State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring
Abstract
:1. Introduction
2. Research Outlines
- Scanning environment: the environment where 3D sensing technology captures the necessary as-built data (indoors, outdoors, or both)
- Level of assessment: The level of progress monitoring data between the as-built model and as-planned model [three-dimensional (3D), four-dimensional (4D), or five-dimensional (5D)]
- Performance of object recognition indicators [recall, accuracy, and precision] (see Section 3)
3. Overview on 3D Sensing Technologies in Construction
3.1. Radio Frequency Identification (RFID)
3.2. Ultra-Wideband (UWB)
3.3. Global Positioning System (GPS)
3.4. Image-Based Methods
3.5. Laser Scanners
4. Critical Analysis for Previous Studies
4.1. Summary of the Current State of the Art
4.2. Statistical Analysis Using Meta-Analysis
4.2.1. Meta-Regression Methods and Procedures
4.2.2. Evaluation of Effect Size and Relative Weight
4.2.3. Deliverables
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Actual | Positive | Prediction | Negative | |
True | False | |||
True Positive (TP) | False Negative (FP) | |||
It happens when the presence of a point cloud is correctly predicted | It happens when a test fails to reveal the presence of a point cloud | |||
False Positive (FP) | True Negative (TN) | |||
It happens when a test incorrectly shows a point cloud is present | It happens when a test correctly predicts the absence of a point cloud |
References | As-Planned vs. As-Built | Performance of Object(s) Recognition | Environment | Notes | |||||
---|---|---|---|---|---|---|---|---|---|
3D | 4D | 5D | Recall (%) | Accuracy (%) | Precision (%) | ||||
1 | [22] | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Indoor | |
2 | [23] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor+Outdoor | It was performed using both RFID and laser scanner |
3 | [24] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | |
4 | [25] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | |
5 | [26] | ✓ | ⊠ | ⊠ | 89.6 | 88.1 | 84.7 | Indoor | |
6 | [27] | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Indoor |
References | As-Planned vs. As-Built | Performance of Object(s) Recognition | Environment | Notes | |||||
---|---|---|---|---|---|---|---|---|---|
3D | 4D | 5D | Recall (%) | Accuracy (%) | Precision (%) | ||||
1 | [29] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor+Outdoor | |
2 | [30] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | It was performed using both UWB and Laser scanner |
3 | [31] | ✓ | ⊠ | ⊠ | N/A | 100 75 | N/A | Indoor | The case study was conducted in two phases. One phase with LED indicator while the other phase without LED indicator |
References | As-Planned vs. As-Built | Performance of Object(s) Recognition | Environment | Notes | |||||
---|---|---|---|---|---|---|---|---|---|
3D | 4D | 5D | Recall (%) | Accuracy (%) | Precision (%) | ||||
1 | [33] | ✓ | ⊠ | ⊠ | 84.8 73.1 81.1 97.8 | 80.3 72.1 76.9 94.2 | 89.6 72.7 83.7 95.7 | Outdoor | It was conducted using both GPS and image-based method |
84.2 | 80.9 | 85.4 |
References | Equipment | As-Planned vs. As-Built | Performance of Object(s) Recognition | Environment | Notes | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
UAV | Camera | 3D | 4D | 5D | Recall (%) | Accuracy (%) | Precision (%) | ||||
1 | [34] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | ||
2 | [35] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | ||
3 | [21] | ✓ | ✓ | ⊠ | ⊠ | N/A | 97.1 | N/A | Outdoor | ||
4 | [36] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | ||
5 | [37] | ✓ | ✓ | ✓ | ⊠ | N/A | 87.5 82.89 91.05 | N/A | Outdoor | Golparvar-Ford performed three case studies. Code names were given to these case studies which are RH1, RH2, and SD | |
6 | [19] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | ||
7 | [46] | ✓ | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | It was conducted using image-based methods and laser scanning | |
8 | [38] | ✓ | ✓ | ✓ | ⊠ | N/A | 60.7 | N/A | Outdoor | ||
9 | [40] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | ||
