Advances in concrete construction

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Deep learning-based smart vision for building and construction application

  • Li Yue (School of Architecture and Engineering, Shanghai Zhongqiao Vocational and Technical University)
  • Received : 2023.10.07
  • Accepted : 2024.02.12
  • Published : 2024.07.25
⟨ Previous Next ⟩

Abstract

The current study was not created with a specific goal in view, which addresses many applications in the field of building and construction. It provides a variety of goals that must be addressed immediately for construction applications. This means that we approach the situation from different angles. The present paper approaches the title in two distinct ways. With regard to "building," we concentrated on damage detection (crack and spall). At the same time, for "construction site application," we mainly depend on worker safety. Since this study is a novel concept with two distinct domains, with one solution. We design an advanced deep learning strategy to address both of the study's objectives. Most previous studies specifically deal with this type of application with various approaches, but the present study gives one solution to address both the objectives. Guaranteeing stable structure of buildings and worker safety are important in the quickly developing field of construction management. By creating an edge-enhanced multi-drone system that makes use of advanced deep learning algorithms, this work provides a novel solution. The chief objectives of this research are to: (1) detect crack damage at building sites; and (2) Focused on employees' compliance by using drone technology. According to this, the proposed model combines two effective strengths called YOLOv3 and Bayesian optimization, an Intelligent Deep Learning (INDEED) model which helps to meet the dual challenges was handled by the study. The system's integration of edge computing principles guarantees real-time processing and decision-making abilities, and providing quick response to abnormalities occurred. Due to this combined ability, the model able to solve both damage detection and safety enhancement procedures in single hand. The evaluation of the study is conducted through two distinct datasets: Crack damage detection was assessed using CSIR-CEERI, Pilani. And the workers safety procedures were evaluated using the UAE based local site samples. The proposed model uses the hardware environment of Jetson-TX2. A multi-drone system, which helps to capture the surroundings of construction and buildings stability. The experiment proves the suggested model efficacy with two distinct validations.

