Special Issue: Managing Risk and Complexity in Construction Projects with Digital Technologies

Guest Editors

Irem Dikmen, University of Reading, i.dikmen@reading.ac.uk

Joseph H. M. Tah, Oxford Brookes University, jtah@brookes.ac.uk

Guzide Atasoy, Middle East Technical University, guzide@metu.edu.tr

Theme

What are the implications of digitalisation on the way risk and complexity are managed in construction projects?

Digitalisation in construction projects refers to the adoption of information and communication technologies during the life cycle of a built asset to improve the processes of designing, constructing, maintaining and operating the built environment. Technologies include and are not limited to BIM, digital twins, and AR/VR for virtualisation, big data analytics and AI for data-driven decision-making and context-aware, autonomous and smart construction objects augmented with sensing, processing and communication capabilities.

Digital transformation, improving the value chains and changing the business models is argued to be the enabler of a sustainable and resilient construction industry (Ernsten et al., 2021). Digitalisation facilitates automating production, data-driven decision-making, and smart project management systems. Apparently, the value of digitalisation is related to the knowledge it delivers for improved decision-making and potential improvements in the way construction projects and companies are managed (Gurdur Broo et al., 2022; Sacks et al.,  2020). In this special issue, the implications of digitalisation on risk-based decision-making and management of risk and complexity will be explored. 

There is a call for new risk assessment and management approaches to capture the complex behaviour of projects and dynamic interactions between people, project systems, and environmental factors (Aven, 2016). This special issue investigates how digital technologies (DT) may address current challenges and change the ways and methods of risk assessment, management, and communication in projects. For example, big data analytics has a potential to enhance risk prediction, decreasing model uncertainty. Fully automated and smart construction industry leveraging cyber-physical systems envisioned for the future would improve predictability, and proactive risk management. AI has a potential to change risk management practices, especially by facilitating dynamic risk mapping and mitigation (Holzmann et al., 2022). On the other hand, digitalisation imposes new risks of interoperability, liability, security and underperformance, creating deep uncertainty due to technological novelty and complexity (Chien et al., 2014). Mental shortcuts resulting in bias may be reduced by automated risk management processes and virtual risk management teams. However, can DT be used to capture tacit knowledge, simulate projects as open socio-technical systems and support sense-making and negotiations between project participants?

Although various studies in the construction management domain demonstrate the utilisation of DT for managing risk and complexity in projects; such as the use of BIM (Abanda et al., 2020; Kang et al., 2013; Lu et al., 2021), digital twins (Kaewunruen et al., 2021; Kamari and Ham 2022; Liu et al., 2022), AR/VR and visualization (Dikmen et al., 2022; Hasanzadeh et al., 2020), big data analytics and deep learning (Ajayi et al., 2020; Lee et al., 2019; Owolabi et al., 2018), and sensors and sensing (Antwi-Afari et al., 2020; Li et al., 2017; Nath et al., 2018; Wang et al., 2015) more research is needed to understand challenges and benefits of using DT for risk and complexity management.

Scope and potential themes to be addressed in the Special Issue

This special issue aims to stimulate research on utilisation of digital technologies for managing risk and opportunity in construction projects. Research papers demonstrating the application of various DT for better identification, assessment, management and communication of risk, as well as conceptualised case studies demonstrating applications of DT to support risk-based decision-making within the construction value-chain are welcome as well as research papers providing a visionary perspective that can be further translated into practical solutions in the future to improve risk management principles and practices. Empirical studies on the impact of DT on projects, companies and industry, utilising different perspectives (such as technical, organisational, economic, and social) and employing methods from different disciplines such as social sciences, cognitive and information science are particularly welcome.

Contributions may address, but are not limited to, the research questions listed below:

Utilising DT in managing risk and complexity in construction projects and companies

• Which digital technologies (e.g., BIM, digital twins, AR/VR, big data analytics, AI, IoT, blockchain etc.) can be used for better identification, assessment, and management of risk and complexity, and how?
• Would DT be used to improve the relibility of risk models? Would predictions be more reliable with big data and AI?
• How can virtualisation and data visualisation be used to support risk identification and communication?
• How can DT  facilitate risk-based decision-making at different stages of a project (e.g., bidding, design, construction, operation, deconstruction) and supporting activities (e.g., contract management, finance) within the construction value chain?
• How would different stakeholders (e.g., owner, designer, contractor, supplier) utilise DT to mitigate complexity and risk in projects?
• How would smart construction objects be used to support construction risk assessment and management?
• How would BIM, digital twins, big data analyics and AI support integrated and smart risk management?
• How would DT enable proactive risk management?
• Considering different performance criteria, such as time, cost, health&safety, decarbonization, and circularity, what would be the impacts of digitilised risk management processes on project success? Can digitalisation lead to the minimisation of risks of poor performance, such as delay, dispute and waste in construction projects?

