REMOTE-CONTROLLED TECHNOLOGY ASSESSMENT FOR SAFER CONSTRUCTION

Page 57

content up to 14.6%. Interestingly, data suggested that compaction at the wheel path was more consistent than other locations (nearly 1/10 of the variability observed at the lane and joint). This may have happened due to Maine DOT’s focus on wheel path compaction for QA/QC testing. Likely contractor practices evolved to produce high level wheel path compaction. However, the report suggests that future implementation of the DPS in Maine must include data from lane and joint as many studies have conclude that compaction in other parts of the pavement, especially at the longitudinal joint are essential for adequate pavement performance.

HWY 2 near Lincoln, Nebraska This survey was a training exercise in which the DPS measurement process and calibration with cores was demonstrated and explained to the Nebraska DOT. Compaction was considered satisfactory and mostly uniform for both areas in the center of the lane and at the joint. However, some points presented scarce areas of low dielectric constant indicating potential issues with compaction (Figure 32). Using DPS in active projects can help improve compaction these locations.

Figure 32: Joint survey [63]

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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
page 39

Figure 19: Temperature segregation identified with thermal imaging [47

0
page 35

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

0
page 25

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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