TRANSPORTATION RESEARCH BOARD. Pavement Maintenance Programming Using 3D Laser Technology. Monday, March 26, :00-3:30 PM ET

Transcription

1 TRANSPORTATION RESEARCH BOARD Pavement Maintenance Programming Using 3D Laser Technology Monday, March 26, :00-3:30 PM ET

2 The Transportation Research Board has met the standards and requirements of the Registered Continuing Education Providers Program. Credit earned on completion of this program will be reported to RCEP. A certificate of completion will be issued to participants that have registered and attended the entire session. As such, it does not include content that may be deemed or construed to be an approval or endorsement by RCEP.

3 Purpose Discuss a systematic approach toward pavement maintenance and rehabilitation planning and programming using 3D laser technology and machine learning algorithms. Learning Objectives At the end of this webinar, you will be able to: Describe recent advances in automatic distress detection using 3D laser technology and machine learning algorithms Discuss the application in pavement maintenance and rehabilitation planning and programming

4 Pavement Maintenance Programming Using 3D Laser Technology Presented by Yichang (James) Tsai, Ph.D., P.E., Professor Georgia Institute of Technology March 26, 2018

5 Outline Overview of automatic pavement distress detection using 3D laser technology. Successful implementation of automatic pavement distress detection and classification on Georgia s Interstate Highway. Asphalt deep patching identification using 3D technology and automatic distress detection and classification. Crack Fundamental Element (CFE) for predicting optimal treatment & timing under a new sensor-based pavement management.

6 A Brief History of 3D Laser Scanning 3D laser scanning started from 1960s The first triangulation-based 3D laser scanning technology was developed in 1978 Other types of 3D laser scanning: time of flight and phase shift Advantage of laser stripe (line laser) Faster than point laser and as accurate as point laser Commercialized systems for pavement engineering (started from 2010) Pathway 3D Imaging Pavemetrics LCMS TxDOT 3D Transverse Profiling System Waylink Ebrahim, M. A. (2011) 3D Laser Scanners: History, Applications, and Future (

7 High-resolution and High-performance 3D Continuous Transverse Profiles (Laurent, et. al., 2008) Resolution Driving direction: 1 5 mm Transverse direction: 1 mm Elevation: 0.5 mm Data points collected per second and width covered 2 (lasers) * 2048 (points/profile/laser) * 5600 HZ = 22,937,600 points/second

8 Cracks with Poor Lighting Contrast Day time At Night (a) (b) (c) (d) (a) 3D laser data collected at daytime (b) Crack segmentation result (crack segmentation score = 98.3) (c) 3D laser data collected at night; (d) Crack segmentation result (crack segmentation score = 98) Tsai, Y., Li, F. (2012) Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using Emerging 3D Laser Technology, ASCE Journal of Transportation Engineering, 138(5),

9 3D Pavement Data and Its Applications a. Texture (IRI; MPD; RVD) c. Joint/crack faulting; potholes b. Cracks d. Rutting 1.Tsai, Y., Chaterjee*, A, (2017) Pothole Detection and Classification Using 3D Technology and Watershed Method, ASCE Journal of Computing in Civil Engineering, 32(2), Tsai, Y., Li*, F. (2012) Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using Emerging 3D Laser Technology, ASCE Journal of Transportation Engineering, 138(5), Tsai, Y., Wu, Y., Lai, J., Geary, G. (2012) Characterizing Micro-milled Pavement Textures Using RVD for Super-thin Resurfacing on I-95 Using A Road Profiler, Journal of The Transportation Research Record, No.2306, pp Tsai, Y., Wu, Y., Ai, C., Pitts, E. (2012) Feasibility Study of Measuring Concrete Joint Faulting Using 3D Continuous Pavement Profile Data, ASCE Journal of Transportation Engineering,138(11), Tsai, Y., Li, F., Wu, Y. (2013) Rutting Condition Assessment Using Emerging 3D Line-Laser Imaging and GPS/GIS Technologies, the International Conference on Road and Airfield Pavement Technology, Taipei, Taiwan, July 14, 2013.

