China’s terrain is complex, both plain, microhill (heavy-hill) and mountainous terrain; the hidden dangers of highway construction are prominent. Construction site management, production safety management, and construction personnel management are difficult, and it is necessary to borrow advanced technology to establish information, and it is necessary to borrow advanced technology to establish information system to realize the visualization of safety monitoring. In the construction of highways, mountainous terrain is often complicated due to complex terrain, high mountains, and deep valleys. Excavation of the mountain mass is required to form high and steep slopes. For successful projects, safety monitoring is particularly important. Multisource data fusion is one of the computer application technologies. It is an information processing technology that is automatically analyzed and synthesized under certain criteria to complete the required decision-making and evaluation tasks. This paper analyzes high-speed data in the context of multisource data fusion. Study on highway slope construction safety monitoring. BP neural network fusion technology of multisource data fusion technology is used. A high-speed breccia-bearing silty clay slope is taken as the research object. The feedback information about the deployed monitoring system is fully used in the slope design and construction. The construction design parameters are reversed to predict the stability of the slope and ensure the safety of construction and operation of similar slopes of the entire expressway. The research in this paper finds that the maximum deviation between the slope displacement value and the measured value obtained by the slope monitoring based on multisource data fusion in this paper is 7.53%, which is less than 10%, which verifies the feasibility of the method in this paper. The research methods and ideas of this paper can also provide a reference for similar engineering research.


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    Titel :

    Safety Monitoring of Expressway Construction Based on Multisource Data Fusion


    Beteiligte:
    Dianliang Xiao (Autor:in) / Tiantao Zhang (Autor:in) / Xudong Zhou (Autor:in) / Guangshun Zheng (Autor:in) / Haoran Song (Autor:in)


    Erscheinungsdatum :

    2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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