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- 저자 : Pengpeng Ding (검색결과 4 건)
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Analysis and Evaluation of Land Subsidence along Linear Engineering Based on InSAR Data
Pengpeng Ding,Chao Jia,Shengtong Di,Jing Wu,Ruchun Wei 대한토목학회 2021 KSCE Journal of Civil Engineering Vol.25 No.9
Land subsidence is a worldwide geological environment problem, which can bring about lasting and serious harm to linear engineering and urban construction. In this paper, land subsidence along the linear engineering was monitored and analyzed by the Synthetic Aperture Radar (SAR) images, high-precision leveling results, groundwater level monitoring data. Combined with Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) technology, spatial analysis technology of geographic information system and contribution rate method, the spatial and temporal evolution characteristics, influencing factors, and the contribution rate of each factor to land subsidence along the Lunan high-speed railway from 2016 to 2018 were researched. The accuracy of subsidence results by InSAR monitoring was verified by the linear fitting method and root mean square error method. The influence of uneven subsidence on linear engineering was discussed based on the evaluation results of subsidence gradient zoning. The results indicate that the maximum accumulated subsidence along the Lunan high-speed railway is 499 mm. Multiple subsidence center areas have been formed in the study area, with the maximum subsidence rate exceeding -55 mm/yr. The maximum subsidence gradient and curvature radius of the Lunan high-speed railway meet the requirements of line smoothness when the train speed is 350 km/h. The coal mining, compressible layer thickness, and changes in groundwater level are positively correlated with land subsidence, the total contribution rate to land subsidence is more than 90%. The research results provide scientific support for the prevention and control of land subsidence along the linear engineering.
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Hui Wang,Chao Jia,Pengpeng Ding,Keyin Feng,Xiao Yang,Xiao Zhu 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.2
As a worldwide environmental and geological disaster, land subsidence may cause serious harm to urban development. Therefore, the prediction of land subsidence is a key scientific problem. Decheng county, Shandong Province in China is taken as the research object. Based on BP neural network (BPNN) and random forest (RF) method, the analysis and prediction of regional land subsidence are carried out by applying multi-source monitoring data, Geographic Information System (GIS), and machine learning algorithm. Combined with Short Baseline Synthetic Aperture Interferometric Radar (SBAS-InSAR) and GIS technology, the spatio-temporal evolution characteristics of land subsidence from 2017 to 2020 are analyzed. The impact of different groundwater levels on land subsidence is quantitatively analyzed by BPNN and RF algorithm. The real-time prediction model of regional land subsidence is established. The results show that: 1) The area with the most serious land subsidence is located in Songguantun town, the maximum annual average subsidence rate is -40.71 mm/yr. 2) Land subsidence is mainly affected by deep groundwater and shallow groundwater in the research area. 3) The accuracy of the prediction results of the BPNN model is higher than that of the RF model when groundwater level change is used to predict land subsidence.
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Monitoring Analysis and Numerical Simulation of the Land Subsidence in Linear Engineering Areas
Chao Jia,Xiao Yang,Jing Wu,Pengpeng Ding,Chao Bian 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.7
Non-uniform land subsidence can cause subgrade deformation and threaten the safety of linear projects such as high-speed railways. The Lunan high-speed railway is taken as the research background. Combined with the hydrogeological and engineering geological conditions, the regional land subsidence is analyzed by the combined method of the differential interferometric synthetic aperture radar (D-InSAR) and small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), which is verified by using precise leveling and GPS monitoring data. The accurate land subsidence analysis of large-scale linear engineering is realized. The distribution situation and change laws of land subsidence in linear engineering areas are revealed. Then, a three-dimensional coupled numerical model of land subsidence along a large-scale linear project is established by using the finite difference method. The spatial distribution characteristics of land subsidence in the linear engineering area are quantitatively analyzed and predicted. The main factors affecting land subsidence and the degree of land subsidence are discussed while keeping the pumping rate constant. The results show that: 1) The combined method of D-InSAR and SBAS-InSAR has high accuracy in the land subsidence monitoring of linear engineering. 2) There are five non-uniform settlement funnels along the Lunan high-speed railway. The groundwater over-extraction and coal mining are the main reasons for the land subsidence. 3) Realizing water source conversion and reduce groundwater exploitation are the main factors in linear engineering areas as soon as possible.
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TCP-PPCC: Online-Learning Proximal Policy for Congestion Control
Shiwei Wang,Jing Li,Yuyao Guan,Pengpeng Ding 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Effective network congestion control strategies are the key to secure the normal operation of complex and changeable networks. The fundamental assumptions of many existing TCP congestion control variants dominated by hand-crafted heuristic algorithms are no longer valid. We propose an algorithm called TCP-Proximal Policy Congestion Control (TCP-PPCC), which is based on deep reinforcement learning algorithm Proximal Policy Optimization (PPO). TCP-PPCC updates the policy offline from the features of the preceding network state and feedback from the current network environment and adjusts the congestion window online with the updated policy. The senders with TCP-PPCC can learn about the changes in network bandwidth more accurately and adjust the congestion window in time. We demonstrate the performance of TCP-PPCC by comparing it with the traditional congestion control algorithm NewReno in four network scenarios with the ns-3 simulator. The results show that in scenario 2, TCPPPCC takes 58.75% improvement in average delay and 27.80% improvement in throughput compared with NewReno.
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