Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Si Tran
 -- 2634 2023-09-22 05:19:31 |
2 layout
Camila Xu
 -149 word(s) 2485 2023-09-22 07:20:44 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Tran, S.V.; Lee, D.; Bao, Q.L.; Yoo, T.; Khan, M.; Jo, J.; Park, C. Human Detection Approach for Intrusion in Hazardous Areas. Encyclopedia. Available online: https://encyclopedia.pub/entry/49504 (accessed on 09 July 2024).
Tran SV, Lee D, Bao QL, Yoo T, Khan M, Jo J, et al. Human Detection Approach for Intrusion in Hazardous Areas. Encyclopedia. Available at: https://encyclopedia.pub/entry/49504. Accessed July 09, 2024.
Tran, Si Van-Tien, Doyeop Lee, Quy Lan Bao, Taehan Yoo, Muhammad Khan, Junhyeon Jo, Chansik Park. "Human Detection Approach for Intrusion in Hazardous Areas" Encyclopedia, https://encyclopedia.pub/entry/49504 (accessed July 09, 2024).
Tran, S.V., Lee, D., Bao, Q.L., Yoo, T., Khan, M., Jo, J., & Park, C. (2023, September 22). Human Detection Approach for Intrusion in Hazardous Areas. In Encyclopedia. https://encyclopedia.pub/entry/49504
Tran, Si Van-Tien, et al. "Human Detection Approach for Intrusion in Hazardous Areas." Encyclopedia. Web. 22 September, 2023.
Human Detection Approach for Intrusion in Hazardous Areas
Edit
This entry is adapted from the peer-reviewed paper 10.3390/buildings13092313

Detecting intrusion in hazardous areas is one of the priorities and duties of safety enhancement. With the emergence of vision intelligence technology, hazardous-area-detection algorithms can support safety managers in predicting potential hazards and making decisions. However, because of the dynamic and complex nature of the jobsite, high-risk zones have a different geometry and can be changed following the schedule and workspace of activity. This leads to hazardous areas being annotated manually.

