پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,953,235 |
| تعداد دریافت فایل اصل مقاله | 89,625,891 |
برآورد بهینه دقت مشاهدات در شبکه‌های کلاسیک جابه‌جاسنجی | ||
| فیزیک زمین و فضا | ||
| مقاله 6، دوره 45، شماره 2، مرداد 1398، صفحه 325-342 اصل مقاله (488.83 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2019.268781.1007058 | ||
| نویسندگان | ||
| سعید فرزانه* 1؛ کمال پروازی2 | ||
| 1استادیار، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران | ||
| 2دانشجوی دکتری، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| روش برآورد مؤلفههای واریانس کمترین‌مربعات زمانی که تنوع مشاهداتی در شبکه وجود داشته باشد کارایی خوبی از خود نشان می‌دهد. با استفاده از این روش برای هر دسته از مشاهدات مختلف یک ضریب مقیاس محاسبه می‌شود. در این تحقیق از روش وزن‌دهی برآورد مؤلفههای واریانس کمترین‌مربعات استفاده شده است. این بهبود دقت برای مختصات نقاط شبکه به‌نحوی است که مقدار نیم قطر بزرگ بیضی خطای مطلق نقاط در حالت استفاده از برآورد مؤلفههای واریانس کمترین مربعات برابر 29 میلی‌متر، در حالی‌که با استفاده از روش فاکتور وریانس ثانویه این مقدار به دو برابر افزایش مییابد. علاوه بر این در هنگام استفاده از روش برآورد مؤلفههای واریانس کمترین‌مربعات اثر ماتریس کوواریانس مجهولات برابر 8/0 میلی‌متر میباشد که نسبت به روش فاکتور وریانس ثانویه مقدار آن به اندازه دو برابر کاهش مییابد. در واقع مزیت روش برآورد مؤلفههای واریانس کمترین‌مربعات برآورد واقعبینانهای از دقت پارامترهای مدل و ابعاد بیضی خطای مطلق میباشد. | ||
| کلیدواژه‌ها | ||
| برآورد مؤلفههای واریانس کمترین‌مربعات؛ فاکتور وریانس ثانویه؛ شبکه‌های ژئودتیک؛ عدد آزادی | ||
| عنوان مقاله [English] | ||
| Optimized Estimation of Observation Precisions In Classical Displacement Network | ||
| نویسندگان [English] | ||
| saeed Farzaneh1؛ Kamal Parvazi2 | ||
| 1Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran | ||
| 2Ph.D. Student Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Any infrastructure such as dams need constant monitoring for the detection of risks of failure and/or to plan civil engineering maintaining work. A recent approach considers precise geodetic instruments and satellite-based geodetic monitoring as a method to estimate potential deformation of such structures. A growing need for a fully automated and continuous monitoring of structural and ground deformations has created new challenges for design and analysis of the monitoring schemes, where multi-sensor geodetic systems can provide essential aid. Combination of different geodetic data helps determining displacements with high precision, hence, the risk of damages is reduced. Corresponding authorities of large man-made structures are faced with the safety problem, as all have aim to reduce risk and cost. Designers try to design large structures to tolerate against different forces like wind, traffic load, temperature, flood, earthquake, land uplift etc. Using geodetic instruments and techniques, we are able to monitor the deformation behavior or deflection in the mentioned structures and eventually provide a structural failure alarm capability (Andersson 2008). It is important to select appropriate sensor and methods to detect the deformation. Slow deforming dams require sub-millimeter to millimeter level accuracy to monitor the displacement and deformation (Lindenbergh et al. 2005). Reaching this level of accuracy is not costly, if geodetic sensors are integrated with other sensors (e.g. geotechnical sensors, and precise total stations, see Hwang et al. 2012). It might be to implement other sensors (e.g. laser scanner and Total Station). Using point clouds data for deformation monitoring is almost new. Gonzalez et al. (2012) studied on point clouds accuracy for applications in civil engineering e.g. deformation monitoring. They showed that the results appear suitable for deformation monitoring, with accuracies less than 1 mm. Bagherbandi et al. (2009) studied on various techniques to find the optimal design of a deformation network using