10 GDOT Raveling Survey Practices Classified into 3 severity levels 1: Loss of substantial number of stones. Could be rejuvenated with fog seal. 2: Loss of most surface. Too many stones lost to rejuvenate the surface and not enough to repave the road. 3: Loss of substantial portion of surface layer ( >1/2 depth). Surface must be removed and repaved. Currently reported by visual inspection (COPACES) Predominant level in % length per mile For convenience, in this study, pavements without raveling were labeled as severity level 0. Level 1 Level 2 Level 3

11 Tsai, Y. and Wang Z. (2015) Development of an Asphalt Pavement Raveling Detection Algorithm Using Emerging 3D Laser Technology and Macrotexture Analysis, National Academy of Science NCHRP IDEA-163 Final Report. Automatic Raveling Detection and Classification Using Machine Learning Procedures Data collection (3D line laser imaging data) Data processing (pre-processing and feature generation) Classification using machine learning, including SVM and Random Forest (output raveling severity levels; classifier needs to be trained first) Data Collection Data Processing Classification

12 Successful Implementation of Automatic Pavement Distress Detection and Classification on Georgia s Interstate Highway 9

13 GDOT Asphalt Pavement Condition Evaluation System (PACES) 10 Distress Types 1. Rut Depth 2. Load Cracking (Level 1, 2, 3 and 4) 3. Block Cracking (Level 1, 2, and 3) 4. Reflection Cracking (Level 1, 2, and 3) 5. Raveling (Level 1, 2, and 3) 6. Edge Distress (Level 1, 2 and 3) 7. Bleeding/Flushing (Level 1 and 2) 8. Corrugations/Pushing (Level 1, 2 and 3) 9. Loss of Section (Level 1, 2 and 3) 10. Patches and Potholes GDOT (2007) Pavement Condition Evaluation System (PACES), Office of Maintenance, Georgia Department of Transportation Identify/determine 1.Distress type, 2.Severity level, 3.Extent of pavement distress

14 Key Components of Existing Automated Crack Evaluation (Detection and Classification) Stage 1: Data Acquisition Stage 2: Crack Detection Stage 3: Crack Classification and Quantification 2-D Raw Data 3-D Crack Map Jiang*, C. and Tsai, Y. (2015) Enhanced Crack Segmentation Algorithm Using 3D Pavement Data, ASCE Journal of Computing in Civil Engineering. 1.Type 2. Severity Level 3. Extent 11

15 Automatic Crack Classification 12

16 Asphalt Pavement Load Cracking Level 1 Level 2 Level 3 Level 4

17 Asphalt Pavement Block Cracking Level 1 Level 2 Level 3

18 Load Cracking Classification Results (Severity Level 1-2) Left Wheelpath LC Level Right Wheelpath LC Level Non Wheelpath BT Level Left Wheelpath None 0 Right Wheelpath LC Level Non Wheelpath BT Level *Measurement Unit: Foot

19 Load Cracking Classification Results (Severity Level 3-4) Left Wheelpath LC Level Right Wheelpath LC Level Non Wheelpath BT Level Left Wheelpath None 0 Right Wheelpath LC Level Non Wheelpath BT Level *Measurement Unit: Foot

20 Successful Implementation of the Developed Technologies to A Large-scale Interstate Highway System (2017 AASHTO High Research Value Award, Sweet 16) Tsai, Y., Wang, Z., Ai, C. (2017) Implementation of Automatic Sign Inventory and Pavement Condition Evaluation on Georgia s Interstate Highways, Final Report, Georgia Department of Transportation. 17

21 Successful Implementation of Automatic Sign and Pavement Condition Evaluation on Georgia s Interstate Highways To implement the automatic traffic sign inventory and pavement distress data collection methods on Georgia s interstate highway system with heavy traffic A complete 22,408 sign data and survey miles of asphalt pavement condition on Georgia s interstate highway system Traffic Signs in Poor Conditions on Interstate Highways in Georgia Surface Failure Post Failure Dirty Obstructed % % % % Figure 1 Traffic signs with poor conditions on interstate highways in Georgia Overhead Traffic Signs on Interstate Highways in Georgia Sign-Bridge Mounted Cantilever Mounted Butterfly Mounted Bridge Mounted 1, % 1, % % % Figure 1 Traffic signs installed on overhead structure on interstate highways in Georgia 18

22 Pavement Condition (COPACES) on Georgia s Interstate Highways Tsai, Y., Wang, Z., Ai, C. (2017) Implementation of Automatic Sign Inventory and Pavement Condition Evaluation on Georgia s Interstate Highways, Final Report, Georgia Department of Transportation.

