LIST OF EQUATIONS
2. Chapter 2 Impact of Climate Change on Pavement Resilience Resilience
2.5. Pavement Performance
46
S.19 Pavement thickness R.3 Pavement
Composition
Harvey et al. (2004) S.20 Availability of material R.3 Pavement
Composition
Pearson (2011) S.21 Bitumen supply and quality R.3 Pavement
Composition
White and Embleton (2015) R.1 Traffic loading R.4 Pavement Strength Pearson (2011) S.22 Insufficient value of structural
number (SN)
R.4 Pavement Strength Pearson (2011)
R.2 Climate change R.5 Pavement Ageing Harvey et al. (2004)
R.1 Traffic loading R.5 Pavement Ageing Harvey et al. (2004) R.6 Subgrade soil R.5 Pavement Ageing Harvey et al. (2004) R.8 Maintenance R.5 Pavement Ageing Harvey et al. (2004) S.17 Increased moisture/excess water R.6 Subgrade Soil Jones and Jefferson (2012) S.13 Selection of construction soil R.6 Subgrade Soil Austroadds (2018) S.17 Increased moisture/excess water R.7 Drainage Haas, Hudson and Zaniewski
(1994)
S.23 Insufficient drainage system R.7 Drainage Haas, Hudson and Zaniewski (1994)
S.24 Delay maintenance R.8 Maintenance Harvey et al. (2004) S.25 Maintenance priorities/plan R.8 Maintenance Harvey et al. (2004) S.26 Limited budget R.8 Maintenance Adlinge and Gupta (2013) S.27 Design and specification R.9 Construction Quality Bubshait (2002) and Abu El-
Maaty, Akal and El- Hamrawy (2016) S.28 Construction process R.9 Construction Quality Bubshait (2002) S.29 Construction management R.9 Construction Quality Abu El-Maaty, Akal and El-
Hamrawy (2016)
47
(2015) added that the state of a pavement would decline due to many factors such as asset ageing and accumulated axle loads. The primary objective is to capture the condition over time; thus, the deterioration rate can be measured. Therefore, many maintenance or rehabilitation alternatives can be introduced to upgrade the condition parameters. Abaza (2004) stated that the condition assessment of a pavement structure at a given period could be conducted using three important performance measurements.
These are Pavement Condition Index (PCI) (presents the distresses on pavement sections), Present Serviceability Index (PSI) (presents the functional condition in terms of ride quality) and International Roughness Index (IRI) (measures the roughness along road profile) (Sayers 1995). Many scholars and engineers have agreed that cracking and excessive deformation of pavement sections, as well as disintegration of pavement material, result from repeated traffic loadings and environmental impacts, which lead to degradation of pavement performance (Haas, Hudson and Zaniewski 1994).
Data collection is a crucial element in assessing pavement performance. In practice, pavement condition data can be gathered using automated or manual methods.
Every agency or municipality establishes their own approach for data collection methodologies, applied software programs and pavement management processes.
Concerning data type, there are four general categories of pavement condition used in maintenance planning for pavements: Surface distress (Pavement Condition Index – PCI), Ride quality (International Roughness Index – IRI), Structural capacity (Falling Weight Deflectometer – FWD) and Friction (Skid resistance). More details are provided in Chapter 5 section 5.4.3.
2.5.1. Pavement Condition Index (PCI)
In the 1980s, the US Army Corps of Engineers introduced the Pavement Condition Index (PCI) rating system. Shiyab (2007) states that the PCI technique has been extensively implemented for airfield pavements, roads and parking lots by various highway authorities worldwide. The primary objective of the PCI is to give a good sign of structural integrity and operational condition of the pavement surface. Mahmood (2015) emphasised the importance of PCI application for highway agencies; he also
48
stated that the PCI covers all types of distress such as cracking, rutting, shoving, etc.
Moreover, the weights of both severity and quantity are expressed in PCI. Abaza (2004) concluded that the PCI techniques had been applied mainly in pavement management applications. Mahmood (2015) adopted the PCI in his research as it gives an excellent presentation of the pavement condition of a network regarding both functional and structural conditions. The PCI measure (or rating) is achieved by visual inspection of pavement distress. The rates of PCI for the surface condition of the pavement depend on the distresses monitored from the surface of the pavement. The primary objective of the data collection is to present the structural integrity and surface operational condition of the pavement section (ASTM 2011). The rating ranges from 0-100, where 100 is the best condition and 0 is the worst. Further analysis is provided in Chapter 5 section 5.4.3.
Also, the PCI was used as a pavement indicator for the deterioration model. More information is presented in Chapter 6 section 6.5.
