Time-series analysis of GPS measurements for modeling and estimating bridge movements using Neural Network model: Case study, Tabiat Bridge

Document Type : Research Article

Authors

1 Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

2 Associate Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

3 M.Sc. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

Abstract

One of the parameters that can be used to predict different types' behavior of civil structures is the displacement components, extracted from structures. Bridges and their health monitoring system are important parts of the land transportation system because of their safety and durability. This study aims to evaluate the safety behavior of the Tabiat pavement bridge using a high-rate geodetic monitoring of the Global Positioning System (GPS). Accurate orbital information (SP3) is also used to process the collected observations as well as the Double Difference Bernese software. Firstly, the data are processed kinematically, using the coordinate conversion system, into the local coordinates of the bridge (N, E, and H). At the beginning, the raw data is pre-processed using the Kalman filter to reduce noise. The time series of GPS observations are estimated with 0.0013 m accuracy by applying the Kalman filter with CWPA method. Afterwards, the MA filter, mean time zone and Median filter are used to estimate the semi-static, static and dynamic components of bridge movements, respectively. The results obtained by applying the MA filter indicate that the semi-static component is extracted with 0.005 m accuracy in the North, 0.003 m in the East and 0.01 m in the Height directions. The Median filter also estimates the dynamic component with precision of 0.0007, 0.0006 and 0.001 m in three directions N, E and H, respectively. In this study, considering the sampling rate of observations, the average of 5 minutes of semi-static movement is considered as a static behavior of each point. In order to extract the dominant frequencies of the bridge, the least squares harmonic estimation (LSHE) method is used. Finally the Artificial Neural Network model (ANN) with 5 hidden layers and 5 delays is used to predict bridge deformation based on data mining. The frequencies at points on the bridge are approximately similar, which shows the similarity of the periodogram to the high hardness of the bridge. In addition, the frequencies in both North and East directions, relative to Height direction, indicate the GPS sensitivity to load effects. The prediction model using the artificial neural network is then fitted to dynamic component, with accuracy of approximately 6×10-5m and 4×10-5m to the dynamic and semi-static components of the bridge motion, respectively. Both the least squares harmonic estimation method and the ANN model are considered as suitable techniques for estimating the performance changes of GPS measurements in the frequency and time domains during the monitoring time period. The results indicate that the bridge behavior safety design range the minimum changes using GPS measurements in the time and frequency domains. Besides, the frequency analysis of the bridge movement time series and the neural network model, can be used to detect significant frequency changes and study the bridge performance rigidity, respectively. These results show that high-frequency satellite geodetic data which properly processed can also be useful for measuring the dynamic displacements of tall buildings, cable-stayed bridges, flexible and rigid civil structures. Also they would be the bases of bridge safety monitoring system primary warning.

