<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study centers on the application of digital intelligence technology in project management, takes the geohazard project in Z area as the research object, and evaluates and predicts the grade of the danger of slope disaster and mudslide disaster in this area. The study begins with an in-depth analysis of the integrated fusion of BIM and GIS technologies. The integrated assessment and prediction model of geologic hazards was constructed using fuzzy comprehensive evaluation and support vector machine algorithm. The study unfolded the analysis of the hazard class and risk prediction of geohazards. The results showed that most of the hazard classes in slope hazards were assessed as low danger, accounting for 33.33%. Meanwhile, about 70% of the mudslide hazards were assessed to have a low to medium hazard rating. Regarding risk prediction, the prediction accuracy of slope and mudslide hazards reached 89% and 90%, respectively. In addition, the detection rate of all geohazards in the ROC (receiver operating characteristic) curve was above 85%, while the false alarm rate remained between 14% and 22%. This result verifies the accuracy of the model in geohazard risk prediction. The comprehensive assessment and prediction model proposed in this paper will help the risk assessment and prediction of geohazards, provide adequate technical support for managing disaster information, and enhance the science and rationality of disaster prevention and mitigation.</p>
</abstract>

This study centers on the application of digital intelligence technology in project management, takes the geohazard project in Z area as the research object, and evaluates and predicts the grade of the danger of slope disaster and mudslide disaster in this area. The study begins with an in-depth analysis of the integrated fusion of BIM and GIS technologies. The integrated assessment and prediction model of geologic hazards was constructed using fuzzy comprehensive evaluation and support vector machine algorithm. The study unfolded the analysis of the hazard class and risk prediction of geohazards. The results showed that most of the hazard classes in slope hazards were assessed as low danger, accounting for 33.33%. Meanwhile, about 70% of the mudslide hazards were assessed to have a low to medium hazard rating. Regarding risk prediction, the prediction accuracy of slope and mudslide hazards reached 89% and 90%, respectively. In addition, the detection rate of all geohazards in the ROC (receiver operating characteristic) curve was above 85%, while the false alarm rate remained between 14% and 22%. This result verifies the accuracy of the model in geohazard risk prediction. The comprehensive assessment and prediction model proposed in this paper will help the risk assessment and prediction of geohazards, provide adequate technical support for managing disaster information, and enhance the science and rationality of disaster prevention and mitigation.

Analysis of the application of BIM combined with GIS in engineering digital management

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study centers on the application of digital intelligence technology in project management, takes the geohazard project in Z area as the research object,...

Analysis of the application of BIM combined with GIS in engineering digital management

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 15 sty 2024

Przyjęty: 20 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0578

Słowa kluczowe
Fuzzy comprehensive evaluation, Support vector machine, BIM technology, GIS technology, Geohazard engineering

© 2024 Junhong Lin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the application of BIM combined with GIS in engineering digital management

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 15 sty 2024

Przyjęty: 20 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0578

Słowa kluczoweFuzzy comprehensive evaluation, Support vector machine, BIM technology, GIS technology, Geohazard engineering

© 2024 Junhong Lin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Fuzzy comprehensive evaluation, Support vector machine, BIM technology, GIS technology, Geohazard engineering