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Editorial
Special Issue on Data Analytics Applications for Architecture and Construction
Buildings, Vol.15(22), pp.1-4
2025
Abstract
Construction processes are lengthy, complex and involve a variety of stakeholders. Each stakeholder holds different roles and responsibilities. As a result, large amounts of data are generated in a variety of formats to serve a variety of purposes during construction projects. Some of these data have specific standards to manage them, whereas others do not; similarly, some data must be manually classified or managed, while others can be automated. Progress in the development of digital devices and software also plays an important role in how these data are generated and managed, which creates inconsistencies. The construction industry is undergoing a digital transformation, with big data emerging as a critical enabler across organizational, costing, and design practices. Despite this, the path to adoption of data analytics is not without obstacles. In Australia, construction professionals have identified over 40 barriers to Big Data implementation, spanning people, technology, and environmental factors. Mapping these challenges offers firms a strategic lens to anticipate and mitigate risks during deployment. In the realm of project costing, automation is revolutionizing how data are extracted and analyzed. New methods now enable the rapid, accurate extraction of cost and quantity data from complex documents, achieving up to 97.5% accuracy. In infrastructure, machine learning is being used to benchmark railway project costs and uncover key cost drivers. Moreover, legal frameworks in public works, such as those in Spain, reveal how regulatory environments influence cost overruns—highlighting the need for data-informed policy reform. With reference to design and construction, big data is enhancing both safety and building efficiency. From semi-automated modeling of residential buildings in Italy to real-time hazard detection using deep learning and IoT wearables, data-driven tools are reshaping how we create the built environment and protect those that use it. Natural language interfaces such as VISA4D are simplifying BIM interactions, while performance evaluation systems and bibliometric analyses are helping teams assess design-phase effectiveness and track research trends. Together, these studies underscore a pivotal shift: Big data is no longer a futuristic concept but a practical necessity. As the industry continues to digitize, embracing data-driven tools will be essential for improving outcomes, reducing costs, and fostering innovation across the construction lifecycle. As part of this Special Issue, 10 papers have been collated, categorised into three main themes: (1) big data application and practice in organizations, (2) construction project costing practice and, and (3) design and construction practice.
Details
- Title
- Special Issue on Data Analytics Applications for Architecture and Construction
- Authors
- Sittimont Kanjanabootra (Corresponding Author) - University of Newcastle AustraliaWaiching Tang - University of Newcastle AustraliaDariusz Alterman - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringBernard Tuffour Atuahene - University of Bradford
- Publication details
- Buildings, Vol.15(22), pp.1-4
- Publisher
- MDPI AG
- Date published
- 2025
- DOI
- 10.3390/buildings15224200
- ISSN
- 2075-5309
- Copyright note
- © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991192144102621
- Output Type
- Editorial
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