[1] Employment by major industry sector : U.S. Bureau of Labor Statistics, (n.d.). https://www.bls.gov/emp/tables/employment-by-major-industry-sector.htm (accessed July 1, 2025).
[2] Number and rate of fatal work injuries, by private industry sector, (n.d.). https://www.bls.gov/charts/census-of-fatal-occupational-injuries/number-and-rate-of-fatal-work-injuries-by-industry.htm (accessed July 1, 2025).
[3] Number and rate of fatal work injuries, by private industry sector, (n.d.). https://www.bls.gov/charts/census-of-fatal-occupational-injuries/number-and-rate-of-fatal-work-injuries-by-industry.htm (accessed August 4, 2023).
[4] W. Fang, P.E.D. Love, H. Luo, L. Ding, Computer vision for behaviour-based safety in construction: A review and future directions, Advanced Engineering Informatics 43 (2020) 100980. https://doi.org/10.1016/J.AEI.2019.100980.
[5] S. Rasouli, Y. Alipouri, S. Chamanzad, Smart Personal Protective Equipment (PPE) for construction safety: A literature review, Saf Sci 170 (2024) 106368. https://doi.org/10.1016/J.SSCI.2023.106368.
[6] W. Fang, L. Ding, H. Luo, P.E.D. Love, Falls from heights: A computer vision-based approach for safety harness detection, Autom Constr 91 (2018) 53–61. https://doi.org/10.1016/J.AUTCON.2018.02.018.
[7] A.S. Kulinan, M. Park, P.P.W. Aung, G. Cha, S. Park, Advancing construction site workforce safety monitoring through BIM and computer vision integration, Autom Constr 158 (2024) 105227. https://doi.org/10.1016/J.AUTCON.2023.105227.
[8] H. Wu, B. Zhong, H. Li, P. Love, X. Pan, N. Zhao, Combining computer vision with semantic reasoning for on-site safety management in construction, Journal of Building Engineering 42 (2021) 103036. https://doi.org/10.1016/J.JOBE.2021.103036.
[9] J. Seo, S. Han, S. Lee, H. Kim, Computer vision techniques for construction safety and health monitoring, Advanced Engineering Informatics 29 (2015) 239–251. https://doi.org/10.1016/J.AEI.2015.02.001.
[10] X. Xu, L. Ma, L. Ding, A framework for BIM-enabled life-cycle information management of construction project, Int J Adv Robot Syst 11 (2014). https://doi.org/10.5772/58445/ASSET/IMAGES/LARGE/10.5772_58445-126-FIG2.JPEG.
[11] H. Malekitabar, A. Ardeshir, M.H. Sebt, R. Stouffs, Construction safety risk drivers: A BIM approach, Saf Sci 82 (2016) 445–455. https://doi.org/10.1016/j.ssci.2015.11.002.
[12] A.B.K. Rabbi, I. Jeelani, AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio based applications, Autom Constr 164 (2024) 105443. https://doi.org/10.1016/J.AUTCON.2024.105443.
[13] Q. Fang, H. Li, X. Luo, L. Ding, H. Luo, T.M. Rose, W. An, Detecting non-hardhat-use by a deep learning method from far-field surveillance videos, Autom Constr 85 (2018) 1–9. https://doi.org/10.1016/J.AUTCON.2017.09.018.
[14] Q. Fang, H. Li, X. Luo, L. Ding, H. Luo, T.M. Rose, Automation in Construction Detecting non-hardhat-use by a deep learning method from far- fi eld surveillance videos, Autom Constr 85 (2018) 1–9. https://doi.org/10.1016/j.autcon.2017.09.018.
[15] B.E. Mneymneh, M. Abbas, H. Khoury, Vision-Based Framework for Intelligent Monitoring of Hardhat Wearing on Construction Sites, Journal of Computing in Civil Engineering 33 (2018) 04018066. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000813.
