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Data Analytics Applications for Architecture and Construction
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Data Analytics Applications for Architecture and Construction

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 11404

Special Issue Editors

Dr. Sittimont Kanjanabootra

E-Mail Website
Guest Editor
School of Architecture and Built Environment, University of Newcastle, Callaghan, NSW 2308, Australia
Interests: big data; digital competencies; climate action; data Analytics; construction
Prof. Dr. Patrick Tang

E-Mail Website
Guest Editor
School of Architecture and Built Environment, University of Newcastle, Callaghan, NSW 2308, Australia
Interests: sustainable and low carbon concrete; advanced composite materials; phase change materials; nanomaterials; energy efficient building design; environmental sustainability; waste management
Special Issues, Collections and Topics in MDPI journals
Dr. Dariusz Alterman

E-Mail Website
Guest Editor
School of Science, Technology & Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: sustainable structures and composite materials; quantitative image analysis; artificial intelligence techniques; structural and stochastic analysis; energy efficiency and smart buildings
Dr. Bernard Tuffour Atuahene

E-Mail Website
Guest Editor
School of the Built Environment, Architecture and Creative Industries, University of Bradford, Bradford BD7 1DP, UK
Interests: big data; sustainability; digital construction; construction management and digital capabilities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information Technologies in the Architecture, Engineering & Construction (AEC) industry has progressed significantly and now play important roles in all aspects of building. Information and data impact how project owners, architects, engineers, energy consultants, contractors, building operators, project managers, construction managers and AEC suppliers conduct their business. There is a very large amount of data generated throughout the building life cycle process yet this data is underutilised relative to other industries such as the retail, finance, supply chain and healthcare sectors. Recent papers have developed a set of questions that should/could frame more focused research which will improve the effectiveness of building design, building processes and construction project management. How can data analytics support building design? How can building owners or governments who are the owners of infrastructure, use data to facilitate their projects/portfolio management in a more effective way? How can data-driven practice facilitate architects, engineers and builders so project teams can deliver projects on time, within budget and sustainably, with safety for workers and with minimisation of waste? How can AEC practitioners use data in a more effective way to enable error elimination from future projects?  

This Special Issue will provide practice and conceptual examples of how buildings and infrastructure designs, contractual and construction process and building maintenance are designed and/or managed through the use of data analytics. Relevant topics to this Special Issue include but are not limited to the following subjects:

  • Building owners data-driven initiatives 
  • Data-driven architecture design
  • Data-driven contractual management 
  • Data-driven project procurement
  • Data-driven construction and logistics management
  • Data-driven building management
  • Data-driven building energy management 
  • Big Data analytics in construction
  • Information systems software applications in construction
  • Information management in construction projects
  • Enabling sustainable construction through adoption of data analytics
  • Data analytics and project infrastructure planning and management
  • Data analytics and building materials performance

Dr. Sittimont Kanjanabootra
Prof. Dr. Patrick Tang
Dr. Dariusz Alterman
Dr. Bernard Tuffour Atuahene
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven construction
  • data-driven architecture
  • big data application
  • data analytics
  • artificial
  • machine learning
  • process analytics
  • design analytics
  • visual analytics in construction