hazardous area 4D BIM computer vision safety monitoring

1. Introduction

Worldwide, the construction industry is recognized as the most dangerous industry in terms of accidents on the jobsite, leading to a significant loss of life and property [1][2]. According to Occupational Safety and Health Administration (OSHA) data [3], the construction sector accounted for 1008 deaths in 2020, with falls from heights accounting for approximately 33% of the fatalities. Moreover, South Korea’s construction industry was responsible for more than half of all industry-wide fatality incidents [4]. The construction industry in Europe has the third highest rate of nonfatal injuries, with the accident rate being higher than the average for all industries [5]. This not only decreases the quality of life of those injured and their families, but also damages the environment. Research has demonstrated that a safe workplace can lower accident occurrence [6][7][8][9]. Potential hazardous zones should be identified during the planning phase, and intrusion should be detected and monitored during the construction phase. Such information can assist safety managers and stakeholders in identifying and preventing potential hazards.
To detect intrusion in hazardous areas, identifying the distance correlation between site employees and hazardous areas is foundational of algorithms [10], even if applying sensing technology or computer vision in place of manual inspection: (1) Sensing technologies utilize signal-processing methods such as ultra-wideband, Bluetooth Low Energy, radio frequency identification, and ZigBee to detect or proximate the distance between other sensor tags. They can be applied to monitor the interactions between on-site person positions and other entities, such as equipment, on-site persons, and temporary facilities, on the construction site. In other words, on-site persons wearing sensor tags could be warned when approaching a hazardous area fitted with another sensor tag. For instance, using Bluetooth technology, Park et al. [11] presented a range-detection and alarm system for dangerous proximity scenarios between highway construction on-site persons and construction vehicles. Jin et al. [10] explored the use of an RFID sensor attached to a hardhat and a portable RFID trigger installed in potentially hazardous working spaces, such as openings. (2) The computer vision technique analyzes the visual data collected from the surveillance systems to automatically detect jobsite entities by utilizing deep-learning algorithms. To prevent hazardous-area entry, Fang et al. [12] developed deep-learning algorithms to detect the structure supports and on-site persons and evaluated the relationship between them. Khan’s approach [13] involved utilizing computer vision to detect the interaction between scaffolding and the on-site person to determine whether the working height was within Korea Occupational Safety and Health Agency (KOSHA) regulations. Researchers have also focused on developing algorithms to detect jobsite entities and their relationships.
A proper hazardous area identification would improve intrusion-detection quality. This is a typical geometry annotation problem in which the aim is to enhance the defining of hazardous areas. Due to the dynamic nature and complexity of construction jobsites, the hazardous areas corresponding to the specific entities defined in the regulations have different geometries and sizes. The geometry varies across different potentially hazardous areas in construction jobsites, such as floor openings, excavation holes, and the area around the scaffolding [14], etc. According to the use of sensor technologies, the active zones of the sensor tag are spherical coverage areas due to the signal processing involved. These necessitate installing numerous sensor tags according to the type of monitored entity. Moreover, during the construction phase, the hazardous area in the same workspace may be changed by shifting to different activities as per the schedule. Hence, the on-site persons have to reinstall the sensor tags for different purposes. For instance, the hazardous area during the excavation phase is the excavation holes. Afterward, when preparing foundation work, safety managers have to consider the hazard posed by the foundation pit area or pile foundation area. Furthermore, current computer vision methods also face challenges in annotating hazardous areas in the intrusion-detection process. The presence of diverse and complex dangerous regions on construction sites is a significant problem as they encounter difficulties in accurately detecting and encompassing a wide range of hazardous areas [15]. Mei et al. [16] labeled the hazardous area manually with the help of image-annotation tools. However, manual labeling faces the challenge of measuring the geometry in a 2D image based on the size of the pixels.
Building information modeling (BIM) has been shown to improve occupational safety, for instance, by facilitating accurate site layouts and safety plans, applying cutting-edge methods for visualizing existing designs, the acquisition of spatial–temporal information, and safety communication [6][17][18][19][20]. Thus, project teams can glean helpful information for furthering the aims of the project. For instance, safety managers may design a safety plan to avert dangers in the workspace, schedule information, and characterize the construction jobs. In addition, more information may be incorporated into the BIM model throughout the building phases for various purposes [20][21][22][23]. Tran et al. [24] proposed the development of a computer-vision-based automatic safety status updating with the support of BIM information. In addition, the 4D-BIM model can provide the geometric information needed, such as the hazardous area boundaries during the period extracted from the schedule. The geometric information from BIM can be transformed as input for applying another technique, for instance, hazardous area object annotation in computer vision.