Keywords

Main Subjects

References

شریفی، م. ع.، فرزانه س. و کوثری، م. 1397،کاهش نوفه تصاویرنجومی با استفاده از معادلات مشتقات جزئی، م. ژئو فیزیک ایران، 11(3)،143-128.
صفری، ع.، شریفی، م. ع. و فرزانه، س.، 1393، تعیین سرعت سینماتیک ماهواره‌های مدار پایین با استفاده از فیلتر کالمن تعمیم‌یافته؛ بررسی موردی: زوج ماهواره GRACE ، م. فیزیک زمین و فضا، 40(4)، 67-82.
گروه دیبا، 1393، پل طبیعت. Retrieved from https://dibats.com/fa/project _.
Aktan, A. E., Catbas, F. N., Grimmelsman, K. A., Pervizpour, M., Curtis, J. M., Shen, K. and Qin, X., 2002, Health monitoring for effective management of infrastructure. Paper presented at the Smart structures and materials 2002: Smart systems for bridges, structures, and highways.
Barner, K. E. and Arce, G. R., 1998, Order-statistic filtering and smoothing of time-series: Part II. Handbook of statistics, 17, 555-602.
Cao, J., Ding, W., Zhao, D., Song, Z. and Liu, H., 2014, Time series forecast of foundation pit deformation based on LSSVM-ARMA model. Rock Soil Mech, 35, 579-586.
Chen, G., Lin, X., Yue, Q. and Liu, H., 2016, Study on separation and forecast of long term deflection based on time series analysis. J. Tongji Univ. Nat. Sci. Ed, 44, 962-968.
Chen, X., Shen, C., Zhang, W.-b., Tomizuka, M., Xu, Y. and Chiu, K., 2013, Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages. Measurement, 46(10), 3847-3854.
Chen, Y., Ye, Y.-q., Sun, B.-n., Lou, W. and Yu, J., 2008, Application of model prediction technology to bridge health monitoring. JOURNAL-ZHEJIANG UNIVERSITY ENGINEERING SCIENCE, 42(1), 157.
Chen, Z., Zhou, J., Tse, K. T., Hu, G., Li, Y. and Wang, X., 2016, Alignment control for a long span urban rail-transit cable-stayed bridge considering dynamic train loads. Science China Technological Sciences, 59(11), 1759-1770.
Frohlich, H., Chapelle, O. and Scholkopf, B., 2003, Feature selection for support vector machines by means of genetic algorithm. Paper presented at the Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.
Furtner, P., Stöger, M. and Schreyer, M., 2013, SHM DATA—management, treatment, analysis and interpretation—a solution for permanent monitoring systems. Paper presented at the 6th International conference on structural health monitoring of intelligent infrastructure, Hong Kong.
Gili, J. A., Corominas, J. and Rius, J., 2000, Using Global Positioning System techniques in landslide monitoring. Engineering geology, 55(3), 167-192.
Górski, P., 2015, Investigation of dynamic characteristics of tall industrial chimney based on GPS measurements using Random Decrement Method. Engineering Structures, 83, 30-49.
Gul, M. and Catbas, F. N., 2009, Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications. Mechanical Systems and Signal Processing, 23(7), 2192-2204.
Harlim, J. and Hunt, B. R., 2007, A non-Gaussian ensemble filter for assimilating infrequent noisy observations. Tellus A: Dynamic Meteorology and Oceanography, 59(2), 225-237.
Harvey, A. C. and Trimbur, T. M., 2003, General model-based filters for extracting cycles and trends in economic time series. Review of Economics and Statistics, 85(2), 244-255.
Haykin, S. 1994, Neural networks, a comprehensive foundation. 1994 New York. NY: MacMillan College Publishing Company.
Hoult, N. A., Dutton, M., Hoag, A. and Take, W. A., 2016, Measuring crack movement in reinforced concrete using digital image correlation: Overview and application to shear slip measurements. Proceedings of the IEEE, 104(8), 1561-1574.
Hudnut, K. W. and Behr, J. A., 1998, Continuous GPS monitoring of structural deformation at Pacoima Dam, California. Seismological Research Letters, 69(4), 299-308.
Hunter, J. S., 1986, The exponentially weighted moving average. Journal of quality technology, 18(4), 203-210.
Im, S. B., Hurlebaus, S. and Kang, Y. J., 2013, Summary review of GPS technology for structural health monitoring. Journal of Structural Engineering, 139(10), 1653-1664.
Kaloop, M. R. and Hu, J. W., 2016, Dynamic performance analysis of the towers of a long-span bridge based on gps monitoring technique. Journal of Sensors, 2016.
Kao, C. Y. and Loh, C. H., 2013, Monitoring of long‐term static deformation data of Fei‐Tsui arch dam using artificial neural network‐based approaches. Structural Control and Health Monitoring, 20(3), 282-303.
Lai, J., Qiu, J., Feng, Z., Chen, J. and Fan, H., 2016, Prediction of soil deformation in tunnelling using artificial neural networks. Computational Intelligence and Neuroscience, 2016.
Larocca, A. P. C., Schaal, R. E., Santos, M. C. and Langley, R. B., 2006, Analyzing the dynamic behavior of suspension bridge towers using GPS. Paper presented at the Proceedings of the 19th International Technical Meeting of the ION Satellite Division—ION GNSS.
