Table 1 Overview of frameworks,... | Emerald Publishing

Table 1

Overview of frameworks, identified gaps, and I4.0 technologies for circular economy implementation in the construction industry

Reviewed studies with frameworks
Reference	Framework synthesis
Jemal et al. (2023)	The framework integrates BIM for digital modeling, IoT for real-time monitoring and data collection, blockchain for secure and transparent data management, big data for analyzing and optimizing resource allocation, and cloud computing for remote access and collaboration. These technologies collectively enhance resource utilization, reduce waste, and improve efficiency throughout the construction lifecycle
Elghaish et al. (2023)	The framework integrates Industry 4.0 tools by using IoT for real-time data collection, blockchain for secure and transparent materials tracking, and AI for analyzing data and optimizing processes. These tools collectively enhance the management of building components throughout their lifecycle, promoting recycling, reuse, and resource efficiency
Kovacic et al. (2020)	The framework uses BIM for digital building models, MPs for tracking material properties, GPR and laser scanning for capturing existing building data, and gamification for user engagement in maintaining digital models
Çetin et al. (2023)	The framework uses AI and computer vision for automated data collection, laser scanning and photogrammetry for detailed material information, and AR/VR for visualizing building data. Machine learning algorithms predict hazardous materials and assess component conditions
Lamptey et al. (2021)	The framework uses BIM for project management and IoT for real-time monitoring and automation, enhancing green design and sustainable construction practices
Teisserenc and Sepasgozar (2021a)	The framework implements I4.0 tools through the Decentralized Digital Twin Cycle (DDTC) model, integrating blockchain with digital twins to enhance data integrity, cybersecurity, traceability, and transparency in the BECOM industry. It decentralizes IT infrastructures, uses smart contracts for automation, and employs decentralized storage and computing to overcome data silos and improve collaboration and information sharing
Teisserenc and Sepasgozar (2021b)	The framework integrates digital twins, IoT, and machine learning to enhance the efficiency and sustainability of construction processes. This digital ecosystem allows for real-time monitoring, data-driven decision-making, and predictive maintenance, ultimately fostering a circular economy in construction by optimizing resource use and extending the lifespan of building materials and components

Reviewed studies with frameworks
Reference	Framework synthesis
Jemal et al. (2023)	The framework integrates BIM for digital modeling, IoT for real-time monitoring and data collection, blockchain for secure and transparent data management, big data for analyzing and optimizing resource allocation, and cloud computing for remote access and collaboration. These technologies collectively enhance resource utilization, reduce waste, and improve efficiency throughout the construction lifecycle
Elghaish et al. (2023)	The framework integrates Industry 4.0 tools by using IoT for real-time data collection, blockchain for secure and transparent materials tracking, and AI for analyzing data and optimizing processes. These tools collectively enhance the management of building components throughout their lifecycle, promoting recycling, reuse, and resource efficiency
Kovacic et al. (2020)	The framework uses BIM for digital building models, MPs for tracking material properties, GPR and laser scanning for capturing existing building data, and gamification for user engagement in maintaining digital models
Çetin et al. (2023)	The framework uses AI and computer vision for automated data collection, laser scanning and photogrammetry for detailed material information, and AR/VR for visualizing building data. Machine learning algorithms predict hazardous materials and assess component conditions
Lamptey et al. (2021)	The framework uses BIM for project management and IoT for real-time monitoring and automation, enhancing green design and sustainable construction practices
Teisserenc and Sepasgozar (2021a)	The framework implements I4.0 tools through the Decentralized Digital Twin Cycle (DDTC) model, integrating blockchain with digital twins to enhance data integrity, cybersecurity, traceability, and transparency in the BECOM industry. It decentralizes IT infrastructures, uses smart contracts for automation, and employs decentralized storage and computing to overcome data silos and improve collaboration and information sharing
Teisserenc and Sepasgozar (2021b)	The framework integrates digital twins, IoT, and machine learning to enhance the efficiency and sustainability of construction processes. This digital ecosystem allows for real-time monitoring, data-driven decision-making, and predictive maintenance, ultimately fostering a circular economy in construction by optimizing resource use and extending the lifespan of building materials and components

