Hypothesis-validated construction accident analysis and framework for potential multi-sensor fusion with 360° camera and LiDAR
DOI: https://doi.org/10.3846/jcem.2026.27071Abstract
Construction sites are among the most hazardous work environments, with frequent accidents such as falls, entrapments, and collisions. Traditional single-sensor detection systems suffer from occlusions, poor lighting, and limited depth perception, reducing reliability in complex conditions. This study addresses these limitations by proposing a realtime multi-sensor fusion framework integrating a 360° camera and LiDAR. A three-year construction accident dataset was analyzed, and Chi-squared tests and ANOVA (p < 0.05) confirmed the statistically significant superiority of the fusion approach over single-sensor systems. Deep learning techniques were applied to enhance real-time detection and prevention capabilities. The results demonstrate that sensor fusion substantially improves detection accuracy, especially in high-risk scenarios such as falls and collisions. This study provides a comprehensive statistical analysis based on national incident records and proposes an AI-driven monitoring framework. The findings offer strong empirical support for multi-sensor fusion as a foundation for next-generation construction site safety systems.
Keywords:
construction safety, multi-sensor fusion, 360° camera, LiDAR-based detection, hypothesis testing, chi-squared testHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bilal, H., Obaidat, M. S., Aslam, M. S., Zhang, J., Yin, B., & Mahmood, K. (2024). Online fault diagnosis of industrial robot using IoRT and hybrid deep learning techniques: An experimental approach. IEEE Internet of Things Journal, 11(19), 31422–31437. https://doi.org/10.1109/JIOT.2024.3418352
Bilal, H., Rehman, A., Aslam, M. S., Ullah, I., Chang, W. J., Kumar, N., & Almuhaideb, A. M. (2025a). Hybrid TrafficAI: A generative AI framework for real-time traffic simulation and adaptive behavior modeling. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2025.3571041
Bilal, H., Muhammad, Y., Ullah, I., Garg, S., Choi, B. J., & Hassan, M. M. (2025b). Identification and diagnosis of chronic heart disease: A deep learning-based hybrid approach. Alexandria Engineering Journal, 124, 470–483. https://doi.org/10.1016/j.aej.2025.03.025
Braun, A., Tuttas, S., Borrmann, A., & Stilla, U. (2020). Improving progress monitoring by fusing point clouds, semantic data and computer vision. Automation in Construction, 116, Article 103210. https://doi.org/10.1016/j.autcon.2020.103210
Cheng, J. P., Wong, P. K. Y., Luo, H., Wang, M., & Leung, P. H. (2022). Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification. Automation in Construction, 139, Article 104312. https://doi.org/10.1016/j.autcon.2022.104312
Construction Safety Management Integrated Information. (2024, December 19). https://www.csi.go.kr/
Fang, W., Ding, L., Luo, H., & Love, P. E. (2018a). Falls from heights: A computer vision-based approach for safety harness detection. Automation in Construction, 91, 53–61. https://doi.org/10.1016/j.autcon.2018.02.018
Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T. M., & An, W. (2018b). Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Automation in Construction, 85, 1–9. https://doi.org/10.1016/j.autcon.2017.09.018
Fang, W., Ding, L., Zhong, B., Love, P. E., & Luo, H. (2018c). Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach. Advanced Engineering Informatics, 37, 139–149. https://doi.org/10.1016/j.aei.2018.05.003
Jeon, Y., Kulinan, A. S., Kim, T., Park, M., & Park, S. (2024). Vision-based motion prediction for construction workers’ safety in real-time multi-camera system. Advanced Engineering Informatics, 62, Article 102898. https://doi.org/10.1016/j.aei.2024.102898
Jeong, J., & Jeong, J. (2021). Novel approach of the integrated work and risk breakdown structure for identifying the hierarchy of fatal incidents in the construction industry. Journal of Building Engineering, 41, Article 102406. https://doi.org/10.1016/j.jobe.2021.102406
Jeong, J., & Jeong, J. (2022). Quantitative risk evaluation of fatal incidents in construction based on frequency and probability analysis. Journal of Management in Engineering, 38(2), Article 04021089. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000998
Kim, J., Chung, D., Kim, Y., & Kim, H. (2022). Deep learning-based 3D reconstruction of scaffolds using a robot dog. Automation in Construction, 134, Article 104092. https://doi.org/10.1016/j.autcon.2021.104092
