Pengesanan pegerakan struktur menggunakan model autoregresi purata bergerak bersepadu

Global Positioning System (GPS) has been widely used to monitor large engineering structures such as dams, bridges, towers and high rise buildings. There are several GPS methods that can be used for coordinate determination including Real Time Kinematic (RTK-GPS). This method uses carrier phase observation in real time to determine position coordinates. The coordinates produced by this method can indicate the displacement due to vibration caused by several factors such as temperature change, wind loading, earthquakes, landslides and other environmental factors. In this study, a technique based on time domain has been developed to analyze the response of a structure or an object. This technique uses time series algorithm to detect movements that occur and produces forecasting of movements of the object. The vibration signal obtained by the GPS sensor is used to model Autoregressive Integrated Moving Average (ARIMA) time series based on Box-Jenkins methodology. This method is used for making forecasting and it uses iterative approach to identify appropriate model. ARIMA model can be accepted after passing through four main steps that is identification of model, estimation of model parameter, model checking and forecasting of model. In this study, the Minitab software was used to develop the ARIMA model which can be used for forecasting of observed GPS data. The results of this study were obtained using the ARIMA model for the X, Y and Z axes. Also the results of this study proved that ARIMA model is capable of detecting movement of structures based on analyses that have been carried out

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 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

The assessment of relative permittivity on diesel vapour in the moisture content of Terap Red soil by ground penetrating radar

In a common agriculture resource, soil contamination monitoring is a prominent area of study. Nowadays, it is crucial to provide a database for the interpretation of ground penetrating radar (GPR) field data in monitoring soil contamination, such as diesel scatter migration. This study aims to assess the association between permittivity properties and soil water content (θw) for diesel contamination in Terap Red soil, which is classified as lateritic soil. Terap Red soil is an agro potential soil and available in more than 40% of distribution areas in Northern Malaysia (Agro-based State). In this research, 800 MHz shielded antenna GPR was applied for 24 hour measurement in a concrete simulation field tank, which was filled with Terap Red soil (1.5 m x 2.6 m x 1.5 m) located at Universiti Teknologi MARA (UiTM) Perlis, Malaysia. Embedded moisture content probe was simultaneously measured to monitor the response of volumetric water content in the contaminated soil. The GPR data were pre-processed and filtered by Reflexw 7.5. The calibrated Agilent Technologies Automated Vector Analyser (VNA) was used to verify the independent relative permittivity value from GPR. As a result, the evaluation of velocities and reflection of GPR data were influenced by the presence of diesel and contaminated vapour. A positive and significant correlation was obtained between relative permittivity and moisture content in the diesel-contaminated soil. In addition, a positive and strong linear regression analysis was also found between relative permittivity and moisture content. This analysis included an accurate total difference of root mean square error (RMSE) difference, which amounted to 0.04, with calibrated dielectric permittivity

Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic

Paddy planting becomes the primary source of income and livelihood for paddy farmers, especially small-scale farmers and landless laborers. Unfortunately, rice production has been threatened by paddy disease. The bacteria leaf blight disease (BLB) is one of Malaysia’s most significant paddy diseases, causing substantial harm to rice production. This study aims to determine the bacteria leaf blight (BLB) disease from the utilized techniques of RGB-Based Vegetation Indices and Fuzzy Logic on the Unmanned Aerial Vehicle (UAV) images during the first paddy season 2022 in Perlis. In this study, the RGB-based indices of Normalized Green Red Different Index (NGRDI) and Green Leaf Index (GLI) were applied to the UAV Images captured at 20m altitudes. Then the fuzzy logic classification technique was applied to identify the BLB disease severity which consists of healthy and infected paddy leaves with the acceptable accuracy of 90.16%. Based on the classified BLB severeness with fuzzy logic, the result shows that the NGRDI was more significant to identify paddy disease in the area. In contrast, the GLI index is more significant to identify the non-paddy area. The NGRDI and GLI index ranges for BLB were found between -0.054 to 0.092 and 0.005 to 0.222. For more improvement of the study, the multispectral UAV Image should be applied to increase the accuracy of paddy disease detection like BLB and the images will also be taken and verified in other paddy plots with the aid of a spectroradiometer