[16] J. Wu, N. Cai, W. Chen, H. Wang, G. Wang, Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset, Autom Constr 106 (2019) 102894. https://doi.org/10.1016/J.AUTCON.2019.102894.
[17] A. Jalil Al-Bayati, A.T. Rener, M.P. Listello, M. Mohamed, PPE non-compliance among construction workers: An assessment of contributing factors utilizing fuzzy theory, J Safety Res 85 (2023) 242–253. https://doi.org/10.1016/J.JSR.2023.02.008.
[18] N.D. Nath, A.H. Behzadan, S.G. Paal, Deep learning for site safety: Real-time detection of personal protective equipment, Autom Constr 112 (2020) 103085. https://doi.org/10.1016/J.AUTCON.2020.103085.
[19] X. Yang, Y. Yu, S. Shirowzhan, S. Sepasgozer, H. Li, Automated PPE-Tool pair check system for construction safety using smart IoT, Journal of Building Engineering 32 (2020) 101721. https://doi.org/10.1016/J.JOBE.2020.101721.
[20] S. Tang, D. Roberts, M. Golparvar-Fard, Human-object interaction recognition for automatic construction site safety inspection, Autom Constr 120 (2020) 103356. https://doi.org/10.1016/J.AUTCON.2020.103356.
[21] N. Khan, S. Farhan, A. Zaidi, A. Sabir, M.S. Abbas, R. Hussain, C. Park, D. Lee, DTR: A Unified Detection-Tracking-Re-identification Framework for Dynamic Worker Monitoring in Construction Sites, International Conference on Construction Engineering and Project Management (2024) 367–374. https://doi.org/10.6106/ICCEPM.2024.0367.
[22] J. Zhao, E. Obonyo, Convolutional long short-term memory model for recognizing construction workers’ postures from wearable inertial measurement units, Advanced Engineering Informatics 46 (2020) 101177. https://doi.org/10.1016/J.AEI.2020.101177.
[23] W. Liu, Q. Meng, Z. Li, X. Hu, Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges, Buildings 2021, Vol. 11, Page 409 11 (2021) 409. https://doi.org/10.3390/BUILDINGS11090409.
[24] R. Akram, M.J. Thaheem, S. Khan, A.R. Nasir, A. Maqsoom, Exploring the Role of BIM in Construction Safety in Developing Countries: Toward Automated Hazard Analysis, Sustainability 2022, Vol. 14, Page 12905 14 (2022) 12905. https://doi.org/10.3390/SU141912905.
[25] A. Rashidi Nasab, H. Malekitabar, H. Elzarka, A. Nekouvaght Tak, K. Ghorab, Managing Safety Risks from Overlapping Construction Activities: A BIM Approach, Buildings 2023, Vol. 13, Page 2647 13 (2023) 2647. https://doi.org/10.3390/BUILDINGS13102647.
[26] R. Chahrour, M.A. Hafeez, A.M. Ahmad, H.I. Sulieman, H. Dawood, S. Rodriguez-Trejo, M. Kassem, K.K. Naji, N. Dawood, Cost-benefit analysis of BIM-enabled design clash detection and resolution, Construction Management and Economics 39 (2021) 55–72. https://doi.org/10.1080/01446193.2020.1802768.
[27] M.S. Dashti, M. RezaZadeh, M. Khanzadi, H. Taghaddos, Integrated BIM-based simulation for automated time-space conflict management in construction projects, Autom Constr 132 (2021) 103957. https://doi.org/10.1016/J.AUTCON.2021.103957.
[28] D. Kim, T. Yoo, S.V.T. Tran, D. Lee, C. Park, D. Lee, Automated Safety Risk Assessment Framework by Integrating Safety Regulation and 4D BIM-Based Rule Modeling, Buildings 2024, Vol. 14, Page 2529 14 (2024) 2529. https://doi.org/10.3390/BUILDINGS14082529.