Identified gaps
Gap	Description	References
Contextual differences	European studies differ in regulatory frameworks and market conditions compared to the UAE, requiring local adaptation	Çetin et al. (2023), Elghaish et al. (2022), Kovacic et al. (2020), Teisserenc and Sepasgozar (2021a, b), Charef et al. (2021)
Technological infrastructure	UAE needs upgrades in data storage, processing, and connectivity to adopt advanced digital technologies	Government of the UAE (2024), Jemal et al. (2023), Elghaish et al. (2023), Elghaish et al. (2022)
Regulatory support	Supportive regulatory frameworks are crucial for adopting new technology in the UAE’s construction industry	Elghaish et al. (2022), Chan et al. (2017), Lamptey et al. (2021)
Stakeholder engagement	Varying readiness and cultural resistance in the UAE construction industry need change management strategies	Çetin et al. (2023), Saradara et al. (2023), Kovacic et al. (2020), Lamptey et al., 2021)
Data privacy and security	Strict data privacy laws and security concerns are significant challenges for IoT and data-intensive technologies in the UAE	PwC (2019), Eghmazi et al. (2024), Tawalbeh et al. (2020a), Kovacic et al. (2020)
Scalability	Proposed frameworks must be scalable to fit the diverse and rapidly growing UAE construction industry	Ministry of Industry and Advanced Technology (2023), Lamptey et al. (2021), Kovacic et al. (2020)
Market readiness	Assessing the market’s readiness for adopting CE practices is crucial for planning and implementation	Elghaish et al. (2023), Jemal et al. (2023), Wuni (2022)

Identified gaps
Gap	Description	References
Contextual differences	European studies differ in regulatory frameworks and market conditions compared to the UAE, requiring local adaptation	Çetin et al. (2023), Elghaish et al. (2022), Kovacic et al. (2020), Teisserenc and Sepasgozar (2021a, b), Charef et al. (2021)
Technological infrastructure	UAE needs upgrades in data storage, processing, and connectivity to adopt advanced digital technologies	Government of the UAE (2024), Jemal et al. (2023), Elghaish et al. (2023), Elghaish et al. (2022)
Regulatory support	Supportive regulatory frameworks are crucial for adopting new technology in the UAE’s construction industry	Elghaish et al. (2022), Chan et al. (2017), Lamptey et al. (2021)
Stakeholder engagement	Varying readiness and cultural resistance in the UAE construction industry need change management strategies	Çetin et al. (2023), Saradara et al. (2023), Kovacic et al. (2020), Lamptey et al., 2021)
Data privacy and security	Strict data privacy laws and security concerns are significant challenges for IoT and data-intensive technologies in the UAE	PwC (2019), Eghmazi et al. (2024), Tawalbeh et al. (2020a), Kovacic et al. (2020)
Scalability	Proposed frameworks must be scalable to fit the diverse and rapidly growing UAE construction industry	Ministry of Industry and Advanced Technology (2023), Lamptey et al. (2021), Kovacic et al. (2020)
Market readiness	Assessing the market’s readiness for adopting CE practices is crucial for planning and implementation	Elghaish et al. (2023), Jemal et al. (2023), Wuni (2022)

