Motivation Bridge Capacity Population Density I- 55 Thomas St I- 40 Walnut Grove Rd I- 240 US Hwy 70 S Germantown Rd Large number of bridges and road structures In the US, > 600,000 bridges E Shelby Dr 0 2.5 5 Miles Large number of aging structures In the US, 1/9 is classified as structurally deficient
Motivation ~ 500,000 bridge inspections/year across the EU and the US
Motivation Bridge Inspection Challenges: A lot of data Or No data
Motivation Bridge Inspection Challenges: Constant need for detailed, updated data throughout the bridge s lifetime New data should be comparable with old Abutment A Scale: 1:200/100
Motivation Bridge Inspection Challenges: Minimize Cost vs. Maximize service level
Bridge management The challenge Bridge management is a complex process
Motivation Almost all BMS are tabular based and hard to manage. Most of them present a similar architectural framework. Some have better bridge visualization.
Motivation Bridge management process Theoretical background Historical data archiving PMS, SMS output Designing bridge Rehab. and Maint. works BMS establishment BMS processing Maintenance management System Maint./rerahb. Project Inspectors recruiting and training Performing Inspections Additional study (NDT/Monitoring) Bridge Maint./Rehab. contractors
Overview SeeBridge is an acronym for Semantic Enrichment Engine for Bridges a proposed process for automatically compiling BIM models of existing concrete highway bridges that includes precise information about their components, geometry and surface defects Automated Compilation of Semantically Enriched BIM models of Bridges
Overview First, SeeBridge uses laser/photogrammetry to survey a bridge.
Overview Then, SeeBridge does 3D geometry reconstruction.
Overview Next, SeeBridge does Semantic Enrichment. Deck slab Shear keys Girder Column Transverse beam Abutment Capping beam
Overview Finally, SeeBridge identifies defects and maps them to bridge BIM model.
Overview Partners are:
Overview Supported by: London Underground AEC 3 Germany
Introduction Infravation is http://www.infravation.net an ERA-NET Plus EU FP7 framework program a pooled research fund to develop transport infrastructure innovations aimed at cost-effective advanced systems, materials and techniques in road infrastructure construction and maintenance, including repair, retrofitting and revamping. The solutions called for include materials technology, methods and processes, and supporting systems, such as for monitoring, communication and energy.
Introduction SeeBridge is a proposed process for automatically compiling BIM models of existing concrete highway bridges that includes precise information about their components, geometry and surface defects
Overview SeeBridge consists of 5 major work packages: WP1: Information Delivery Manual WP2: Point Cloud Data Acquisition WP3: Point Cloud to Geometry Processing WP4: Semantic Enrichment WP5: Damage Detection and Identification
WP 1 Information delivery manual
WP1 WP2 WP3 WP4 WP5 Information Delivery Manual SeeBridge IDM Information Delivery Manual describes: the processes the data exchange scenarios the data requirements involved with data exchanges basis for Model View Definition (MVD)
WP1 WP2 WP3 WP4 WP5 Information Delivery Manual SeeBridge IDM Process Map Exchange Scearios Exchange Requeriments
Vision
Vision
Vision Exchange Requirements
WP1 WP2 WP3 WP4 WP5 Model View Definition SeeBridge MVD Subset of the IFC4 Schema Rules for structures, attributes and geometry SeeBridge items mapped to IFC entities SeeBridge Exchange Models mapped to IFC Model View Exchange Requirements On the basis of the IDM an mvdxml template was generated featuring relevant Concept Templates.
