Presentation in Two Parts: Research Results from Study Funded by Texas Department of Transportation Proposed Future Work for funding from DHS

Transcription

1 Impact Assessment Of Hydro- Meteorological Events On Texas Pavements And Development Of Resiliency Strategy

2 Overview Presentation in Two Parts: Research Results from Study Funded by Texas Department of Transportation Proposed Future Work for funding from DHS 2

3 TxDOT Funded Study Problem Approach Framework Adopted Climate Model Data Vulnerability Assessment of the Pavements Adaptation Methods Probabilistic Analysis Conclusions 3

4 Why Hydrometerological Events are problem? Rutting result of change in temperature and soil moisture Flooding: wetter surface, stripping increases, water enters pavement structures 4 Hurricane Harvey Damage

5 Approach Future Cliamte Projections for Regional and Local Parameters Impacting Pavement Infrastructure Framework for Evaluating the Performance of the Pavements Vulnerability Assessment of Pavements: Current and projected variability of climate change Evaluating effects of climatic change on the performance of pavement structures Nature and severity of each parameter The sensitivity of roads to climate change Adaptation Methods for Climate Change Probabilistic Analysis 5

6 Emission Scenarios Special Report on Emission Scenario (IPCC AR4, 2000) Representative Concentrative Pathways (IPCC AR5, 2014) Downscaling Methods Statistical Downscaling Dynamic Downscaling Temporal: Daily, 3-hourly Spatial Resolution: 50x50 km Conduct Probabilistic Analysis 6

7 Climate Data Source used in this Study Climate Source/ Downscaling method/ Emission scenario North American Regional Climate Change Assessment Program (NARCCAP) uses Dynamic downscaling and is based on Emission Scenario: SRES A2. Parameters/ Resolution Temporal resolution: Daily (maximum and minimum surface air temperature) 3-hourly (Climate Parameters: precipitation, surface air temperature, cloud fraction, wind speed, relative humidity) Spatial resolution: 50x50 km 7

8 Climate Models NARCCAP includes six RCMs and four GCMs namely: RCMs: 1) Hadley Regional Climate Model Version 3 (HRM3) 2) Regional Climate Model 3.0 (RCM3) 3) The Canadian Regional Climate Model (CRCM) 4) Experimental Climate Prediction Center Regional Spectral Model (ECPC) 5) Mesoscale Meteorological Model Version 5.0 (MM5I) 6) The Weather Research and Forecasting Model (WRFG) GCMs: 1) The Hadley Centre Climate Model (HadCM3) 2) Community Climate System Model (CCSM) 3) The Canadian Global Climate Model (CGCM3) 8 4) The Geophysical Fluid Dynamics Laboratory (GFDL) model

9 Mean Annual Temperature Historical Climate ( ) CRCM-CGCM3 Future ( ) 9

10 Bias-Correction for Temperature Fort Worth Mean Annual Temperature ( F) Existing Temperature (Pavement ME) Model Simulate Current (CRCM-CCSM) Model Simulate Future (CRCM-CCSM) Bias corrected Model Simulate Current Bias corrected Model Simulate Future Time Period (yrs)

11 Mean Annual Precipitation (in) Bias-Correction for Precipitation Corpus Christi Exixting Precipitation (Pavement ME) Model Simulate Future (CRCM-CCSM Bias Corrected Model Simulate Future Model Simulate Current (CRCM-CCSM) Bias Corrected Model Simulate Current Time Period (years) 11

12 According to George E. P. Box All models are wrong but some are useful 12

13 120 Fort Worth 100 Monthly Mean Temperature ( F) CRCM-CGCM3 HRM3-HADCM3 MM5I-CCSM MM5I-HADCM3 HRM3-GFDL CRCM-CCSM Existing ECP2-GFDL RCM3-CGCM3 RCM3-GFDL WRFG-CGCM3 WRFG-CCSM 0 Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

14 80 70 Pavement ME Climate MM5I-HADCM3 MM5I-CCSM HRM3-HADCM3 HRM3-GFDL CRCM-CGCM3 CRCM-CCSM ECP2-GFDL RCM3-CGCM3 RCM3-GFDL WRFG-CGCM3 WRFG-CCSM 60 Mean Annual Temperature ( F) Amarillo Austin Corpus Christi Dallas El Paso Fort Worth Houston Lubbock Mc Allen San Antonio

15 Pavement ME Climate MM5I-CCSM HRM3-GFDL CRCM-CCSM RCM3-CGCM3 WRFG-CGCM3 MM5I-HADCM3 HRM3-HADCM3 CRCM-CGCM3 ECP2-GFDL RCM3-GFDL WRFG-CCSM Mean Annual Precipitation (in.) Amarillo Austin Corpus Christi Dallas El Paso Fort Worth Houston Lubbock Mc Allen San Antonio

