Disaster reduction stick equipment: A method for monitoring and early warning of pipeline-landslide hazards

Yan, Yan; Yang, Da-shen; Geng, D. Y.; Hu, Sheng; Wang, Ziang; Wang, Hu; Yin, Shuyao

doi:10.1007/s11629-019-5613-6

Cited by 17 publications

(9 citation statements)

References 29 publications

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“…The characteristics of bamboo ice pressure disaster in different slope directions are: the bamboo forest on the north slope is the most affected, followed by the South and West slopes, and the bamboo forest on the east slope is less affected. The average disaster rate and disaster index of the forest on the north slope are higher than those on the east slope by percentage points and respectively [14]. The average disaster rate, disaster index and the average length of bamboo rupture increase with the East, South, West and north slopes.…”

Section: Figure 2disaster Level At All Levelsmentioning

confidence: 88%

Relationship between Snow and Ice Disasters in Phyllostachys Pubescens Forest and Environmental and Biological Characteristics in the Context of Big Data

2021

AJEB

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With the development of science and technology, big data(BD) technology is gradually applied to all aspects of life. At the same time, under the trend of global warming, the frequency of Snow and Ice Disaster(SAID) in PPF is also gradually increasing. Therefore, based on BD, this paper studies and analyzes the relationship between SAIDs in PPF and environmental and biological characteristics(BC). Taking the SAID of PP Forest(PPF) in a province as an example, the characteristics of SAID of PPF, altitude gradient and ice pressure disaster of different bamboo ages were studied and analyzed, and then the relationship between SAID of PPF and environment and the relationship between SAID of PPF and BC were discussed; It provides a scientific basis for promoting the rejuvenation of PPF, the improvement of production capacity and the restoration of ecological functions, and also provides a reference for the sustainable development of bamboo ecosystem.

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Section: Figure 2disaster Level At All Levelsmentioning

confidence: 88%

Relationship between Snow and Ice Disasters in Phyllostachys Pubescens Forest and Environmental and Biological Characteristics in the Context of Big Data

2021

AJEB

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“…We identified the key monitoring parameters as: 1) rainfall conditions, specifically, hourly, and daily totals; 2) pore water pressure in the shallow surface soil; 3) angular displacement and tilting deformation of unstable slopes; 4) horizontal displacement of the unstable slope to obtain cumulative deformation and the deformation rate. We used our self-developed pipeline slope hazard monitoring stick equipment system (Yan et al, 2019) to measure these parameters.…”

Section: Monitoring and Early Warningmentioning

confidence: 99%

“…The safety of pipelines under the action of landslides can be evaluated using strength theory based on monitoring surface displacement and strain of the pipeline slope (Feng and Huang, 2009;Liu et al, 2010;Marinos et al, 2019). To deal with the particularity and complexity of pipeline landslides, Yan et al (2019) developed a multi-parameter comprehensive monitoring system that could be applied to pipeline evaluation using a mechanical model of failure probability (Alvarado-Franco et al, 2017). However, most monitoring approaches separate the pipeline from the slope hazard and only assess hazard occurrence; the impact of the hazard on the pipeline, and the vulnerability of the pipeline, needs further research.…”

Section: Introductionmentioning

confidence: 99%

A Whole Process Risk Management System for the Monitoring and Early Warning of Slope Hazards Affecting Gas and Oil Pipelines

et al. 2022

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Pipelines are important methods of oil and gas transportation and are fundamental to many country’s economies. Pipeline safety is a critical issue; over 96% of pipeline accidents due to ground movement are caused by slope hazards and these can lead to serious personnel and property losses. Therefore, effective pipeline slope hazard monitoring and early warning is crucial, but there are many limitations to existing measures. The recent advance in remote sensing technologies enables the collection of slope hazards information that maps the spatial distribution of landslide. But this approach cannot provide real-time monitoring and early warning as there is a time lag due to image processing. Also, pipelines are considered separately from the slope hazard, with only slope event occurrence assessed rather than quantification of the impact of the hazard on the pipeline. Here, we report on a whole process risk management system for the pipeline slope hazard, incorporating monitoring and early warning of pipeline slope hazards. Three sites at risk of slope hazard on the Guangdong Dapeng Liquefied Natural Gas (LNG) Company pipeline in Guandong, South China - Zhangmutou, Huoshaogang and Dapeng New District - were selected for research and implementation of the whole process risk management, monitoring and early warning system. The system is shown to operate well and, overall, we found that the three sites are relatively stable at present. This research provides widely applicable guidance for the prevention, control, and early warning of pipeline slope hazards.

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“…pipeline geological hazards can be effectively identified and early warnings issued (Vasconez et al, 2010). In recent years, with the development of artificial intelligence algorithms, pipeline geological hazard monitoring and early warning have gradually moved toward artificial intelligence-based approaches (Cao et al, 2012;Yan et al, 2019b). Although these monitoring and early warning studies are becoming more mature and effective, the monitoring equipment is often limited to identified pipeline geological hazard areas and cannot yet meet the needs of large-scale identification of pipeline geological hazards.…”

Section: Introductionmentioning

confidence: 99%

Detection and Numerical Simulation of Potential Hazard in Oil Pipeline Areas Based on UAV Surveys

Yan

Yin

et al. 2021

Front. Earth Sci.

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Western China is rich in oil and gas resources, and many oil and gas pipelines are under construction or have been completed. However, many water-related natural hazards, such as landslides, collapses, rockfalls, and debris flows, have developed in the areas passed through by oil and gas pipelines and seriously threaten the operational safety of these pipelines. Therefore, it is urgent to carry out large-scale identification and assessment of pipeline geological hazards. At present, conventional on-site investigation, evaluation, monitoring, and early warning methods are difficult to apply for rapid identification and evaluation of pipeline geological hazards across large-scale areas. Based on this, this study takes the pipeline of Sinopec Marketing South China Branch in Yunnan Province as the research area. In this research, unmanned aerial vehicle (UAV) and photogrammetry technology were used to quickly and accurately obtain multi-phase images of an oil pipeline passing through the study area, and the images were post-processed to obtain multi-phase high-resolution, high-precision digital orthophoto maps and digital terrain models (DTMs) to identify landform changes and deformation. The focus of this research is to propose a set of technical methods for UAV point cloud filtering. The DTMs obtained based on this method can effectively identify unstable areas of oil pipelines. In addition, we have carried out numerical simulations under different motion scenarios in unstable regions, providing scientific support for future geological hazard prevention and mitigation and engineering practices in oil pipeline areas.

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Disaster reduction stick equipment: A method for monitoring and early warning of pipeline-landslide hazards

Cited by 17 publications

References 29 publications

Relationship between Snow and Ice Disasters in Phyllostachys Pubescens Forest and Environmental and Biological Characteristics in the Context of Big Data

Relationship between Snow and Ice Disasters in Phyllostachys Pubescens Forest and Environmental and Biological Characteristics in the Context of Big Data

A Whole Process Risk Management System for the Monitoring and Early Warning of Slope Hazards Affecting Gas and Oil Pipelines

Detection and Numerical Simulation of Potential Hazard in Oil Pipeline Areas Based on UAV Surveys

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