Applied IT & Engineering
Information and engineering sciences | Online ISSN 3068-0115
31
Citations
36.6k
Views
23
Articles
Volume 1 Number 1 2023
RESEARCH ARTICLE (Open Access)
Adoption and Effectiveness of AI-Enabled Digital Inspection Systems for Infrastructure Safety and Construction Quality Assurance
Nipa Akter 1*
Applied IT & Engineering 1 (1) 1-8 https://doi.org/10.25163/engineering.1110699
Submitted: 26 December 2022 Revised: 06 March 2023 Accepted: 13 March 2023 Published: 14 March 2023
Abstract
Background: Infrastructure inspection has long depended on manual, experience-driven approaches, yet increasing structural complexity and safety demands are gradually exposing the limitations of such methods. In recent years, artificial intelligence (AI) and digital technologies—particularly drones, computer vision, and sensor-based systems—have been proposed as transformative tools. Still, their real-world adoption remains uneven, and professional perceptions of their effectiveness are not fully understood.Methods: This study employed a cross-sectional survey of 160 infrastructure professionals across the United States, including engineers, inspectors, managers, and safety officers. The survey explored technology adoption patterns, perceived effectiveness, operational benefits, and implementation barriers. Data were analyzed using descriptive statistics, Chi-square tests, and logistic regression to identify factors associated with AI system acceptance and perceived performance.Results: Drone-based inspection (25%), computer vision (21%), and BIM-integrated systems (19%) emerged as the most widely adopted technologies. A majority of respondents (62.5%) rated AI-based inspection as effective or very effective. Key perceived benefits included improved defect detection (26.3%), enhanced worker safety (21.2%), and faster inspection processes (18.8%). Statistical analysis indicated that technology adoption (β = 0.47, p = 0.041), professional experience (p = 0.049), digital training (p = 0.047), and organizational support (p = 0.043) significantly influenced perceived effectiveness.Conclusion: AI-enabled inspection systems show clear promise, yet their success appears to depend less on technological capability alone and more on organizational readiness, training, and practical integration into existing workflows.
Keywords: Artificial intelligence; infrastructure inspection; digital construction; quality assurance; technology adoption
References
Ammar, M., Haleem, A., Javaid, M., Walia, R., & Bahl, S. (2021). Improving material quality management and manufacturing organizations system through Industry 4.0 technologies. Materials Today Proceedings, 45, 5089–5096. https://doi.org/10.1016/j.matpr.2021.01.585
Arden, N. S., Fisher, A. C., Tyner, K., Yu, L. X., Lee, S. L., & Kopcha, M. (2021). Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future. International Journal of Pharmaceutics, 602, 120554. https://doi.org/10.1016/j.ijpharm.2021.120554
Behzadan, A. H., Dong, S., & Kamat, V. R. (2015). Augmented reality visualization: A review of civil infrastructure system applications. Advanced Engineering Informatics, 29(2), 252–267. https://doi.org/10.1016/j.aei.2015.03.005
Besada, J. A., Bergesio, L., Campaña, I., Vaquero-Melchor, D., López-Araquistain, J., Bernardos, A. M., & Casar, J. R. (2018). Drone mission definition and implementation for automated infrastructure inspection using airborne sensors. Sensors, 18(4), 1170. https://doi.org/10.3390/s18041170
Bradley, A., Li, H., Lark, R., & Dunn, S. (2016). BIM for infrastructure: An overall review and constructor perspective. Automation in Construction, 71, 139–152. https://doi.org/10.1016/j.autcon.2016.08.019
Chen, Y., Lai, Y., & Lin, Y. (2019). BIM-based augmented reality inspection and maintenance of fire safety equipment. Automation in Construction, 110, 103041. https://doi.org/10.1016/j.autcon.2019.103041
Cheung, W., Lin, T., & Lin, Y. (2018). A Real-Time construction safety monitoring system for hazardous gas integrating wireless sensor network and building information modeling technologies. Sensors, 18(2), 436. https://doi.org/10.3390/s18020436
De Melo, R. R. S., Costa, D. B., Álvares, J. S., & Irizarry, J. (2017). Applicability of unmanned aerial system (UAS) for safety inspection on construction sites. Safety Science, 98, 174–185. https://doi.org/10.1016/j.ssci.2017.06.008
Ham, Y., Han, K. K., Lin, J. J., & Golparvar-Fard, M. (2016). Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works. Visualization in Engineering, 4(1). https://doi.org/10.1186/s40327-015-0029-z
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-Driven digital twins in Industry 4.0: smart manufacturing and Advanced Robotics. Sensors, 21(19), 6340. https://doi.org/10.3390/s21196340
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. https://doi.org/10.3390/su13042051