16 16 14 Pavement ME Climate MM5I-CCSM HRM3-GFDL CRCM-CCSM RCM3-CGCM3 WRFG-CGCM3 MM5I-HADCM3 HRM3-HADCM3 CRCM-CGCM3 ECP2-GFDL RCM3-GFDL WRFG-CCSM 12 Mean Annual Wind Speed (mph) Amarillo Austin Corpus Christi Dallas El Paso Fort Worth Houston Lubbock Mc Allen San Antonio

17 Pavement ME Climate MM5I-CCSM HRM3-GFDL CRCM-CCSM RCM3-CGCM3 WRFG-CGCM3 MM5I-HADCM3 HRM3-HADCM3 CRCM-CGCM3 ECP2-GFDL RCM3-GFDL WRFG-CCSM Mean Annual Relative Humidity (%) Amarillo Austin Corpus Christi Dallas El Paso Fort Worth Houston Lubbock Mc Allen San Antonio

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19 Ride Quality IRI (International Roughness Index) Rut Depth in Asphalt Concrete Layer IRI is calculated from longitudinal profile measured with a road profiler in both wheelpaths. The average IRI of the two wheelpaths is reported as the roughness of the pavement section. 19

20 Conduct Probabilistic Analysis 20

21 IH 30 Frontage Road for Process Demonstration AADTT: 828 Percentage of Trucks: 3.6% Growth rate 1.86 % for 20 years Annual average daily traffic (AADT) of 22,990 Asphalt Concrete Cement Treated Base Layer Material Properties Asphalt Concrete Type D, PG Cement Treated Base 120 ksi Subgrade Semi Infinite Subgrade 4.5 ksi 21

22 Influence of Climate Models on Pavement Performance 22

23 IRI(in./mile) CRCM_CCSM CRCM_CGCM3 ECP2_GFDL ECP2-HADCM3 HRM3_GFDL HRM3_HADCM3 MM5I_CCSM MM5I_HADCM3 RCM3_CGCM3 RCM3_GFDL WRFG_CCSM WRFG_CGCM3 Pavement ME Climate Maintenance Range variation due to different Climatic Predictions Pavement Age(years)

24 AC Rutting(in.) Threshold Value CRCM_CCSM CRCM_CGCM3 ECP2_GFDL ECP2-HADCM3 HRM3_GFDL HRM3_HADCM3 MM5I_CCSM MM5I_HADCM3 RCM3_CGCM3 RCM3_GFDL WRFG_CCSM WRFG_CGCM3 Pavement ME Climate Early Maintenance with climate change projections Pavement Age(years) 15.8

25 Influence of Extreme Event on Pavement Performance Assumptions: Extreme rainfall will occur at an interval of 1, 2, 3, 5, 10, and 15 years of service life of the pavements. Saturation will last at a time for 7.5 days or 15 days or one month or two months after occurrence of these events 25

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27 Base+Subgrade Rutting (in.) Pavement ME Climate 2 Month 1 Month 15 Days 7.5 Days Pavement Age (years) 27

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29 4,500 4,000 3,500 Subgrade Modulus (psi) 3,000 2,500 2,000 1,500 1, Pavement ME Climate 1 Month Pavement Age (yrs) 29

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31 Adaptation Methods Adaptation Methods for Climate Change Increasing the Thickness of AC Layer Binder Change Changing Mix Type Increasing the Thickness of AC Layer and Changing Binder Grade Adaptation Methods for Extreme Events Improving Subgrade Modulus Adaptation to Extreme Event and Climate Change Improving Subgrade Modulus and Increasing the Thickness of AC Layer 31

32 Changing Material Type with Pavement ME AC Rutting (inch) SMA PG SP-C PG Type C PG Type D PG CC_SMA PG CC_SP-C PG CC_Type C PG CC_Type D PG Pavement Age (yrs) With changing climate the average increase in AC rutting is 0.18 in. for any material type IRI (in/mile) SMA PG SP-C PG Type C PG Type D PG CC_SMA PG CC_SP-C PG CC_Type C PG CC_Type D PG Pavement Age (yrs) Increase in IRI is about 5 % for each material type with changing climate

33 Changing Thickness IRI (in/mile) Maintenance Type D PG CC_Type D PG 70-22_1 in CC_Type D PG PSI Pavement Age (yrs) 5.0

34 Monte-Carlo Simulations for Probabilistic Analysis 34

35 Annual Average Mean Temperature Annual Average Mean Precipitation Climate Models Pavement ME Mean Temperature: F Future Simulation ( ) ( F) Current Simulation ( ) ( F) Bias Corrected ( F) CRCM-CCSM CRCM-CGCM ECP2-GFDL ECP2-HADCM HRM3-GFDL HRM3-HADCM MM5I-CCSM MM5I-HADCM RCM3-CGCM RCM3-GFDL WRFG-CCSM WRFG-CGCM Pavement ME Mean Precipitation: inches Climate Models Future Simulation ( ) (inches) Current Simulation ( ) (inches) Bias Corrected (inches) CRCM-CCSM CRCM-CGCM ECP2-GFDL ECP2-HADCM HRM3-GFDL HRM3-HADCM MM5I-CCSM MM5I-HADCM RCM3-CGCM RCM3-GFDL WRFG-CCSM WRFG-CGCM

