Grant: Alliance Grants
Université d'Ottawa
$30,104.00
Artificial Intelligence for the Condition Assessment of Critical Infrastructure
EF 2023-24
Résumé
The goals of this partnership are to improve testing speed, reproducibility, and accuracy of tools and techniques commonly used for monitoring structural deterioration of concrete. We will use artificial intelligence (AI) and deep learning (DL) to develop automated tools for on-site visual crack inspection and for lab-based microscopic techniques for concrete damage assessment. Our solutions will include rapid and reliable tools to i) automate computation of the cracking index (CI), using a smartphone application ("app") to analyze, in real time, pictures taken in-situ during visual inspections of structures, and ii) to automate the Damage Rating Index (DRI) calculation by applying image analysis (IA) techniques to high-resolution stereomicroscope images of concrete specimens. The proposed technology will significantly enhance two important concrete monitoring techniques: a) computation of the CI, a preliminary assessment of cause and extent of damage based on visual inspection of structures, and b) computation of the DRI, in which core samples retrieved from structures are inspected in a lab-based microscopic protocol to diagnose cause and extent of damage in concrete affected by distress mechanisms such as internal swelling reactions (ISR). Both methods have important limitations: CI requires in-situ qualitative investigations and, although useful as a preliminary indicator, its ability to assess damage of affected concrete is still unclear for structures presenting multiple distress mechanisms and under distinct degrees of confinement and exposure conditions. Computation of the DRI is a time-consuming, expertise-based, lab procedure. Both CI and DRI are subjective in nature and rely heavily on the skill and experience of the person performing the analysis. This project will enable rapid and reliable assessments of the nature and extent of concrete damage in critical infrastructure prevalent in Canada, such as bridges, dams, and buildings. This will, in turn, enable timely and cost-effective preventative rehabilitation strategies, thus ensuring a safer and more reliable built environment.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
569563-2021
Name nom
Moretti Sanchez, Leandro Francisco LF
Department département
Civil Engineering
Organization id
28
Institution établissement
Université d'Ottawa
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2021
Award amount
30104
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
602
Area of application group en
Construction, urban and rural planning
Area of application group fr
Construction, urbanisme et aménagement rural
Area of application en
Materials performance
Area of application fr
Performance des matériaux
Research subject code
1103
Research subject group en
Structural engineering
Research subject group fr
Concrete structures
Research subject en
Génie des structures
Research subject fr
Structures en béton
Application title
Artificial Intelligence for the Condition Assessment of Critical Infrastructure
Keyword
Concrete Durability; Condition assessment; Convolutional Neural Networks (CNN); Cracking Index (CI); Damage Rating Index (DRI); Deep Learning (DL); Diagnosis; Machine Learning (ML); Prognosis; Structural Performance
Application summary
The goals of this partnership are to improve testing speed, reproducibility, and accuracy of tools and techniques commonly used for monitoring structural deterioration of concrete. We will use artificial intelligence (AI) and deep learning (DL) to develop automated tools for on-site visual crack inspection and for lab-based microscopic techniques for concrete damage assessment. Our solutions will include rapid and reliable tools to i) automate computation of the cracking index (CI), using a smartphone application ("app") to analyze, in real time, pictures taken in-situ during visual inspections of structures, and ii) to automate the Damage Rating Index (DRI) calculation by applying image analysis (IA) techniques to high-resolution stereomicroscope images of concrete specimens. The proposed technology will significantly enhance two important concrete monitoring techniques: a) computation of the CI, a preliminary assessment of cause and extent of damage based on visual inspection of structures, and b) computation of the DRI, in which core samples retrieved from structures are inspected in a lab-based microscopic protocol to diagnose cause and extent of damage in concrete affected by distress mechanisms such as internal swelling reactions (ISR). Both methods have important limitations: CI requires in-situ qualitative investigations and, although useful as a preliminary indicator, its ability to assess damage of affected concrete is still unclear for structures presenting multiple distress mechanisms and under distinct degrees of confinement and exposure conditions. Computation of the DRI is a time-consuming, expertise-based, lab procedure. Both CI and DRI are subjective in nature and rely heavily on the skill and experience of the person performing the analysis. This project will enable rapid and reliable assessments of the nature and extent of concrete damage in critical infrastructure prevalent in Canada, such as bridges, dams, and buildings. This will, in turn, enable timely and cost-effective preventative rehabilitation strategies, thus ensuring a safer and more reliable built environment.