摘要：Landslide disasters are one of the main risks involved with theoperation of long-distance oil and gas pipelines. Because previouslyestablished disaster risk models are too subjective, this paper presents aquantitative model for regional risk assessment through an analysis of thepatterns of historical landslide disasters along oil and gas pipelines. Using theGuangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distancemultiproduct oil pipeline (82km) in China as a case study, we successivelycarried out two independent assessments: a susceptibility assessment and avulnerability assessment. We used an entropy weight method to establish asystem for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct thesusceptibility assessment. The risk assessment was carried out on the basisof two assessments. The first, the system of the vulnerability assessment,considered the pipeline position and the angle between the pipe and thelandslide (pipeline laying environmental factors). We also used aninterpolation theory to generate the standard sample matrix of the LM-BPneural network. Accordingly, a landslide susceptibility risk zoning map wasobtained based on susceptibility and vulnerability assessment. The resultsshow that about 70% of the slopes were in high-susceptibility areaswith a comparatively high landslide possibility and that the southern sectionof the oil pipeline in the study area was in danger. These results can beused as a guide for preventing and reducing regional hazards, establishingsafe routes for both existing and new pipelines, and safely operatingpipelines in the Guangyuan area and other segments of the LCC oilpipeline.
