Roughness Model Accounting for Heterogeneity Based on In-Service Pavement Performance Data

Hong, Feng; Prozzi, Jorge A

doi:10.1061/(asce)0733-947x(2010)136:3(205)

Cited by 46 publications

(17 citation statements)

References 6 publications

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“…One uncertainty that is slowly gaining attention with articles cite the core of Figure 4 (1 -Statistical Deterioration Models) and the Pavement Management path is the relationship between deterioration and maintenance expenditure, geographical locations and climate (Anastasopoulos et al, 2012) over the long term (Lee, Blumenstein, Guan, & Loo, 2013;Lee, Guan, Loo, & Blumenstein, 2012). Beside these contextual uncertainties, Articles in the many technical difficulties remain unresolved in the models themselves such as 'autocorrelation bias' (Li, Sheng, Li, & Zhou, 2010) or interpretive bias (Farran & Zayed, 2012;Hong & Prozzi, 2010) and demonstrate the reliability of models (Tsai, Li, Purcell, & Rabun, 2012). Similarly, articles that cite from the Water and Utility Infrastructure path argue that using standard asset lifetimes to predict performance is biased (Burn, Marlow, & Tran, 2010) due to often differing ages (Ugarelli & Di Federico, 2010).…”

Section: Research Orientations Among Retrieved Articlesmentioning

confidence: 99%

Research orientations towards the ‘management’ of infrastructure assets: an intellectual structure approach

Schraven

Hartmann

Dewulf

2013

Structure and Infrastructure Engineering

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In recent decades, research on the management of infrastructure assets has increased steadily. However, there are concerns raised about the contribution of studies to a coherent body of knowledge. There is a call for a more structured understanding of the knowledge that is emerging around the management of infrastructure assets. This paper attempts to answer this call through an empirical study based on the reference lists of over 8200 articles that present their study relevant to the management of infrastructure assets. In so doing, we apply recognised techniques from bibliometric and social network analyses to visualise and identify major and minor topics, where researchers have oriented and contributed. We find that managing infrastructure assets traditionally was object-oriented, such as pavements, bridges, water and utility networks, and that attention is only now emerging on the life-cycle decision-making and organisational aspects, although the latter remains weakly linked with technical aspects. We conclude with shared research orientations in 'managing' infrastructure assets.

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Section: Research Orientations Among Retrieved Articlesmentioning

confidence: 99%

Research orientations towards the ‘management’ of infrastructure assets: an intellectual structure approach

Schraven

Hartmann

Dewulf

2013

Structure and Infrastructure Engineering

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“…This study adopts an index that measures the pavement condition by its roughness. Since roughness directly affects the ride quality experienced by road users, it is a good indicator for measuring pavement condition and has been widely used both in the United States and internationally (19)(20)(21). On the basis of a previous study (22) a linearized deterioration was adopted:…”

Section: Pavement Deterioration Functionmentioning

confidence: 99%

Managing Transportation Facilities in Design–Build–Finance–Operate Partnerships

Zhang

2013

Transportation Research Record: Journal of the Transportation R

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In recent years, transportation planning has been challenged by an increasing need for infrastructure development, a shortfall of revenue from the public sector, and political trending toward deregulation of transportation infrastructure development. These factors have led to increased interest in privatization of transportation infrastructure and the development of public–private partnerships, such as design–build–finance–operate. Although the overall goal of a transportation infrastructure project is to provide safe, reliable transportation systems for the public, parties involved in public–private partnerships take different roles and responsibilities. The public sector leads in laying out the terms and standards to regulate the obligations between the state departments of transportation and private entities. The private sector makes capital investment to provide agreed-upon services as well as to assume various investment risks, including project operational and financial risks. Toll-pricing strategies are a key component for the public sector in regulating the operation of a public–private partnership facility and for the private sector in controlling investment risks. This study investigated the applicability of deterministic dynamic optimization models for determining toll-pricing strategies that can help improve mobility, secure the public interest, and attract investment from the private sector. A case study of a design–build–finance–operate project was completed. Results showed that the proposed model provides a useful tool to assist both the public and private sectors in making more informed decisions, including study of optimal strategies to seek investment return and determination of the predefined contract regulations.

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“…Econometric models were also used in these works. For example, Loizos and Karlaftis [12] use a duration model to predict pavement failure times, based on data from Europe's road network. In this study, average daily temperature, number of days per year with a maximum temperature above 25 • C, number of days per year with a minimum temperature below 0 • C, freezing index, and average yearly precipitation were used as explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Seasonal and Annual Weather Variations on Network-Level Pavement Performance

2019

Self Cite

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This study was aimed to determine the extent of association between network-level pavement condition and seasonal and annual weather variations. Pavement condition data recorded in the Texas Department of Transportation’s Pavement Management Information System (PMIS) database between 2000 and 2008 was highlighted. Meteorological data collected at different districts across the whole state were used in the explanatory variables. Dynamic panel data analysis was used in the deterioration models to quantify the effects of temperature and precipitation variations on pavement conditions. Based on the statistical implication from the model estimation results, significant correlations were identified between pavement conditions, the average monthly rainfall, and the average monthly temperature recorded 1 to 23 months prior to pavement condition inspection.

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Roughness Model Accounting for Heterogeneity Based on In-Service Pavement Performance Data

Cited by 46 publications

References 6 publications

Research orientations towards the ‘management’ of infrastructure assets: an intellectual structure approach

Research orientations towards the ‘management’ of infrastructure assets: an intellectual structure approach

Managing Transportation Facilities in Design–Build–Finance–Operate Partnerships

Impacts of Seasonal and Annual Weather Variations on Network-Level Pavement Performance

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