Malleswaran, M., Vaidehi, V. and Sivasankari, N., 2014, A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques. Aerospace Science and Technology, 32(1), 169-179.
McClelland, J. L., Rumelhart, D. E. and Group, P. R., 1986, Parallel distributed processing. Explorations in the Microstructure of Cognition, 2, 216-271.
Moschas, F. and Stiros, S., 2011, Measurement of the dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer. Engineering Structures, 33(1), 10-17.
Moschas, F. and Stiros, S., 2013, Noise characteristics of high-frequency, short-duration GPS records from analysis of identical, collocated instruments. Measurement, 46(4), 1488-1506.
Moschas, F. and Stiros, S., 2015, Dynamic deflections of a stiff footbridge using 100-Hz GNSS and accelerometer data. Journal of Surveying Engineering, 141(4), 04015003.
Ossandón, S. and Bahamonde, N., 2011, On the nonlinear estimation of GARCH models using an extended Kalman filter. Paper presented at the Proceedings of the World Congress on Engineering.
Samadi Alinia, H., 2017, New GPS Time Series Analysis and a Simplified Model to Compute an Accurate Seasonal Amplitude of Tropospheric Delay.
Sayed, M. A., Kaloop, M. R., Kim, E. and Kim, D., 2017, Assessment of acceleration responses of a railway bridge using wavelet analysis. KSCE Journal of Civil Engineering, 21(5), 1844-1853.
Sirca Jr, G. and Adeli, H., 2012, System identification in structural engineering. Scientia Iranica, 19(6), 1355-1364.
Shumway, R. H. and Stoffer, D. S., 1982, An approach to time series smoothing and forecasting using the EM algorithm. Journal of time series analysis, 3(4), 253-264.
Souza, E. M. and Negri, T. T., 2017, First prospects in a new approach for structure monitoring from GPS multipath effect and wavelet spectrum. Advances in Space Research, 59(10), 2536-2547.
Taha, M. R., Noureldin, A., Lucero, J. and Baca, T., 2006, Wavelet transform for structural health monitoring: a compendium of uses and features. Structural health monitoring, 5(3), 267-295.
Teferle, F. N., Williams, S. D., Kierulf, H. P., Bingley, R. M. and Plag, H.-P., 2008, A continuous GPS coordinate time series analysis strategy for high-accuracy vertical land movements. Physics and Chemistry of the Earth, Parts A/B/C, 33(3-4), 205-216.
Van Le, H. and Nishio, M., 2015, Time-series analysis of GPS monitoring data from a long-span bridge considering the global deformation due to air temperature changes. Journal of Civil Structural Health Monitoring, 5(4), 415-425.
Xie, Y., Zhang, Y. and Ye, Z., 2007, Short‐term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Computer‐Aided Civil and Infrastructure Engineering, 22(5), 326-334.
Xin, J., Zhou, J., Yang, S. X., Li, X. and Wang, Y., 2018, Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model. Sensors, 18(1), 298.
Yang, J., Zhou, Y., Zhou, J. and Chen, Y., 2013, Prediction of bridge monitoring information chaotic using time series theory by multi-step BP and RBF neural networks. Intelligent Automation & Soft Computing, 19(3), 305-314.
Yi, T.-H., Li, H.-N. and Gu, M., 2013a, Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge. Measurement, 46(1), 420-432.
Yi, T. H., Li, H. N. and Gu, M., 2013b, Recent research and applications of GPS‐based monitoring technology for high‐rise structures. Structural Control and Health Monitoring, 20(5), 649-670.
Yi, T.-H., Li, H.-N., Song, G. and Guo, Q., 2016, Detection of shifts in GPS measurements for a long-span bridge using CUSUM chart. International Journal of Structural Stability and Dynamics, 16(04), 1640024.
Yigit, C. O., 2016, Experimental assessment of post-processed kinematic Precise Point Positioning method for structural health monitoring. Geomatics, natural hazards and risk, 7(1), 360-383.
Yu, J., Meng, X., Shao, X., Yan, B. and Yang, L., 2014, Identification of dynamic displacements and modal frequencies of a medium-span suspension bridge using multimode GNSS processing. Engineering Structures, 81, 432-443.
Yu, J., Yan, B., Meng, X., Shao, X. and Ye, H., 2016, Measurement of bridge dynamic responses using network-based real-time kinematic GNSS technique. Journal of Surveying Engineering, 142(3), 04015013.
Yu, M., Guo, H. and Chengwu, Z., 2006, Application of wavelet analysis to GPS deformation monitoring. Paper presented at the 2006 IEEE/ION Position, Location, And Navigation Symposium.
Zhou, J., Li, X., Xia, R., Yang, J. and Zhang, H., 2017, Health monitoring and evaluation of long-span bridges based on sensing and data analysis: A survey. Sensors, 17(3), 603.
Journal of the Earth and Space Physics
Volume 46, Issue 3
November 2020
Pages 473-495

Files

Share

How to cite

Statistics