Kim, J., Kim, J., Paik, S., & Kim, H. (2024). Point cloud registration considering safety nets during scaffold installation using sensor fusion and deep learning. Automation in Construction, 159, Article 105277. https://doi.org/10.1016/j.autcon.2024.105277
Kou, Y., & Liu, K. (2025). Optimization path for construction safety resilience in megaprojects from the perspective of configuration. International Journal of Occupational Safety and Ergonomics, 31(2), 603–614. https://doi.org/10.1080/10803548.2025.2454802
Lee, J., Jeong, J., Soh, J., & Jeong, J. (2022). Quantitative analysis of the accident prevention costs in Korean construction projects. Buildings, 12(10), Article 1536. https://doi.org/10.3390/buildings12101536
Liu, J., He, Y., Feng, R., & Chu, Q. (2025). Enhancing construction safety management through multivariable grey model analysis and variable selection optimization. Journal of Civil Engineering and Management, 31(6), 561–579. https://doi.org/10.3846/jcem.2025.24348
Luo, H., Xiong, C., Fang, W., Love, P. E., Zhang, B., & Ouyang, X. (2018a). Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction, 94, 282–289. https://doi.org/10.1016/j.autcon.2018.06.007
Luo, X., Li, H., Cao, D., Yu, Y., Yang, X., & Huang, T. (2018b). Towards efficient and objective work sampling: Recognizing workers’ activities in site surveillance videos with two-stream convolutional networks. Automation in Construction, 94, 360–370. https://doi.org/10.1016/j.autcon.2018.07.011
Ma, Z., Chong, H. Y., & Liao, P. C. (2020). Real-time safety inspection and planning: A first application of the Fagan nomogram. Canadian Journal of Civil Engineering, 47(4), 438–449. https://doi.org/10.1139/cjce-2018-0500
Mun, H., Jeong, J., Jeong, J., & Kumi, L. (2026). Unsupervised learning approach for benchmark models to identify construction projects with high accident risk levels. Engineering, Construction and Architectural Management, 33(5), 3777–3798. https://doi.org/10.1108/ECAM-06-2024-0815
Park, M., Kulinan, A. S., Bak, J., & Park, S. (2024). Preventing falls from floor openings using quadrilateral detection and construction worker pose-estimation. Automation in Construction, 165, Article 105536. https://doi.org/10.1016/j.autcon.2024.105536
Parsons, T., Sheikhha, F. H., & Seo, J. (2024). RGB-LiDAR sensor fusion for dust de-filtering in autonomous excavation applications. Automation in Construction, 168, Article 105850. https://doi.org/10.1016/j.autcon.2024.105850
Satoh, M. (2022). Digital twin-based collision avoidance system for autonomous excavator with automatic 3D LiDAR sensor calibration. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (pp. 1267–1273). IEEE. https://doi.org/10.1109/CASE49997.2022.9926653
Ullah, R., Wahab, F., Bilal, H., Ayouni, S., & Phanomchoeng, G. (2025). CrackFormer: A reinforcement learning-enhanced Swin Transformer V3 framework for high-fidelity detection and segmentation of pavement surface cracks. Alexandria Engineering Journal, 131, 183–198. https://doi.org/10.1016/j.aej.2025.08.011
Ullah, R., Bilal, H., Aslam, M. S., Ayouni, S., Majid, A., Vasilakos, A. V., & Gadekallu, T. R. (2026). Conformer-PhyFaultNet: Physics-informed spectral attention conformer for generalizable bearing fault diagnosis. IEEE Internet of Things Journal, 13(8), 17538–17552. https://doi.org/10.1109/JIOT.2026.3662413
Wu, Z., Chen, Y., Meng, X., Sun, J., Zhu, Y., & Li, T. (2023). Speed monitoring of heavy vehicles on construction plants by fusing camera visual image with UAV LiDAR point cloud. In IGARSS 2023 – IEEE International Geoscience and Remote Sensing Symposium (pp. 5105–5108). IEEE. https://doi.org/10.1109/IGARSS52108.2023.10282169
Yousaf, D., Khan, M. B., Bilal, H., et al. (2025). AI-driven framework for text neck syndrome detection using non-contact software-defined RF sensing and sequential deep learning. Telecommunication Systems, 88(3), Article 99. https://doi.org/10.1007/s11235-025-01330-x
Zhang, M., Shi, R., & Yang, Z. (2020). A critical review of vision-based occupational health and safety monitoring of construction site workers. Safety Science, 126, Article 104658. https://doi.org/10.1016/j.ssci.2020.104658
Zhou, F., Zhao, H., & Nie, Z. (2021). Safety helmet detection based on YOLOv5. In 2021 IEEE International Conference on Power Electronics and Computer Applications (ICPECA) (pp. 6–11). IEEE. https://doi.org/10.1109/ICPECA51329.2021.9362711
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.