Life-cycle performance model based on homogeneous Markov processes

Puž, Goran; Radić, Jure

doi:10.1080/15732470802532943

Cited by 13 publications

(2 citation statements)

References 3 publications

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“…Furthermore, Hallberg and Racutanu [6] presented a service-life performance analysis model based on the Markov chain model and different concrete degradation models. Puz and Radic [53] presented a homogeneous Markov processes model based on the exponential distribution, utilizing chloride-induced corrosion models to determine sojourn times. They established the relationship between states of the exponential Markov process and chloride-induced corrosion by dividing deterioration into four stages (protected, vulnerable, attacked, and damaged).…”

Section: Relationship Between the Semi-markov Process And The Carbonamentioning

confidence: 99%

Condition Prediction of Existing Concrete Bridges as a Combination of Visual Inspection and Analytical Models of Deterioration

et al. 2019

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In most state-of-the-art Bridge Management Systems, structural condition is predicted by a homogeneous Markov chain model that uses condition ratings assigned during visual inspections. Although generally accepted, such an approach exhibits certain shortcomings, one of which is not considering the nature of actual physical phenomena that cause deterioration. To overcome this shortcoming, this article presents a framework that combines both information on condition ratings through the semi-Markov process and knowledge of bridge properties using analytical deterioration models. In this manner, and contrary to current practice, not only are the results of visual inspection taken into account, but also information such as environmental loading, as well as material and structural properties. The presented framework was implemented in the case study bridge, in which the deterioration caused by carbonation-induced corrosion was studied. Along with the implementation in the case study, the article contained a detailed overview of the subject of carbonation-induced corrosion and emphasized issues that require additional research in order to develop the framework into a comprehensive and fully applicable tool for condition prediction. Accounting for its adaptability to other material types and deterioration processes and its consideration of the historic deterioration path, the framework presents a suitable alternative to frameworks presently implemented for condition prediction.

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Section: Relationship Between the Semi-markov Process And The Carbonamentioning

confidence: 99%

Condition Prediction of Existing Concrete Bridges as a Combination of Visual Inspection and Analytical Models of Deterioration

et al. 2019

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“…The ability to make a prediction about the remaining service life of a technical asset provides valuable information for optimal prioritisation of maintenance, rehabilitation or replacement of assets (Syachrani, Jeong, & Chung, 2013). Solving the problem of forecasting and predicting the future state of an asset implicitly or explicitly implies a theoretical model of the complex process of pipe deterioration (Puz & Radic, 2011). An extensive amount of factors (Salehi, Jalili Ghazizadeh, & Tabesh, 2017) affect this process, which makes the prediction when a pipe will fail a difficult task (Ana & Bauwens, 2010).…”

Section: Introductionmentioning

confidence: 99%

Pipe failure modelling for water distribution networks using boosted decision trees

Winkler

Haltmeier

Kleidorfer

et al. 2018

Structure and Infrastructure Engineering

114

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Pipe failure modelling is an important tool for strategic rehabilitation planning of urban water distribution infrastructure. Rehabilitation predictions are mostly based on existing network data and historical failure records, both of varying quality. This paper presents a framework for the extraction and processing of such data to use it for training of decision tree-based machine learning methods. The performance of trained models for predicting pipe failures is evaluated for simple as well as more advanced, ensemblebased, decision tree methods. Bootstrap aggregation and boosting techniques are used to improve the accuracy of the models. The models are trained on 50% of the available data and their performance is evaluated using confusion matrices and receiver operating characteristic curves. While all models show very good performance, the boosted decision tree approach using random undersampling turns out to have the best performance and thus is applied to a real world case study. The applicability of decision tree methods for practical rehabilitation planning is demonstrated for the pipe network of a medium sized city.

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