[29] K. Kim, Y. Cho, S. Zhang, Integrating work sequences and temporary structures into safety planning: Automated scaffolding-related safety hazard identification and prevention in BIM, Autom Constr 70 (2016) 128–142. https://doi.org/10.1016/J.AUTCON.2016.06.012.
[30] S.V.T. Tran, N. Khan, D. Lee, C. Park, A Hazard Identification Approach of Integrating 4D BIM and Accident Case Analysis of Spatial–Temporal Exposure, Sustainability 2021, Vol. 13, Page 2211 13 (2021) 2211. https://doi.org/10.3390/SU13042211.
[31] A.N. Tak, H. Taghaddos, A. Mousaei, A. Bolourani, U. Hermann, BIM-based 4D mobile crane simulation and onsite operation management, Autom Constr 128 (2021) 103766. https://doi.org/10.1016/J.AUTCON.2021.103766.
[32] A. Salzano, S. Cascone, E.P. Zitiello, M. Nicolella, Construction Safety and Efficiency: Integrating Building Information Modeling into Risk Management and Project Execution, Sustainability 2024, Vol. 16, Page 4094 16 (2024) 4094. https://doi.org/10.3390/SU16104094.
[33] Y. Lu, P. Gong, Y. Tang, S. Sun, Q. Li, BIM-integrated construction safety risk assessment at the design stage of building projects, Autom Constr 124 (2021) 103553. https://doi.org/10.1016/J.AUTCON.2021.103553.
[34] M.H. Tamanaeifar, V. Shahhosseini, Automated fall hazard analysis in the design stage using Building Information Modeling (BIM), Civil Engineering and Environmental Systems (2025). https://doi.org/10.1080/10286608.2025.2524412;CTYPE:STRING:JOURNAL.
[35] M. Dadashi Haji, B. Behnam, M.H. Sebt, A. Ardeshir, A. Katooziani, BIM-Based Safety Leading Indicators Measurement Tool for Construction Sites, International Journal of Civil Engineering 21 (2023) 265–282. https://doi.org/10.1007/S40999-022-00754-9/TABLES/11.
[36] M. Dadashi Haji, B. Behnam, An automated BIM and system dynamics tool for assessing safety leading indicators in construction projects, International Journal of Building Pathology and Adaptation 43 (2023) 414–439. https://doi.org/10.1108/IJBPA-05-2022-0072/FULL/XML.
[37] J.D. Nunez-Morales, S.H. Hsu, A. Ibrahim, M. Golparvar-Fard, New Metrics to Benchmark and Improve BIM Visibility Within a Synthetic Image Generation Process for Computer Vision Progress Tracking, Lecture Notes in Civil Engineering 498 LNCE (2025) 209–221. https://doi.org/10.1007/978-3-031-61499-6_16.
[38] A. Pal, J.J. Lin, S.H. Hsieh, M. Golparvar-Fard, Automated vision-based construction progress monitoring in built environment through digital twin, Developments in the Built Environment 16 (2023) 100247. https://doi.org/10.1016/J.DIBE.2023.100247.
[39] A.S. Kulinan, M. Park, P.P.W. Aung, G. Cha, S. Park, Advancing construction site workforce safety monitoring through BIM and computer vision integration, Autom Constr 158 (2024) 105227. https://doi.org/10.1016/J.AUTCON.2023.105227.
[40] R. Khanam, M. Hussain, YOLOv11: An Overview of the Key Architectural Enhancements, (2024). https://arxiv.org/pdf/2410.17725 (accessed July 3, 2025).
[41] C.-Y. Wang, A. Bochkovskiy, H.-Y.M. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, (2022). https://doi.org/10.48550/arxiv.2207.02696.
[42] J. Wu, N. Cai, W. Chen, H. Wang, G. Wang, Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset, Autom Constr 106 (2019) 102894. https://doi.org/10.1016/J.AUTCON.2019.102894.