skip to content

Construction Information Technology Laboratory

 

Current Projects

AI-OPTIMISED PATHWAYS FOR SCHEDULE EXECUTION

March 2019 - February 2021

NPlan Ltd, Kier Infrastructure and Overseas Ltd  PI: Ioannis Brilakis, £846,753

The proposed project seeks to develop a novel automated 'schedule learning platform' that applies data science and machine learning to thousands of previous project schedules, offering a unique scalable solution for improved certainty and confidence in project planning for future projects. The solution is based on thousands of previous construction projects, allowing the platform to learn across projects what was planned to happen and what actually happened, thus reducing the effect of human bias, subjectivity and inaccuracy. Schedule data is analysed, similar tasks and relationships are automatically grouped, with patterns drawn using Artificial Intelligence, enabling the platform to predict the most likely outcome for every task and provide optimal paths/recommendations to mitigate risks/delays.

 

Past Projects

COMPUTER VISION AUTOMATED PRODUCTIVITY MEASUREMENT

October 2013 – August 2017

EPSRC and Laing O’Rourke Ltd, £85,624

The objective of this research was to investigate the performance of a new method that can automatically detect the task cycles of construction activities, measure their duration, and compile reliable statistics based on 100% of the observed data. This can be achieved by applying primarily computer vision and machine learning techniques to process jobsites’ video data. The extracted construction entities’ trajectories will then be used for the detection of possible repeated work cycles by implementing statistical pattern recognition models.

Leading researcher: Eirini Konstantinou.

 

RP 11-12 TRAINING AND CERTIFICATION FOR CONSTRUCTION INSPECTORS

May 2011 – May 2013
Georgia Department of Transportation (GDOT), PI: Ioannis Brilakis / £130,719

The purpose of this research effort was to provide material for training and certification courses to GDOT construction inspectors as well as a delivery system which helps inspectors learn inspection methods and techniques in an easy to understand, comprehensible fashion. The project also aimed to improve construction inspection practices employed by the GDOT.
Under this research effort, existing literature regarding inspection practices was reviewed to identify best practices for each type of construction inspection. These practices were then used to develop the manual for each construction inspection. The final manual was then used to convert material into training and certification modules for GDOT inspectors.

Leading researchers: Linda Hui, Stefania Radopoulou and Eric Marks.

IREE: AUTOMATED VISION TRACKING OF PROJECT RELATED ENTITIES

August 2007 – August 2011
NSF Grant #0738417, PI: Ioannis Brilakis, £24,346

This international collaboration sent 3 US students to the Aristotle University of Thessaloniki (AUTh), Greece, for 4 months. The students tested the tracking method invented and prototyped in the project above on several types of sites of the Egnatia Odos motorway project, such as cantilevered bridge construction, tunnel face excavation, and interchange construction. These sites were under heavy equipment, personnel and materials traffic. Egnatia Odos is an $8 billion, 670km project that aims to create a central East-West artery to connect Turkey in the east with the Ionian Sea port of Igoumenitsa in the west. The foreign collaborator of this project, Pr. Demos Angelides, is the Chairman of the Civil and Environmental Engineering Department at AUTh and acted as the host and local advisor for our students.

Leading student researcher: Gauri Jog.

 

AUTOMATED VISION TRACKING OF PROJECT RELATED ENTITIES

August 2006 – August 2011
NSF Grant #0625643, PI: Ioannis Brilakis, £195,907

This research aimed to design an automated vision tracking method that reports the 4D location (spatial coordinates and time) of distinctly shaped, project related entities, such as construction equipment, personnel, and materials of standard sizes and shapes. Under this method, two or more self-calibrated, outdoor wireless video cameras are initially placed at a project site and collect video-streams. Using construction materials and shapes visual recognition techniques, each project related entity on the cameras' field of view is identified as an "interesting" pattern to track. Established tracking tools are then used in each subsequent frame of the video stream to track the movement of the identified "interesting" entity while it operates within the cameras' viewing spectrum. 

Leading student researcher: Man Woo Park.

 

 

Latest news

PhD studentships advertised as part of the Cloud-based Building Information Modelling (CBIM) training network

13 November 2019

The following PhD studentships are available at the University of Cambridge on the European Commission-funded, Marie Sklodowska-Curie "CBIM" European...

Infrastructure Computer Vision – Book available for pre-order

28 October 2019

Pre-order the soon-to-be-released Infrastructure Computer Vision to explore advanced information and sensing technologies, and how to apply them to individual projects.