540 Table 16-5: Results generated from Vensim based on degradation model when traffic and speed are de. 553 Table 16-19: Descriptive Analysis for Data of Subsoil Risk for Multiple Regression Analysis Method554 Table 16-20: Results of Estimated Coefficients and ANOVA for Subsoil Risk. 558 Table 16-25: Descriptive Analysis for Vehicle Speed Risk Data for Multiple Regression Analysis Method 558 Table 16-26: Descriptive Analysis for Pavement Thickness Risk Data.
563 Table 16-35: Construction quality Risk factor based on regression analysis with three different measures 564 Table 16-36: Rutting risk factor based on regression analysis with three different measures.
15 and 20 years using CurveExpert Professional software ---338 Figure 10-3: Deterioration curve for different climate change scenarios based on Markov chain model --339 Figure 10-4: Percent loss of elasticity based on chain Markov ---342 Figure 10-5: Pavement deterioration curves based on IRI under different climate change risks. using system dynamics) ---344 Figure 10-6: Percentage loss of elasticity based on system dynamics ---347 Figure 10-7: Comparison of results for loss of elasticity based on system dynamics and Markov.
LIST OF ABBREVIATIONS
ΔRIs Incremental change in roughness due to structural deterioration during the year of analysis, in m/km IRI. ΔRIc Incremental change in roughness due to cracks during the year of analysis, in m/km IR ΔRIr Incremental change in roughness due to ruts during the year of analysis, in m/km IRI. ΔRIe Incremental change in roughness due to the environment during the year of analysis, in m/km IRI SNc Changed structural number for the pavement at the beginning of the year of analysis.
VIM Voids in the mix for road asphalt material of type m on road section i during year t SP Softening point of binder for road section i with material of type m during year t sh Average speed of heavy vehicles on section i, in km/h during year t . 𝐅𝐢𝐤𝐭 Average annual daily traffic (PPDP) of commercial vehicles of class k in one direction on road section i in year t. GF Traffic growth factor for adjusting the existing traffic flow data to the desired year t Pi Share of commercial vehicles on the heavily loaded lane of the road section i.
LIST OF EQUATIONS
Chapter 1 Introduction
- Introduction
- Research Context and Background Information
- Research Problem
- Climate Change Impact
- Pavement Failure Risk
- Current Deterioration Prediction Models
- Shortcomings of Deterministic Modelling
- Motivations for the Research
- Research Significance and Knowledge Expansion
- Aim, Objectives and Research Questions
- Research Aim
- Research Objectives
- Research Questions
- Thesis Structure
Moreover, the literature is sparse on the question of how to capture a real system of pavement deterioration under the impact of climate change using system dynamics models. Introduces a new system dynamics model (Casual Loop Diagram and Stock and Flow) that measures pavement deterioration rate for different pavement performance indicators such as Pavement Condition Index (PCI) and International Roughness Index (IRI) with respect to the impact of future climate change with and without pavement risk. To propose a Markov chain model for the projection of pavement deterioration rate for different pavement performance indicators such as pavement condition index (PCI) and International Roughness Index (IRI) with respect to the impact of future climate change.
Index (PCI) and International Roughness Index (IRI) in relation to the impacts of future climate change. To propose a pavement failure risk system dynamics model to project pavement deterioration rates for various pavement performance indicators such as Pavement Condition Index (PCI) and International Roughness Index (IRI) under future climate change impacts. The HDM-4 model is used to build a new deterministic model regarding the impact of climate change.
The chapter examines the impact of the pavement deterioration model with respect to generic risks that was developed in Chapter 3. The interdependence between pavement failure risks due to climate change and pavement deterioration constructs was determined by causal loop diagrams.
Chapter 2 Impact of Climate Change on Pavement Resilience Resilience
- Introduction
- Climate Change
- Theory on Climate Change
- Climate Change Projection
- Observations on Climate Change
- Climate Change Impact on Pavement Structure
- Pavement Structure
- Pavement Deterioration
- Pavement Failure
- Pavement Deterioration Distress Risks
- Pavement Performance
- Pavement Condition Index (PCI)
- International Roughness Index
- Resilience Review
- Resilience Background and Definitions
- Resilience Dimensions
- Resilience in the Transport Context
- The Relationship between the Climate Change Risk and Resilience Resilience
- Review of Pavement Deterioration and Resilience Prediction Models Models
- Deterministic Model
- Probabilistic Deterioration
- Simulation Models
- Measuring Resilience through Pavement Performance
- Summary
In addition, the ice ages, which caused extremely cold periods on the planet, are clear evidence of natural changes in climate. The thickness of the protective layer in the atmosphere has become very thick due to the concentration of carbon dioxide (CO2). For example, the IPCC report predicts that average global warming will increase between 1.1°C and 6.4°C by the end of the 21st century (see Figure 2-2).