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

24 pages, 8329 KiB  
Article
Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management
by Li-Wei Lung, Yu-Ren Wang and Yung-Sung Chen
Buildings 2025, 15(8), 1325; https://doi.org/10.3390/buildings15081325 - 17 Apr 2025
Viewed by 240
Abstract
The construction industry faces persistent occupational health and safety challenges, with numerous risks arising from construction sites’ complex and dynamic nature. Accidents frequently result from inadequate safety distances and poorly managed work-er–machine interactions, highlighting the need for advanced safety management solutions. This study [...] Read more.
The construction industry faces persistent occupational health and safety challenges, with numerous risks arising from construction sites’ complex and dynamic nature. Accidents frequently result from inadequate safety distances and poorly managed work-er–machine interactions, highlighting the need for advanced safety management solutions. This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. The system achieves a mean average precision (mAP) of 0.922 and an F1 score of 0.88 at a 0.595 confidence threshold, detecting hazards in under 1 s. Integrating IoT-enabled smart wearable devices provides real-time monitoring, delivering instant hazard alerts and personalized safety warnings, even in areas with limited network connectivity. The system employs the DIKW knowledge management framework to extract, transform, and load (ETL) high-quality labeled data and optimize worker and machinery recognition. Robust feature extraction is performed using convolutional neural networks (CNNs) and a fully connected approach for neural network training. Key innovations, such as perspective projection coordinate transformation (PPCT) and the security assessment block module (SABM), further enhance hazard detection and warning generation accuracy and reliability. Validated through extensive on-site experiments, the system demonstrates significant advancements in real-time hazard detection, improving site safety, reducing accident rates, and increasing productivity. The integration of IoT enhances scalability and adaptability, laying the groundwork for future advancements in construction automation and safety management. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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25 pages, 10769 KiB  
Article
Semi-Automated Dataset Generation for Residential Buildings Using Graph-Based Topological Modelling
by Angelo Massafra, Dania H. Al-Harasis, Lorenzo Stefanini and Wassim Jabi
Buildings 2025, 15(8), 1283; https://doi.org/10.3390/buildings15081283 - 14 Apr 2025
Viewed by 627
Abstract
Most of Italy’s residential building stock predates contemporary structural safety and energy efficiency regulatory frameworks. Today, policymakers face the challenge of choosing whether to prioritise renovation or opt for demolition and reconstruction; both options carry significant socio-economic and environmental consequences and require extensive [...] Read more.
Most of Italy’s residential building stock predates contemporary structural safety and energy efficiency regulatory frameworks. Today, policymakers face the challenge of choosing whether to prioritise renovation or opt for demolition and reconstruction; both options carry significant socio-economic and environmental consequences and require extensive knowledge of the built heritage. However, detailed architecture-specific data remain scarce, as existing databases lack granular information. Moreover, traditional urban-level knowledge mapping approaches may be resource-intensive. To address this data gap, this study proposes a semi-automated methodology for generating graph-based digital models representing residential building floor plans. Using graph theory, floor spatial layouts are mapped into connectivity graphs and transformed into topological models. These models are enriched with functional data about spaces by assigning conditional topological rules based on node centrality metrics. The method was tested on 98 buildings in Bologna, Italy, yielding an 89.8% success rate and demonstrating its effectiveness in data-limited contexts. The resulting dataset facilitates the analysis of floor spatial configurations and the extraction of geometric attributes, laying the foundation for future analyses that will integrate machine learning techniques for functional detection and typological clustering. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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20 pages, 1162 KiB  
Article
Big Data Value Proposition in UK Facilities Management: A Structural Equation Modelling Approach
by Ashwini Konanahalli, Marina Marinelli and Lukumon Oyedele
Buildings 2024, 14(7), 2083; https://doi.org/10.3390/buildings14072083 - 7 Jul 2024
Cited by 1 | Viewed by 1707
Abstract
Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client-centric service model, [...] Read more.
Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client-centric service model, founded on data delivering continuous value and achieving optimal performance, whilst also upgrading their own decision-making and reporting processes. This article aims to explore how UK FM organizations are currently capitalizing on BDA to drive innovation and ‘added value’ in their operations. The objective is to shed light on the initial BDA adoption efforts within the UK’s FM sector, particularly capturing the benefits experienced by FM organizations in relation to customer value and improved decision-making processes. Drawing upon exploratory sequential research including a qualitative stage with 12 semi-structured interviews and an industry-wide questionnaire survey with 52 responses, a novel fifteen-variable model for BDA outcomes was developed. Exploratory Factor Analysis (EFA) and a Higher-Order model using Partial Least Square Structural modelling (PLS-SEM) were used to validate the scale. The EFA output generated three dimensions with 14 items. The dimensions included Improved client value, FM business operations added value, and Improved efficiency added value. Furthermore, the results of PLS-SEM confirmed the validity of the scale items and the reflective–formative measurement model. The findings suggest that the contemporary digitization trend offers the FM service the unique opportunity to develop a smarter, client-centric strategy resulting in more personalized services and stronger customer relationships. Furthermore, efficient resource management and planning powered by analytics and data-driven insights emerge as a key driver for competitive differentiation in the field. As one of the first studies to develop and validate scale items measuring specific dimensions of BDA adoption outcomes, the study makes significant contributions to the literature. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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20 pages, 2590 KiB  
Article
The Relationship between Cost Overruns and Modifications for Construction Projects: Spanish Public Works and Their Legal Framework
