Application of Artificial Intelligence Technology in Risk Assessment and Management of Pile Foundation Engineering of Offshore Wind Power Projects

被引:0
|
作者
Huang, Peipei [1 ]
Ou, Xiaolin [1 ]
机构
[1] Guangzhou Inst Sci & Technol, Sch Architectural Engn, Guangzhou 510000, Guangdong, Peoples R China
关键词
Artificial intelligence technology; offshore wind power projects; fuzzy analysis; risk assessment system;
D O I
10.1145/3675249.3675286
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In the context of the continuous progress of today's society, the level of science and technology is also developing rapidly. In particular, the extensive application of information technology such as artificial intelligence, big data and cloud computing has penetrated into all walks of life. In the process of intelligent building construction, computer network system plays a crucial role. Artificial intelligence can analyze the risk trends and patterns of wind power project pile foundation engineering by processing a large amount of historical risk data, and predict possible future risks. This helps the project team to develop corresponding risk management strategies and preventive measures. The risk assessment and management of pile foundation project of wind power project is of great significance to enhance the competitiveness of construction enterprises. Based on the analysis and research of relevant literatures at home and abroad and combined with artificial intelligence algorithm, this paper designs a risk assessment system for offshore wind power projects, so as to effectively reduce the risk incidence of pile foundation projects of wind power projects and improve the economic benefits of enterprises. Then, the accuracy performance of the system is tested. The test results show that as the fuzzy output value continues to rise from 0.2 to 1, the accuracy score also shows an increasing trend, indicating that the accuracy of the risk prediction system has improved. Based on this data, the performance of the risk prediction system can be evaluated and the prediction algorithm or model can be further improved, thus increasing the level of accuracy.
引用
收藏
页码:200 / 205
页数:6
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