Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India

Research Article

Application of Data Mining Technology in Civil Engineering Systems

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342713,
        author={Yu  Xiu},
        title={Application of Data Mining Technology in Civil Engineering Systems},
        proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India},
        publisher={EAI},
        proceedings_a={ICSETPSD},
        year={2024},
        month={1},
        keywords={data mining civil engineering neural networks support vector machines engineering quality},
        doi={10.4108/eai.17-11-2023.2342713}
    }
    
  • Yu Xiu
    Year: 2024
    Application of Data Mining Technology in Civil Engineering Systems
    ICSETPSD
    EAI
    DOI: 10.4108/eai.17-11-2023.2342713
Yu Xiu1,*
  • 1: School of Environmental Arts&Architectural Engineering, Heilongjiang University Of Technology, Jixi, Heilongjiang, China
*Contact email: 13945823636@163.com

Abstract

The level of science and technology and management is gradually advancing, and the management of engineering projects is gradually shifting from traditional management mode to modern management mode. Data mining (DM) technology is increasingly widely used in engineering project construction, but currently, exploration in this area is still in the initial stage, lacking corresponding theoretical basis and practical experience to guide practice. This article first introduced the theoretical development status of DM, then analyzed the main problems that need to be solved in the decision-making process of civil engineering systems. Finally, with examples, it explained how to use neural network algorithms and support vector machine models in DM technology to analyze, organize, and extract engineering information, in order to optimize design schemes and improve engineering quality. A comparison was made between the traditional method and the DM technology based health status detection of bridge construction structures. The experimental data showed that the evaluation results based on DM technology were superior to traditional methods, and the accuracy of the detection results was improved by about 4.43%. This can effectively achieve the engineering cost management goals and provide a reliable basis for project investment decision-making.