lead enterprises to encounter more and larger risks during the construction process of engineering projects. Traditional engineering construction risk recognition model can’t already fully and effectively identify risks. Therefore a new type of intelligent risk recognition mode is in urgently needed in construction of engineering projects. This paper proposes a kind of risk recognition model based on neural network so as to intelligently recognize different forms of potential risks, thus decreasing the damage from risks to a minimum. Relevant model below is established according to characteristics of engineering projects on purpose of doing quantitative analysis of risk rating in the engineering construction industry at present. It utilizes Genetic Algorithm to correct network, whose process increases accuracy and stability of all networks. Based on neural network, this paper establishes the model which could do risk rating during the project operation process in the engineering construction industry. It also does classification and research according to characteristics of potential risks in engineering projects, thus helping a project leader to more accurately predict, prevent and control risks, which guarantees safe and smooth operation of engineering projects.
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Luo, J., Pan, R. (2018). Research and Implementation of Intelligent Risk Recognition Model Based on Engineering Construction of Neural Network. In: Mizera-Pietraszko, J., Pichappan, P. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2016. Advances in Intelligent Systems and Computing, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-60744-3_34
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DOI: https://doi.org/10.1007/978-3-319-60744-3_34
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