Change detection and deformation monitoring is an active area of research within the field of engineering surveying and other overlapping areas such as structural and civil engineering. This paper reviews the application of terrestrial laser scanning in the monitoring of structures and discusses registration and georeferencing of scan data. Past terrestrial laser scanning research work has shown trends in addressing issues such as accurate registration and georeferencing of scans, error modelling, point cloud processing techniques for deformation analysis, scanner calibration and detection of millimetre deformations. However, several issues are still open to investigation such as robust methods of point cloud processing for detecting change and deformation, incorporation of measurement geometry in deformation measurements, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. A three-stage process model for deformation analysis is proposed as conceptualised from the material reviewed.

During the design phase of a bridge, certain characteristics are assumed, such as traffic and wind loading. In reality, however, these characteristics can vary, therefore it is very important that the health of such a structure is monitored to evaluate whether it is holding up to the true loading. Due to the ever-increasing size and cost of these structures, it is vital that any anomalies from the expected deflections are detected as soon as possible, allowing remedial action to be taken, and hence trying to prevent disastrous consequences. The use of a kinematic global positioning system (GPS) now allows subcentimeter accuracy to be achieved at a rate of up to 20 Hz, and at maximum distances from the reference GPS receivers to the bridge receivers of up to 20 km. This makes such a system capable of detecting the deflections of long bridges. In addition, accelerometers have been proven useful for such monitoring, allowing precise readings at rates of up to 1,000 Hz. However, both systems have their limita...

Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm.