The South-to-North Water Diversion (SNWD) is a multi-decadal infrastructure project in China aimed at alleviating severe water shortages in north China. It has imposed broad social, economic, environmental, and ecological impacts since 2015, particularly in the Beijing-Tianjin metropolitan area. Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) (2014–2021), Global Positioning System (GPS) (2010–2021), and hydraulic-head data are used to assess the impacts on ongoing land subsidence in Tianjin in this study. Additionally, the Principal Component Analysis (PCA) is employed to highlight primary factors controlling the recent land subsidence. Our results show that the reduced groundwater pumping has slowed down the overall subsidence since 2019 due to SNWD. As of 2021, the subsiding area (>5 mm/year) has reduced to about 5400 km², approximately 85% of the subsiding area before SNWD; the areas of rapid subsidence (>30 mm/year) and extremely rapid subsidence (>50 mm/year) have reduced to 1300 km² and 280 km², respectively, approximately 70% and 60% of the areas before SNWD. Recent subsidence (2016–2021) was primarily contributed by the inelastic compaction of clays in deep aquifers of Aquifers III and IV ranging from approximately 200 to 450 m below the land surface. The ongoing rapid subsidence (>30 mm/year) in Tianjin is limited to border areas adjacent to large industrial cities (e.g., Langfang, Tanshan, Cangzhou) in Hebei Province. Ongoing subsidence will cease when hydraulic heads in the deep Aquifers (IV and V) recover to the new pre-consolidation head, approximately 45 m below the land surface, and subsidence will not be reinitiated as long as the hydraulic heads remain above the new pre-consolidation head. This study reveals the importance of coordinating groundwater and surface water uses at local, regional, and national scales for land subsidence mitigation.

We have established a stable regional geodetic reference frame using long-history (13.5 years on average) observations from 55 continuously operated Global Navigation Satellite System (GNSS) stations adjacent to the Gulf of Mexico (GOM). The regional reference frame, designated as GOM20, is aligned in origin and scale with the International GNSS Reference Frame 2014 (IGS14). The primary product from this study is the seven-parameters for transforming the Earth-Centered-Earth-Fixed (ECEF) Cartesian coordinates from IGS14 to GOM20. The frame stability of GOM20 is approximately 0.3 mm/year in the horizontal directions and 0.5 mm/year in the vertical direction. The regional reference frame can be confidently used for the time window from the 1990s to 2030 without causing positional errors larger than the accuracy of 24-h static GNSS measurements. Applications of GOM20 in delineating rapid urban subsidence, coastal subsidence and faulting, and sea-level rise are demonstrated in this article. According to this study, subsidence faster than 2 cm/year is ongoing in several major cities in central Mexico, with the most rapid subsidence reaching to 27 cm/year in Mexico City; a large portion of the Texas and Louisiana coasts are subsiding at 3 to 6.5 mm/year; the average sea-level-rise rate (with respect to GOM20) along the Gulf coast is 2.6 mm/year with a 95% confidence interval of ±1 mm/year during the past five decades. GOM20 provides a consistent platform to integrate ground deformational observations from different remote sensing techniques (e.g., GPS, InSAR, LiDAR, UAV-Photogrammetry) and ground surveys (e.g., tide gauge, leveling surveying) into a unified geodetic reference frame and enables multidisciplinary and cross-disciplinary research.

Timely risk information acquisition and diagnosis during foundation pit excavation (FPE) processes are vital for ensuring the safe and effective construction of underground urban infrastructures. Unfortunately, diverse geological and hydrogeological conditions and complex shapes of the foundation pit create barriers for reliable FPE risk prognosis and control. Furthermore, typical support systems during FPE use temporary measures, which have limited capacity to confront excessive loads, large deformations, and seepage. This study aims to establish an intelligent risk prognosis and control framework based on digital twin (DT) for ensuring safe and effective FPE processes. Previous studies have conducted extensive experimental and numerical analyses for examining unsafe conditions during FPE. How to enable intelligent risk prognosis and control of tedious FPE processes by integrating physics-based models and sensory data collected in the field is still challenging. DT could help to establish the interaction and feedback mechanisms between the physical and virtual space. In this study, the authors have established a DT model that consists of a physical space model and a high-fidelity physics-based model of a foundation pit in virtual space. As a result, a mechanism for effective acquisition and fusion of heterogeneous information from both physical and virtual space is established. Then, the authors proposed an integrated model and data-driven approach for examining safety risks during FPE. In the end, the authors have validated the proposed method through a case study of the FPE of the Wuhan Metro Line. The results show that the proposed method could provide theoretical and practical support for future intelligent FPE.

