Table 2 Seminal publications Authors... | Emerald Publishing

Table 2

Seminal publications

Authors	Year	Title	Publication	Citation	CPY	Key finding
Jeelani and Gheisari	2021	Safety challenges of UAV integration in construction: Conceptual analysis and future research roadmap	Safety Science	136	45.33	UAVs pose physical, attentional, and psychological risks to construction workers, highlighting the need for empirical research and regulatory measures to ensure their safe integration into construction sites
Alizadehsalehi et al.	2020	The effectiveness of an integrated BIM/UAV model in managing safety on construction sites	International Journal of Occupational Safety and Ergonomics	170	42.5	Integrating 4D BIM with UAV monitoring significantly enhances hazard identification and mitigation, potentially reducing fatal, non-fatal, and property damage-causing accidents on construction sites
Martinez et al.	2020	UAV integration in current construction safety planning and monitoring processes: Case study of a high-rise building construction project in Chile	Journal of Management in Engineering	151	37.75	Integrating UAV-generated aerial visuals into safety planning and monitoring significantly improves hazard identification, especially for high-risk conditions at heights and reduces the time required for site inspections in high-rise construction projects with limited safety personnel
Shanti et al.	2022	Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning	Journal of Safety Research	57	28.5	The integrated deep learning and drone-based system can accurately detect fall protection equipment and safety violations in real-time, achieving 90% accuracy and demonstrating strong potential for improving worker safety at heights on construction sites
Wu et al.	2021	Rapid safety monitoring and analysis of foundation pit construction using unmanned aerial vehicle images	Automation in Construction	68	22.67	UAV imagery combined with point cloud reconstruction enables accurate and efficient detection of local deformations in foundation pits, offering a reliable alternative to traditional safety monitoring methods

Authors	Year	Title	Publication	Citation	CPY	Key finding
Jeelani and Gheisari	2021	Safety challenges of UAV integration in construction: Conceptual analysis and future research roadmap	Safety Science	136	45.33	UAVs pose physical, attentional, and psychological risks to construction workers, highlighting the need for empirical research and regulatory measures to ensure their safe integration into construction sites
Alizadehsalehi et al.	2020	The effectiveness of an integrated BIM/UAV model in managing safety on construction sites	International Journal of Occupational Safety and Ergonomics	170	42.5	Integrating 4D BIM with UAV monitoring significantly enhances hazard identification and mitigation, potentially reducing fatal, non-fatal, and property damage-causing accidents on construction sites
Martinez et al.	2020	UAV integration in current construction safety planning and monitoring processes: Case study of a high-rise building construction project in Chile	Journal of Management in Engineering	151	37.75	Integrating UAV-generated aerial visuals into safety planning and monitoring significantly improves hazard identification, especially for high-risk conditions at heights and reduces the time required for site inspections in high-rise construction projects with limited safety personnel
Shanti et al.	2022	Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning	Journal of Safety Research	57	28.5	The integrated deep learning and drone-based system can accurately detect fall protection equipment and safety violations in real-time, achieving 90% accuracy and demonstrating strong potential for improving worker safety at heights on construction sites
Wu et al.	2021	Rapid safety monitoring and analysis of foundation pit construction using unmanned aerial vehicle images	Automation in Construction	68	22.67	UAV imagery combined with point cloud reconstruction enables accurate and efficient detection of local deformations in foundation pits, offering a reliable alternative to traditional safety monitoring methods

Note(s): Citations Per Year (CPY)

Source(s): Authors' own work