With the advent of Industry 4.0, smart construction sites have seen significant development in China. However, accidents involving digitized tower cranes continue to be a persistent issue. Among the contributing factors, human unsafe behavior stands out as a primary cause for these incidents. This study aims to assess the human reliability of tower crane operations on smart construction sites. To proactively enhance safety measures, the research employs text mining techniques (TF-IDF-Truncated SVD-Complement NB) to identify patterns of human errors among tower crane operators. Building upon the SHEL model, the study categorizes behavioral factors affecting human reliability in the man-machine interface, leading to the establishment of the Performance Shaping Factors (PSFs) system. Furthermore, the research constructs an error impact indicator system for the intelligent construction site tower crane operator interface. Using the DEMATEL method, it analyzes the significance of various factors influencing human errors in tower crane operations. Additionally, the ISM-MICMAC method is applied to unveil the hierarchical relationships and driving-dependent connections among these influencing factors. The findings indicate that personal state, operating procedures, and physical environment directly impact human errors, while personal capability, technological environment, and one fundamental organizational management factor contribute indirectly.
References
[1]
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M. and Yin, B. (2018) Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access, 6, 6505-6519. https://doi.org/10.1109/ACCESS.2017.2783682
[2]
Wang, J., Guo, F., Song, Y., Liu, Y., Hu, X. and Yuan, C. (2022) Safety Risk Assessment of Prefabricated Buildings Hoisting Construction: Based on IHFACS-ISAM-BN. Buildings, 12, Article 81https://doi.org/10.3390/buildings12060811
[3]
Fang, Y., Cho, Y.K., Durso, F. and Seo, J. (2018) Assessment of Operator’s Situation Awareness for Smart Operation of Mobile Cranes. Automation in Construction, 85, 65-75. https://doi.org/10.1016/j.autcon.2017.10.007
[4]
Liu, C., Sun, S.M., Zhao, Y.J. and Ding, M.R. (2022) Statistical Analysis of Construction Tower Crane Accidents from 2016 to 2020. Industrial Safety and Environmental Protection, 48, 53-55+63.
[5]
Raviv, G., Fishbain, B. and Shapira, A. (2017) Analyzing Risk Factors in Crane-Related Near-Miss and Accident Reports. Safety Science, 91, 192-205.
https://doi.org/10.1016/j.ssci.2016.08.022
[6]
Fang, Y., Cho, Y.K. and Chen, J. (2016) A Framework for Real-Time Pro-Active Safety Assistance for Mobile Crane Lifting Operations. Automation in Construction, 72, 367-379. https://doi.org/10.1016/j.autcon.2016.08.025
[7]
Jiang, W., Ding, L. and Zhou, C. (2020) Cyber Physical System for Safety Management in Smart Construction Site. Engineering, Construction and Architectural Management, 28, 788-808. https://doi.org/10.1108/ECAM-10-2019-0578
[8]
Stone, R.W. (1931) Industrial Accident Prevention. H. W. Heinrich. Social Service Review, 5, 323-324. https://doi.org/10.1086/630904
[9]
Porthin, M., Liinasuo, M. and Kling, T. (2020) Effects of Digitalization of Nuclear Power plant Control Rooms on Human Reliability Analysis—A Review. Reliability Engineering & System Safety, 194, Article ID: 106415.
https://doi.org/10.1016/j.ress.2019.03.022
[10]
Jeon, M. (2017) Chapter 1—Emotions and Affect in Human Factors and Human-Computer Interaction: Taxonomy, Theories, Approaches, and Methods. In: Jeon, M., Ed., Emotions and Affect in Human Factors and Human-Computer Interaction, Academic Press, San Diego, 3-26.
https://doi.org/10.1016/B978-0-12-801851-4.00001-X
[11]
Zhou, W., Zhao, T., Liu, W. and Tang, J. (2018) Tower Crane Safety on Construction Sites: A Complex Sociotechnical System Perspective. Safety Science, 109, 95-108.
https://doi.org/10.1016/j.ssci.2018.05.001
[12]
Lingard, H., Cooke, T., Zelic, G. and Harley, J. (2021) A Qualitative Analysis of Crane safety Incident Causation in the Australian Construction Industry. Safety Science, 133, Article ID: 105028. https://doi.org/10.1016/j.ssci.2020.105028
[13]
Sadeghi, H., Zhang, X. and Mohandes, S.R. (2023) Developing an Ensemble Risk Analysis Framework for Improving the Safety of Tower Crane Operations under Coupled Fuzzy-Based Environment. Safety Science, 158, Article ID: 105957.
https://doi.org/10.1016/j.ssci.2022.105957
[14]
Zhang, X., Zhang, W., Jiang, L. and Zhao, T. (2020) Identification of Critical Causes of Tower-Crane Accidents through System Thinking and Case Analysis. Journal of Construction Engineering and Management, 146, Article ID: 04020071.
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001860
[15]
Ordu, M. and Der, O. (2023) Polymeric Materials Selection for Flexible Pulsating Heat Pipe Manufacturing Using a Comparative Hybrid MCDM Approach. Polymers, 15, Article 2933. https://doi.org/10.3390/polym15132933
[16]
Chandrasegaran, D., Ghazilla, R.A.R. and Rich, K. (2020) Human Factors Engineering Integration in the Offshore O&G Industry: A Review of Current State of Practice. Safety Science, 125, Article ID: 104627. https://doi.org/10.1016/j.ssci.2020.104627
[17]
Dewi, C. and Chen, R.-C. (2022) Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, T.-P., Trawiński, B. and Szczerbicki, E., Eds., Intelligent Information and Database Systems, Vol. 13757, Springer International Publishing, Cham, 81-93.
https://doi.org/10.1007/978-3-031-21743-2_7
[18]
Zhang, L., Liu, J. and Zou, Y. (2020) Framework of Performance Shaping Factors for Human Reliability Analysis of Digitized Nuclear Power Plants. In: Long, S. and Dhillon, B.S., Eds., Man-Machine-Environment System Engineering, Vol. 645, Springer Singapore, Singapore City, 1013-1020.
https://doi.org/10.1007/978-981-15-6978-4_116
[19]
Boparai, J.K., Singh, S. and Kathuria, P. (2019) How to Design and Validate a Questionnaire: A Guide. Current Clinical Pharmacology, 13, 210-215.
https://doi.org/10.2174/1574884713666180807151328
[20]
Li, G., Wu, X., Han, J.-C., Li, B., Huang, Y. and Wang, Y. (2023) Flood Risk Assessment by Using an Interpretative Structural Modeling Based Bayesian Network Approach (ISM-BN): An Urban-Level Analysis of Shenzhen, China. Journal of Environmental Management, 329, Article ID: 117040.
https://doi.org/10.1016/j.jenvman.2022.117040
[21]
Asano, T., et al. (2014) Depth-First Search Using O(n) Bits. In: Ahn, H.-K. and Shin, C.-S., Eds., Algorithms and Computation, Vol. 8889, Springer International Publishing, Cham, 553-564. https://doi.org/10.1007/978-3-319-13075-0_44
[22]
Saxena, J.P., Sushil and Vrat, P. (1990) Impact of Indirect Relationships in Classification of Variables—A Micmac Analysis for Energy Conservation. Systems Research, 7, 245-253. https://doi.org/10.1002/sres.3850070404