2.5.2. International Roughness Index
ASTM (2003) defined roughness as “the deviation of a surface from a true planar surface with characteristic dimensions that affect vehicle dynamics and ride quality”. Sayers, Gillespie and Queiroz (1986) introduced another definition of roughness. They stated that it is “variations in elevation of the surface that induce vibrations in traversing vehicles at a given point of time”. Fwa (2006) added another definition: “the irregularities in the pavement profile which causes uncomfortable, unsafe, and economical riding”. Qiao (2015) presented roughness as the measurement of the longitudinal unevenness of the pavement. Overall, roughness is a crucial sign of vehicle operating costs and the safety, comfort and speed of travel. The rougher the pavement, the higher the in-road user’s cost (Archondo-Callao and Faiz 1994).
Basically, a higher roughness magnitude affects pavement serviceability and has more impact on vehicle operating costs (VOCs). Haas, Hudson and Zaniewski (1994) classified the rating of roughness based on vehicle speed, vehicle passengers’ and driver’s patience and attitude. On the other hand, Gillespie (1992) defined the main factors that cause roughness in a pavement such as environmental impact, traffic loads and defective material applied in the construction of the pavement. A newly constructed pavement segment is also considered to have some initial roughness. For such a section,
49
the roughness value will start to increase with time due to pavement deterioration generated from both traffic loads and environmental impact. Therefore, missing of inadequate maintenance measures will lead to increase the severity and magnitude of distresses, which will eventually affect vehicle speed, and the safety and comfort of road users (Sayers, Gillespie and Queiroz 1986). The road roughness is not only a crucial element in achieving the pavement performance, it is also widely applied in forming the rehabilitation and maintenance priorities plan, especially with a limited budget (Shahin 2005). Therefore, many countries consider road roughness as a valuable tool for defining vehicle operating costs (VOCs).
To measure the roughness, in the 1970s the World Bank introduced a calibration standard that was first applied and tested in Brazil. Finally, a conceptual International Roughness Index (IRI) was developed to achieve an accepted scale that evaluated roughness using a fixed index. The International Roughness Index (IRI) model consists of a series of differential equations that link to the motions of a simulated quarter-car and the road profile. The International Roughness Index (IRI) is expressed in metres per km. Figure 2-7 shows IRI roughness with different scale.
Figure 2-7: IRI roughness scale (Source: Sayers, Gillespie and Queiroz 1998)
50
Many different studies have been conducted on roughness to identify the progression trends and factors affecting roughness value (Kargah-Ostadi, Stoffels and Tabatabaee 2010). Many limitations were recorded in these studies such as complexity of the equations, a large number of variables applied, difficulties in collecting data relating to the variables (especially for the mechanistic-empirical models), and the limited and short period of prediction (Madanat, Nakat and Sathaye 2005). Therefore, it has been concluded that research is still required to discover a more efficient empirical model to be used at the network level. This is still an ongoing matter for pavement scientists and practitioners. In this study, it is proposed to establish a pavement performance indicators model in terms of International Roughness Index (IRI) and Pavement Condition Index (PCI) and then introduce a new pavement deterioration model using Markov chain and system dynamics. Chapter 6 section 6.5 introduces the proposal of an empirical model that concludes on how the International Roughness Index (IRI) can be measured with respect to different climate change impact scenarios.
2.5.3. Pavement Structural Condition
Rebecchi and Sharp (2009) discussed pavement structural condition through the pavement surface deflection under an applied load (traffic load). Alaswadko (2016) added that pavement strength is indicated by the Structural Number (SN). Paterson (1987) considered that Structural Number (SN) is widely used as a strength parameter in roughness progression models. Abd El-Raof et al. (2018) defined the structural number of existing pavement as “a numerical value used as indicator of the pavement strength and its structural capacity at any age”. Moreover, recently, the pavement strength concept was developed to quantify the contribution of all pavement layers (pavement structures and subgrade) through an adjusted or modified structural number (SNP) (Rolt and Parkman, 2000). This new concept is incorporated in the HDM-4 model (Morosiuk, Riley and Odoki 2004). The primary goal of applying Non-Destructive Testing (NDT) is to quantify the pavement structural responses to heavy dynamic loads produced by heavy trucks. ‘Falling Weight Deflectometer’ is the most popular Deflection Measuring System for Non-Destructive Testing (NDT). The FWD is a device
51
used to obtain the readings of stress/strain parameters of pavement structures including subgrades.
The strength data obtained from the Falling Weight Deflectometer (FWD) measure the surface deflection. The modified structural number (SNC) has been used to highlight the total strength of both the pavement and the subgrade. Such a combination can easily predict the performance of asphalt pavement structures at a network level (Watanatada et al., 1987). The variable of the SNC is the essential element in the equation of structural component of roughness based on default HDM-4 model. More details are presented in Chapter 6 sections 6.2.1 and 6.3.1.