Arima model time-series forecasting for structural monitoring using RTK-GPS
Many studies have been reported by researchers on the deployment of high precision GPS sensors on large engineering structures such as dams, bridges, towers and tall building to provide real time measurement for the indication of displacements and vibrations caused due to temperature changes, wind l...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2008
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| Online Access: | http://eprints.utm.my/7769/1/ARIMA_MODEL_TIME-SERIES_FORECASTING_FOR_STRUCTURAL_MONITORING_USING_RTK-GPS.pdf |
| _version_ | 1825910095367634944 |
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| author | Anshah, Siti Aminah Ahmad, Anuar Mat Amin, Zulkarnaini |
| author_facet | Anshah, Siti Aminah Ahmad, Anuar Mat Amin, Zulkarnaini |
| author_sort | Anshah, Siti Aminah |
| collection | ePrints |
| description | Many studies have been reported by researchers on the deployment of high precision GPS sensors on large engineering structures such as dams, bridges, towers and tall building to provide real time measurement for the indication of displacements and vibrations caused due to temperature changes, wind loading, distant earthquakes, landslides, etc. Similarly, current researches on Global Positioning System (GPS) and its applications to structural monitoring have been conducted but eventually no detailed or
thorough studies on the analysis of the various changes in unusual events as well as structure change or damage have been discussed or explained. As consequence, a new analysis technique has been proposed in this paper. This technique analyzed the response of the object or structure’s response in the time domain. In this paper, a time series algorithm is presented for damage identification and forecasting to
detect any movement of the structure. The vibration signals obtained from GPS are modelled as autoregressive integrated moving average (ARIMA) time series. The Box-Jenkins methodology of forecasting was used and it is different from the other methods because it does not assume any particular pattern in the historical data of the series to be forecasted. It uses an iterative approach of identifying a possible model from a general class of models. The chosen model is then checked against the historical data to see whether it accurately describes the series. The model fits well if the residuals are generally small, randomly distributed, and contain no useful information. By using Minitab software, this Box- Jenkins methodology can be implemented in the model building strategy and the model can be used for forecasting. |
| first_indexed | 2024-03-05T18:11:55Z |
| format | Conference or Workshop Item |
| id | utm.eprints-7769 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:11:55Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | utm.eprints-77692017-09-10T04:16:54Z http://eprints.utm.my/7769/ Arima model time-series forecasting for structural monitoring using RTK-GPS Anshah, Siti Aminah Ahmad, Anuar Mat Amin, Zulkarnaini T Technology (General) Many studies have been reported by researchers on the deployment of high precision GPS sensors on large engineering structures such as dams, bridges, towers and tall building to provide real time measurement for the indication of displacements and vibrations caused due to temperature changes, wind loading, distant earthquakes, landslides, etc. Similarly, current researches on Global Positioning System (GPS) and its applications to structural monitoring have been conducted but eventually no detailed or thorough studies on the analysis of the various changes in unusual events as well as structure change or damage have been discussed or explained. As consequence, a new analysis technique has been proposed in this paper. This technique analyzed the response of the object or structure’s response in the time domain. In this paper, a time series algorithm is presented for damage identification and forecasting to detect any movement of the structure. The vibration signals obtained from GPS are modelled as autoregressive integrated moving average (ARIMA) time series. The Box-Jenkins methodology of forecasting was used and it is different from the other methods because it does not assume any particular pattern in the historical data of the series to be forecasted. It uses an iterative approach of identifying a possible model from a general class of models. The chosen model is then checked against the historical data to see whether it accurately describes the series. The model fits well if the residuals are generally small, randomly distributed, and contain no useful information. By using Minitab software, this Box- Jenkins methodology can be implemented in the model building strategy and the model can be used for forecasting. 2008 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/7769/1/ARIMA_MODEL_TIME-SERIES_FORECASTING_FOR_STRUCTURAL_MONITORING_USING_RTK-GPS.pdf Anshah, Siti Aminah and Ahmad, Anuar and Mat Amin, Zulkarnaini (2008) Arima model time-series forecasting for structural monitoring using RTK-GPS. In: 7th International Symposium & Exhibition on Geoinformation (ISG 2008), 13-15 October 2008, Putra World Trade Centre(PWTC), Kuala Lumpur, Malaysia. |
| spellingShingle | T Technology (General) Anshah, Siti Aminah Ahmad, Anuar Mat Amin, Zulkarnaini Arima model time-series forecasting for structural monitoring using RTK-GPS |
| title | Arima model time-series forecasting for structural monitoring using RTK-GPS |
| title_full | Arima model time-series forecasting for structural monitoring using RTK-GPS |
| title_fullStr | Arima model time-series forecasting for structural monitoring using RTK-GPS |
| title_full_unstemmed | Arima model time-series forecasting for structural monitoring using RTK-GPS |
| title_short | Arima model time-series forecasting for structural monitoring using RTK-GPS |
| title_sort | arima model time series forecasting for structural monitoring using rtk gps |
| topic | T Technology (General) |
| url | http://eprints.utm.my/7769/1/ARIMA_MODEL_TIME-SERIES_FORECASTING_FOR_STRUCTURAL_MONITORING_USING_RTK-GPS.pdf |
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