various criteria such as precision, cost and reliability. Better results can be achieved using the control network, provided that an optimal network design is performed for detecting deformations (Kuang 1996). In addition, the methods of geodetic network process can affect the results (Bagherbandi 2016). The aim of this study is primarily to evaluate different deformation monitoring methods and possibilities to physically interpret the deformation and evaluate the risk of failures. In this research, the idea of assigning weights for the observations by least square variance components estimation (LS-VCE) is used (Amiri-Simkooei 2007; Teunissen and Amiri-Simkooei 2008) in order to improve accuracy of adjustment results, which differs from the applied method in Bagherbandi (2016) to determine the variance components. Some issues and parameters should be investigated in LS-VCE such as the effect of variance components estimation on the observations final accuracy, the absolute error ellipsoid estimation, the study of the necessary conditions in a network to achieve higher accuracy and its effect on obtaining real results from the reliability matrix. All results obtained from adjustment by element, LS-VCE, and Tikhonov regularization are compared using a simulated geodetic network and real data. Results from this study provide important information in studying deformation that can be used to interpret the deformation mechanism, which may reduce the risk of potential disasters in large structures. We will evaluate the above-mentioned methods in Jamishan dam in Iran and utilize the geodetic techniques and observations to monitor the deformation of the dam. | ||
| کلیدواژه‌ها [English] | ||
| Geodetic Network, least squares variance component estimation, Deformation | ||
| مراجع | ||
|
Amiri-Simkooei, A. R., 2001, Strategy for Designing Geodetic Network with High Reliability and Geometrical Strength Criteria. Journal of Surveying Engineering, 127(3), 104-117. Amiri-Simkooei, A. R., 2004, A New Method for Second-order Design of Geodetic Networks: Aiming at High Reliability. Survey Review, 37(293), 552-560. Amiri-Simkooei, A. R., 2007, Least-squares variance component estimation: theory and GPS applications (Doctoral dissertation, TU Delft, Delft University of Technology). Amiri-Simkooei, A. R., Asgari, J., Zangeneh-Nejad, F. and Zaminpardaz, S., 2012, Basic concepts of optimization and design of geodetic networks. Journal of Surveying Engineering, 138(4), 172-183. Amiri-Simkooei, A. R., Zaminpardaz, S. and Sharifi, M. A., 2014, Extracting tidal frequencies using multivariate harmonic analysis of sea level height time series. Journal of Geodesy, 88(10), 975-988. Andersson, J. V., 2008, A complete model for displacement monitoring based on undifferenced GPS observations (Doctoral dissertation, KTH). Baarda, W., 1968, A testing procedure for use in geodetic networks, Netherland Geodetic Commission, Delft, Netherlands. Bagherbandi, M., Eshagh, M. and Sjöberg, L. E., 2009, Multi-objective versus single-objective models in geodetic network optimization. Nordic Journal of Surveying and Real Estate Research, 6(1), 7-20. Bagherbandi, M., 2016, Deformation monitoring using different least squares adjustment methods: A simulated study. KSCE Journal of Civil Engineering, 20(2), 855-862. Barnett, V. and Lewis, T., 1974, Outliers in statistical data. Wiley. Ben-Gal, I., Maimon, O. and Rockach, L.,2005, Data Mining and Knowledge Discovery Handbook A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers. Chen, Y.Q., Chrzanowski, A. and Secord, J.M., 1990, A strategy for the analysis of the stability of reference points in deformation surveys. CISM Journal, 44(2), 39-46. Cross, P. A., 1985, Numerical Methods in Network Design. In: Grafarend & Sanso, eds. Optimization and Design of Geodetic Networks. Berlin: Springer, 132-168. Davies, L. and Gather, U., 1993, The identification