Tools categorization
Phase	I4.0 technologies	Applications	References
Design	BIM Digital Twins AR and VR Material Passports Blockchain	BIM provides detailed digital representations for resource assessment and predictive modeling Digital twins enhance visualization and simulation, ensuring efficient material use AR & VR allow immersive visualizations for stakeholders BIM-supported Material Passports document material lifecycle data Blockchain ensures secure, immutable records, enhancing trust	Kovacic et al. (2020), Meng et al. (2023), Jemal et al. (2023), Elghaish et al. (2022, 2023), Teisserenc and Sepasgozar (2021a), Figueiredo et al. (2022)
Planning	BIM Digital Platforms IoT AI Big Data Analytics Cloud Computing Edge Computing	BIM facilitates collaboration and detailed visualization for decision-making Digital platforms provide real-time data on material availability and compliance IoT collects site data for optimizing plans AI and Big Data analytics predict and optimize resource use and waste generation Cloud and Edge computing offer scalable, on-demand computing resources	Basbagill et al. (2013), Çetin et al. (2022, 2023), Jemal et al. (2023), Rejeb et al. (2022), Porter et al. (2014), Pal and Yasar (2023), Bibri et al. (2023), Elghaish et al. (2022), Oluleye et al. (2023), Mell and Grance (2011), Akbari (2023)
Construction	IoT AI BIM Additive Manufacturing Digital Platforms Cloud Computing Edge Computing AR and VR	IoT monitors material consumption and waste generation in real-time AI optimizes processes and minimizes waste Big Data analytics suggest improvements based on performance data Cloud and Edge computing facilitate real-time data exchange and collaboration AR/VR improve on-site activities and training Additive manufacturing allows on-demand production and resource savings	Çetin et al. (2023), Alahi et al. (2023), Çetin et al. (2022), Moon et al. (2019), Jemal et al. (2023), Yue et al. (2022), Ahmed (2019), Sauerwein et al. (2019), Chougan et al. (2023)
Operation and maintenance	IoT AI Digital Twins	IoT collects real-time data on energy consumption and material wear AI-driven predictive analytics forecast maintenance needs Digital twins identify inefficiencies and optimize resource use	Damianou et al. (2019), Meng et al. (2023), Preut et al. (2021), Liu et al. (2022a, b)
End of life	Material Passports Blockchain IoT AI Digital Twins	BIM-based MPs provide detailed documentation of materials for recovery and reuse Blockchain ensures accurate documentation and verification of deconstruction and recycling IoT and AI technologies enhance Urban Mining by providing real-time data Digital twins facilitate efficient deconstruction and resource recovery	Munaro and Tavares (2021), Shojaei et al. (2021), Çetin et al. (2023), Koutamanis et al. (2018), Jemal et al. (2023), Lu (2017), Preut et al. (2021)

Tools categorization
Phase	I4.0 technologies	Applications	References
Design	BIM Digital Twins AR and VR Material Passports Blockchain	BIM provides detailed digital representations for resource assessment and predictive modeling Digital twins enhance visualization and simulation, ensuring efficient material use AR & VR allow immersive visualizations for stakeholders BIM-supported Material Passports document material lifecycle data Blockchain ensures secure, immutable records, enhancing trust	Kovacic et al. (2020), Meng et al. (2023), Jemal et al. (2023), Elghaish et al. (2022, 2023), Teisserenc and Sepasgozar (2021a), Figueiredo et al. (2022)
Planning	BIM Digital Platforms IoT AI Big Data Analytics Cloud Computing Edge Computing	BIM facilitates collaboration and detailed visualization for decision-making Digital platforms provide real-time data on material availability and compliance IoT collects site data for optimizing plans AI and Big Data analytics predict and optimize resource use and waste generation Cloud and Edge computing offer scalable, on-demand computing resources	Basbagill et al. (2013), Çetin et al. (2022, 2023), Jemal et al. (2023), Rejeb et al. (2022), Porter et al. (2014), Pal and Yasar (2023), Bibri et al. (2023), Elghaish et al. (2022), Oluleye et al. (2023), Mell and Grance (2011), Akbari (2023)
Construction	IoT AI BIM Additive Manufacturing Digital Platforms Cloud Computing Edge Computing AR and VR	IoT monitors material consumption and waste generation in real-time AI optimizes processes and minimizes waste Big Data analytics suggest improvements based on performance data Cloud and Edge computing facilitate real-time data exchange and collaboration AR/VR improve on-site activities and training Additive manufacturing allows on-demand production and resource savings	Çetin et al. (2023), Alahi et al. (2023), Çetin et al. (2022), Moon et al. (2019), Jemal et al. (2023), Yue et al. (2022), Ahmed (2019), Sauerwein et al. (2019), Chougan et al. (2023)
Operation and maintenance	IoT AI Digital Twins	IoT collects real-time data on energy consumption and material wear AI-driven predictive analytics forecast maintenance needs Digital twins identify inefficiencies and optimize resource use	Damianou et al. (2019), Meng et al. (2023), Preut et al. (2021), Liu et al. (2022a, b)
End of life	Material Passports Blockchain IoT AI Digital Twins	BIM-based MPs provide detailed documentation of materials for recovery and reuse Blockchain ensures accurate documentation and verification of deconstruction and recycling IoT and AI technologies enhance Urban Mining by providing real-time data Digital twins facilitate efficient deconstruction and resource recovery	Munaro and Tavares (2021), Shojaei et al. (2021), Çetin et al. (2023), Koutamanis et al. (2018), Jemal et al. (2023), Lu (2017), Preut et al. (2021)

Source(s): Authors’ own creation