23 How to Utilization the Automatically Extracted Pavement Distress Data? 20

24 Asphalt Deep Patching Identification Using 3D Technology and Automatic Distress Detection and Classification (Resurfacing Project on SR26/US80, Savannah, GA) Tsai, Y., Price, G., Wu, Y. (2018) A Cost-effective and Objective Full-Depth Patching Identification Method Using 3D Sensing Technology with Automated Crack Detection and Classification, Journal of Transportation Research Record, National Academy of Sciences, in press 21

25 Milling/Resurfacing with Localized Deep Patching Step 1: Identify the isolated locations for deep patching based on pavement distresses Step 2: Conduct 4-inch deep patching on the identified, localized area Step 3: Conduct 1.5-inch milling and resurfacing on the entire project. Step 2 Step 3 4 deep patching mm Superpave 2 19 mm 6 25 mm Base 10 Graded Aggregate Base Subgrade 1.5 resurfacing

26 GDOT (2007) Pavement Condition Evaluation System (PACES), Office of Maintenance, Georgia Department of Transportation Crack Fundamental Element (CFE) Classification Load Cracking Severity 1 Single longitudinal cracks with possible transverse spurs Severity 2 Two to three longitudinal cracks with transverse cracks connecting them, starting to form polygons Severity 3+ More than three longitudinal cracks, lots of polygons, often accompanied by rutting

27 Rutting Detection and Measurement 3D Rut Shape (02/17/2016) 1.Li, F. (2012). A Methodology for Characterizing Pavement Rutting Condition Using Emerging 3D Line Laser Imaging Technology. Ph.D. dissertation, Georgia Institute of Technology. 2.Tsai, Y., Li, F., Wu, Y. (2013) A New Rutting Measurement Method Using Emerging 3D Line-Laser Imaging System, Int. Journal of Pavement Research and Technology, Vol. 6(5): Wang, C. (2016). A Spatiotemporal Methodology for Pavement Rut Characterization and Deterioration Analysis Using Long-Term 3D Pavement Data. Ph.D. dissertation, Georgia Institute of Technology

28 Determination of Deep Patching Locations TCL: Total Crack Length LWW: Lengthweighted crack width LC severity level : Classified load cracking severity Rut Depth Note: LC: Load Cracking, longitudinal cracks on the wheelpath (From GDOT) Tsai, Y., Price, G., Wu, Y. (2018) A Cost-effective and Objective Full-Depth Patching Identification Method Using 3D Sensing Technology with Automated Crack Detection and Classification, Journal of Transportation Research Record, National Academy of Sciences, in press

29 Before and After Deep Patching 1) Before deep patching (identified locations & milling) 2) After deep patching

30 Crack Fundamental Element (CFE) for Predicting Optimal Treatment & Timing under A New Sensor-based Pavement Management System Jiang, C., Tsai, Y., Wang, Z. (2016) Crack Deterioration Analysis Using 3D Pavement Surface Data: A Pilot Study on Georgia State Route 26. Journal of Transportation Research Record, National Academy of Sciences, 2016 (2589): Tsai, Y., Jiang*, C., Huang, Y. (2014) A Multi-scale Crack Fundamental Element Model for Real World Pavement Crack Classification, ASCE Journal of Computing in Civil Engineering, vol. 28, no. 4,

31 Project-level Pavement Condition Evaluation Cost-effective Treatment Decision Treatment method and timing e.g. new parameters Simulate Existing Types, Severity, and Extent of Distresses e.g. Crack Sealing Distress Classification Fundamental Crack Elements Segmentation Methods

32 Multi-Scale Crack Fundamental Element Model 4.N Large-Scale Crack Network (Extent) Model Rules & Criteria Applications Maintenance Operations 4.2 Medium-Scale Crack Network (density of curves and pieces) 4.1 Small-Scale Crack Network (location, density of curves and pieces) Crack Type Crack Severity 3. Crack Piece (polygon or spall type, angle and area) 2. Crack Intersection (number and location of key points) 1. Predominant Crack Curve (location, extent, width, depth, and orientation) 0. Crack Fundamental Element Approximating Intersecting Extending Crack Characteristics Location Length Orientation Width Depth Etc. Tsai, Y., Jiang*, C., Huang*, Y. (2014) A Multi-scale Crack Fundamental Element Model for Real World Pavement Crack Classification, ASCE Journal of Computing in Civil Engineering, vol. 28, no. 4, 2014

33 Crack Propagation - Spatial and Temporal Analysis on US 80, Savannah, Georgia Oct. 15, 2011 Dec. 07,

34 Property: Crack Length Total Crack Length (Meter) The following slides will show that the propagation on transverse direction is more significant than on longitudinal direction.

