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<p>High-risk areas of geologic disasters need to select appropriate foundation treatment techniques according to different risk assessment levels to ensure the overall quality and safety of housing construction projects. In this paper, for the three foundation treatment technologies for different geologies, the geologic condition sensing measurement algorithm is designed according to the principle of InSAR technology, and the informativeness-logistic regression algorithm is proposed for vulnerability risk evaluation. The TRIGRS algorithm that has been improved is used to construct the geologic hazard risk assessment model. Then, the data preparation work is carried out in the practical application, and input data are connected through a series of preprocessing operations. Taking the landslide geohazards occurring in a certain place as an example, the geologic deformation curve generated by the system was used to analyze them, and the risk assessment of geohazard susceptibility was also carried out in a certain area. It was found that the cumulative deformation curve from the 20th week to the 30th week of 2020 decreased significantly by more than 100mm. The actual major landslide disaster occurred at the beginning of June 2020, and the results of the system operation are in line with the time of the actual disaster. For all 11 factors, logistic regression analysis yielded significant results. The proportion of 5-level susceptibility areas of geologic hazards is 31.5%, 24.6%, 20.2%, 14.7%, and 9%, respectively, and the distribution of assessment results is reasonable. This study provides new ideas and effective methods for defining the level of geohazard risk assessment and selecting foundation treatment technology.</p>
</abstract>

High-risk areas of geologic disasters need to select appropriate foundation treatment techniques according to different risk assessment levels to ensure the overall quality and safety of housing construction projects. In this paper, for the three foundation treatment technologies for different geologies, the geologic condition sensing measurement algorithm is designed according to the principle of InSAR technology, and the informativeness-logistic regression algorithm is proposed for vulnerability risk evaluation. The TRIGRS algorithm that has been improved is used to construct the geologic hazard risk assessment model. Then, the data preparation work is carried out in the practical application, and input data are connected through a series of preprocessing operations. Taking the landslide geohazards occurring in a certain place as an example, the geologic deformation curve generated by the system was used to analyze them, and the risk assessment of geohazard susceptibility was also carried out in a certain area. It was found that the cumulative deformation curve from the 20th week to the 30th week of 2020 decreased significantly by more than 100mm. The actual major landslide disaster occurred at the beginning of June 2020, and the results of the system operation are in line with the time of the actual disaster. For all 11 factors, logistic regression analysis yielded significant results. The proportion of 5-level susceptibility areas of geologic hazards is 31.5%, 24.6%, 20.2%, 14.7%, and 9%, respectively, and the distribution of assessment results is reasonable. This study provides new ideas and effective methods for defining the level of geohazard risk assessment and selecting foundation treatment technology.

Research on Coupling Model of Foundation Treatment and Geological Hazard Risk Assessment

Applied Mathematics and Nonlinear Sciences

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

High-risk areas of geologic disasters need to select appropriate foundation treatment techniques according to different risk assessment levels to ensure the...

Research on Coupling Model of Foundation Treatment and Geological Hazard Risk Assessment

Publicado en línea: 02 jul 2024

Páginas: -

Recibido: 27 feb 2024

Aceptado: 22 may 2024

DOI: https://doi.org/10.2478/amns-2024-1528

Palabras clave
InSAR, Particle swarm algorithm, Logistic regression, TRIGRS, Geologic hazards

© 2024 Kangyue An et al., published by Sciendo

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

Research on Coupling Model of Foundation Treatment and Geological Hazard Risk Assessment

Publicado en línea: 02 jul 2024

Páginas: -

Recibido: 27 feb 2024

Aceptado: 22 may 2024

DOI: https://doi.org/10.2478/amns-2024-1528

Palabras claveInSAR, Particle swarm algorithm, Logistic regression, TRIGRS, Geologic hazards

© 2024 Kangyue An et al., published by Sciendo

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

Palabras clave
InSAR, Particle swarm algorithm, Logistic regression, TRIGRS, Geologic hazards