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المؤلفDouier, K.
المؤلفHussein, M.F.M.
المؤلفRenno, J.
تاريخ الإتاحة2024-06-02T06:20:09Z
تاريخ النشر2022
اسم المنشورProceedings of the International Congress on Sound and Vibration
المصدرScopus
الرقم المعياري الدولي للكتاب23293675
معرّف المصادر الموحدhttp://hdl.handle.net/10576/55705
الملخصRoad roughness can cause ride discomfort and contribute to the emission of ground-borne noise and vibration. A robust monitoring regime of road roughness is essential for the efficient maintenance of a country's road network. The International Organization for Standardization (ISO) presents an International Roughness Index (IRI) in Standard ISO 8606 to classify road roughness into eight different categories ranging from class A to class H. ISO 8606 summarizes the level of road roughness into a single number that could be used to describe the state of the road. The IRI can be measured using well-established techniques such as profilometers, profilographs, and automated road meters. However, these traditional techniques are time-consuming and/or not cost-effective. We propose using vehicle acceleration and artificial neural networks (ANNs) as an efficient and cost-effective alternative to the aforementioned techniques. We use a multi-degree of freedom model to simulate the dynamics of a vehicle traveling on a road with various road roughness classes. The road profile will be predicted using two methods. The first method uses an ANN which was trained using a library of inputs (dynamic vehicle accelerations) and outputs (road profiles), to predict the road profile. In the second method, we formulate the inverse problem in the frequency domain to obtain the road profile from the dynamic vehicle accelerations. The second method will serve as our benchmark solution. We compare the accuracy and computational efficiency of both methods and demonstrate that the trained ANN has a noticeable advantage over the inverse problem in the frequency domain.
راعي المشروعThe authors would like to acknowledge funding from Qatar University through a Graduate Assistantship. The authors would also like to acknowledge funding from Qatar Rail through grant number QUEX-CENG-QR-21/22-1.
اللغةen
الناشرSociety of Acoustics
الموضوعartificial neural network
machine learning
road roughness
vibrations
العنوانPREDICTING ROAD ROUGHNESS PROFILE USING DYNAMIC VEHICLE ACCELERATIONS AND ARTIFICIAL NEURAL NETWORKS
النوعConference
الصفحات-
dc.accessType Abstract Only


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