Open Access
Issue
E3S Web Conf.
Volume 702, 2026
Second International Conference on Innovations in Sustainable and Digital Construction Practices (ISDCP 2026)
Article Number 06016
Number of page(s) 12
Section Structural Engineering
DOI https://doi.org/10.1051/e3sconf/202670206016
Published online 01 April 2026
  1. Y. Han, R. Jin, H. Wood, and T. Yang, Investigation of Demographic Factors in Construction Employees Safety Perceptions. KSCE J. Civ. Eng. 23, 2815–2828 (2019). doi: https://doi.org/10.1007/s12205-019-2044-4 [Google Scholar]
  2. L. Lei, D. Haijuan, and C. Bing, Experimental Investigation on Concrete Using Corn Stalk and Magnesium Phosphate Cement under Compaction Forming Technology. J. Mater. Civ. Eng. 32, 4020370 (2020). doi: 10.1061/(ASCE)MT.1943-5533.0003487. [Google Scholar]
  3. R. Ali, J.H. Chuah, M.S.A. Talip, N. Mokhtar, and M.A. Shoaib, Structural crack detection using deep convolutional neural networks. Autom. Constr. 133, 103989 (2022). doi: https://doi.org/10.1016/j.autcon.2021.103989. [Google Scholar]
  4. R. Amhaz, S. Chambon, J. Idier, and V. Baltazart, Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection. IEEE Trans. Intell. Transp. Syst. 17, 2718–2729 (2016). doi: 10.1109/TITS.2015.2477675. [Google Scholar]
  5. S.E. Park, S.-H. Eem, and H. Jeon, Concrete crack detection and quantification using deep learning and structured light. Constr. Build. Mater. 252, 119096 (2020). doi: https://doi.org/10.1016/Lconbuildmat.2020.119096 [Google Scholar]
  6. M. Antoun, C.A. Issa, G. Aouad, and N. Gerges, Sustainable masonry blocks: Olive wood waste as substitute for fine aggregates. Case Stud. Constr. Mater. 15, e00590 (2021). doi: https://doi.org/10.1016/j.cscm.2021.e00590 [Google Scholar]
  7. N. Kumar and M. Barbato, Effects of sugarcane bagasse fibers on the properties of compressed and stabilized earth blocks. Constr. Build. Mater. 315, 125552 (2022). doi: https://doi.Org/10.1016/j.conbuildmat.2021.125552 [Google Scholar]
  8. C.V. Dung and L.D. Anh, Autonomous concrete crack detection using deep fully convolutional neural network. Autom. Constr. 99, 52–58 (2019). doi: https://doi.org/10.1016/j.autcon.2018.11.028 [Google Scholar]
  9. S.S. Man, S. Alabdulkarim, A.H.S. Chan, and T. Zhang, The acceptance of personal protective equipment among Hong Kong construction workers: An integration of technology acceptance model and theory of planned behavior with risk perception and safety climate. J. Safety Res. 79, 329–340 (2021). doi: https://doi.org/10.1016/j.jsr.2021.09.014 [Google Scholar]
  10. Z. Fan, S. Member, Y. Wu, J. Lu, and W. Li, Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network. 1–9. https://doi.org/10.48550/arXiv.1802.02208 [Google Scholar]
  11. B.K. Biskaborn, L. Nazarova, T. Kroger, L.A. Pestryakova, L. Syrykh, G. Pfalz, U. Herzschuh and B. Diekmann, Late Quaternary Climate Reconstruction and Lead- Lag Relationships of Biotic and Sediment-Geochemical Indicators at Lake Bolshoe Toko, Siberia. Front. Earth Sci. 9, 733533 (2021). doi: 10.3389/feart.2021.737353. [Google Scholar]
  12. M.A. Khan, S.-H. Kee, A.-S.K. Pathan, and A.-A. Nahid, Image Processing Techniques for Concrete Crack Detection: A Scientometrics Literature Review, Remote Sensing, 15, 9 (2023). doi: 10.3390/rs15092400. [Google Scholar]
  13. D. Ai, G. Jiang, S.-K. Lam, P. He, and C. Li, Computer vision framework for crack detection of civil infrastructure—A review, Eng. Appl. Artif. Intell. 117, 105478(2023).doi: https://doi.org/10.1016/j.engappai.2022.105478. [Google Scholar]
  14. T. Jiang, Y. Hong, J. Zheng, L. Wang, and H. Gu, Crack Detection of FRP- Reinforced Concrete Beam Using Embedded Piezoceramic Smart Aggregates, sensors, 19, 3390 (2019). doi: 10.3390/s19091979. [Google Scholar]
  15. C. He, B. McCabe, and G. Jia, Effect of leader-member exchange on construction worker safety behavior: Safety climate and psychological capital as the mediators. Saf. Sci. 142, 105401, (2021). doi: https://doi.org/10.1016/j.ssci.2021.105401 [Google Scholar]

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