Challenges and Drivers for Data Mining in the AEC Sector

Vian Ahmed, Zeeshan Aziz, Algan Tezel, Zainab Riaz

Research output: Contribution to journalArticlepeer-review


The purpose of this paper is to explore the current challenges and drivers for data mining in the AEC sector. Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals. Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and Big Data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for data mining in the AEC sector with a practical focus.
Original languageEnglish
Pages (from-to)1436-1453
Number of pages18
JournalEngineering, Construction and Architectural Management
Issue number11
Early online date2 Oct 2018
Publication statusPublished - 22 Oct 2018

Bibliographical note

Funding: The authors would like to acknowledge the contribution of support of XpertRule [] and TSB for their support for this study.


  • Business analytics
  • Data mining
  • Data analytics
  • AEC
  • Facilities management


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