Based on the highlighted literature, one part of the research is to examine the evolving role of temperature in the context of the impact of climate change. A direct impact can be seen in the poor condition of the pavement surface, leading to safety issues and increased travel times. He also added that insufficient maintenance and pavement failure assessment programs with a limited budget lead to deterioration of the pavement structure.
Mubaraki (2010) emphasized the importance of the pavement age factor in constructing a pavement deterioration prediction model. An example of the pavement permeability index can be seen in the degradation of the bond between the pavement layers or the removal of the binder from the aggregate. According to statistics, drainage maintenance costs account for 7% of all maintenance costs in Great Britain (Pearson 2011).
More details on the impact of climate change on pavement were discussed earlier in this chapter, in section 3.4.1. He stated that rutting occurs in the form of plastic flow of the asphalt material generated by stress. The rates of PCI for the surface condition of the pavement depend on the loads monitored from the surface of the pavement.
On the other hand, Gillespie (1992) identified the main factors that cause pavement roughness as environmental impact, traffic loads and defective material used in pavement construction. Practice focuses on people and process on the structure of infrastructure and resources. For example, infrastructure (system) quality was assumed to be 100% before a disruptive event (earthquake), which is not a common scenario.
In the supply chain context, Carvalho et al. 2012) presented a simulation study for a real case related to automobiles in Portugal.
Chapter 3 Theoretical Methods for Modelling Pavement Deterioration Deterioration
- Introduction
- Climate Change Model
- Pavement Temperature Model
- Thornthwaite Moisture Index model for pavements
- Highway Development and Management Model (HDM-4)
- Calibration of Highway Development and Management Software (HDM-4) Software (HDM-4)
- Thornthwaite Moisture Index in the HDM-4 Model
- Modelling Pavement Indicators
- Deriving the Relationship between PCI and IRI
- Deriving the Relationship between Pavement Distress (Rutting and Cracking) and IRI and Cracking) and IRI
- Total Change in Roughness Model
- Modelling The HDM-4 Structural Component of Roughness (ΔRIs) (ΔRIs)
- Modelling The HDM-4 Rutting Component of Roughness
- Development Change in Rutting (ΔRDS) Model based on Climate Change Climate Change
- Modelling the HDM-4 Cracking Component of Roughness
- Modelling the HDM-4 Environmental Component of Roughness Roughness
- Risk Analysis for Pavement Failure
- Traffic Loading Modification to Risk
- Environmental Loading Adjustment to Risk
- Pavement Composition Adjustment to Risk
- Pavement Strength Adjustment to Risk
- Pavement Ageing Adjustment to Risk
- Subgrade Soil Adjustment to Risk
- Drainage Risk Adjustment to Risk
- Maintenance Adjustment to Risk
- Construction Quality Adjustment to Risk
- Rutting Adjustment to Risk
- Cracking Adjustment to Risk
- Heavy Vehicle Speed Adjustment to Risk
- Pavement Thickness Adjustment to Risk
- Measuring Resilience Loss for the Pavement Network
- Summary
The selection of the temperature change due to the impact of climate change is based on the interests of the author and other factors are not studied in the study. In this study, the calibration factors for climate change road surface deterioration models are determined with the combination of the standard equation HDM-4 model for total roughness change due to cracks and environment. Some of the standard HDM-4 model sections rely on the application of TMI as the primary climate factor included in the model.
In terms of the TMI, Martin and Choummanivong (2010) conducted a project to build a model to evaluate the strength of pavement or subsoil using a custom structural number (SNC). Dewan and Smith (2002) investigated the relationship between the condition of the asphalt pavement surface and its roughness. In this study, research on the relationship between PCI and IRI is conducted based on their model.