by Guillermo Alonso-Iglesias, Francisco Ortega-Fernández, Vicente Rodríguez-Montequín, Martin Skitmore and Olabode Emmanuel Ogunmakinde
Buildings 2023, 13(10), 2626; https://doi.org/10.3390/buildings13102626 - 18 Oct 2023
Cited by 1 | Viewed by 2101
Abstract
Cost overruns are a common problem for public works projects, often due to modifications to the original design. While the causes of these modifications have been studied, the legal framework’s impact and limitations on these modifications have received extensive treatment, with no specific [...] Read more.
Cost overruns are a common problem for public works projects, often due to modifications to the original design. While the causes of these modifications have been studied, the legal framework’s impact and limitations on these modifications have received extensive treatment, with no specific case studies from different countries. This paper explores the relationship between modifications in Spanish public works projects and their compliance with legal limits, investigating the alignment between base bidding prices and eventual costs after adjustments. The study also delves into the strategic behaviour of construction companies in Spain, which frequently involves manipulating project costs to match the initially proposed bidding price. Statistical methods, such as the Spearman correlation test and graphical analysis, confirm a nearly exact relationship between base bid price and final price. Also, a modification costs comparison for two different legal periods highlights the legal framework’s influence, as a less restrictive framework leads into higher average cost overruns. It provides valuable information to avoid malpractice for tendering institutions, practitioners, and legal developers, as well as those interested in the Spanish public works sector, and opens the door for future research on solving this problem. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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13 pages, 2730 KiB  
Article
An Automated Method for Extracting and Analyzing Railway Infrastructure Cost Data
by Daniel Adanza Dopazo, Lamine Mahdjoubi and Bill Gething
Buildings 2023, 13(10), 2405; https://doi.org/10.3390/buildings13102405 - 22 Sep 2023
Viewed by 1118
Abstract
The capability of extracting information and analyzing it so that it is in a common format is essential for performing predictions, comparing projects through cost benchmarking, and having a deeper understanding of the project costs. However, the lack of standardization and the manual [...] Read more.
The capability of extracting information and analyzing it so that it is in a common format is essential for performing predictions, comparing projects through cost benchmarking, and having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of data make this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway infrastructure cost data. To validate the suggested approach, data from 23 real historical projects from the client network rail were extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing cost benchmarking to be performed. The presented method proves the benefits of data extraction for gathering, analyzing, and benchmarking each project in an efficient manner, and to develop a deeper understanding of the relationships and the relevant factors that matter in infrastructure costs. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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11 pages, 1681 KiB  
Article
A Method to Enable Automatic Extraction of Cost and Quantity Data from Hierarchical Construction Information Documents to Enable Rapid Digital Comparison and Analysis
by Daniel Adanza Dopazo, Lamine Mahdjoubi and Bill Gething
Buildings 2023, 13(9), 2286; https://doi.org/10.3390/buildings13092286 - 8 Sep 2023
Viewed by 1538
Abstract
Context: Despite the effort put into developing standards for structuring construction costs and the strong interest in the field, most construction companies still perform the process of data gathering and processing manually. This provokes inconsistencies, different criteria when classifying, misclassifications, and the process [...] Read more.
Context: Despite the effort put into developing standards for structuring construction costs and the strong interest in the field, most construction companies still perform the process of data gathering and processing manually. This provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly in large projects. Additionally, the lack of standardization makes cost estimation and comparison tasks very difficult. Objective: The aim of this work was to create a method to extract and organize construction cost and quantity data into a consistent format and structure to enable rapid and reliable digital comparison of the content. Methods: The approach consisted of a two-step method: firstly, the system implemented data mining to review the input document and determine how it was structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data were processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A large variety of information coming from real historical projects was successfully extracted and processed into a common format with 97.5% accuracy using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusions: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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18 pages, 734 KiB  
Article
Mapping the Barriers of Big Data Process in Construction: The Perspective of Construction Professionals
by Bernard Tuffour Atuahene, Sittimont Kanjanabootra and Thayaparan Gajendran
Buildings 2023, 13(8), 1963; https://doi.org/10.3390/buildings13081963 - 1 Aug 2023
Cited by 1 | Viewed by 2283
Abstract
This study identifies, maps and thematizes the barriers to the big data process in the construction industry from the perspective of construction professionals. Australian construction professionals with varying experiences in the big data process were interviewed. Qualitative data analysis identified forty barriers in [...] Read more.
This study identifies, maps and thematizes the barriers to the big data process in the construction industry from the perspective of construction professionals. Australian construction professionals with varying experiences in the big data process were interviewed. Qualitative data analysis identified forty barriers in the big data process and five themes: people, knowledge, technology, data, and environment. The barriers were further mapped, with some transcending more than one stage in the big data process. Many of the barriers have not been empirically identified in previous studies. By implication, mapping the barriers across the big data process enables professionals/construction firms to visualize the potential lapses before and/or during implementation. Therefore, the study offers professionals/construction firms strategic insights and operational perspectives for planning and deploying big data processes. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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