Construction project and processes intelligence across the project lifecycle

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Digital transformation of the construction industry is expected to provide solutions to the historical challenges of the industry, including the lack of data accessibility and stagnant productivity. The proposed partnership between Concordia University and Aedo AI company aims to take the existing Aedo's construction project data platform to the next level by incorporating 'intelligence' through a semantic foundation and process mining analytics. The project has three main Research Objectives (RO's), each of which will be achieved through a number of research tasks. (RO1) Top-down Intelligence through project lifecycle ontology - This RO will include (i) a requirements analysis; and (ii) modeling and validation of a cross-faceted ontology for construction projects across their entire lifecycle. (RO2) Process Mining with Aedo - This RO will be achieved by (i) enhancing Aedo data schema to support event-logs; and (ii) developing algorithms for extracting process analytics, i.e., process discovery, compliance checking, bottleneck analysis, project social network analysis, etc. (RO3) Semantic cross-search among projects - Three tasks are defined to achieve this objective. (i) Implementing the ontology and process mining results for a cross-faceted analysis of construction project; (ii) using data mining for understanding influence patterns among construction and other phases of the = lifecycle; and (iii) developing KPIs for evaluation of construction projects based on interactions among various facets, as well as impacts from/on different phases of the lifecycle.This project will recruit five main HQP (three PhD and two MASc) and will train more MEng students, throughout the course of three years. Equity, Diversity, and Inclusion (EDI) is a fundamental attribute of team building, and a central theme of the training plan for this project. Furthermore, the project's outcome, i.e. data-driven analysis of processes' and actors' performance is expected to help break the stereotypical barriers against the presence of underrepresented groups in construction, by removing biases from the traditional performance assessment procedures.