This paper describes the results of a project which employed impulse radar to detect faults existing beneath the surface of an asphalt-covered bridge deck at Papineau Creek, Ontario. A simple but enlightening mathematical analysis is provided which accurately predicts the waveforms actually observed. The methodology clearly demonstrates the usefulness of impulse radar as a diagnostic tool in remote sensing. Amongst the problems discussed and solved are: the identification of faults due to debonding, scaling, and delamination; the measurement of asphalt thickness at selected locations on the bridge deck and hence the determination of average thickness over the entire bridge deck; and the automated processing of the data using a computer. A comparison of the radar detected faults with those detected using conventional methods is provided. A set of parameters has been developed which permits data to be reduced to a simple set of measurements that can be plotted for visual inspection. Dans cet article, on decrit les r6sultats d'un projet dans lequel on a utilise un radar impulsionnel pour detecter les defauts sur la surface d'un pont asphalte a PapineauCreek, Ontario. On utilise une methode d'analyse simple qui calcule avec precision les formes d'onde observees. La methodologie demontre clairement l'application du radar impulsionnel comme un outil pratique dans les applications de detection des objets eloignes. On etudie les problemes suivants: l'identification des defauts dus aux "debonding, scaling" et a la "delamination." On fait une etude com parative pour la detection des defauts observes par la methode proposee et par d'autres methodes usuelles. On etablit un ensemble des parametres qui permet la reduction des donnees en un ensemble de mesures pour inspection visuelle. |
Search and rescue satellite-aided tracking (SARSAT) has been responsible for saving the lives of many victims of airplane crashes and boating ac cidents. The system, in operation since 1982, employs satellites in low polar orbits which relay the emergency signals from distressed platforms to an earth station where signal processing techniques are used to measure doppler frequency shift. This paper provides a theoretical bound which can be used to evaluate the performance of SARSAT signal processors. Results show that, for a processing duration of 125 ms, an emergency locator transmitter (ELT) signal can be detected within ± 8 Hz when the carrier-to-noise density ratio is only 15 dB-Hz. La poursuite par satellite de recherche et sauvetage (SARSAT) a r£ussi a sauver la vie de nombreuses victimes d'accidents d'avion et de bateau. Le systeme qui fonctionne depuis 1982, utilise des satellites dans de faibles orbites polaires qui transmittent les signaux d'urgence depuis les plates-formes en detresse jusqu'a une station sur terre ou des techniques de traitement des signaux servent a mesurer le d6calage de frequence Doppler. Ce document fournit une mite theorique qui peut servir a ^valuer la performance des processeurs de signaux SARSAT. Les r&ultats montrent que, pour une dur6e de traitement de 125 ms, un signal d'Smetteur localisateur d'urgence (ELT) peut etre d6tect6 dans des limites de ± 8 Hz lorsque le rapport onde-porteuse-bruit n'est que de 15 dB-Hz.CAN. ELECT. ENG.
In this paper, it is demonstrated that multilayer neural networks, trained with the backpropagation algorithm and radial basis functions, can classify impulse radar waveforms from three different asphalt-covered bridge decks, each with its own structure. It might be thought that the thickness of asphalt and the depth of concrete over the reinforcing bars would be nearly constant for any one bridge deck; however in practice this is not the case. There are often significant changes in the thickness of the asphalt and the cover over reinforcement. Furthermore, a certain amount of damage to the concrete caused by severe winter climate often produces a random variation in the reflected waveforms obtained from different locations. These factors lead to a significant number of combinations of waveforms that can be obtained from any given structural type of deck. The classification accuracies achieved ranged between 89.9% and 100%. The accuracies achieved after using principal components analysis to reduce the dimensionality of the input data ranged between 95.6% and 100%.
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