of multiple outliers, Journal of the American Statistical Association, 88(423), 782-792. Fan, H., 2010, Theory of Errors and Least Squares Adjustment, Stockholm: Royal Institue of Technology (KTH). González-Ferreiro, E., Diéguez-Aranda, U. and Miranda, D., 2012, Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry, 85(2), 281-292. Grafarend, E. W., 1974, Optimization of geodetic networks. Bolletino di Geodesia a Science Affini, 33(4), 351-406. Grafarend, E., Kleusberg, A. and Schaffrin, B., 1980, An introduction to the variance-covariance component estimation of Helmert type. Zeitschrift für Vermessungswesen, 105(4), 161-180. Helmert, F. R., 1907, Die Ausgleichungsrechnung nach der Methode der kleinsten Quadrate: mit Anwendungen auf die Geod sie, die Physik und die Theorie der Messinstrumente. BG Teubner. Hwang, J., Yun, H., Park, S.K., Lee, D. and Hong, S., 2012, Optimal methods of RTK-GPS/accelerometer integration to monitor the displacement of structures. Sensors, 12(1), 1014-1034. Jin, X. X. and de Jong, C.D., 1996, Relationship between satellite elevation and precision of GPS code observations. The Journal of Navigation, 49(2), 253-265. Kern, M., Preimesberger, T., Allesch, M., Pail, R., Bouman, J. and Koop, R., 2005, Outlier detection algorithms and their performance in GOCE gravity field processing. Journal of Geodesy, 78(9), 509-519 Koch, K. R., 1985, First Order Design: Optimization of the Configuration of a Network by Introducing Small Position Changes. In: Grafarend & Sanso, eds. Optimization and Design of Geodetic Networks. Berlin: Springer, pp. 56-73. Kuang, S., 1991, Optimization and Design of Deformation Monitoring Schemes, Fredericton, Canada: Department of Surveying Engineering. Kuang, S., 1996, Geodetic Network Analysis and Optimal Design: Concepts and Applications. Chelsea, Michigan, USA: Ann Arbor Press, Inc. Lerch, F. J., 1991, Optimum data weighting and error calibration for estimation of gravitational parameters. Bulletin géodésique, 65(1), 44-52. Lindenbergh, R., Pfeifer, N. and Rabbani, T., 2005, September. Accuracy analysis of the Leica HDS3000 and feasibility of tunnel deformation monitoring. In Proceedings of the ISPRS Workshop, Laser scanning, 36(3), 24-29. Lucas, J.R. and Dillinger, W.H., 1998, MINQUE for block diagonal bordered systems such as those encountered in VLBI data analysis. Journal of Geodesy, 72(6), 343-349. Teunissen, P.J., 1988, Towards a least-squares framework for adjusting and testing of both functional and stochastic model. Internal research memo, Geodetic Computing Centre, Delft. A reprint of original 1988 report is also available in 2004, No. 26, http://www.lr.tudelft.nl/mgp. Teunissen, P.J., 2000, Adjustment theory: an introduction series on mathematical geodesy and positioning. Delft University Press, Washington, D.C. Teunissen, P. J. and Amiri-Simkooei, A.R., 2008, Least-squares variance component estimation. Journal of geodesy, 82(2), pp.65-82. Williams, S. D., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R. M., Prawirodirdjo, L., Miller, M. and Johnson, D. J., 2004, Error analysis of continuous GPS position time series. Journal of Geophysical Research: Solid Earth, 109(B3), 1-19 Xu, P., Liu, Y., Shen, Y. and Fukuda, Y., 2007, Estimability analysis of variance and covariance components. Journal of Geodesy, 81(9), 593-602. Yetkin, M. and Inal, C., 2015, Optimal Design of Deformation Monitoring Networks Using the Global Optimization Methods. In The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11) (pp. 27-31). Springer, Cham. Zhang, J., Bock, Y., Johnson, H., Fang, P., Williams, S., Genrich, J., Wdowinski, S. and Behr, J., 1997, Southern California Permanent GPS Geodetic Array: Error analysis of daily position estimates and site velocities. Journal of geophysical research: solid earth, 102(B8), 18035-18055. | ||
|
آمار تعداد مشاهده مقاله: 934 تعداد دریافت فایل اصل مقاله: 460 |
||