35 Example of Longitudinal Propagation Oct Range Image Oct Crack Map Dec Range Image Dec Crack Map

36 Example of Transverse Propagation Oct Range Image Oct Crack Map Dec Range Image Dec Crack Map

37 SEP-11 OCT-11 NOV-11 DEC-11 JAN-12 FEB-12 MAR-12 APR-12 MAY-12 JUN-12 JUL-12 AUG-12 SEP-12 OCT-12 NOV-12 DEC-12 JAN-13 FEB-13 MAR-13 APR-13 MAY-13 JUN-13 JUL-13 AUG-13 SEP-13 OCT-13 NOV-13 DEC-13 JAN-14 Comparison between crack propagation along longitudinal and other directions Longitudinal Crack Length (m) Transverse Crack Length (m) The following slides will show that the propagation on other directions is more significant than on longitudinal direction.

38 Comparison between crack propagation inside and outside the wheelpaths

39 Example of Branching Out (Crack Intersection Points) Dec Range Image Dec Crack Map Dec Range Image Dec Crack Map

40 Property: Crack Intersection Points Number Of Crack Intersections Jiang, C.*, Tsai, Y., Wang, Z. (2016) Crack Deterioration Analysis Using 3D Pavement Surface Data: A Pilot Study on Georgia State Route 26. Journal of Transportation Research Record, National Academy of Sciences, 2016 (2589):

41 Example of Forming Polygons (Crack Polygons) Dec Range Image Dec Crack Map Dec Range Image Dec Crack Map

42 Property: Crack Polygons Number Of Crack Polygons

43 Summary High-resolution 3D pavement data provides great opportunities to advance the development of sensor-based pavement performance models and pavement maintenance programming: New, valuable performance indicators, like crack intersections and polygons, etc., defined in the crack fundamental element (CFE) need to be devised to characterize the detailed pavement distresses. Linkage needs to be established between new indicators and the commonly used composite rating, as well as the optimal treatment method and timing. Small-scale, localized treatments (homogeneous pavement condition sections) can be identified and planned cost effectively using the detailed pavement distress data and the corresponding pavement performance and deterioration models Need for developing the accurate pavement performance and forecasting models using existing and new indicators. Need for developing a new method to quantify raveling (rather than current qualitative H, M, L severity levels) for supporting the forecasting of optimal timing for fog seal treatment.

44 Acknowledgements Sponsors The Office of the Assistant Secretary for Research and Technology (OST-R), USDOT NCHRP IDEA program Georgia Department of Transportation Research team Research engineers: Dr. Zhaohua Wang and Yiching Wu Previous students: Dr. Feng Li, Dr. Chenglong Jiang, Dr. Chieh Wang, and Dr. Chengbo Ai, Geoffrey Price Current students: Anirban Chatterjee, Georgene Geary, Lauren Gardner and April Gadsby

45 Thanks Questions Contact: Yichang (James) Tsai, Ph.D., P.E., Professor Georgia Institute of Technology 42

46 Network-level Crack Sealing/Filling Programming using 3D Laser Technology Zhaohua Wang, PhD, P.E. Senior Research Engineer Center for Spatial Planning Analytics and Visualization Georgia Institute of Technology March 26, 2018

47 Acknowledgement This research was funded by the National Center for Transportation Systems Productivity and Management (NCTSPM) (a USDOT Tier 1 University Transportation Center (UTC)) and the Georgia Department of Transportation.

48 Outline Research need Research summary CS/CF performance modeling Estimation of CS/CF workload A prototype model for network-level CS/CF programming Ongoing Research

49 Research Need (1) As one of the most commonly used pavement preservation techniques, crack sealing and crack filling (CS/CF) has become an integral part of network-level pavement maintenance program in many state DOTs (e.g. Georgia DOT). Two challenges still remain. How to determine the performance gain when a given candidate project is treated using CS/CF? How to estimate CS/CF cost for a given candidate project? The above two questions need to be answered in order to integrate CS/CF in an existing Pavement Management System.

50 Research Need (2) The commonly used pavement surface rating systems (e.g. PCI, PSR, etc.) are not sufficient to address the above issues because CS/CF is a local treatment and needs more detailed information about cracks such as crack location, length, width, etc. It is infeasible to acquire the above detailed crack characteristic data using manual, visual survey method. The emerging 3D laser technology and the advanced, automatic data extraction methods can be used to fill the gap.

