Leveraging Digital Twins for Quality Risk Management in Medium-Rise Residential Constructions

Leveraging Digital Twins for Quality Risk Management in Medium-Rise Residential Constructions

Traditional construction risk management approaches used by contractors to ensure the delivery of high-quality projects have been questioned. These approaches have been found to be inadequate, inefficient, and siloed. As a result, quality issues prevail in many construction projects. While the problem is well known, the lack of literature on quality risk management (QRM) in medium-rise residential construction (MRRC) is concerning. To address this gap, the current study reviews the literature on QRM in MRRCs and proposes digital twin (DT) as a technology to address such concerns. Accordingly, an integrated DTQRM framework is proposed based on the reviewed literature to encourage the adoption of DT for QRM in MRRCs. This study employs the preferred reporting items for systematic and meta-analyses (PRISMA) methodology for systematic and meta-analyses. The Scopus repository is utilized to gather resources by searching for keywords and phrases that pertain to the implementation of DTQRM in MRRCs. A review of 58 Scopus-retrieved articles identified three themes related to QRM: decision-making and prediction capabilities, technology integration into sustainable building Practices, and sustainability and advancements in green building technologies. Thematic analysis is conducted to examine current applications, adoption strategies, and future directions for the identified themes. Overall, DT and QRM are reportedly used for structural health monitoring, health care, traffic management, environmental protection, site safety, and waste management. Adoption strategies include increasing practitioners’ knowledge and understanding, providing them with monetary rewards, implementing focused work methods, developing necessary infrastructure, and enhancing contractors’ ability to handle the changes. It is desirable to develop more risk-informed, data-driven models for addressing construction quality issues in future studies. This research provides a conceptual data-driven framework for practitioners, enabling them to identify and prioritize quality risk (QR). As a benefit of DTQRM adoption in MRRCs, it can be used to automate QR identification, assessment, and prediction before they occur. Researchers can develop specific strategies, policies, and implementation guidelines based on the proposed framework to enable a holistic adoption of DTQRM in MRRC projects.