10 | [39] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | It was conducted using both image-based method and laser scanning | |
11 | [41] | ✓ | ✓ | ✓ | ⊠ | N/A | 95.9 | N/A | Outdoor | ||
12 | [54] | ✓ | ✓ | ✓ | ⊠ | ⊠ | N/A N/A | N/A N/A | N/A N/A | Outdoor | |
13 | [18] | ✓ | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Indoor | ||
14 | [47] | ✓ | ✓ | ✓ | ⊠ | N/A | 90 | N/A | Outdoor | It was conducted using Image-based and laser scanning methods | |
15 | [48] | ✓ | ✓ | ✓ | ⊠ | N/A | 91 | N/A | Outdoor | ||
16 | [49] | ✓ | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | ||
17 | [50] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | ||
18 | [51] | ✓ | ✓ | ✓ | ⊠ | N/A | 82~84 | 50~72 | Outdoor | ||
19 | [55] | ✓ | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | It was conducted using both image-based method and laser scanning |
20 | [42] | ✓ | ✓ | ⊠ | ⊠ | 79.5 79.1 | N/A N/A | 93.9 90.7 | Outdoor | There were two case studies, Project 1 and project 2 | |
21 | [43] | ✓ | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | ||
22 | [52] | ✓ | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | ||
23 | [53] | ✓ | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | ||
24 | [44] | ✓ | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor |
References | As-Planned vs. As-Built | Performance of Object(s) Recognition | Environment | Notes | |||||
---|---|---|---|---|---|---|---|---|---|
3D | 4D | 5D | Recall (%) | Accuracy (%) | Precision (%) | ||||
1 | [23] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor+Outdoor | Mentioned before, in Table 2 |
2 | [10] | ✓ | ✓ | ⊠ | 98 | N/A | 96 | Outdoor | |
3 | [57] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | |
4 | [56] | ✓ | ✓ | ⊠ | 51 | N/A | 98 | Outdoor | |
5 | [58] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | |
6 | [59] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | |
7 | [60] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | |
8 | [30] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | Mentioned before, in Table 3 |
9 | [61] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor +Indoor | |
10 | [46] | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | Mentioned before, in Table 5 |
11 | [39] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | Mentioned before, in Table 5 |
12 | [62] | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Indoor | |
13 | [47] | ✓ | ⊠ | ⊠ | N/A | 68 | N/A | Outdoor | Mentioned before, in Table 5 |
14 | [63] | ✓ | ✓ | ✓ | 100 | 99.3 | 99.2 | Outdoor | The set of results is only for columns. |
15 | [64] | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor | |
16 | [55] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Outdoor | Mentioned before in Table 5 |
17 | [65] | ✓ | ⊠ | ⊠ | N/A | N/A | N/A | Indoor | |
18 | [66] | ✓ | ✓ | ⊠ | N/A | N/A | N/A | Outdoor |
Study | Effect Size (ES.) | % Changes in Object Recognition Indicators’ Performance | p-Value | ||||
---|---|---|---|---|---|---|---|
Relative Weight | Lower 95% | Estimate | Upper 95% | ||||
1 | [26] | −0.56 | 0.161 | +0.27 | +0.6 | +1.17 | |
2 | [33] | 2.78 | 0.159 | +7.30 | +16 | +36 | |
3 | [38] | 1.79 | 0.135 | +1.2 | +6 | +30.63 | |
4 | [49] | 2.93 | 0.168 | +15 | +19 | +23.7 | |
5 | [57] | 4.79 | 0.129 | +19.6 | +120 | +735 | |
6 | [56] | 1.66 | 0.138 | +1 | +5.3 | +24.75 | |
7 | [64] | 4.89 | 0.109 | +7.29 | +133 | +1480 | |
2.48 | +3.102 | +11.84 | +45.22 | 0.0003 |
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ElQasaby, A.R.; Alqahtani, F.K.; Alheyf, M. State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring. Sensors 2022, 22, 3497. https://doi.org/10.3390/s22093497
ElQasaby AR, Alqahtani FK, Alheyf M. State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring. Sensors. 2022; 22(9):3497. https://doi.org/10.3390/s22093497
Chicago/Turabian StyleElQasaby, Ahmed R., Fahad K. Alqahtani, and Mohammed Alheyf. 2022. "State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring" Sensors 22, no. 9: 3497. https://doi.org/10.3390/s22093497
APA StyleElQasaby, A. R., Alqahtani, F. K., & Alheyf, M. (2022). State of the Art of BIM Integration with Sensing Technologies in Construction Progress Monitoring. Sensors, 22(9), 3497. https://doi.org/10.3390/s22093497