51 Research Summary This research studied the network-level CS/CF programming using automatically extracted crack characteristics, e.g. crack locations, crack length, crack widths, etc. Proposed a model to quantify the performance gain (e.g., extension of service life) of a CS/CF-treated pavement project. Determined the workload and cost of CS/CF for a CS/CF project using automatically detected crack maps Examined a prototype optimization model for network-level CS/CF programming

52 Crack Sealing and Crack Filling Crack filling Routine maintenance Non-working cracks Crack sealing Preventive maintenance Working cracks (>3 mm yearly horizontal move, defined by FHWA) Routing is normally needed Functions Prevent water intrusion to underlying layer of pavement structures Reduce incompressibles, thus reducing crack growth and raveling

53 Framework of Network-level Programming Pavement network Candidate CS/CF projects Pavement preservation guide Performance model Performance gain for each candidate project Cost for each candidate project Cost estimation Goal/objectives (e.g. total performance gain) Constraints (e.g. total cost) Optimized programming

54 CS/CF Project Selection Criteria The commonly used CS/CF project selection criteria in a State DOT define the feasibility of a candidate project. They can be used for selecting candidate projects at network level. Georgia DOT Illinois DOT Reference: Bureau of Design and Environment Manual, Illinois Department of Transportation

55 Crack Characteristics Crack Type As a preventive maintenance method, CS/CF is only applied to structurally sound pavements. Thus, most highway agencies don t recommend performing CS/CF on alligator cracking. Working cracks: transverse thermal and reflective cracks; longitudinal cracks in wheel paths. Non-working cracks: longitudinal cracks outside of wheel paths.

56 Crack Characteristics Crack Density Crack density is used to measure the crack-related pavement conditions

57 Crack Characteristics Crack Width Crack width is considered in terms of workability and effectiveness. Very tight crack makes it difficult for sufficient sealant to enter it. Very wide crack might indicate structural failure and needs crack repair instead of sealing. Normally, a crack should be wider than 5 mm and narrower than 25 mm for it to be sealed

58 A Generalized Performance Model Model estimation using Georgia DOT s practices Reference: Wang, Z., Tsai, Y. and Ding, M. (2016). Use of Crack Characteristics in Crack Sealing Performance Modeling and Network-Level Project Selection. Journal of TRB, DOI: /

59 Issue of CS/CF Cost Estimation By total gallon used (Source: Caltrans) By lane mile or unit area (Source: ntinteractive.org) By crack linear foot

60 Automatic CS/CF Workload Estimation Reference: Tsai, Y., Wang, Z., Jiang, C., Mahfood, B. and Rone, J., Innovative Crack Sealing Analysis and Cost Estimation for Airport Runway Shoulders Using 3D Laser Technology and Automatic Crack Detection Algorithms. Proceedings of International Airfield & Highway Pavements Conference, pp , Miami, Florida, June 7-10, 2015.

61 A Case Study at Atlanta Airport

62 100-ft Section Overview

63 Estimated Results Initial Crack Width Depth for Routing 0 3/16 No routing or sealing required 3/16 1/2 1 1/2 3/4 1-1/2 3/4 1 2 Greater than 1 2 Section Length: 100 ft. Transverse Coverage: ~13 ft. Total Coverage: 1300 square ft. Depth for Routing Crack Length (ft) No routing or sealing required / Total 321

64 Network-level Programming Model The proposed CS/CF performance model can be applied to optimally select CS/CF projects at network level under the constraint of insufficient budget

65 A Case Study One mile test road was selected on State Route 26 / US 80 near Savannah, Georgia. AADT is 24,020; Truck% is 12%. It is divided into ft-long segments. 3D pavement laser data was collected on December 6, 2011 March 21, 2012 December 7, 2013 June 15, 2015 February 17, 2016

66 CS/CF-Treated Pavement Performance

67 Network-level CS/CF Project Selection Given budget is $1,500. Unit cost for CS is assumed to be $1.5 per linear foot; for CF, it is $0.5 per linear foot. The allowable minimum performance gain is simplified as zero. The pavement conditions are chosen as the ones in 2011

68 Ongoing Research Field experimental tests are to be conducted through a research project sponsored by Georgia DOT. 10 test sites were selected in North (4 sites) and South (6 sites) Georgia. A five year monitoring will be performed.

