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Construction Information Technology Laboratory

Machine Learning for Construction Scheduling

Exploring innovative opportunities for flexible project schedule design and execution.

After the launch of the research partnership between the University of Cambridge’s Construction Information Technology (CIT) Lab, nPlan and Kier, the Cambridge team continues to make progress in the exploration of the integrated schedule execution framework comprised of process benchmarking and delay-prediction tools. This research programme, sponsored by the InnovateUK grant, is titled “AI-Optimised Pathways for Schedule Execution (AI-OPSE)”. The project team aims to improve project management by developing an innovative schedule-learning platform which applies data science and machine learning to thousands of historic project schedules. The project will offer a unique and scalable solution for improving reliability and confidence in project planning. There are seven phases in this 24-month industrial research programme. The Cambridge team is in charge of framework development on learning optimal sequences of tasks and learning comparative project performance.

At present, the Cambridge team is designing and developing the prototype framework that uses an identified artificial intelligence (AI) model to read sequences of tasks based on high-level human input. The expected system output will be a probabilistic estimation of delays which can help to train a second AI model to rearrange the existing tasks for the purpose of minimising risks and possible delays. This novel approach will empower the automation of benchmarking and determination of task efficiency and project performances.  

The research team has highly qualified members led by Dr. Ioannis Brilakis, director of the CIT laboratory, which is part of the Laing O'Rourke Centre for Construction Engineering and Technology, in the Department of Engineering, University of Cambridge. Dr. Brilakis is a recipient of the 2019 ASCE J. James R. Croes Medal, and has authored or co-authored over 190 papers in peer-reviewed journals and conference proceedings. His research interests lie broadly in the field of construction engineering with a focus on construction automation and information technologies.


Dr. Haiyan Xie (Sally) joined the research team as a Research Associate on the 16th of August 2019. Her recent research focuses on Smart City, Big Data Analytics, Artificial Intelligence in Construction and Operation, Simulation and Control, and Innovative Technology in Construction Engineering and Management. She is a Professor at Illinois State University (ISU), since 2013. She has over 75 published research articles, books, and reports. Recently, she received the International Outstanding Research Award of the Associated Schools of Constructionon April 12th, 2019. Dr. Xie has over 15 years of experience in teaching and research in the areas of construction materials and project management, as well as seven years of construction industry experience as a project manager. She has supervised multiple undergraduate and graduate students conducting research work and honours projects in renewable energy and construction management areas.

Dr. Ying Hong joined the research team on the 1st of August 2019. She received a doctorate degree in civil and environmental engineering from the University of New South Wales in 2019. Her research interests cover the area of machine learning, deep learning, construction management, and optimisation. In the AI-OPSE project, Ying leads the development of Artificial Intelligent models. Her goal is to build up an intelligent construction industry.


For more information on this project, please contact:

Dr Ioannis Brilakis

Phone: +44(0) 1223 332718

E-mail address:


Further reading regarding the AI-OPSE research programme: Kier article - Will artificial intelligence change how we deliver construction projects?

Latest news

A fully funded PhD studentship is now available

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The project is a fully funded 4-year EPSRC Industrial CASE PhD Studentship, commencing on 1st October 2021, to investigate methods for generating...

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13 April 2021

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