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Road surface profile monitoring based on vehicle response and artificial neural network simulation
Thesis (PhD)--University of Pretoria, 2015.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2015
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| _version_ | 1867613537151483904 |
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| access_status_str | Open Access |
| author2 | Heyns, P.S. (Philippus Stephanus) |
| author_browse | Heyns, P.S. (Philippus Stephanus) |
| author_facet | Heyns, P.S. (Philippus Stephanus) |
| collection | Thesis |
| dc_rights_str_mv | © 2015 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD)--University of Pretoria, 2015. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/43788 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:43.241Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/43788 Road surface profile monitoring based on vehicle response and artificial neural network simulation Heyns, P.S. (Philippus Stephanus) Ngwangwa, Harry Magadhlela Mechanical vibrations Non-linear AutoRegressive with eXogenous inputs (NARX) Artificial neural networks PSD roughness classification International Roughness Index (IRI) UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Thesis (PhD)--University of Pretoria, 2015. Road damage identification is still largely based on visual inspection methods and profilometer data. Visual inspection methods heavily rely on expert knowledge which is often very subjective. They also result in traffic flow interference due to the need for redirection of traffic to alternative routes during inspection. In addition to this, accurate high-speed profilometers, such as scanning vehicles, are extremely expensive often requiring strong economic justifications for their acquisition. The low-cost profilometers are very slow, typically operating at or less than walking speeds, causing their use to be labour-intensive if applied to large networks.This study aims at developing a road damage identification methodology for both paved and unpaved roads based on modelling the road-vehicle interaction system with an artificial neural network. The artificial neural network is created and trained with vehicle acceleration data as inputs and road profiles as targets. Then the trained neural network is consequently used for reconstruction of road profiles upon simulating it with vertical vehicle accelerations. The simulation process is very fast and can often be completed in a very short time thus making it possible to implement the methodology in real-time. Three case studies were used to demonstrate the feasibility of the methodology and the results on field tests carried out on mine vehicles with crudely measured road profiles showed a majority of the tested roads were reconstructed to within a fitting accuracy of less than 40% at a correlation level of greater than 55% which in this study was found to be practically acceptable considering the limitations imposed by the sizes of the haul trucks and their tyres as well as the quality of the road profiles and lack of control in the vehicle operation. mi2025 Mechanical and Aeronautical Engineering Unrestricted SDG-09: Industry, innovation and infrastructure SDG-11: Sustainable cities and communities SDG-12: Responsible consumption and production 2015-02-23T12:37:02Z 2015-02-23T12:37:02Z 2015-04 2015 Thesis Ngwangwa, HM 2015, Road surface profile monitoring based on vehicle response and artificial neural network simulation, PhD thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/43788> A2015 http://hdl.handle.net/2263/43788 en © 2015 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Mechanical vibrations Non-linear AutoRegressive with eXogenous inputs (NARX) Artificial neural networks PSD roughness classification International Roughness Index (IRI) UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Road surface profile monitoring based on vehicle response and artificial neural network simulation |
| title | Road surface profile monitoring based on vehicle response and artificial neural network simulation |
| title_full | Road surface profile monitoring based on vehicle response and artificial neural network simulation |
| title_fullStr | Road surface profile monitoring based on vehicle response and artificial neural network simulation |
| title_full_unstemmed | Road surface profile monitoring based on vehicle response and artificial neural network simulation |
| title_short | Road surface profile monitoring based on vehicle response and artificial neural network simulation |
| title_sort | road surface profile monitoring based on vehicle response and artificial neural network simulation |
| topic | Mechanical vibrations Non-linear AutoRegressive with eXogenous inputs (NARX) Artificial neural networks PSD roughness classification International Roughness Index (IRI) UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production |
| url | http://hdl.handle.net/2263/43788 |