Starting point: 

How can computational methods, machine learning, and artificial intelligence be used to enhance sustainability outcomes and optimise value creation in the Architecture, Engineering, and Construction (AEC) sector?

This topic may include consideration of: computational methods, machine learning, and artificial intelligence insights to help the AEC sector overcome challenges that consider key decision factors around resource use, carbon emissions, waste production, project timelines, project budgets, and project benefits; how manufacturing information databases and systems can be leveraged to inform decision-making across key aspects of sustainable built assets. 

Project Summary: 

The Architecture, Engineering, and Construction (AEC) sector is renowned for being  highly inefficient. Lying at the intersection of process science and data science, Process  Mining (PM) has accrued many benefits to many industries and sectors through reducing inefficiencies. Task mining (TM), a subset of process mining concerned with finding bottlenecks in users’ software workflows using event logs, has facilitated Robotic Process Automation (RPA) optimisations. We contend that, compared to other industries, AEC is yet to reap rich dividends through PM, TM, and RPA. With that being said, several scientific studies have delved into the PM-AEC realm, but the count, output, and realworld impact of such research are not on par with the size of the AEC sector–with a huge  turnover and climate impact.  

In this research, we try to explore possibilities to (1) discover inefficient software processes, (2) find substitute best practices (by interviewing stakeholders), and (3) suggest automation/optimisation steps accordingly. Aiming to cater to the needs of different organisational user levels, we follow a Process Level of Detail (PLOD) approach. Four PLODs have been envisioned, namely “top-level management (Strategic level)”, “middle-level management (Tactical level)”, “lower-level management (operational level)”, and “user/staff level  (executive level)”. Candidates for each of these PLODs, from our industry partners, will  be interviewed before, during, and after the data analysis–using a Design Science Research (DSR) methodology–to ensure research output relevance to real-world problems. 

Keywords 

Process Mining; Task Mining; Robotic Process Automation (RPA); User Interface Interaction Optimisation; Architecture, Engineering, and Construction (AEC) Expected Outcomes