2. Preventing Entry to Hazardous Areas on Construction Jobsites

Safety is essential at every construction jobsite, which is indicated in laws/regulations such as section 292 of subsection 6.4 of the Model Work Health and Safety Regulations in Australia, Act 85 of 1993 in South Africa, and Section 29 CFR 1910.120(b) of the OSHA regulations in the United States. However, the project employees practically fail to follow safety guidelines, including entering hazardous areas that lead to incidents at job sites. The theory of task dynamics, proposed by Winsemius [25], can help to explain why some project employees may be more prone to intrusion behavior at construction sites, as they are likely to weigh the convenience of a quicker route against the potential risks of entering a hazardous area and underestimate the possible consequences of their actions. Hence, safety professionals need to be aware of this tendency and take steps to prevent construction on-site persons and other individuals from engaging in risky behaviors at the jobsite. Therein, providing clear guidance on safe practices and procedures is required. For instance, in the trench safety infographics, the Center for Construction Research and Training (CPWR) recommends keeping rocks, soil, materials, and equipment away from the edge of the trench. This would shape the hazardous zone around the trench, which the safety managers should consider to practice trench safety [26]. Moreover, there is a need for monitoring on-site persons at workplaces associated with hazardous areas during the construction phase.
Construction safety monitoring is critical in recognizing and warning of potential hazards to prevent unacceptable intrusion. Site observations and inspections have traditionally been utilized as monitoring tools in the construction industry to evaluate the dangers involved in ongoing works and existing site conditions [27]. However, these systems are expensive, and using them is time-consuming because supervisors or safety staff have to perform visual inspections and write down their findings. Manual inspection also has drawbacks, such as a lack of timely access to comprehensive and accurate information. Detecting dangers, such as risky situations and activities, depends on the observer’s capacity to see and comprehend the scenario.
As listed in Table 1, Xu et al. [28] investigated the benefit of smart sensing technologies and integrated them with manual inspection to develop collaborative safety monitoring. In preventing hazardous-area entry, Kanan et al. [29] proposed a sensing approach for preventing site laborers who work within hazardous zones. Shuang et al. [30] considered age and gender variables that affected the intrusion behavior of 147 laborers over four months on construction jobsites. To conduct the experiment, the authors utilized sensing technologies for location tracking combined with BIM. In Jin’s proposed solutions [10], an RFID sensor mounted to a hardhat and portable RFID trigger is placed in potentially dangerous working places. However, using sensing technologies, such as RFID sensors, to prevent hazardous area entry has limitations. (1) Reliability: Sensing technologies may not always work as intended due to interference, malfunctions, or maintenance issues. This can lead to false alarms or missed detections, compromising the system’s effectiveness. (2) Limited Range: Sensing technologies may be restricted, making them unable to identify objects or individuals beyond a specific distance. Additionally, the active zones encompass spherical coverage regions because of signal processing. In contrast, building sites have diverse forms and geometries that might provide hazards, such as apertures, excavation pits, and the vicinity around scaffolding. (3) Cost: Implementing and maintaining a sensing system can be expensive, especially if it requires installing and maintaining many sensors or other devices. (4) Convenience: The hazardous area inside the workstation may undergo modifications during the construction phase because of transitioning to different activities according to the predetermined timetable. Accordingly, the on-site persons have to re-install the sensor tags for different purposes.
Table 1. Intrusion detection in hazardous areas in the construction domain.
Reference Solution
Approach		 Determining
On-Site Persons		 Type of Hazardous Area Method of Hazardous Area Annotation
Xu [28] Sensor Sensor embedded Opening signal range of sensors
Jin [10] Sensor Sensor embedded Opening signal range of sensors
Huang [31] Computer vision Skeleton identification Opening, Lifting operation area manual annotation
Mei [16] Computer vision Object detection Foundation pitch, opening, limb, scaffold manual annotation
Kim [15] Sensor Sensor embedded Extracted geometric information from BIM The 3D information on hazardous areas was extracted
Wan [32] Computer vision Helmet color detection Foundation pitch, opening, limb, scaffold, machinery manual annotation
Kim [33] Computer vision Object detection Area around excavator excavator object annotation
Park [11] Sensor Sensor embedded Construction equipment signal range of sensors
This study Computer vision + 4D BIM Object detection Extracted geometric information from 4D BIM The 3D information on hazardous areas was extracted and transformed into objects for supporting the vision monitoring system

3. Intrusion Detection in Hazardous Areas Based on Computer Vision

Vision intelligence technology is also a trend that academic and industry professionals have also studied and implemented vision intelligence technology for the automated detection of individuals entering hazardous areas at construction sites, as listed in Table 1. Deep learning algorithms have been developed based on specific scenarios from expert knowledge and regulations, then deployed into the monitoring system to support stakeholders in identifying potential hazards of its scenarios. Studies have developed an approach for detecting unsafe behavior from the distance correlation between entities. For instance, Khan’s approach [13] applied computer vision to detect the relationship between the scaffolding and the on-site person to determine whether their working height was higher or lower than KOSHA stipulated. Kim et al. [33] presented a method for measuring the distance between employees and other construction entities using YOLO-V3.
Similarly, hazardous areas have to be identified before analyzing the distance correlation between them and on-site persons. However, computer vision-based hazardous area detection face challenges due to the complex and dynamic environments of construction job sites. Hazardous areas can take many different forms, such as slippery surfaces, openings, or uneven terrain. Thus, different geometries and sizes cover the specific entity, as defined in laws/regulations. For instance, according to Hua et al. [34], various hazardous areas were simulated and highlighted with red color in a three-dimensional illustration. Huang et al. [31] defined a buffer zone for a hazardous area warning around the lifting operation area or foundation ditch. Thus, researchers manually labeled the hazardous area object using annotation tools [16]. Further, the site conditions may be changed, such as lighting conditions, moving equipment, and other obstacles. Hazardous area can also be changed following the schedule at a specific workspace. Consequently, this leads to a hazardous area that has its own set of unique characteristics that make it difficult to develop computer vision algorithms.

