## 5 Discussion  
During shallow tunnel excavations, the main objective of deformation monitoring is to detect minor abrupt-displacements as well as slow gradual-displacements and alert the excavation to prevent potential damages. As acceptable deformation levels are usually very low, accurate and reliable measurements are required and, very often, near real-time detection is needed. We introduced a static positioning approach that integrates PPP and short-baseline DD processing to achieve few-millimeter-accuracy for displacement measurements. The accuracy depends on the length of the observational sessions. Longer observational sessions often result in a higher accuracy but delay the identification of field events. In the practice of near real-time monitoring, a trade-off between  
Figure 10: Comparisons of the GPS-measured and Peck-predicted settlements of the riverbed. timeliness and accuracy has to be considered. The magnitude of structural deformation is often minor and the process is often slow. As a consequence, it is difficult to identify minor displacement-steps by eye in near real-time. We introduced a CPD algorithm combining backward and forward average-filtering to aid in detecting minor abrupt-displacements. In practice, no CPD algorithm operates perfectly because it must inspect new data before determining if a change point occurred between the old and new data points. Incorporating variable observational sessions (e.g., every 15, 30 min, one hour, one day) for static positioning and variable sliding-windows (e.g., 5 + 1 points, 10 + 1 points) for the CPD filtering may provide a better solution to identify and confirm the abrupt and gradual displacements in time. The monitoring system introduced in this study was able to detect 5-mm and larger abrupt-deformations in near real-time and slow gradual-deformations at a couple of millimeters per day within a week. In general, the detection ability at this level would meet the requirement of most monitoring projects with the preliminary purpose of assessing the safety of structures. However, there are outliers that can cause certain problems in near real-time structural deformation monitoring using the GPS-only observations. Figure 9 indicates that there are several outliers in the GPS-derived displacement time series, particularly in the vertical component. The outliers can be identified and removed in post-processing by comparing with the overall datasets. However, it is a challenge to judge an incoming measurement that is further away from the mean as an outlier or as a real positional change. There is a wide range of possible reasons that lead to outliers in GPS-derived displacement time series. Some reasons are related to specific satellite signals at a specific epoch, such as multipath reflections, cycle clips, and satellite clock errors. An outlier occurring in GPS-derived displacement time series may not occur in the displacement time series derived from other satellite systems since the outlier could be satellite specific. Near real-time positioning results from BeiDou may provide a second thought to confirm or decline a potential displacement event obtained from GPS-only observations. As of May 2019, there are more than 110 operational GNSS satellites in orbits, which comprises of approximately 31 GPS satellites, 33 BeiDou satellites, 25 GLONASS satellites, and 22 Galileo satellites. These 33 operational BeiDou satellites include 6 satellites in geostationary orbit (GEO), 6 satellites in 55-degree inclined geosynchronous orbits (IGSO), and 21 satellites in medium Earth orbits (MEO) ([[http://www.csno-tarc.cn](http://www.csno-tarc.cn)]([http://www.csno-tarc.cn](http://www.csno-tarc.cn))). Furthermore, eight satellites (3 in MEO, 2 in GEO, 3 in IGSO) are undergoing testing or commissioning. The full BeiDou constellation will consist of 35 operational satellites and will be completed in 2020. A greater number of simultaneously visible satellites theoretically brings many advantages.