Integrating RC bridge defect information into BIM models

Reinforced Concrete bridges are a vitally important part of our infrastructure. The status of this infrastructure needs to be monitored on a continuous basis in order to ensure its safety and functionality. This is currently being done by authorities worldwide via bridge inspection reports. The format and storage of these reports varies considerably across different authorities around the world and is sometimes comprised into a bridge management system (BMS). The lack of standardization severely hinders the use of inspection information for knowledge generation use cases of both practitioners and researchers. This paper presents an exploratory analysis and as a result an information model and a candidate binding to IFC to categorize inspection information of RC bridges and to standardize storage of this information in a format that is suitable for sharing and comparing it between different users and varying requirements. We were able to show in three steps, that IFC in its latest version IFC 4 provides sufficient functionality to serve as a basis for integrating relevant defect information and imagery. Firstly, we extracted types of defects and properties needed for bridge assessment from existing bridge inspection manuals. Secondly, we modelled the defect entities, their properties and relations and thirdly, mapped them to appropriate IFC entities. A prototypical implementation serves as a proof of concept for automated sharing and comparing of information needed in RC bridge inspections and for establishing a knowledge base for bridge performance over time and across authorities.This work is partly funded by Trimble Inc. and by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 31109806.0007. SeeBridge is co-funded by Funding Partners of the ERA-NET Plus Infravation and the European Commission. The Funding Partners of the Infravation 2014 Call are: Ministerie van Infrastructuur en Milieu, Rijkswaterstaat, Bundesministerium für Verkehr, Bau und Stadtentwicklung, Danish Road Directorate, Statens Vegvesen Vegdirektoratet, Trafikverket – Trv, Vegagerðin, Ministere de L’ecologie, du Developpement Durable et de L’energie, Centro para el Desarrollo Tecnologico Industrial, Anas S.P.A., Netivei Israel – National Transport Infrastructure Company Ltd. and Federal Highway Administration USDOT

SeeBridge Next Generation Bridge Inspection: Overview, Information Delivery Manual and Model View Definition

Innovative solutions for rapid and intelligent survey and assessment methods are required in maintenance, repair, retrofit and rebuild of enormous numbers of bridges in service throughout the world. Motivated by this need, a next-generation integrated bridge inspection system, called SeeBridge, has been proposed. An Information Delivery Manual (IDM) was compiled to specify the technical components, activities and information exchanges in the SeeBridge process, and a Model View Definition (MVD) was prepared to specify the data exchange schema to serve the IDM. The MVD was bound to the IFC4 Add2 data schema standard. The IDM and MVD support research and development of the system by rigorously defining the information and data that structure bridge engineers' knowledge. The SeeBridge process is mapped, parts of the data repositories are presented, and the future use of the IDM is discussed. The development underlines the real potential for automated inspection of infrastructure at large, because it demonstrates that the hurdles in the way of automated acquisition of detailed and semantically rich models of existing infrastructure are computational in nature, not instrumental, and are surmountable with existing technologies

Cambridge Bridge Inspection Dataset

A labelled dataset for the scope of visual bridge inspection is not publically available. We have composed a new dataset for evaluating the performance of different detection methods on this dataset

Cambridge Bridge Inspection Dataset

A labelled dataset for the scope of visual bridge inspection is not publically available. We have composed a new dataset for evaluating the performance of different detection methods on this dataset

Challenges of bridge maintenance inspection

Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing

Challenges of bridge maintenance inspection

Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing

Multi-classifier for reinforced concrete bridge defects

Classifying concrete defects during a bridge inspection remains a subjective and laborious task. The risk of getting a false result is approximately 50% if different inspectors assess the same concrete defect. This is significant in the light of an over-aging bridge stock, decreasing infrastructure maintenance budgets and catastrophic bridge collapses as happened in 2018 in Genoa, Italy. To support an automated inspection and an objective bridge defect classification, we propose a three-staged concrete defect classifier that can multi-classify potentially unhealthy bridge areas into their specific defect type in conformity with existing bridge inspection guidelines. Three separate deep neural pre-trained networks are fine-tuned based on a multi-source dataset consisting of self-collected image samples plus several Departments of Transportation inspection databases. We show that this approach can reliably classify multiple defect types with an average mean score of 85%. Our presented multi-classifier is a contribution towards developing a mostly or fully inspection schema for a more cost-effective and more objective bridge inspection

Integrating RC bridge defect information into BIM models

Reinforced Concrete bridges are a vitally important part of our infrastructure. The status of this infrastructure needs to be monitored on a continuous basis in order to ensure its safety and functionality. This is currently being done by authorities worldwide via bridge inspection reports. The format and storage of these reports varies considerably across different authorities around the world and is sometimes comprised into a bridge management system (BMS). The lack of standardization severely hinders the use of inspection information for knowledge generation use cases of both practitioners and researchers. This paper presents an exploratory analysis and as a result an information model and a candidate binding to IFC to categorize inspection information of RC bridges and to standardize storage of this information in a format that is suitable for sharing and comparing it between different users and varying requirements. We were able to show in three steps, that IFC in its latest version IFC 4 provides sufficient functionality to serve as a basis for integrating relevant defect information and imagery. Firstly, we extracted types of defects and properties needed for bridge assessment from existing bridge inspection manuals. Secondly, we modelled the defect entities, their properties and relations and thirdly, mapped them to appropriate IFC entities. A prototypical implementation serves as a proof of concept for automated sharing and comparing of information needed in RC bridge inspections and for establishing a knowledge base for bridge performance over time and across authorities