Bihter Erol

Height transformation using regional geoids and GPS/levelling in Turkey

Abstract Transformation of ellipsoidal heights derived from the Global Positioning System (GPS) to orthometric heights using geoid models is investigated in the north and west parts of Turkey. Although the transformation depends on a simple relation between ellipsoidal h, orthometric H and geoid N heights, the accuracy of the resulting orthometric heights after transformation is crucial in geodetic and surveying applications. Various factors which affect this accuracy, such as measurement errors, datum inconsistencies and theoretical assumptions, are investigated in this study, while testing different methods in three test networks (Sakarya in the Northwest, Çankırı in the North and Izmir in the West). The study consists of three steps. In the first step the regional Turkey geoids TG99A, TG03 and the European gravimetric geoid EGG97 are tested comparing geoid heights derived from models and GPS/levelling at co–located benchmarks. In the second step, regional geoid models are combined with GPS/levelling using Least Squares Adjustment of height differences and corrector surface models. In this step, additionally, Variance Component Estimation (VCE) using Minimum Norm Quadratic Unbiased Estimation (MINQUE) approach is performed, in order to combine the height sets. In the last step, GPS/levelling surface type local geoids are determined and their performances are tested in transformation of GPS–heights. Finally, the resulting accuracies are compared and practical aspects of those approaches in deriving orthometric heights from GPS measurements in geodetic and surveying applications are discussed.

Learning-based computing techniques in geoid modeling for precise height transformation

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.

Evaluation of High-Precision Sensors in Structural Monitoring

One of the most intricate branches of metrology involves the monitoring of displacements and deformations of natural and anthropogenic structures under environmental forces, such as tidal or tectonic phenomena, or ground water level changes. Technological progress has changed the measurement process, and steadily increasing accuracy requirements have led to the continued development of new measuring instruments. The adoption of an appropriate measurement strategy, with proper instruments suited for the characteristics of the observed structure and its environmental conditions, is of high priority in the planning of deformation monitoring processes. This paper describes the use of precise digital inclination sensors in continuous monitoring of structural deformations. The topic is treated from two viewpoints: (i) evaluation of the performance of inclination sensors by comparing them to static and continuous GPS observations in deformation monitoring and (ii) providing a strategy for analyzing the structural deformations. The movements of two case study objects, a tall building and a geodetic monument in Istanbul, were separately monitored using dual-axes micro-radian precision inclination sensors (inclinometers) and GPS. The time series of continuous deformation observations were analyzed using the Least Squares Spectral Analysis Technique (LSSA). Overall, the inclinometers showed good performance for continuous monitoring of structural displacements, even at the sub-millimeter level. Static GPS observations remained insufficient for resolving the deformations to the sub-centimeter level due to the errors that affect GPS signals. With the accuracy advantage of inclination sensors, their use with GPS provides more detailed investigation of deformation phenomena. Using inclinometers and GPS is helpful to be able to identify the components of structural responses to the natural forces as static, quasi-static, or resonant.

Monitoring and Analysing Structural Movements with Precise Inclination Sensors

A multistorey high—rise building has been monitored using microradian precision inclination sensors and the structural movements have been analyzed as consequence of 40 days observation period. In the first step, the time series obtained from inclination sensors’ data were processed with Least Squares Spectral Analysis technique. In documenting the periodicity of the data, the correlation functions were also used as a straightforward way of evaluating the time series. Afterwards the results from the analysis were interpreted and linked to the other observed data such as temperature changes, wind load effects, population in the building and instant earthquake information in the observation period. Further on the results were compared with processed data from consecutive GPS campaigns processes. The study has shown that precise inclination sensors are efficient tools for continuously monitoring and investigating structural movements of large engineering structures.