H.5.10. Applications
S. Shoorabi Sani
Abstract
In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a ...
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In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a conventional medium sized bridge (length of 50 meters, height of 10 meters, and with 2 piers) was examined for the purpose of this study. This method includes installing two sensor nodes with the ability of measuring temperature and humidity on both side of the bridge deck. The data collected by the system including temperature and humidity values are received by a LABVIEW-based software to be analyzed and stored in a database. Proposed SHM monitoring system is equipped by a novel method of using data mining techniques on the database of climatic conditions of past few years related to the location of the bridge to predict the occurrence and severity of future damages. In addition, this system has several alarm levels which are based on analysis of bridge conditions with fuzzy inference method, so it can issue proactive and precise warnings and alarms in terms of place of occurrence and severity of possible damages in the bridge deck to ensure total productive (TPM) and proactive maintenance. Very low costs, increased efficiency of the bridge service, and reduced maintenance costs makes this SHM system a practical and applicable system. The data and results related to all mentioned subjects were thoroughly discussed .
H.5.10. Applications
Z. Dorrani; M.S. Mahmoodi
Abstract
The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant ...
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The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant colony optimization (ACO) that detect edges. We here used ACO for edge detection of noisy images with Gaussian noise and salt and pepper noise. As the image edge frequencies are close to the noise frequency band, the edge detection using the conventional edge detection methods is challenging. The movement of ants depends on local discrepancy of image’s intensity value. The simulation results compared with existing conventional methods and are provided to support the superior performance of ACO algorithm in noisy images edge detection. Canny, Sobel and Prewitt operator have thick, non continuous edges and with less clear image content. But the applied method gives thin and clear edges.