69 3D Data to Analyze Jointed Plain Concrete Pavements (JPCP) in Space and Time Georgene M. Geary, PhD Student Georgia Tech Space- slab, 0.1 mile (~25 slabs), mile or project (>250 slabs) Time Changes in pavement distress over time and space Slab Maintenance with 3D Data

70 3D data allows for Multi-scale models in concrete pavements 1) Slab Level a) Orientation and Location of Cracking b) Faulting: slabs or cracks 2) 0.1 mile/ LTPP section length Pavement ME calibration criteria joint Transverse C Wheel path Longitudinal joint 0.1 mile Direction of Travel 3) 1 mile/project Level a) % slabs cracked- Pavement ME b) Changes over time

71 LTPP Distress Protocol & Topological Slab states LTPP Crack Types Transverse Cracking Slab State T1 or T2 Longitudinal Cracking L1 or L2 Corner Breaks CC (corner crack) Not separately identified by LTPP criteria SS (Shattered Slab)

72 Typical Slab State Progression L1 Longitudinal crack 1 L2 Longitudinal crack 2 NC Not Cracked L1/T1 L2/T2/CC SS Shattered Slab T1 Transverse crack 1 T2 Transverse crack 2 CC Corner crack 4

73 GT Slabviewer2 App Used to View and Categorize Individual Slabs

74 GT Slabviewer2.1 App Used to Compare Individual Slabs by Time

75 3D Data and Slab State identification 3 years worth of data Slabs Note: Not Cracked (NC) = 0 7

76 Example 3D data: Two Interstate pavement sections each 1 mile in length Constructed at same time (1972) with same typical section in same project Located 1 mile apart from each other (MP 17 and MP 15) Typical Section 9 inch thick undoweled jointed plain concrete 6 inch soil cement base Random Skewed Joints (ave ~20 ft) 9 inch JPC 6 inch soil cement Soil subgrade 8

77 Distribution of Slab states in one mile section ( 3 years time) Age of the pavement in years NC L1 T1 L2 T2 SS

78 Changes in Slab States over Time Transition from T1 to T2 in one year Transition from T2 to SS in one year Year 43 Year 42 3D view Range view

79 Changes in Slab states in one mile section (Year 41 to Year 42) SS T Year 42 L2 T L NC 166 NC L1 T1 L2 T2 SS Year 41

80 Slabs Jumping States in One Years Time L1 to SS

81 Comparison of Slab states in both sections (267 total slabs in each) Age of the pavement in years MP 17 MP 15 Changes in slab state over time- MP NC L1 T1 L2 T2 SS NC L1 T1 L2 T2 SS

82 Changes in SS over time and space MP 17 Age of the pavement in years Distance along the mile 14

83 A methodology for determining rehabilitation needs- slab replacement quantities 3D Pavement Data Replacement criteria Slab-level distress info Engineering rules SV2 Quantity and Costs of Rehabilitation Needs 15

84 Automated Slab Replacement Quantity Estimation Determine if slab portion needs to be replaced Add 1ft on each side to include dowel bars Check for min replaced slab length: 6ft Check for max replaced slab length: 15ft Check for min remaining slab length: 6-8ft Remaining slab Original Slab Replacement Area Replaced Slab Addition for dowel bars 16

85 Review Slab Replacement Recommendations Replacement area can be added or removed manually 17

86 3D data for Concrete Pavement Maintenance and Rehabilitation Deterioration can be tracked, modeled and predicted Patterns of deterioration can provide insight on causes JPCP maintenance can be optimized not only spatially but also temporally 18

87 Acknowledgement This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Thank You for your Time and Attention! Next: Q and A

88 Today s Participants Hasan Ozer, University of Illinois at Urbana- Champaign, hozer2@illinois.edu Yichang (James) Tsai, Georgia Institute of Technology, james.tsai@ce.gatech.edu Zhaohua Wang, Georgia Institute of Technology, zhaohua.wang@gatech.edu Georgene Geary, Georgia Institute of Technology, ggeary@gatech.edu

89 Get Involved with TRB Getting involved is free! Join a Standing Committee ( Become a Friend of a Committee ( Networking opportunities May provide a path to become a Standing Committee member Sponsoring Committees: AHD18, AHD20, AHD25 For more information: Create your account Update your profile

90 Receiving PDH credits Must register as an individual to receive credits (no group credits) Credits will be reported two to three business days after the webinar You will be able to retrieve your certificate from RCEP within one week of the webinar