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Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Erzincan University Journal of Science & Technology is the property of Erzincan Binali Yildirim Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of transformation between geodetic ellipsoidal coordinates (φ, λ, h) and three-dimensional (3D) global Cartesian coordinates (X, Y, Z) is a common one. Only one method for solving this transformation problem has been found. An examination of the current studies reveals that the application and testing of possible alternative techniques for transforming geodetic ellipsoidal coordinates to 3D global Cartesian coordinates have not been fully addressed. The aim of this study was to investigate the performance of a method of transforming geodetic ellipsoidal coordinates to 3D global Cartesian coordinates using a multilayer perceptron artificial neural network (MLP-ANN). For the prediction, 594-point datasets of the Turkish National Fundamental GPS Network (TNPGN) were used. As a result of many trials, the Bayesian Regulation was determined as the training algorithm and the number of hidden layers was 2. The mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) were used for the performance evaluation of the MLP-ANN model. According to the test results, the MSE value for the components of the MLP-ANN and the 3D global Cartesian coordinates varied between 0.4536 cm and 0.9411 cm, the MAE value ranged between 0.3883 cm and 0.8165 cm, and the R2 value was calculated as 0.9999 for all models. To examine the results in more detail, the difference between the estimated values and the calculated values was determined. Accordingly, it was found that the difference in the values was quite close to zero except for a few number of data. According to these statistical criteria, it was concluded that the MLP-ANN used in this study could be used as an alternative to the classical coordinate transformation method. [ABSTRACT FROM AUTHOR]

Civil engineering structures (e.g., bridges, dams) are exposed to deformation under the influence of various factors such as water level changes, landslides, tectonic phenomena, etc. These deformations must be periodically monitored. Various deformation analysis approaches have been developed to describe the behaviour of a structure or natural process. The most significant task in deformation analysis is to correctly classify whether the points are stable or unstable. In this study, various robust weight functions for determination of stable/unstable points were applied to the Deriner Dam using GNSS (Global Navigation Satellite System) data measured over four different periods. The robust weight functions examined included the Andrews, Beaton-Tukey, Cauchy, Danish, Fair, German-McClure, Hampel, Huber, L1, and L1-L2. Test results were evaluated, and the performances of the different deformation analysis methods were determined. It was concluded that the horizontal deformations based on GNSS data determined by these robust weight functions were in good agreement with each other, except for the L1-L2. The results of all approaches were also compared with the results of the θ2-Criteria method. According to the results obtained, although the θ2-Criteria and the robust methods yielded nearly similar results, the results of the θ2-Criteria method were thought to be more reliable. [ABSTRACT FROM AUTHOR]

Dams, one of the country's most natural and cheapest way to product energy, are built for energy production, agricultural activities and flood protection. Dams with high construction costs are subject to deformation due to some physical factors. Therefore, dams should be kept safe to prevent possible dam accidents, loss of life and property. Engineering structures (such as dams) should be monitored periodically by geodetic and non-geodetic techniques. Deriner dam is Turkey's highest double-curved concrete arch dam. In this study, we monitored deformation with GPS measurements. For this purpose, two period static GPS measurements were performed on the reference and object points in the study. Afterwards, GPS measurements were adjusted separately with respect to free adjustment method and then deformation analysis were carried out by using the adjusted coordinates and their cofactor vectors. Iterative Weighted Similarity Transformation (IWST) and Least Absolute Sum (LAS) methods were used as deformation methods to detect the displacement of the reference and object points. [ABSTRACT FROM AUTHOR]

One of such transformation methods, the two-dimensional similarity transformation, is widely used in geodetic studies. The outliers in the measure group should be detected so that the model established during the transformation process gives accurate results. In transformation practices, conventional outlier detection test procedures based on the least squares estimation (LSE) and robust estimation methods are widely used for the detection of outliers. The aim of this study was to compare performances of the result data obtained by multilayer perceptron artificial neural networks (MLPNNs) including various activation functions and training algorithms of artificial neural networks (ANNs), which has recently begun to be widely used in scientific studies and engineering fields, and of the result data obtained using various methods to detect outliers in two-dimensional similarity transformation process between two different coordinate systems. In ANNs consisting of three layers, eight different network configurations were generated using different activation functions and training algorithms. The coordinates of the control points calculated by two-dimensional similarity and ANNs methods were compared with known coordinate values. Differences between the coordinates calculated using two-dimensional similarity transformation and eight different network configurations and the coordinates of control points were examined in terms of the root mean square error, and network configuration which uses a combination of 'tansig-purelin' activation functions and Bayesian regulation algorithm provided the best result. [ABSTRACT FROM AUTHOR]