Stochastic Deterioration Processes for Bridge Lifetime Assessment

Aboura, Khalid; Samali, Bijan; Crews, Keith; Li, J.

doi:10.1109/broadcom.2008.6

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Condition state 1 represents the 'as new' condition, no-deterioration state, while condition states 2…, n mark increasing levels of deterioration. For example a concrete deck slab (CDSL) could have 60% in condition 1, 20% in condition 2, 15% in condition 3 and 5% in condition 4, condition 4 being the worst condition (Aboura, Samali, Crews, & Li, 2009). An element on a bridge is divided in a number of units.…”

Section: Statistical Methodologymentioning

confidence: 99%

“…Using the 15 years of data, we checked the different assumptions of the gamma process. While obviously some are hard to check, such as the independence of incremental deterioration, the data provided ample evidence of a good distributional fit (Aboura, Samali, Crews, & Li, 2009). The study of fit was done with the analysis of the distribution of the deterioration variables Z 2 and dZ 7, the increase associated with Z 7 .…”

Section: Gamma Process As a Deterioration Modelmentioning

confidence: 99%

See 1 more Smart Citation

The Information System for Bridge Networks Condition Monitoring and Prediction

Aboura

Samali

2012

International Journal of Information Technologies and Systems Approach

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This paper introduces an information system for estimating lifetime characteristics of elements of bridges and predicting the future conditions of networks of bridges. The Information System for Bridge Networks Condition Monitoring and Prediction was developed for the Roads and Traffic Authority of the state of New South Wales, Australia. The conceptual departure from the standard bridge management systems is the use of a novel stochastic process built out of the gamma process. The statistical model was designed for the estimation of infrastructure lifetime, based on the analysis of more than 15 years of bridge inspection data. The predictive curve provides a coherent mathematical model for conducting target level constrained and funding based maintenance optimization.

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Section: Statistical Methodologymentioning

confidence: 99%

Section: Gamma Process As a Deterioration Modelmentioning

confidence: 99%

The Information System for Bridge Networks Condition Monitoring and Prediction

Aboura

Samali

2012

International Journal of Information Technologies and Systems Approach

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“…Past studies (e.g., Aboura et al 2008;Cesare et al 1992) have introduced schemes to rate the state of a bridge based on the subjective analysis of practitioners. The rating scheme used in this study is based on the rating scheme proposed by HAZUS -MH2.1.…”

Section: State Modelmentioning

confidence: 99%

Optimal Timing of the Seismic Vulnerability Reduction Measures Using Real Options Analysis

Asghari

Kashani

Hsu

2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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The uninterrupted performance of the infrastructure systems of a community is essential to its welfare and prosperity. Infrastructure assets are continuously subject to degradation, which can compromise their performance and increase their seismic vulnerability.Measures such as seismic retrofits should be adopted to ensure the uninterrupted performance of infrastructure assets. Proper timing of these interventions can reduce the total costs incurred by the communities. To this end, an appropriate investment valuation method is needed. This study proposes an investment valuation framework that addresses this need. At the core of this framework is a real options model that determines the optimal time to implement seismic vulnerability reduction measures considering various uncertainties about the state of the asset, the probable future hazards, and the costs incurred by the community. The main contribution of this study to the body of knowledge is the creation of a framework based on real options analysis that is tailored to the context of investment decision-making and timing of seismic vulnerability reduction measures. Practitioners can use this framework to plan the implementation of seismic vulnerability reduction measures, avoid over-or under-investment in these interventions, enhance their flexibility in using limited available financial resources, and achieve resilience enhancement objectives.

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