Beijing Capital International Airport (BCIA) has suffered from uneven land subsidence since 1935, which affects the smoothness of airport runways and seriously threatens the safety of aircrafts. In this paper, a spaceborne interferometric synthetic aperture radar (InSAR) with high-resolution Cosmo-SkyMed SAR data was utilized at BCIA for the first time to diagnose the subsidence hazard. The results show that subsidence is progressing at BCIA at a maximum rate of 50 mm/year, which is mainly distributed in the northwest side of the airport. It was found that the Shunyi-Liangxiang fault directly traverses Runway2 and Runway3 and causes uneven subsidence, controlling the spatial subsidence pattern to some degree. Four driving factors of subsidence were investigated, namely: the over-exploitation of groundwater, active faults, compressible soil thickness, and aquifer types. For the future sustainable development of BCIA, the influence of Beijing new airport and Beijing Daxing International Airport (BDIA), was analyzed and predicted. It is necessary to take relevant measures to control the uneven subsidence during the initial operation of BDIA and conduct long-term monitoring to ensure the regular safe operation of BCIA. This case demonstrates a remote sensing method of diagnosing the subsidence hazard with high accuracy and non-contact, providing a reliable alternative for the geohazard diagnosis of key infrastructures in the future.

A series of small-magnitude earthquakes (M_w 2.9~M_w 4.9) occurred in Rong County, Sichuan Province, China between 30 March 2018 and December 2020, which threatened the safety of local residents. Determining the surface displacement and estimating the damage caused by these earthquakes are significant for earthquake relief, post-earthquake disaster assessment and hazard elimination. This paper integrates the Generic Atmospheric Correction Online Service (GACOS) with interferometry synthetic aperture radar (InSAR) to accurately detect the displacement of the series of small-magnitude earthquakes in Rong County based on 45 Sentinel-1 ascending/descending images acquired from January 2018 to December 2020. We analyze the influence of some factors involved in surface displacement, including earthquake magnitude, focal depth and the distance from the epicenter to the fault. The above measurement for small-magnitude earthquakes and statistics analysis for the displacement have not been performed before, so this can help better understand the displacement features of small-magnitude earthquakes, which are important for post-earthquake hazard assessment and disaster prevention.

With the popularization and rise in BIM technology usage, BIM education for undergraduate students in architecture, engineering, and construction (AEC) related disciplines has emerged as a priority. This study assesses the BIM learning outcomes of students participating in the National BIM Graduation Design Innovation Competition of Colleges and Universities. In total, 2777 valid questionnaire responses were obtained for this study. The Cronbach’s alpha coefficient method and principal component factor analysis method were used to verify the reliability of the data set (Cronbach’s alpha = 0.962, KMO = 0.965). The t-test (ANOVA) was used to verify that gender, school type, major, grade, study duration and use BIM related software, as well as other demographic attributes, displayed significant inter-group differences. Seven common factors affecting BIM learning performance were obtained by exploratory factor analysis: (1) ability of the instructor, (2) school (college) atmosphere, (3) teamwork, (4) individual ability, (5) understanding of BIM industry applications, (6) social environment incentives, and (7) achievement demand. Finally, the results of an ordered logistic regression revealed that the demographic attributes of participants, the comprehensive ability of the instructor, teamwork, individual ability, and achievement demand significantly affects BIM learning performance. Based on these findings, this paper puts forward suggestions for improving BIM learning performance and provides theoretical support for BIM education and learning in AEC related undergraduate majors.