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This study explores the potential of digital twin (DT) technology to enhance education and training in the construction industry. It aims to provide a clear understanding of how DT can be applied for educational purposes and proposes a framework to facilitate the adoption of DT in construction training.

A systematic literature review was conducted to examine the current applications of DT technology in construction education and training. A total of 19 relevant studies were identified and analysed to evaluate the tools, technologies, educational objectives and integration methods used in developing DT models for the construction sector. Based on this analysis, a conceptual framework was developed to guide the integration of DT technology into construction education, addressing gaps in the current literature and practices.

The analysis revealed a strong consensus on the effectiveness of DT technology in supporting education and training objectives within the construction industry. The study highlighted the fragmented nature of the current literature and proposed a comprehensive framework designed to facilitate the integration of DT in construction education. This framework offers a structured approach to bridging the gap between theoretical learning and real-world application.

The research presents a new systematic framework developed based on an in-depth review for utilising DT in education, training and learning (ETL) processes in construction. The framework provides a novel and structured learning process to integrate theoretical knowledge with practical skills to support workforce development in the construction industry. This framework offers a structured roadmap for future research and practical applications.

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.