Benefits, challenges and secondary risks of digitalised risk and complexity management

• What could be the benefits and challenges of utilising DT for risk and complexity management in construction projects?
• Will digitalisation also increase complexity and impose secondary risks of security and underperformance? How would the organisations respond to the risks posed by the adoption of digitalization in the construction industry and digital footprint? What are the educational, regulatory and awareness-related implications of digitalization-related risks?
• Can digitalisation facilitate capturing the behaviour of projects as socio-technical systems and various stakeholders as agents? For example, can digital twins be used to simulate sense-making processes during risk-based decision-making and automate the risk management process ?

Future of managing risk and complexity in projects with digital technologies and impacts on the construction industry

• How would DT potentially change the way risk and complexity are conceptualised, assessed, and managed in the construction sector? What would the future of project risk management be in the digital age?
• What would be the function/role of a virtual risk manager with DT, for example, digital twins?
• Would digitalisation lead to revisions/changes in traditional risk management process models, principles (such as precautionary principle), project management standards, and maturity frameworks?
• Would utilisation of DT lead to changes in the construction industry? Does digitalisation of risk management have the potential to create new sources of competitive advantage and lead to new business models? How would digitalisation affect risk allocation between the parties and risk-reward structures? Will it lead to new project delivery systems?

Notes for Prospective Authors

We invite the submission of original manuscripts that advance empirical, theoretical, and conceptual understanding of the impacts of digital technologies on managing complexity and risk in construction projects. Manuscripts must have substantial implications for theory and practice and need to contribute to the existing body of knowledge. We welcome both empirical papers and conceptual theory development papers as well as case studies.

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper. Submissions will be reviewed according to the journal’s rigorous standards and procedures through a double-blind peer review by at least two qualified reviewers.

Submission Process

Please prepare the manuscript according to IEEE-TEM’s guidelines (http://ieee-tmc.org/tem-guidelines) and submit to the journal’s Manuscript Central site (https://mc.manuscriptcentral.com/tem-ieee). Please upload the paper on the IEEE TEM Editorial Manager clearly indicating it is submission for the IEEE TEM Special Issue on Managing Risk and Complexity in Construction Projects with Digital Technologies.

Schedule

Papers submitted by December 31, 2023

 Papers will be evaluated on a rolling basis.

Guest Editor Bios

Irem Dikmen is a professor of construction management and engineering in the School of the Built Environment of the University of Reading, UK. Her main research interests include construction management, risk assessment and management, mega construction projects, public-private partnerships and strategic planning. She has widely published in the area of application of AI as a decision support for project risk management. Her work has been published among others in the ASCE Journal of Construction Engineering and Management, Automation in Construction and International Journal of Project Management.

Joseph H. M. Tah is Professor of Project Management and Pro Vice-Chancellor and Dean of the Faculty of Technology, Design and the Environment at Oxford Brookes University in the UK. His research work is inter-disciplinary, focusing at the interface between the built environment, computer science and artificial intelligence. He has a long standing research interest in the application of artificial intelligence techniques in construction project risk management and has published widely in this area. He is a Fellow of the Royal Institution of Chartered Surveyors (FRICS), a member of the Chartered Institute of Building (MCIOB) and the Association of Computing Machinery (ACM).

Guzide Atasoy is an assistant professor in the construction engineering and management division of civil engineering department at Middle East Technical University, Turkey. She received her PhD degree in civil and environmental engineering from Carnegie Mellon University, USA. Her teaching and research interests include construction management, risk management, sustainable construction, and the use of visualization and information technologies for project management. She published on these topics in journals, including ASCE Journal of Construction Engineering and Management, Automation in Construction, and Expert Systems with Applications.

References

Abanda, F. H., Musa, A. M., Clermont, P., Tah, J. H., & Oti, A. H. (2020). A BIM-based framework for construction project scheduling risk management. International Journal of Computer Aided Engineering and Technology, 12(2), 182-218.

