Democratize GC access to public sector construction using data- ON-392Desired discipline(s): Engineering - computer / electrical, Engineering
Company: PledgX Inc.
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 1
Preferred institutions: Concordia University, University of Toronto, University of Waterloo
About the company:
PledgX is developing a digital marketplace for the construction industry that supports general contractors in bidding on government projects. Our solution is designed to help GCs to bid more efficiently, secure Bonding, and monitor project performance for the benefit of all stakeholders.
Please describe the project.:
We want to use data related to public sector construction, most of which is available from public sources, to develop a model that can predict the chances of winning a bid at a certain price. Other data such as current market prices for labour and materials, and who is bidding, will augment the historical bid data to predict the probability of a bid being successful. AI/machine learning technology would make such algorithm improve over time.
Further, we want to augment the credit profile of a small contractor with up to date performance data on contracts under execution. Data from the construction site would be gathered by IoT devices and cameras, and observers, to build a progress report in relation to plan. All stakeholders, including bonding companies and banks would benefit from such insight to not only release payments on time but also address performance issues on time. Over time such performance data would help public sector entities, bonding companies and banks assess the capability of a contractor and predict future performance using several variables.
The objective of the research would be to:
- assess the availability and robustness of construction contract data as well as labour and material costs
- develop an algorithm or algorithms to use such data intelligently and predict the probability of a price winning a bid
- use a variety of data collected from construction site to map progress against plan
- use available data to predict future performance of a contractor under certain variables
Artificial Intelligence, data science and machine learning