REMOTE-CONTROLLED TECHNOLOGY ASSESSMENT FOR SAFER CONSTRUCTION

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captured by UAS can be used to calculate earthwork and stockpile volumes. Several studies have been conducted in this area in the US and abroad. Most studies report minimal error in comparison with more traditional methods like GPS and Light Detecting and Ranging (LiDAR) without UAS. The success of the measurements depends on the post processing of the images to build the 3D models [17, 18].

2.2.2 Pavement Marking 2.2.2.1 Automated Marker Placement Traditional marker placement methods require a worker to manually install the marker on the pavement (Figure 5). This exposes the worker to traffic and high-temperature adhesives. Automated marker placement systems install the marker without exposing the worker who remains inside the vehicle controlling the device (Figure 5b). In initial tests, comparative results showed that the automated system matched productivity and quality of high-end marker teams [19].

(a)

(b)

Figure 5: Marker placement with (a) manual method and (b) automated system [19] 2.2.3 Inspection 2.2.3.1 Remote-Controlled Ground Penetrating Radar (GPR) for Asphalt Density Uniform and adequate asphalt compaction is critical for pavement performance. Minimal reductions in asphalt density can cause huge impacts on pavement service life. Conventional quality control for asphalt density involves in-situ random determination of density from cores or 11


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Appendix B: Technology Transfer Workshops

14min
pages 91-100

Appendix A: IRISE survey

1min
pages 89-90

References

13min
pages 82-88

operated cart

1min
pages 80-81

Figure 38: AIPV system layout [97

4min
pages 67-69

accuracy tests: (a) following accuracy, (b)lane changing, (c) roundabout operation, (e) minimum turn radius, (f) U-turn [86

12min
pages 71-79

Figure 35: Impact testing of TMA on a tractor [89

1min
page 64

Figure 37: AIPV system overview [95

1min
page 66

Figure 36: Accident involving IPV of the Virginia DOT [92

1min
page 65

Figure 33: Dielectric Maps from Joint Surveys of I-95 near Pittsfield, Maine [63

0
page 59

Figure 32: Joint survey [63

1min
pages 57-58

Figure 27: A prototype of MnDOT remotely operated rolling asphalt density meter

6min
pages 50-53

Figure 30: Real-time data visualization and comparison with cores [63

1min
page 55

Figure 31: Cherryfield, Maine calibration model [63

1min
page 56

Figure 24: Cleaned temperature profile [52

4min
pages 42-44

Figure 23: Examples of Pave Project ManagerTM detailed reports with temperature profiles and paver speed or time diagram [53

1min
pages 40-41

Figure 25: PDP instrument background principle of operation [73

1min
page 48

Table 3: Specification recommendations for LaDOTD [48

5min
pages 45-47

Figure 22: On-board computer output for real time feedback [53

0
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Figure 19: Temperature segregation identified with thermal imaging [47

0
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Figure 6: Conduit remote inspection using (a) crawler robot (b) UAS [22

1min
page 22

Figure 5: Marker placement with (a) manual method and (b) automated system [19

2min
pages 20-21

Figure 21: Infrared sensors attached to paver for real-time thermal data acquisition [52,53

1min
page 38

Figure 20: Distress due to temperature segregation causing inadequate compaction [50

3min
pages 36-37

Figure 9: Infrared sensors attached to paver for real-time thermal data acquisition [26] and the latest version of IR temperature scanners [27

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Figure 18: Autonomous impact protection vehicle [44

2min
pages 33-34

Figure 4: Example of bridge deck demolition using a remote-controlled robot [15

1min
page 19
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