Ajayi, A., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., Davila Delgado, J. M., & Akanbi, L. (2020). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, 40(10), 2019-2039.

Antwi-Afari, M. F., Li, H., Umer, W., Yu, Y., & Xing, X. (2020). Construction activity recognition and ergonomic risk assessment using a wearable insole pressure system. Journal of Construction Engineering and Management, 146(7), 04020077.

Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1-13.

Chien, K. F., Wu, Z. H., & Huang, S. C. (2014). Identifying and assessing critical risk factors for BIM projects: Empirical study. Automation in Construction, 45, 1-15.

Dikmen, I., Atasoy, G., Erol, H., Kaya, H. D., & Birgonul, M. T. (2022). A decision-support tool for risk and complexity assessment and visualization in construction projects. Computers in Industry, 141, 103694.

Ernstsen, S. N., Whyte, J., Thuesen, C., & Maier, A. (2021). How innovation champions frame the future: Three visions for digital transformation of construction. Journal of Construction Engineering and Management, 147(1), 05020022.

Gurdur Broo, D., Bravo-Haro, M., & Schooling, J. (2022). Design and implementation of a smart infrastructure digital twin. Automation in Construction, 136, 104171.

Hasanzadeh, S., Polys, N. F., & Jesus, M. (2020). Presence, mixed reality, and risk-taking behavior: a study in safety interventions. IEEE Transactions on Visualization and Computer Graphics, 26(5), 2115-2125.

Holzmann, V., Zitter, D. & Peshkess, S. (2022). The expectations of project managers from Artifical Intelligence : A Delphi study. Project Management Journal, https://doi.org/10.1177/87569728211061779.

Kang, L. S., Kim, S. K., Moon, H. S., & Kim, H. S. (2013). Development of a 4D object-based system for visualizing the risk information of construction projects. Automation in Construction, 31, 186-203.

Kaewunruen, S., Sresakoolchai, J., Ma, W., & Phil-Ebosie, O. (2021). Digital twin aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions. Sustainability, 13(4), 2051.

Kamari, M., & Ham, Y. (2022). AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning. Automation in Construction, 134, 104091.

Lee, J., Yi, J. S., & Son, J. (2019). Development of automatic-extraction model of poisonous clauses in international construction contracts using rule-based NLP. Journal of Computing in Civil Engineering, 33(3), 04019003.

Li, C. Z., Zhong, R. Y., Xue, F., Xu, G., Chen, K., Huang, G. G., & Shen, G. Q. (2017). Integrating RFID and BIM technologies for mitigating risks and improving schedule performance of prefabricated house construction. Journal of Cleaner Production, 165, 1048-1062.

Liu, Z. S., Meng, X. T., Xing, Z. Z., Cao, C. F., Jiao, Y. Y., & Li, A. X. (2022). Digital twin-based intelligent safety risks prediction of prefabricated construction hoisting. Sustainability, 14(9), 5179.

Lu, Y., Gong, P., Tang, Y., Sun, S., & Li, Q. (2021). BIM-integrated construction safety risk assessment at the design stage of building projects. Automation in Construction, 124, 103553.

Nath, N. D., Chaspari, T., & Behzadan, A. H. (2018). Automated ergonomic risk monitoring using body-mounted sensors and machine learning. Advanced Engineering Informatics, 38, 514-526.

Owolabi, H. A., Bilal, M., Oyedele, L. O., Alaka, H. A., Ajayi, S. O., & Akinade, O. O. (2018). Predicting completion risk in PPP projects using big data analytics. IEEE Transactions on Engineering Management, 67(2), 430-453.

Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1: e14.

Wang, J., Zhang, S., & Teizer, J. (2015). Geotechnical and safety protective equipment planning using range point cloud data and rule checking in building information modeling. Automation in Construction, 49, 250-261.

IEEE Transactions on Engineering Management is journal of the Technology and Engineering Management Society of IEEE, published quarterly since 1954. It is dedicated to the publication of peer-reviewed original contributions, by researchers and practitioners, regarding the theory and practice of engineering, technology, and innovation management.

Editor in Chief

Tugrul U Daim, PhD PICMET Fellow

Professor and Director

Technology Management Doctoral Program

Department of Engineering and Technology Management

Maseeh College of Engineering and Computer Science

Portland State University, Portland OR

United States