36 Using Bias-corrected Range for Temperature & Precipitation 36

37 Findings Selected model simulation showed a change in future All prediction models simulated an increase in mean annual precipitation, which leads to premature failure Change in environmental condition adversely impacts the pavements functionality by reducing the service life of pavements Increasing the thickness of the asphalt concrete layer, binder grade, and mix type are some of the options for mitigating impact of hydrometerological events 37

38 Issues TxDOT manages roughly 73,000 miles of highways. Assuming two lane highway, an increase in 1 layer thickness would cost roughly 20 billion dollars to mitigate impact of environmental conditions. Ignoring impact of extreme events is not an option either. Identifying and enhancing resiliency of critical pavement infrastructure is an option. 38

39 Part II Department of Homeland Security Funding Request 39

40 Resiliency Beginning of 19 th Century Behavior After Strain Hardening From Standard Materials Book (Campbell, Flake C. (2008). Elements of Metallurgy and Engineering Alloys. ASM International

41 Resiliency Linkov, Igor & Bridges, Todd & Creutzig, Felix & Decker, Jennifer & Fox-Lent, Cate & Kröger, Wolfgang & Lambert, James & Levermann, Anders & Montreuil, Benoit & Nathwani, Jatin & Nyer, Raymond & Renn, Ortwin & Scharte, Benjamin & Scheffler, Alexander & Schreurs, Miranda & Clemen, Thomas. (2014). Changing the resilience paradigm. Nature Climate Change /nclimate2227.

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43 43 P. Bocchini, M. Asce, D.M. Frangopol, D.M. Asce, T. Ummenhofer, T. Zinke Resilience and sustainability of civil infrastructure: toward a unified approach

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45 Resiliency of Critical Transportation Infrastructure Accumulate Information on Infrastructure (Transportation Asset Management) Identification of critical components of infrastructure through Simulation of Scenarios, traffic patterns, and experience. Develop a Resilience Assessment Strategy for Selection and Mitigation Develop Multi-criteria Decision Making Framework for Evaluating and 45 Selecting Efficient and Environmentally Friendly Alternative

46 Transportation Asset Management Framework 46 Adopted from TRB Report 551 on Transportation Asset Management

47 Critical Transportation Infrastructure 47

48 Simulations to Identify Critical Infrastructure 48

49 Network Dependencies Roberto Guidotti, Hana Chmielewski, Vipin Unnikrishnan, Paolo Gardoni, Therese McAllister & John van de Lindt (2016) Modeling the resilience of critical infrastructure: the role of network dependencies, Sustainable and Resilient Infrastructure, 1:3-4, , DOI: /

50 Urban Road Efficiency Model by Ganin et al. (2017) 50

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52 Resilience Assessment Strategy Proposed by New Zealand Transportation Agency 52

53 Resilience Assessment Strategy Proposed by New Zealand Transportation Agency 53

54 Resilience Assessment Strategy Proposed by New Zealand Transportation Agency 54

55 T. P. Bostick; E. B. Connelly; J. H. Lambert; I. Linkov2018: Resilience science, policy and investment for civil infrastructure 55

56 Decision Model To identify an effective decision making model which can handle multiple criteria (subjective as well as objective) To develop a group decision model Multi Criteria Decision Model Group Decision Model

57 Decision Model: Multi Criteria Decision Model Analytical Hierarchy Process (AHP) is traditionally used for decision making in Civil Engineering Pros Cons 57

58 Group Decision Model Current Practice Arithmetic or Geometric Mean of individual decisions. Manipulating individual Pros Cons decisions. 58

59 Group Decision Model Data Envelopment Analysis (DEA) based preference aggregation method (commonly used in business community) can be selected for group decision. Pros Cons Linear Programming nn mmmmmmmm jj = uu kk Ѳ jjjj kk=1 SSSSSSSSSSSSSSSSSS tttt nn uu kk Ѳ jjjj 1 jj = 1,2,, nn Model kk=1 59 uu 1 2uu 2 nnnn nn 2 uu nn Ɛ = pppp(nn + 1)

60 Group Decision Model A new constrained optimization method αpso is proposed by Takahama and Sakai (2004) which is a combination of the α constrained method and PSO. Pros Simplistic Approach. Optimizes in minimal time frame. Can be used for many engineering problems. Cons Not applicable in a constrained environment. 60

61 Decision Model For the study a decision model that integrates the following things can be used: Analytic Hierarchy Process (AHP) (handles multiple criteria) Fuzzy logic (lessens the drawbacks of AHP) Data Envelopment Analysis (handles group decision) Hybrid Particle Swarm Optimization (handles the linear programming model and minimizes running time). DEA H- PSO AHP Group Decision Model 61

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63 Thanks TxDOT, DHS, and CIRI for the Support 63