Keywords

References

  1. Akinsemoyin, A., Awolusi, I., Chakraborty, D., Al-Bayati, A.J. and Akanmu, A. (2023), "Unmanned aerial systems and deep learning for safety and health activity monitoring on construction sites", Sensors, 23(15), 6690. https://doi.org/10.3390/s23156690
  2. Al Zarooni, M., Awad, M. and Alzaatreh, A. (2022), "Confirmatory factor analysis of work-related accidents in UAE", Safety Sci., 153, 105813. https://doi.org/10.1016/j.ssci.2022.105813
  3. Ali, R., Chuah, J.H., Talip, M.S.A., Mokhtar, N. and Shoaib, M.A. (2022), "Structural crack detection using deep convolutional neural networks", Automat. Constr., 133, 103989. https://doi.org/10.1016/j.autcon.2021.103989
  4. Alnuaimi, G.H. (2022), PREVALENCE OF LOST TIME INJURIES AND FATALITIES AMONG CONSTRUCTION WORKERS IN AL AIN, UNITED ARAB EMIRATES (UAE).
  5. Awolusi, I., Akinsemoyin, A., Chakraborty, D. and Al-Bayati, A. (2022), "Worker Safety and Health Activity Monitoring in Construction Using Unmanned Aerial Vehicles and Deep Learning", In: Construction Research Congress 2022.
  6. Azimi, M., Eslamlou, A.D. and Pekcan, G. (2020), "Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review", Sensors, 20(10), 2778. https://doi.org/10.3390/s20102778
  7. Baduge, S.K., Thilakarathna, S., Perera, J.S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A. and Mendis, P. (2022), "Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications", Automat. Constr., 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440
  8. Chen, K., Reichard, G., Xu, X. and Akanmu, A. (2021), "Automated crack segmentation in close-range building façade inspection images using deep learning techniques", J. Build. Eng., 43, 102913. https://doi.org/10.1016/j.jobe.2021.102913
  9. Chen, J., Song, Y., Li, D., Lin, X., Zhou, S. and Xu, W. (2024), "Specular removal of industrial metal objects without changing lighting configuration", IEEE Transact. Industr. Inform., 20(3), 3144-3153. https://doi.org/10.1109/TII.2023.3297613
  10. Chinnasamy, P., Sathya, K.B., Jebamani, B.J., Nithyasri, A. and Fowjiya, S. (2023), "Deep Learning: Algorithms, Techniques, and Applications — A Systematic Survey", In: L. Ashok Kumar, D. Karthika Renuka, & S. Geetha (Eds.), Deep Learning Research Applications for Natural Language Processing, pp. 1-17. https://doi.org/10.4018/978-1-6684-6001-6.ch001
  11. Falorca, J.F., Miraldes, J.P. and Lanzinha, J.C.G. (2021), "New trends in visual inspection of buildings and structures: Study for the use of drones", Open Eng., 11(1), 734-743. https://doi.org/10.1515/eng-2021-0071
  12. Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T.M. and An, W. (2018), "Detecting non-hardhat-use by a deep learning method from far-field surveillance videos", Automat. Constr., 85, 1-9. https://doi.org/10.1016/j.autcon.2017.09.018
  13. Hou, L., Chen, H., Zhang, G. and Wang, X. (2021), "Deep learning-based applications for safety management in the AEC industry: A review", Appl. Sci., 11(2), 821. https://doi.org/10.3390/app11020821
  14. Hu, C., Dong, B., Shao, H., Zhang, J. and Wang, Y. (2023), "Toward purifying defect feature for multilabel sewer defect classification", IEEE Transact. Instrum. Measur., 72, 1-11. https://doi.org/10.1109/TIM.2023.3250306
  15. Hu, Z., Qi, W., Ding, K., Liu, G. and Zhao, Y. (2024a), "An adaptive lighting indoor vSLAM with limited on-device resources", IEEE Internet Things J., 11(17), 28863-28875. https://doi.org/10.1109/JIOT.2024.3406816
  16. Hu, D., Hu, Y., Yi, S., Liang, X., Li, Y. and Yang, X. (2024b), "Surface Settlement Prediction of Rectangular Pipe-Jacking Tunnel Based on the Machine-Learning Algorithm", J. Pipeline Syst. Eng. Pract., 15(1), 04023061. https://doi.org/10.1061/JPSEA2.PSENG-1453
  17. Jiang, Y., Han, S. and Bai, Y. (2021), "Building and infrastructure defect detection and visualization using drone and deep learning technologies", J. Perform. Constr. Facil., 35(6), 04021092. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001652
  18. Kordestani, H., Zhang, C. and Arab, A. (2024), "An Investigation into the Application of Acceleration Responses' Trendline for Bridge Damage Detection Using Quadratic Regression", Sensors, 24(2), 410. https://doi.org/10.3390/s24020410
  19. Koch, C., Georgieva, K., Kasireddy, V., Akinci, B. and Fieguth, P. (2015), "A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure", Adv. Eng. Inform., 29(2), 196-210. https://doi.org/10.1016/j.aei.2015.01.008
  20. Kumar, P., Batchu, S. and Kota, S.R. (2021), "Real-time concrete damage detection using deep learning for high rise structures", IEEE Access, 9, 112312-112331. https://doi.org/10.1109/ACCESS.2021.3102647
  21. Lea, F.C. and Middleton, C. (2002), Reliability of Visual Inspection of Highway Bridges. Department of Engineering, University of Cambridge, Cambridge, UK.
  22. Lei, J., Fang, H., Zhu, Y., Chen, Z., Wang, X., Xue, B., Yang, M. and Wang, N. (2024), "GPR detection localization of underground structures based on deep learning and reverse time migration", NDT & E Int., 143, 103043. https://doi.org/10.1016/j.ndteint.2024.103043
  23. Mi, C., Liu, Y., Zhang, Y., Wang, J., Feng, Y. and Zhang, Z. (2023), "A vision-based displacement measurement system for foundation pit", IEEE Transact. Instrum. Measur., 72. https://doi.org/10.1109/TIM.2023.3311069
