LIST OF EQUATIONS
2. Chapter 2 Impact of Climate Change on Pavement Resilience Resilience
2.7. Review of Pavement Deterioration and Resilience Prediction Models Models
2.7.2. Probabilistic Deterioration
2.7.2.4. Measuring Resilience Using Probabilistic Approaches
Chang and Shinozuka (2004) provided an example of measuring resilience using a probabilistic approach. They used this technique for assessing resilience associated with an earthquake. Originally, their studies were based on Bruneau et al. (2003). They defined resilience associated with various infrastructure systems under seismic stress such as an earthquake. They also developed a notion of integrating all dimensions of community resilience using probabilistic frameworks. Two variable factors were applied in measuring the resilience: loss of performance and length of recovery (Hosseini, Barker and Ramirez-Marquez 2016; Tamvakis and Xenidis 2013). Hosseini, Barker and Ramirez-Marquez (2016) stated that Chang and Shinozuka's (2004) approach could be applied not only to quantify infrastructure resilience and communities’ resilience following an earthquake but also to any other systems and disruptions. However, they highlighted some limitations when the two variables (loss of performance and recovery length) surpass their maximum satisfactory values.
Hosseini, Barker and Ramirez-Marquez (2016) also summarised a few examples where quantification of resilience was carried out based on probabilistic approaches. For instance, Franchin and Cavalieri (2015) tested the system resilience under the event of an
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earthquake, and their philosophy was based on the efficiency and accuracy of defining the position of an infrastructure network. Hosseini, Barker and Ramirez-Marquez (2016) concluded that their method could also be applicable for other infrastructures such as power plants and potable water networks. Mubaraki (2010) summarised the deterministic and probabilistic models, as detailed in Table 2-10. Moreover Summary of different pavement deterioration models are shown in Table 2-11.
Table 2-10: Models’ comparison proposed by Mubaraki (2010)
Model Advantage Disadvantage
Regression
1. Microcomputer software packages are now widely available for analysis which makes modelling easy and less time consuming.
2. These models can be easily installed in a PMS.
3. Models take less time and storage to run.
1. Needs large database for a better model.
2. Works only within the range of input data.
3. Faulty data sometimes get mixed up and induce poor prediction.
4. Needs data censorship.
5. Selection of proper form is difficult and time consuming.
Mechanistic
Prediction is based on cause-and effect relationship, hence gives the
best result.
1. Needs maximum computer power, storage and time.
2. Uses large number of variables (e.g. material properties,
environment conditions, geometric elements, loading characteristics, etc.).
3. Predicts only basic material responses.
Mechanistic- empirical
1. Primarily based on cause-and-effect relationship, hence its prediction is better.
2. Easy to work with the final empirical model.
3. Needs less computer power and time.
1. Depends on field data for the development of empirical model.
2. Does not lend itself to subjective inputs.
3. Works within a fixed domain of independent variable.
4. Generally works with large number of input variables (material properties, environment conditions, geometric elements, etc.) which are often not available in a PMS.
Markov
1. Provides a convenient way to incorporate data feedback.
2. Reflects performance trends regardless of non-linear trends.
1. No ready-made software is available.
2. Past performance has no influence
3. It does not provide guidance on physical factors which contribute to change.
4. Needs large computer storage and time.
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Table 2-11: Summary of different pavement deterioration models by the author
Author(s) Model Name Model Type Independent Variables Output
(Dependent)
Kargah- Ostadi, Stoffels and
Tabatabaee (2010)
Artificial Neural Networks (ANNs)
for Roughness
Artificial Intelligence
Initial roughness, pavement age, traffic, climatic conditions, pavement structural properties,
subgrade properties, drainage type and conditions, and maintenance and rehabilitation
treatments
Predicted future roughness
trends
Lorino et al.
(2012)
Non-linear mixed-effects
modelling
Deterministic Ageing Progression in
cracking Lethanh and
(Adey 2013)
Exponential
hidden Markov Probabilistic Ageing
Deterioration of road sections
Park et al.
(2008)
Bayesian distress prediction utilising Markov
chain Monte Carlo (MCMC)
methods
Probabilistic Ageing Longitudinal
cracking
Obaidat and Al- kheder (2006)
Multiple
regression Deterministic
ADT(traffic), distance from maintenance unit (R), section area and
pavement age
Distresses quantities
Abaza (2016)
discrete-time Markov
model
Probabilistic Distress (cracking and deformation)
Predicting future pavement condition (deterioration
rate) Anyala, Odoki
and Baker (2014)
Bayesian
regression Probabilistic
Climate, traffic, properties of materials
and the design of pavements
Predict rutting in asphalt surfacing Mubaraki
(2016) Linear regression Deterministic Three distresses (cracking, rutting, and ravelling)
International Roughness Index (IRI)
Bianchini and Bandini
(2010)
Neuro-fuzzy Artificial Intelligence
Indicators of the structural and functional serviceability of the pavement structure. For example, pavement conditions,
traffic increment, and change in pavement
serviceability
Performance of flexible (SPI, IRI) pavements
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Amador- Jiménez and
Mrawira (2012)
Rut depth progression model/Bayesian
regression
Probabilistic
Traffic loading and pavement deflection under a moving standard dual wheel
(single axle) for which pavement layer thickness and
material properties were given
Rut depth
Amador- Jiménez and
Mrawira (2009)
Markov chain
deterioration Probabilistic Ageing PCI
Abaza (2004)
Unique performance
curve
Deterministic Aging, traffic and potential pavement design
Present Serviceability
Index (PSI) Hong and
Wang (2003)
Non- homogeneous Markov chain
Probabilistic Ageing
Pavement performance
degradation Mandiartha et
al. (2017)
Markov chain/The network-level
effectiveness model
Probabilistic International Roughness Index
(IRI) Predicted IRI
In terms of the deterministic approach, a strategy similar to that of Mubaraki (2016) was applied in this research to measure the pavement resilience. Mubaraki (2016) investigated the relationship between independent variables of three distresses (cracking, rutting and ravelling) and the dependent variable of the International Roughness Index (IRI) using linear regression. The proposed research is carried out to define the relationship between the independent variable of the International Roughness Index (IRI) and the dependent variable of the Pavement Condition Index (PCI). More details are shown in Chapter 6 section 6.5 and Chapter 10 section 10.2.
In terms of the stochastic approach, the discrete-time Markov model proposed by Abaza (2016) was applied with different proposed outcomes (International Roughness Index, IRI) to measure pavement resilience. The transitions probability matrix was determined from an empirical approach. More details are provided in Chapter 7 section 7.3 and Chapter 10 section 10.3