A University of Queensland research team is using state government bridge data, stored in QCIF’s cloud computer, to develop a new computational tool for automated generation of bridge information and analysis models.
Project lead Dr Joe Gattas, a Senior Lecturer in UQ’s School of Civil Engineering, said the tool will enable more information to be available for quicker and closer analysis.
“This project will provide a higher level of confidence to bridge engineers and network owners in the data used for design, construction, operation and monitoring of bridge assets,” he said.
Joe acknowledged the Queensland Government’s Department of Transport and Main Roads (TMR) had a sophisticated database but that his project, which began in January 2020, aimed to improve the reliability and usability of bridge asset 3D point cloud data records.
“We are finding new ways to collect and analyse point cloud data throughout the entire lifecycle of bridge assets, giving new insight into their current and future performance. This will lead ultimately to better decisions in road infrastructure.”
In 2013, QCIF and TMR began collaborating on hosting and sharing a trove of high-quality data that represents more than 6,000 kilometres of state-controlled roads, road assets and surrounding terrain.
TMR’s Survey Technologies team has collected a vast array of 3D point cloud data, predominantly using Mobile LiDAR (Light Detection and Ranging) Scanning (MLS) and Aerial LiDAR Scanning (ALS).
A point cloud is a set of data points in space. In geographic information systems, point clouds are one of the sources used to make digital elevation models of terrain or used to generate 3D models of urban environments.
Stored on QCIF’s QRIScloud, TMR’s data collection is freely available for researchers to use.
“QCIF is focused on adding value for our member institutions by providing access to government data for research,” said Troy Lockett, QCIF’s Business Development and Communications Manager.
Joe learnt about the data collection through Professor Mark Hickman, a UQ Transport Engineer and Chair of TMR’s Transport Academic Partnership (TAP).
Joe and his team worked with Troy to access the data in QRIScloud.
“Access to existing data has allowed us to quickly test a range of initial ideas for analysis of bridge network point cloud data,” said Joe.
The data helped the research team (which also comprises Gabbi Hodge, a UQ Civil Engineering PhD candidate, and Dr Surya Singh, a Senior Lecturer in UQ’s School of Information Technology and Electrical Engineering) to develop a proof of concept approach to automatically detecting and isolating bridges from point cloud data.
Preliminary work has also separated the isolated bridge point cloud data into bridge superstructure and substructure regions.
Current work is exploring how LiDAR scans of sub-elements can be used to provide accurate as-built representations and information for bridge components, which could then be incorporated into a global bridge model or QRIScloud data records.
The team is also finding other ways to collect more data, for example during bridge fabrication and construction.
“Adding all that information to a unified data record gives you a complete and accurate record of that bridge’s history. Scans of existing bridges in QRIScloud may also be useable in reconstruction of existing assets. This will be a high-quality database for QRIScloud,” said Joe.
As well as providing information for researchers looking at aspects of Queensland transportation, the data collection is also a valuable ecological resource. The LiDAR scanning has captured high-resolution landscape data suitable for ecological analysis.
Martin Tuckwood, the TMR manager of the collection, can share the data directly with researchers using QRIScloud. For more details please email: ET_GDC_GT_Survey_Technologies@tmr.qld.gov.au.
Read QCIF’s first article about the TMR data collection
Image above: A point cloud derived from a terrestrial laser scan of the Tim Fischer Bridge, Bruce Highway near Wallaville. Colours represent intensity values collected by the scanner. (Image courtesy of TMR.)