4. BIM for Construction Safety

Building Information Modeling (BIM) has increased on-site person safety by analyzing spatial and temporal information and enhancing safety communication [35][36][37]. For instance, Table 2 lists examples of applying/developing BIM for construction safety. Tran et al. [38] utilized 4D BIM data to develop a surveillance installation strategy and schedule work areas. In addition, this study simulated virtual cameras that could provide a virtual view of their cameras. In another study, they examined these data to detect any possible scheduling or workplace conflicts that may lead to accidents before the building process started [6]. BIM offers a visual model and database for storing data gathered or generated throughout the construction phase. Arslan et al. [39] visualized the intrusion using BIM to capture unsafe behaviors in dynamic environments. Other researchers have investigated the integration of the BIM model with a sensor-based monitoring system [40][41]. Using visual intelligence, 4D BIM can provide practical and valuable information for construction safety monitoring. The plans, which describe the activities, locations, and schedules, support the vision system in applying the appropriate algorithms to achieve good results and provide the safety manager with practical information. The 4D-BIM model can also provide the geometric information needed, such as the hazardous area boundaries during the period extracted from the schedule. Moreover, combining computer vision technology with BIM enhances construction safety by enabling the real-time monitoring and analysis of the construction site, facilitating proactive risk management and the identification of potential hazards before they become problematic [42].
Table 2. Examples of applying/developing BIM for construction safety.
Reference Functionality Benefits for Safety Management
Tran [6] 4D BIM Identifying hazards of spatial–temporal exposure cases
Arslan [39] 3D visualization Visualization of intrusions in dynamic construction settings with the objective of enhancing worker safety
Feng [36] Rule-based modeling Using BIM to automate planning for scaffolding on construction sites
Tran [38] 4D BIM Developing a surveillance camera installation plan for safety purposes
Zhou [41] Cloud-based BIM Developing building fire alarm system

5. Need for BIM-Based Information Extraction to Support Computer Vision System

The literature review on construction site accidents and existing safety monitoring procedures reveals several gaps. Hence, a hybrid monitoring system, which incorporates 4D BIM information and computer vision technologies, is suggested for overcoming the following:
  • Monitoring on-site persons entering hazardous areas: The proposed approach must detect on-site persons in the field of view of the camera and the correlation between them and the hazardous areas.
  • Multiple workspace shapes: The approach must detect different hazardous areas at construction jobsites as stipulated in the laws and regulations.
  • Inefficient monitoring by the site safety manager: Manually monitoring every construction on-site person is challenging for site managers. Therefore, the suggested monitoring approach must utilize computer vision and BIM to ensure on-site person safety in the danger zone.
  • Inconvenience: During the construction phase, the hazardous area in the same workspace may change due to shifting to other activities as stipulated in the schedule. Accordingly, the laborers have to reinstall the sensor tags for different purposes. Hence, the proposed system should predefine the hazardous area before construction to monitor intrusion.

