Application of Semi-Markov Decision Process in Bridge Management

Hajdin, Rade; Mašović, Snežana; Stošić, Saša

doi:10.2749/222137815818358204

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…In cases where condition prediction is purely based on condition ratings, the knowledge of how materials and elements are affected of large infrastructure assets, such as water transmission pipes and trunk sewers. Mašović et al [36] implemented the semi-Markov process with the Weibull distribution of sojourn times on the Serbian bridge database. Wu et al [37] presented the life-cycle optimization model using the semi-Markov process also based on the Weibull distribution.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…24,25 One can cite in particular the semi-Markov (assuming a Weibull distribution for the sojourn time) and hidden Markov models together with Artificial Neural Networks (ANNs), which have been reported in the literature as reliable deterioration prediction models. [26][27][28] Gamma process representations also offer an alternative to discrete Markov models for the description of degradation processes. Gamma processes are continuous-time stochastic processes with independent, non-negative increments that follow gamma distributions with typically identical scale parameters and a time dependent shape parameter.…”

Section: Modelling Of Deterioration and Effects Of Interventionsmentioning

confidence: 99%

IABSE Survey of Implemented Decision-making Models used by Public and Private Owners/Operators of Road- and Railway Infrastructures

Strauß

Orcesi

Λαμπρόπουλος

et al. 2023

Structural Engineering International

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Infrastructure systems, such as bridges, are a driver for the economic growth and sustainable development of countries. Similarly, the development of operation and maintenance strategies for infrastructure systems may aim at optimal management using Key Performance Indicators (KPIs) such as reliability, redundancy, availability, safety, economy, environmental performance and resilience. Recent research and development projects, such as COST TU1406, highlight that infrastructure managers make decisions based on a mix of qualitative and quantitative data from various sources paired with models of various levels of complexity as well as expert judgement. Similarly, recent state-of-the-art academia reports on a variety of different decision-making models applicable to the optimal management of infrastructure systems may be used. Within IABSE Commission 5 on Existing Structures, Task Group 5.4 has performed a survey on implemented decision-making models among 23 infrastructure managers from 20 countries. It highlights some similarities in relation to KPIs, condition rating and limit state checks. This has stimulated the standardisation of decision making. The application of risk-based methods, performance prediction and intervention modelling are somewhat more scattered and may call for further research and development as well as training. The need to bridge the gap between implemented decision-making models and research is of paramount importance.

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“…Fang et al [22] proposed a semi-Markov process model based on Weibull distribution for the prediction of urban bridge deterioration by considering the time-dependent reliability in the bridge deterioration process, and showed that the prediction accuracy of the semi-Markov model at the network level was better than that of the regression analysis method. Masovic et al [23] introduced the semi-Markov decision process and found that determining the optimal strategy in a finite time range has high mathematical complexity. Zambon et al [24] proposed a state rating model based on a semi-Markov process, which can overcome the shortcomings of the original model that did not take into account the properties of actual physical phenomena leading to deterioration.…”

Section: Introductionmentioning

confidence: 99%

State-Based Technical Condition Assessment and Prediction of Concrete Box Girder Bridges

Zhu,

Ye,

et al. 2024

Buildings

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The technical condition of bridges has become a crucial issue for organizing the maintenance and repairs in bridge management systems. It is of great practical engineering significance to construct an effective model for predicting the technical condition degradation of the bridge through the use of the historical inspection data. Based on the semi-Markov random process, this paper proposes a useful deterioration prediction model for bridges in the highway network. From the historical inspection data of the prefabricated concrete box girder bridges, the degradation curves of technical condition rating are obtained. The effect of bridge length on degradation rate of the prefabricated concrete box girder bridges is analyzed. According to the Weibull distribution parameters of different condition grades, the technical state degradation models for a bridge group and an individual bridge are proposed to predict the performance of the overall bridge and superstructure of the bridge. The results show that with the increase in bridge length, the degradation rate of bridge technical condition increases. The degradation rate of the technical condition of the superstructure is faster than that of the overall bridge. The proposed semi-Markov stochastic degradation model for the bridge group can not only predict the different condition ratings of the bridges at any time, but also predict the future deterioration trend of an individual bridge under any ratings.

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Application of Semi-Markov Decision Process in Bridge Management

Cited by 11 publications

References 7 publications

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

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

IABSE Survey of Implemented Decision-making Models used by Public and Private Owners/Operators of Road- and Railway Infrastructures

State-Based Technical Condition Assessment and Prediction of Concrete Box Girder Bridges

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