WP1 WP2 WP3 WP4 WP5 Model View Definition SeeBridge MVD Online database BIM*Q from AEC3 for mapping of the IDM onto IFC4 No extension of the IFC schema necessary Usage of predefined types and PropertySets The mvdxml is exported from BIM*Q
WP1 WP2 WP3 WP4 WP5 Model View Definition SeeBridge MVD IFC models must be generated according to the MVD The MVD is used to check IFC models for compliance Tool: XBIM Explorer Helps to ensure quality of delivered IFC files
Vision SeeBridge MVD Modeling of defects as surface features (IfcSurfaceFeature) A defect can span several elements and is comprised of element defects Current IFC tools can correctly visualize modeled defects
WP 2 Point Cloud Data Acquisition
Data Acquisition 14 bridge datasets were collected: Leica Scanstation C-10 Cambridge, UK 10 Atlanta, USA 3 Haifa, Israel 1 Sony alpha 7R/II DSLR camera Sony 70 400mm F4 5.6 G SSM II zoom lens Trimble TX5 Faro Focus 3D X330 WP1 WP2 WP3 WP4 WP5
WP1 WP2 WP3 WP4 WP5 Data Acquisition On-site data collection:
SEEBridge
Acworth Bridge Cambridge University
Gwinnett Bridge Cambridge University
1A Terrestrial Data Collection: Results: What is possible Acworth, GA 067-5252-0
1A Terrestrial Data Collection: Results: What is possible Gwinnett, GA 135-0115-0
1A Terrestrial Data Collection: Results: What is possible Cambridge, UK #2 Cambridge, UK #8
41
WP1 WP2 WP3 WP4 WP5 Point Cloud Data Atlanta Haifa Cambridge
WP1 WP2 WP3 WP4 WP5 Image Data High Res Close-Ups
WP 3 Volume Geometry Generation
WP1 WP2 WP3 WP4 WP5 Detecting Objects Objective: To detect bridge objects at LOD 300 in bridge point cloud datasets To fit labelled, 3D solid components of the bridge structure. Deliverables:
WP1 WP2 WP3 WP4 WP5 Option 1: Bottom-up model (e.g. bridge) assemblies components surfaces points
WP1 WP2 WP3 WP4 WP5 Option 1: Bottom-up 1. Surface primitive and parametric model extraction 2. Detection and classification of bridge components from primitives 3. Bridge component parser for generating IFC model files. IFC Model Surface primitive estimation
WP1 WP2 WP3 WP4 WP5 Option 2: Top-down two major tasks Object detection in PCD Point clusters fitting Point cloud segmentation Point cluster classification
WP1 WP2 WP3 WP4 WP5 Option 2: Top-down Slicing algorithm
WP1 WP2 WP3 WP4 WP5 Option 2: Top-down Object detection in PCD Point clusters fitting 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 merging
WP1 WP2 WP3 WP4 WP5 WP 4 Semantic Enrichment
WP1 WP2 WP3 WP4 WP5 Semantic Enrichment Objective: To build and test a software application capable of upgrading a 3D bridge model to an information model that is sufficiently rich to serve as a central component for Bridge Management Systems. Deliverables:
WP1 WP2 WP3 WP4 WP5 Semantic Enrichment The enrichment process Plain geometry model in IFC file Additional Info from BMS Numbering Classification Axes Reconstruction Aggregation Occlusion Enriched model of the bridge Semantic enrichment of building models refers to the automatic or semi-automatic addition of meaningful information to a digital model of a building or other structure by software that can deduce new information by processing rules or by applying machine-learning (Bloch et al. 2017)
WP1 WP2 WP3 WP4 WP5 Classification: Identifying objects
Aggregation: grouping objects Types of groups Functional systems e.g. structure, lighting, safety, drainage etc. Mutually exclusive subsystems e.g. superstructure and substructure. Concept groups WP1 WP2 WP3 WP4 WP5 e.g. span between axes A and B
Reconstruction of axes Longitudinal and lateral axes Longitudinal curvature of multi-span bridges WP1 WP2 WP3 WP4 WP5
Occlusion: repairing geometry Lengthening the occluded beams Inserting placeholders for missing objects Recreating deck structure WP1 WP2 WP3 WP4 WP5
Girder lengthening algorithm to the capping beam no Should they be adjacent? yes Is the girder too short? yes Take one girder and one capping beam Find the minimal distance from edge of girder to axis of capping beam in XY projection Do nothing From the start From the start or the end? From the end Calculate the correct length and new start point Copy new beam to start point & set the correct length
Output model IFC and MVD compliance Post processor WP1 WP2 WP3 WP4 WP5
WP 5
Texture Mapping and Defect Detection Goal: The goal of this step is to take a bridge geometry model and high resolution imagery of a bridge as input and generate bridge element surface texture and defect information as output. Crack IF C BIM Integration Texture Reconstruction Approach: Photogrammetry Inverted Ray Tracing Defect Detection Deep learning (CNN) Dataset DoT s + own collection Inspection Manual Analysis Build information model WP1 WP2 WP3 WP4 WP5
Defect Detection - Step 2
Integrating defect information into BIM models Add defects and build defect history over time
Results
Technology readiness level (TRL) Information Delivery Manual (IDM) and Model View Definition (MVD) Point Cloud Data Acquisition Laser Scanning Photogrammetry 3D Geometry Reconstruction Bottom-up (GT) Top-down (Cambridge) Semantic Enrichment Damage Detection and Modeling Slab Bridges Now we are here TRL 6 TRL 7 TRL 6 TRL 6 TRL 6 TRL 8 TRL 6 TRL 7 Girder Bridges TRL 6 We started here TRL 6 TRL 6 Note: the data from 3D reconstruction, semantic enrichment, and damage detection must all be compliant with the SeeBridge MVD
Gaps in Technology What is missing? - Extensive testing - Rule sets for additional bridge types (only girder bridges and slab bridges dealt with) - Integration of models in Bridge Management Systems What further research is needed? - Better solutions for 3D reconstruction of girder bridges - Extensive testing to explore quantitatively how much user input is still required for BIM compilation
Technology Transfer What opportunities are there for commercialization? - Licensing of the procedure - Implementation - Stand-alone bridge inspection tool - Embedded in Bridge Management System software tools
Closing This project has received funding from 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, Federal Highway Administration USDOT