Incorporating local environmental factors into railway bridge asset management

Yianni, Panayioti C.; Neves, Luís; Rama, Dovile; Andrews, John; Dean, Robert

doi:10.1016/j.engstruct.2016.09.038

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Stochastic models measure the bridge deterioration process as one or more random variables and attributed occurrence probabilities of a specific state within a sample. The third method, artificial intelligence models, exploits computer techniques that aim to automate intelligent behaviors [12].…”

Section: Bridge Deterioration Modelsmentioning

confidence: 99%

Analysis of the brazilian federal bridge inventory

Oliveira

Greco

Bittencourt

2019

Rev. IBRACON Estrut. Mater.

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Bridge Management aims to provide an appropriate support to decision-making for maintenance, rehabilitation and repair strategies under constraint of limited budgets. In this regard, the Federal Brazilian Department of Transportation (DNIT) has developed the SGO - a Brazilian Bridge Management System (B-BMS) - and promoted the most comprehensive road bridge inventory under its direct administration. To improve the management of these bridge assets, the DNIT is working to develop a statistical model to predict the future condition of bridge: the most efficient and effective tool in a BMS to planning when the maintenance actions will be required. The current paper reports on findings of inventory, predominantly composed of reinforced concrete bridges, focusing on potential deterioration agents reported and checking their influence on deterioration conditions. Based on national database, the paper proposes a methodology to forecast Brazilian bridges deterioration rates. An example of application is demonstrated and satisfactory prediction accuracy obtained, even for few inspection cycles and under restricted database information.

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Section: Bridge Deterioration Modelsmentioning

confidence: 99%

Analysis of the brazilian federal bridge inventory

Oliveira

Greco

Bittencourt

2019

Rev. IBRACON Estrut. Mater.

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“…In practice, the deterioration rate may be affected by heavy use and aggressive environment conditions (see for example Yianni et al 32 ). Ideally, the maintainers' knowledge of these effects could be combined with statistical failure data gathered from a population where use and environment vary.…”

Section: Modelling Of Available Data Typesmentioning

confidence: 99%

“…Each factor has three states: low, medium and high. An example of how to estimate their states can be adopted from Yianni et al 32 Take loading of railway bridges as an example; data on equivalent million gross tonnes per annum (EMGTPA) passing over the bridge can be used to categorise the level of loadings. Loadings less than 3.5 EMGTPA, they are grouped as low, between 3.5 and 12 EMGTPA are medium and more than 12 EMGTPA are high.…”

Section: Modelling Of Available Data Typesmentioning

confidence: 99%

Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

Zhang

Marsh

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration's causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice.

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“…Deterioration can be modelled with Petri nets by considering that each place is a condition state of the classification system adapted, tokens indicate the current condition of an element, and timed transitions define the movement between condition states (Le, 2014, Yianni et al, 2016. In this work, a five condition levels Petri net scheme is defined.…”

Section: Deterioration Petri Net Modelmentioning

confidence: 99%

Stochastic Petri net-based modelling of the durability of renderings

Ferreira

Neves

Silva

et al. 2018

Automation in Construction

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In this study, a methodology to model and predict the life-cycle performance of building façades based on Stochastic Petri Nets is proposed. The proposed model evaluates the performance of rendered façades over time, evaluating the uncertainty of the future performance of these coatings. The performance of rendered façades is evaluated based on a discrete qualitative scale composed of five condition levels, established according to the physical and visual degradation of these elements. In this study, the deterioration is modelled considering that the transition times between these condition states can be modelled as a random variable with different distributions. For that purpose, a Stochastic Petri Nets model is used, as a formal framework to describe this problem. The model's validation is based on probabilistic indicators of performance, computed using Monte-Carlo simulation and the probability distribution parameters leading to better fit are defined as those maximizing the likelihood, computed using Genetic Algorithm. In this study, a sample of 99 rendered façades, located in Portugal, is analysed, and the degradation condition of each case study is evaluated through in-situ visual inspections. The model proposed allows evaluating: i) the transition rate between degradation conditions; ii) the probability of belonging to a given degradation condition over time; and iii) the mean time of permanence in each degradation condition. The use of Petri Nets shows to be more accurate than a more traditional approach based on Markov Chains, but

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Incorporating local environmental factors into railway bridge asset management

Cited by 13 publications

References 21 publications

Analysis of the brazilian federal bridge inventory

Analysis of the brazilian federal bridge inventory

Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

Stochastic Petri net-based modelling of the durability of renderings

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