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AuthorDouier, Kais
AuthorHussein, Mohammed F. M.
AuthorRenno, Jamil
Available date2024-06-02T06:20:07Z
Publication Date2024
Publication NameLecture Notes in Mechanical Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-981-99-5922-8_19
ISSN21954356
URIhttp://hdl.handle.net/10576/55694
AbstractThe monitoring of road roughness is one of the first and most critical steps in road maintenance. Road networks need constant maintenance to function properly and avoid any hazardous accidents or blockage of the traffic flow. The International Organization of Standardization (ISO) developed the International Roughness Index (IRI) to unify road monitoring systems and to categorize roads based on their roughness levels. The ISO 8608 standard divides road roughness levels into eight different classifications ranging from best (Class A) to worst (Class H) roads. The road roughness profile of a certain road section could be measured by traditional equipment such as a dipstick profilometer, profilograph, or an automated road meter. However, these traditional methods are time-consuming and costly. Thus, this research proposes the use of dynamic vehicle accelerations of a moving regular car and artificial neural networks to estimate the road profile. This research also compares the accuracy and efficiency of using a full car (7 degrees of freedom) numerical model and a quarter-car (2 degrees of freedom) numerical model in training the neural network and estimating the road profile. These numerical models will be used to create a library of input data sets (vehicle accelerations) and output data sets (road roughness profiles) which will be used to train the artificial neural networks (ANNs).
SponsorThe 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.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectArtificial neural network
Road roughness
Vehicle acceleration
Vibrations
TitleRoad Profile Estimation Using Full/Quarter-Car Model with Artificial Neural Networks
TypeConference Paper
Pagination203-210
dc.accessType Abstract Only


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