Coupling effects of various loading conditions can cause deflections, settlements and even failure of in-service bridges. Although it is one of the most critical loads, unfortunately, loading conditions of moving vehicles are difficult to capture in real time by bridge monitoring systems currently in place for sustainable operation. To fully understand the status of a bridge, it is essential to obtain instantaneous vehicle load distributions in a dynamic traffic environment. Although there are some methods that can identify overweight vehicles, the captured vehicle-related information is scattered and incomplete and thus cannot support effective bridge structural health monitoring (BSHM). This study proposes a noncontact, vision-based approach to identification of vehicle loads for real-time monitoring of bridge structural health. The proposed method consists of four major steps: (1) establish a dual-object detection model for vehicles using YOLOv7, (2) develop a hybrid coordinate transformation model on a bridge desk, (3) develop a multiobject tracking model for real-time trajectory monitoring of moving vehicles, and (4) establish a decision-level fusion model for fusing data on vehicle loads and positions. The proposed method effectively visualizes the 3D spatiotemporal vehicular-load distribution with low delay at a speed of over 30FPS. The results show that the hybrid coordinate transformation ensures that the vehicle position error is within 1 m, a 5-fold reduction compared with the traditional method. Wheelbase is calculated through dual-object detection and transformation and is as the primary reference for vehicle position correction. The trajectory and real-time speed of vehicles are preserved, and the smoothed speed error is under 5.7%, compared with the speed measured by sensors. The authors envision that the proposed method could constitute a new approach for conducting real-time SHM of in-service bridges.

Structural monitoring and engineering surveys that use the Global Positioning System (GPS) have traditionally performed using a so-called carrier-phase double-difference method in data processing. The method requires at least one reference GPS station to be continuously operated in the field during the monitoring period. The stability and data quality of the reference station would directly affect the accuracy of displacement measurements at the rover station. Furthermore, it becomes increasingly difficult to maintain “ideal” reference stations in urban environments. This article introduces a method for using stand-alone GPS units to conduct millimeter-accuracy structural deformation monitoring for poststudy or for near real-time structural health monitoring. Two years of continuous GPS observations obtained from two high-rise buildings in Beijing were used to depict the detailed method. The method comprises three steps: (1) solving GPS antenna positions with respect to a global reference frame (IGS08), (2) establishing a stable local reference frame and transforming the global positions into the local reference frame, and (3) deriving a regional seasonal model and correcting GPS-derived positional time series with the regional model. It is concluded that 2- to 3-mm horizontal accuracy and 5- to 7-mm vertical accuracy can be achieved for daily solutions with the proposed method in the Beijing metropolitan area. The key products from this study are the Stable Beijing Reference Frame (SBJRF) and the local seasonal model for GPS-derived (vertical) positional time series. SBJRF will be incrementally improved and periodically updated to synchronize with the update of the IGS reference frame. The general theory and method presented in this article could be applied to other urban areas for conducting structural health monitoring in near real time.

Shield tunneling under rivers often requires monitoring riverbed deformations in near real-time. However, it is challenging to measure riverbed deformation with conventional survey techniques. This study introduces a comprehensive method that uses the Global Positioning System (GPS) of the USA and the BeiDou navigation satellite system (BeiDou) of China to monitor riverbed deformation during the construction of twin tunnels beneath the Hutuo River in Shijiazhuang, China. A semi-permanent GPS network with one base station outside the river and six rover stations within the river was established for conducting near real-time and long-term monitoring. The distances between the base and the rover antennas are within two kilometers. The network was continuously operating for eight months from April to December 2018. The method is comprised of three components: (1) Monitoring the stability of the base station using precise point positioning (PPP) method, a stable regional reference frame, and a seasonal ground deformation model; (2) monitoring the relative positions of rover stations using the carrier-phase double-difference (DD) positioning method in near real-time; and (3) detecting abrupt and gradual displacements at both base and rover stations using an automated change point detection algorithm. The method is able to detect abrupt positional-changes as minor as five millimeters in near real-time and gradual positional-changes at a couple of millimeters per day within a week. The method has the flexibility of concurrent processing different GPS and BeiDou data sessions (e.g., every 15 minutes, 30 minutes, one hour, one day) for diffident monitoring purposes. This study indicates that BeiDou observations can also achieve few-millimeter-accuracy for measuring displacements. Parallel processing GPS and BeiDou observations can improve the reliability of near real-time structural deformation monitoring and minimize false alerts. The method introduced in this article can be applied to other urban areas for near real-time and long-term structural health monitoring.