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Grant: Alliance Grants

The University of British Columbia

$111,522.00

Automated Low-Cost Change Detection of Road Infrastructure Assets Using Remote Sensing and AI

EF 2023-24

Résumé
Highway infrastructure across Canada is a key national asset that is critical to the mobility and economic prosperity of Canadians. Unfortunately, maintaining highway infrastructure is a costly and resource draining endeavor. Provincial transportation agencies dedicate billions of dollars to the process on an annual basis. In current practice inspecting the conditions of highway infrastructure assets is a tedious process whereby a crew of at least two individuals drive thousands of kilometers of road at low speeds to identify assets' where the conditions have changed since the initial installation. For primary highways the same highway is driven every day and crew stop by at assets in search for any changes in the condition. To help automate the process and improve its efficiency and accuracy, this project will involve developing a low-cost framework that uses remote sensing technology to create virtual 3D models of infrastructure assets. Algorithms that employ principles of machine learning and Artificial Intelligence will then be developed to process the 3D models and provide inspection crew with information about the changes in asset conditions without the need to visit each asset during long and tedious site visits. The results will be shared with the project partner who will work on large scale deployment of the developed technology to improve highway maintenance practice across Canada. This will cause significant improvements in the way infrastructure asset inspections are conducted resulting in safer and more resilient roads at a significantly lower cost to tax-payers.
Programme
Alliance Grants
Emplacement
British Columbia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
576485-2022
Name nom
Gargoum, Suliman SA
Department département
Applied Science, Faculty of
Organization id
2
Institution établissement
The University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2022
Award amount
111522
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
1003
Research subject group en
Civil engineering
Research subject group fr
Transportation engineering
Research subject en
Génie civil
Research subject fr
Génie du transport
Application title
Automated Low-Cost Change Detection of Road Infrastructure Assets Using Remote Sensing and AI
Keyword
Artificial Intelligence; Asset Management; Highway Design; Highway Engineering; Infrastructure Management; LiDAR; Machine Learning; Remote Sensing; Traffic Safety
Application summary
Highway infrastructure across Canada is a key national asset that is critical to the mobility and economic prosperity of Canadians. Unfortunately, maintaining highway infrastructure is a costly and resource draining endeavor. Provincial transportation agencies dedicate billions of dollars to the process on an annual basis. In current practice inspecting the conditions of highway infrastructure assets is a tedious process whereby a crew of at least two individuals drive thousands of kilometers of road at low speeds to identify assets' where the conditions have changed since the initial installation. For primary highways the same highway is driven every day and crew stop by at assets in search for any changes in the condition. To help automate the process and improve its efficiency and accuracy, this project will involve developing a low-cost framework that uses remote sensing technology to create virtual 3D models of infrastructure assets. Algorithms that employ principles of machine learning and Artificial Intelligence will then be developed to process the 3D models and provide inspection crew with information about the changes in asset conditions without the need to visit each asset during long and tedious site visits. The results will be shared with the project partner who will work on large scale deployment of the developed technology to improve highway maintenance practice across Canada. This will cause significant improvements in the way infrastructure asset inspections are conducted resulting in safer and more resilient roads at a significantly lower cost to tax-payers.