  24. Mahmoudi, H., Bitaraf, M., Salkhordeh, M. and Soroushian, S. (2023), "A rapid machine learning-based damage detection algorithm for identifying the extent of damage in concrete shear-wall buildings", Structures, 47, 482-499. https://doi.org/10.1016/j.istruc.2022.11.041
  25. Nath, N.D., Behzadan, A.H. and Paal, S.G. (2020), "Deep learning for site safety: Real-time detection of personal protective equipment", Automat. Constr., 112, 103085. https://doi.org/10.1016/j.autcon.2020.103085
  26. Neuhausen, M., Pawlowski, D. and König, M. (2020), "Comparing classical and modern machine learning techniques for monitoring pedestrian workers in top-view construction site video sequences", Appl. Sci., 10(23), 8466. https://doi.org/10.3390/app10238466
  27. Ni, Y., Mao, J., Wang, H., Xi, Z. and Xu, Y. (2023), "Toward high precision crack detection in concrete bridges using deep learning", J. Perform. Constr. Facil., 37(3), 04023017. https://doi.org/10.1061/JPCFEV.CFENG-4275
  28. Perez, H. and Tah, J.H. (2021), "Deep learning smartphone application for real‐time detection of defects in buildings", Struct. Control Health Monitor., 28(7), e2751. https://doi.org/10.1002/stc.2751
  29. Qi, H., Zhou, Z., Irizarry, J., Deng, X., Yang, Y., Li, N. and Zhou, J. (2024), "Modification of HFACS model for path identification of causal factors of collapse accidents in the construction industry", Eng. Constr. Architect. Manag. https://doi.org/10.1108/ECAM-02-2023-0101
  30. Shanti, M.Z., Cho, C.-S., Byon, Y.-J., Yeun, C.Y., Kim, T.-Y., Kim, S.-K. and Altunaiji, A. (2021), "A novel implementation of an ai-based smart construction safety inspection protocol in the uae", IEEE Access, 9, 166603-166616. https://doi.org/10.1109/ACCESS.2021.3135662
  31. Shanti, M.Z., Cho, C.-S., de Soto, B.G., Byon, Y.-J., Yeun, C.Y. and Kim, T.Y. (2022), "Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning", J. Safety Res., 83, 364-370. https://doi.org/10.1016/j.jsr.2022.09.011
  32. Umar, T. (2019), "Developing toolkits and guidelines to improve safety performance in the construction industry in Oman", Ph.D. Thesis; London South Bank University, London, UK. https://doi.org/10.18744/lsbu.89yz9
  33. Wang, J., Lin, S.Q., Tan, D.Y., Yin, J.H., Zhu, H.H. and Kuok, S.C. (2024), "A Novel Method for Integrity Assessment of Soil Nailing Works with Actively Heated Fiber-Optic Sensors", J. Geotech. Geoenviron. Eng., 150(8), 04024063. https://doi.org/10.1061/JGGEFK.GTENG-11790
  34. Wu, J., Zhu, J., Zhang, J., Dang, P., Li, W., Guo, Y., Fu, L., Lai, J., You, J., Xie, Y. and Liang, C. (2023), "A dynamic holographic modelling method of digital twin scenes for bridge construction", Int. J. Digital Earth, 16(1), 2404-2425. https://doi.org/10.1080/17538947.2023.2229792
  35. Xiong, C., Zayed, T. and Abdelkader, E.M. (2024), "A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks", Constr. Build. Mater., 414, 135025. https://doi.org/10.1016/j.conbuildmat.2024.135025
  36. Xu, H., Li, Q. and Chen, J. (2022), "Highlight removal from a single grayscale image using attentive GAN", Appl. Artif. Intell., 36(1), 1988441. https://doi.org/10.1080/08839514.2021.1988441
  37. Xu, X., Fu, X., Zhao, H., Liu, M., Xu, A. and Ma, Y. (2023), "Three-Dimensional Reconstruction and Geometric Morphology Analysis of Lunar Small Craters within the Patrol Range of the Yutu-2 Rover", Remote Sensing, 15(17), 4251. https://doi.org/10.3390/rs15174251
  38. Yang, J., Zhang, L., Chen, C., Li, Y., Li, R., Wang, G., Jiang, S. and Zeng, Z. (2020), "A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection", Inform. Sci., 540, 117-130. https://doi.org/10.1016/j.ins.2020.05.090
  39. Yang, J., Li, H., Zou, J., Jiang, S., Li, R. and Liu, X. (2022), "Concrete crack segmentation based on UAV-enabled edge computing", Neurocomputing, 485, 233-241. https://doi.org/10.1016/j.neucom.2021.03.139
  40. Yao, R., Ge, Z., Wang, D., Shang, N. and Shi, J. (2024), "Selfsensing joints for in-situ structural health monitoring of composite pipes: A piezoresistive behavior-based method", Eng. Struct., 308, 118049. https://doi.org/10.1016/j.engstruct.2024.118049
  41. Yin, L., Wang, L., Lu, S., Wang, R., Yang, Y., Yang, B., Liu, S., AlSanad, A., AlQahtani, S.A., Yin, Z. and Li, X. (2024a), "Convolution-Transformer for Image Feature Extraction", Comput. Model. Eng. Sci., 141(1), 87-106. https://doi.org/10.32604/cmes.2024.051083
  42. Yin, L., Wang, L., Lu, S., Wang, R., Ren, H., AlSanad, A., AlQahtani, S.A., Yin, Z., Li, X. and Zheng, W. (2024b), "AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms", Comput. Model. Eng. Sci., 140(3), 2315-2347. https://doi.org/10.32604/cmes.2024.050853
  43. Zhang, R., Yin, L., Jia, J., Yin, Y. and Li, C. (2019), "Application of ATS-GWIFBM Operator Based on Improved Time Entropy in Green Building Projects", Adv. Civil Eng., 2019, 3519195. https://doi.org/10.1155/2019/3519195
  44. Zhou, P., Peng, R., Xu, M., Wu, V. and Navarro-Alarcon, D. (2021), "Path planning with automatic seam extraction over point cloud models for robotic arc welding", IEEE Robot. Automat. Lett., 6(3), 5002-5009. https://doi.org/10.1109/LRA.2021.3070828
  45. Zurub, H.H. (2021), "The Effectiveness of the Occupational Health and Safety Management System in the United Arab Emirates", Ph.D. Thesis, Aston University, UK.