Kelm, A., Laußat, L., Meins-Becker, A., Platz, D., Khazaee, M. J., Costin, A. M., Helmus, M., & Teizer, J. (2013). Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites. Automation in Construction, 36, 38–52. https://doi.org/10.1016/j.autcon.2013.08.009
Kim, M., Wang, Q., Park, J., Cheng, J. C., Sohn, H., & Chang, C. (2016). Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM. Automation in Construction, 72, 102–114. https://doi.org/10.1016/j.autcon.2016.08.035
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2), 196–210. https://doi.org/10.1016/j.aei.2015.01.008
Li, Y., & Liu, C. (2018). Applications of multirotor drone technologies in construction management. International Journal of Construction Management, 19(5), 401–412. https://doi.org/10.1080/15623599.2018.1452101
Liu, D., Chen, J., Hu, D., & Zhang, Z. (2019). Dynamic BIM-augmented UAV safety inspection for water diversion project. Computers in Industry, 108, 163–177. https://doi.org/10.1016/j.compind.2019.03.004
Ma, Z., Cai, S., Mao, N., Yang, Q., Feng, J., & Wang, P. (2018). Construction quality management based on a collaborative system using BIM and indoor positioning. Automation in Construction, 92, 35–45. https://doi.org/10.1016/j.autcon.2018.03.027
Mahadevaiah, G., Rv, P., Bermejo, I., Jaffray, D., Dekker, A., & Wee, L. (2020). Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance. Medical Physics, 47(5), e228–e235. https://doi.org/10.1002/mp.13562
Manzoor, B., Othman, I., & Pomares, J. C. (2021). Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry—A Bibliometric—Qualitative Literature Review of Research activities. International Journal of Environmental Research and Public Health, 18(11), 6135. https://doi.org/10.3390/ijerph18116135
Mohassel, R. R., Fung, A., Mohammadi, F., & Raahemifar, K. (2014). A survey on Advanced Metering Infrastructure. International Journal of Electrical Power & Energy Systems, 63, 473–484. https://doi.org/10.1016/j.ijepes.2014.06.025
Morgenthal, G., Hallermann, N., Kersten, J., Taraben, J., Debus, P., Helmrich, M., & Rodehorst, V. (2018). Framework for automated UAS-based structural condition assessment of bridges. Automation in Construction, 97, 77–95. https://doi.org/10.1016/j.autcon.2018.10.006
Omar, T., & Nehdi, M. L. (2016). Data acquisition technologies for construction progress tracking. Automation in Construction, 70, 143–155. https://doi.org/10.1016/j.autcon.2016.06.016
Outay, F., Mengash, H. A., & Adnan, M. (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges. Transportation Research Part a Policy and Practice, 141, 116–129. https://doi.org/10.1016/j.tra.2020.09.018
Qian, Q., & Lin, P. (2016). Safety risk management of underground engineering in China: Progress, challenges and strategies. Journal of Rock Mechanics and Geotechnical Engineering, 8(4), 423–442. https://doi.org/10.1016/j.jrmge.2016.04.001
Rakha, T., & Gorodetsky, A. (2018). Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones. Automation in Construction, 93, 252–264. https://doi.org/10.1016/j.autcon.2018.05.002
Sheng, D., Ding, L., Zhong, B., Love, P. E., Luo, H., & Chen, J. (2020). Construction quality information management with blockchains. Automation in Construction, 120, 103373. https://doi.org/10.1016/j.autcon.2020.103373
Teizer, J. (2015). Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics, 29(2), 225–238. https://doi.org/10.1016/j.aei.2015.03.006
Wang, J., Sun, W., Shou, W., Wang, X., Wu, C., Chong, H., Liu, Y., & Sun, C. (2014). Integrating BIM and LiDAR for Real-Time Construction Quality Control. Journal of Intelligent & Robotic Systems, 79(3–4), 417–432. https://doi.org/10.1007/s10846-014-0116-8
Zhang, M., Cao, T., & Zhao, X. (2017). Applying Sensor-Based Technology to improve construction safety management. Sensors, 17(8), 1841. https://doi.org/10.3390/s17081841
Zhou, W., Whyte, J., & Sacks, R. (2011). Construction safety and digital design: A review. Automation in Construction, 22, 102–111. https://doi.org/10.1016/j.autcon.2011.07.005
Zhou, Y., Luo, H., & Yang, Y. (2017). Implementation of augmented reality for segment displacement inspection during tunneling construction. Automation in Construction, 82, 112–121. https://doi.org/10.1016/j.autcon.2017.02.007
Zou, Y., Kiviniemi, A., & Jones, S. W. (2016). A review of risk management through BIM and BIM-related technologies. Safety Science, 97, 88–98. https://doi.org/10.1016/j.ssci.2015.12.027
Recommended articles
Illuminating Biological Dark Matter: Integrating Metagenomics, Synthetic Biology, and AI to Unlock Microbial and Genomic Potential for Therapeutics and Biotechnology
RETRACTED ARTICLE: Predicting Parkinson’s Disease Progression Using Statistical And Neural Mixed Effects Models: Comparative Study On Longitudinal Biomarkers
Article metrics
View details
0
Downloads
0
Citations
35
Views
0
Save
Save
0
Citation
Citation
35
View
View
0
Share
Share