Total change in roughness is a summation of the complete annual incremental change in roughness and initial roughness value according to the following equation, 3-5. Based on the HDM-4 model, the total annual incremental change in roughness is the sum of the various components, such as rutting, cracking, environmental and structural components. He also added other elements based on the TRL (1993) report, such as the gradient of the pavement section and asphalt mixture properties.
Morosiuk, Riley and Odoki (2004) defined the softening point (SP) as “the temperature at which bitumen reaches a certain level of consistency”. Anyala, Odoki, and Baker (2014) considered the age-related softening point (AGE) of an asphalt layer in their research model using the following equation, 3-8. RIa Roughness at the beginning of the year of analysis, in m/km IRI TMI Thornthwaite moisture index.
The primary objective of the questionnaire is to determine the risk associated with pavement failure due to the impact of climate change. In summary, this chapter presented a theoretical method for different modeling of pavement deterioration and climate change impacts. The HDM-4 model was used to build new degradation modeling under the influence of climate change.
Chapter 4 Methodology
- Introduction
- Research Background
- Research Scope
- Delivery of Scope
- Questionnaire
- Deterministic Model Approach
- Numerical Analysis
- Non-linear Regression Modelling
- Estimating Total Change in Roughness in the Default HDM-4 Model Model
- Estimating Total Change in Roughness on the Modified HDM- 4 Model 4 Model
- Pavement Condition Index (PCI) Model
- Model Checking and Validation
- Define Number of Condition States
- Determine Pavement Deterioration Rate (Transition Probability Matrix) Probability Matrix)
- Determine Current Pavement Condition
- Model Simulation
- Markov Model Assumptions
- System Dynamics
- Stage 1 – Problem Definition
- Stage 2 – System Conceptualisation
- Stage 3 – Data Collection
- Stage 4 – Simulation Model Formulation
- Stage 5 – Evaluation
- Measuring Resilience through Pavement Performance
- Summary
- Introduction
- The Study Area
- Study Background
- Data Types and Sources
- International Roughness Index (IRI)
- Pavement Condition Index
- Reliability of Data from the Conditional Survey
- Traffic Data
- Automatic Temporary Counts (ATCs)
- Traffic Flow Data
- Traffic Loading (YE4)
- Heavy Vehicle Speed (sh)
- Asphalt Surfacing Thickness (Hs)
- Asphaltic Ageing
- UAE Weather Data
- Developments of Model Inputs
- Maximum Air temperature (Tmax) in the UAE
- Pavement Temperature (TPmax)
- TMI for Future Climate Change Scenario
- Binder Softening Point (SP)
- Voids in Mix (VIM)
- Respondents’ General Information
- Descriptive Statistics for Pavement Failure Risk
- Summary
Data is collected from the roads department in the Ministry of Public Works of the United Arab Emirates, Al Ain City Municipality and the National Center of Meteorology and Seismology. Measuring resilience loss of the pavement network in the UAE under different climate change scenarios and different risks using attenuation curves generated from both system dynamics model and Markov chain model. In this research, such analysis is used to define the relationship between different variables in the HDM-4 model.
One of the most reliable simulation methods used in policy optimization is system dynamics. This chapter concludes with a description of the statistical methods used in the analysis and the results of the pavement failure risk questionnaire. A summary of essential roads under the jurisdiction of the Ministry of Public Works in the UAE is highlighted in Appendix 1.
A summary of the length of roads used in the study is also given in Appendix 1. Conditional survey conducted by Ministry of Public Works in the UAE and of Al Ain City Municipality. This was average data taken from another agency in the UAE (Al Ain City Municipality).
The average annual daily traffic (AADT) of commercial vehicles (F) on the roads in the research network was calculated by the Ministry of Public Works in the UAE. The National Center for Meteorology and Seismology is the main source of climate data in the UAE. The selection of the highest air temperature recorded in the UAE was made to develop the model based on the worst.
The determination of softening point based on the pavement structure age in the UAE is shown in the table in Appendix 1; samples of the results are given in tables 5-33 and 5-34. Number of pavement engineering experts who participated in the questionnaire survey. Experience of pavement engineering experts who participated in the questionnaire survey.
Data suitable for use in the development of the proposed models in Chapter 6 are collected, analyzed and summarized in this chapter.
Chapter 6 Developing Pavement Deteriorations Indicators
- Introduction
- Chapter 6 Roadmap