References

  1. Zhang, M.; Shi, R.; Yang, Z. A critical review of vision-based occupational health and safety monitoring of construction site workers. Saf. Sci. 2020, 126, 104658.
  2. Fang, W.; Ding, L.; Love, P.E.D.; Luo, H.; Li, H.; Peña-Mora, F.; Zhong, B.; Zhou, C. Computer vision applications in construction safety assurance. Automat. Constr. 2020, 110, 103013.
  3. OSHA Fatality Report. Available online: https://www.osha.gov/stop-falls (accessed on 6 October 2022).
  4. Construction Work|Statistics Korea. Available online: http://kostat.go.kr/portal/eng/pressReleases/4/5/index.board (accessed on 6 October 2022).
  5. Accidents at Work Statistics—Statistics Explained. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Accidents_at_work_statistics#Number_of_accidents (accessed on 2 February 2023).
  6. Tran, S.V.T.; Khan, N.; Lee, D.; Park, C. A Hazard Identification Approach of Integrating 4D BIM and Accident Case Analysis of Spatial–Temporal Exposure. Sustainability 2021, 13, 2211.
  7. Luo, X.; Li, H.; Wang, H.; Wu, Z.; Dai, F.; Cao, D. Vision-based detection and visualization of dynamic workspaces. Automat. Constr. 2019, 104, 1–13.
  8. Bao, L.; Tran, S.V.T.; Nguyen, T.L.; Pham, H.C.; Lee, D.; Park, C. Cross-platform virtual reality for real-time construction safety training using immersive web and industry foundation classes. Automat. Constr. 2022, 143, 104565.
  9. Chen, H.; Li, H.; Goh, Y.M. A review of construction safety climate: Definitions, factors, relationship with safety behavior and research agenda. Saf. Sci. 2021, 142, 105391.
  10. Jin, R.; Zhang, H.; Liu, D.; Yan, X. IoT-based detecting, locating and alarming of unauthorized intrusion on construction sites. Automat. Constr. 2020, 118, 103278.
  11. Park, J.; Asce, S.M.; Marks, E.; Cho, Y.K.; Suryanto, W. Performance Test of Wireless Technologies for Personnel and Equipment Proximity Sensing in Work Zones. J. Constr. Eng. Manag. 2015, 142, 04015049.
  12. Fang, W.; Zhong, B.; Zhao, N.; Love, P.E.D.; Luo, H.; Xue, J.; Xu, S. A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network. Adv. Eng. Inform. 2019, 39, 170–177.
  13. Khan, M.; Khalid, R.; Anjum, S.; Tran, S.V.-T.; Park, C. Fall Prevention from Scaffolding Using Computer Vision and IoT-Based Monitoring. J. Constr. Eng. Manag. 2022, 148, 04022051.
  14. Yang, J.; Lee, D.; Baek, C.; Park, C.; Lan, B.Q.; Lee, D. Leveraging Blockchain for Scaffolding Work Management in Construction. IEEE Access 2022, 10, 39220–39238.
  15. Kim, H.; Lee, H.S.; Park, M.; Chung, B.Y.; Hwang, S. Automated hazardous area identification using laborers’ actual and optimal routes. Automat. Constr. 2016, 65, 21–32.
  16. Mei, X.; Zhou, X.; Xu, F.; Zhang, Z. Human Intrusion Detection in Static Hazardous Areas at Construction Sites: Deep Learning–Based Method. J. Constr. Eng. Manag. 2023, 149, 04022142.
  17. Akram, R.; Thaheem, M.J.; Nasir, A.R.; Ali, T.H.; Khan, S. Exploring the role of building information modeling in construction safety through science mapping. Saf. Sci. 2019, 120, 456–470.
  18. Alizadehsalehi, S.; Yitmen, I.; Celik, T.; Arditi, D. The effectiveness of an integrated BIM/UAV model in managing safety on construction sites. Int. J. Occup. Saf. Ergon. 2018, 26, 829–844.
  19. Khan, M.; Nnaji, C.; Khan, M.S.; Ibrahim, A.; Lee, D.; Park, C. Risk factors and emerging technologies for preventing falls from heights at construction sites. Autom. Constr. 2023, 153, 104955.
  20. Huang, M.Q.; Ninić, J.; Zhang, Q.B. BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives. Tunn. Undergr. Space Technol. 2021, 108, 103677.
  21. Xu, W.; Wang, T.K. Dynamic safety prewarning mechanism of human–machine–environment using computer vision. Eng. Constr. Archit. Manag. 2020, 27, 1813–1833.
  22. Yang, B.; Zhang, B.; Zhang, Q.; Wang, Z.; Dong, M.; Fang, T. Automatic detection of falling hazard from surveillance videos based on computer vision and building information modeling. Struct. Infrastruct. Eng. 2022, 18, 1049–1063.
  23. Tang, S.; Shelden, D.R.; Eastman, C.M.; Pishdad-Bozorgi, P.; Gao, X. A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automat. Constr. 2019, 101, 127–139.
