Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Dense-graded asphalt pavement macrotexture measurement using tire/road noise monitoring
Abstract Data collection and the associated automated methods are one of the main building blocks of pavement management systems. One of the main problems associated with automated data collection methods is the high cost of the equipment involved. The aim of this research is to develop a cost-effective system for dense-graded asphalt pavement macrotexture monitoring. To this end, a system based on the tire/road noise, utilizing microphones and employing cepstral signal processing has been developed. The proposed method is compared with the current state of the art PCA based method and is shown that the precision error of the proposed method is about 7%, which outperforms the previous state of the art. To enhance the precision of the cepstral method for the vehicle speed variation amongst the collected dataset, the combination of the cepstral signal processing with Gaussian mixture models is proposed which results in the final precision error of 8%.
Highlights Narrow range macrotexture can be evaluated by targeting the vibration mechanism Macrotexture evaluation using Cepstrum method provides a low precision error of 7% Cepstral coefficients are appropriate features for macrotexture monitoring Combination of GMM with cepstral coefficients has resulted in an improved model
Dense-graded asphalt pavement macrotexture measurement using tire/road noise monitoring
Abstract Data collection and the associated automated methods are one of the main building blocks of pavement management systems. One of the main problems associated with automated data collection methods is the high cost of the equipment involved. The aim of this research is to develop a cost-effective system for dense-graded asphalt pavement macrotexture monitoring. To this end, a system based on the tire/road noise, utilizing microphones and employing cepstral signal processing has been developed. The proposed method is compared with the current state of the art PCA based method and is shown that the precision error of the proposed method is about 7%, which outperforms the previous state of the art. To enhance the precision of the cepstral method for the vehicle speed variation amongst the collected dataset, the combination of the cepstral signal processing with Gaussian mixture models is proposed which results in the final precision error of 8%.
Highlights Narrow range macrotexture can be evaluated by targeting the vibration mechanism Macrotexture evaluation using Cepstrum method provides a low precision error of 7% Cepstral coefficients are appropriate features for macrotexture monitoring Combination of GMM with cepstral coefficients has resulted in an improved model
Dense-graded asphalt pavement macrotexture measurement using tire/road noise monitoring
Ganji, Mohammad Reza (Autor:in) / Golroo, Amir (Autor:in) / Sheikhzadeh, Hamid (Autor:in) / Ghelmani, Ali (Autor:in) / Arbabpour Bidgoli, Mohammad (Autor:in)
20.06.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
ANN , artificial neural network , CPX , close-proximity , CTM , circular texture meter , DCT , discrete cosine transform , ETD , estimated pavement depth , GMM , Gaussian mixture model , IFI , international friction index , IRI , international roughness index , M & R , maintenance and rehabilitation , MPD , mean pavement depth , MTD , mean texture depth , OBSI , on-board sound intensity , PCA , Principle component analysis , PCI , Pavement condition index , PMS , pavement management system , PSD , power spectral density , SMTD , sensor measured texture depth , SN , structural number , SVM , support vector machine , TPTA , tire/pavement test apparatus , VAD , voice activity detection , Pavement management systems , Macrotexture , Cepstral signal processing , Tire/road noise , Gaussian mixture models , Automated condition monitoring
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Experimental Study on Macrotexture of Asphalt Pavement
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