Abstract
A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of pre-stressed concrete I-type (PSCI) girder bridges using a particle filtering technique which is an advanced Bayesian updating method based on big data analysis. The method enables predicting maintenance cost fitted in the current structural status, which includes the recent information by inspection with bridge-monitoring. The method is adapted in the Mokdo Bridge which is currently being used for evaluating the efficiency of maintenance cost by effects on updated probabilistic values with two different scenarios. As the result, it is shown that the proposed method is effective in predicting maintenance costs.
Highlights
An accurate the deterioration prediction of the existing bridge is very important for sustainable maintenance
Intending to establish a model with a practical application of the bridge condition and performance changes over time during establishing a maintenance scenario strategy, we developed a deterioration model based on the secondary function, which allowed for maintaining the initial condition of bridges that were being used and the reflection of the acceleration zones
The deterioration model was estimated in the designing and planning stage through the particle filtering technique that could be updated by probabilistically combining the damage inspection data results acquired from the eddy-current sensor (ECS) based monitoring techniques
Summary
An accurate the deterioration prediction of the existing bridge is very important for sustainable maintenance. Maintaining bridges with optimal maintenance activities by forecasting bridge performance with periodical inspection data is an interesting issue. These days, the bridge deterioration model based on regression analysis is conducted to derive an efficient maintenance method for bridge management system in advanced countries, such as the USA, Japan, and Australia [1,2,3]. Bayesian deterioration model updating for maintenance cost estimation in steel box girder bridges has been proposed [16]. These studies are insufficient to predict maintenance costs accurately. TheheprporcoecsesssstsetpepofotfhtehecocnodnidtiiotinonrartaintigngdadtaatafofroPr SPCSCI gI igrdiredre. r(.a()aD) Dataatadidstisrtibriubtuiotinonbebfeofroere prporcoecsessinsign;g(b; )(bre)mroevmeotvhee etrhreor edrartoarodr aretapeoatredredpaetaat;e(dc) rdeamtao;ve(ct)heredmatoavwe htihceh hdaavtea thwehmicahinhteanvaenctehe mmeaasiunrten; (adn)creemeoavseutrhee; (cdo)nrdeimtionvehtihsteocryonddaittaiownhhiicshtoirsyodnlayta1.which is only 1
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