  24. Tran, S.V.; Bao, L.Q.; Nguyen, L.T.; Pedro, A. Development of Computer Vision and BIM-cloud based Automated Status Updating for Construction Safety Monitoring. In Proceedings of the 22nd International Conference on Construction Applications of Virtual Reality (CONVR2022), Seoul, Republic of Korea, 16–19 November 2022.
  25. Winsemius, W. Some ergonomic aspects of safety. Ergonomics 2007, 8, 151–162.
  26. CPWR|Practice Trench Safety. It Saves Lives. Available online: https://www.cpwr.com/practice-trench-safety-it-saves-lives/ (accessed on 8 December 2022).
  27. Hinze, J.; Godfrey, R. An evaluation of safety performance measures for construction projects. J. Constr. Res. 2003, 04, 5–15.
  28. Xu, Q.; Chong, H.Y.; Liao, P.C. Collaborative information integration for construction safety monitoring. Automat. Constr. 2019, 102, 120–134.
  29. Kanan, R.; Elhassan, O.; Bensalem, R. An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automat. Constr. 2018, 88, 73–86.
  30. Shuang, D.; Heng, L.; Skitmore, M.; Qin, Y. An experimental study of intrusion behaviors on construction sites: The role of age and gender. Saf. Sci. 2019, 115, 425–434.
  31. Huang, H.; Hu, H.; Xu, F.; Zhang, Z.; Tao, Y. Skeleton-based automatic assessment and prediction of intrusion risk in construction hazardous areas. Saf. Sci. 2023, 164, 106150.
  32. Wan, H.-P.; Zhang, W.-J.; Ge, H.-B.; Luo, Y.; Todd, M.D. Improved Vision-Based Method for Detection of Unauthorized Intrusion by Construction Sites Workers. J. Constr. Eng. Manag. 2023, 149, 04023040.
  33. Kim, D.; Liu, M.; Lee, S.H.; Kamat, V.R. Remote proximity monitoring between mobile construction resources using camera-mounted UAVs. Automat. Constr. 2019, 99, 168–182.
  34. Hua, Y.; He, J.; Gong, J.; Zhao, J. Hazardous Area Risk-Based Evacuation Simulation and Analysis of Building Construction Sites. J. Constr. Eng. Manag. 2020, 146, 5.
  35. Tran, S.; Ali, A.K.; Khan, N.; Lee, D.; Park, C. A Framework for Camera Planning in Construction Site using 4D BIM and VPL. In Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), Kitakyushu, Japan, 27–28 October 2020.
  36. Feng, C.-W.; Lu, S.-W. Using BIM to Automate Scaffolding Planning for Risk Analysis at Construction Sites. In Proceedings of the 34th International Symposium on Automation and Robotics in Construction, ISARC 2017, Taipei, Taiwan, 28 June–July 2017; International Association for Automation and Robotics in Construction: Edinburgh, UK, 2017; pp. 610–617.
  37. Getuli, V.; Capone, P.; Bruttini, A.; Isaac, S. BIM-based immersive Virtual Reality for construction workspace planning: A safety-oriented approach. Automat. Constr. 2020, 114, 103160.
  38. Tran, S.V.T.; Nguyen, T.L.; Chi, H.L.; Lee, D.; Park, C. Generative planning for construction safety surveillance camera installation in 4D BIM environment. Automat. Constr. 2022, 134, 104103.
  39. Arslan, M.; Cruz, C.; Ginhac, D. Visualizing intrusions in dynamic building environments for worker safety. Saf. Sci. 2019, 120, 428–446.
  40. Valinejadshoubi, M.; Moselhi, O.; Bagchi, A.; Salem, A. Development of an IoT and BIM-based automated alert system for thermal comfort monitoring in buildings. Sustain. Cities Soc. 2021, 66, 102602.
  41. Zhou, X.; Li, H.; Wang, J.; Zhao, J.; Xie, Q.; Li, L.; Liu, J.; Yu, J. CloudFAS: Cloud-based building fire alarm system using Building Information Modelling. J. Build. Eng. 2022, 53, 104571.
  42. Tran, S.V.T.; Nguyen, T.L.; Park, C. A BIM Integrated Hazardous Zone Registration Using Image Stitching. In Proceedings of the 38th International Symposium on Automation and Robotics in Construction, Dubai, United Arab Emirates, 2–4 November 2021; pp. 176–181.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Construction & Building Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Si Van-Tien Tran
,
Doyeop Lee
,
Quy Lan Bao
,
Taehan Yoo
,
Muhammad Khan
,
Junhyeon Jo
,
Chansik Park
View Times: 454
Revisions: 2 times (View History)
Update Date: 22 Sep 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback