Performance Evaluation and Remaining Life Prediction of an Aged Bridge by J-BMS

Emoto, Hisao; Takahashi, Jun; Widyawati, Ratna; Miyamoto, Atsushi

doi:10.1016/j.proeng.2014.12.166

Cited by 9 publications

(5 citation statements)

References 2 publications

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“…Therefore, small hydrogenerations were so quickly built in the Caucasus, where the water flow in the winter practically does not freeze over, and so slowly, reluctantly they are built in Siberia [15][16]. The problem can be solved by creating a reservoir of sufficient volume, which would somehow accumulate power in the summer in order to deliver it in the winter.…”

Section: Fig 1 Chemal Hydroelectric Station (Altai Territory)mentioning

confidence: 99%

Construction of large dams: problems and development trends

Akhmetshin

Kovalenko

2019

MATEC Web Conf.

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In this article, the authors consider the problems of using large dams, and analyze environmental and economic indicators. Reducing climate pollution and eradicating poverty are two of the most serious challenges facing the world today. More than 50,000 large dams block at least 60% of the world's rivers. The consequences of this massive engineering program are devastating. Large dams destroy species; flood vast areas of wetlands, forests, and agricultural land; moving tens of millions of people, and influencing half a billion people living downstream.

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Section: Fig 1 Chemal Hydroelectric Station (Altai Territory)mentioning

confidence: 99%

Construction of large dams: problems and development trends

Akhmetshin

Kovalenko

2019

MATEC Web Conf.

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“…The bridge evaluation and remaining service life prediction processes were conducted in order to efficiently maintain existing bridges, it was based on the results of periodical visual inspections [5]. Conventional methods for crack detection were applied by experienced inspectors who mark the cracks manually and read the width of the cracks with their naked eyes [6,7].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy Based Bridge Structural Health Rating of Existing Bridges using Accelerometer Sensors: Case of Zamora Bridge

et al. 2018

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This study presents the development of a fuzzy-based procedure for condition assessment and bridge rating in structural health monitoring of existing bridges. A sample bridge, Zamora Bridge, was assessed to determine its structural health through fuzzy-based analysis utilizing accelerometer sensors installed in the bridge. Sensors were installed on superstructure and substructure components to record the three-dimensional accelerations of each element. Accelerations were then compared to the designed acceleration of the member to obtain structural ratings. Using fuzzy logic, the resultant rating set for the bridge has been evaluated based on specific ratings and importance factor for elements of the bridge. The final rating for the bridge can be evaluated by converting the collective fuzzy rating that defines the membership value. Comparison between the data of fuzzy based analysis for structural health assessment and visual bridge inspection for condition rating verified the reliability of the findings of this study.

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“…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]. Domestic and foreign research trends related to bridge maintenance denote that they have focused on estimating the bridge performance changes with high accuracy through monitoring technologies based on unmanned inspection devices (sensors, drones, or robots) and by establishing an optimal maintenance decision making process system [4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

et al. 2019

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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.

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Performance Evaluation and Remaining Life Prediction of an Aged Bridge by J-BMS

Cited by 9 publications

References 2 publications

Construction of large dams: problems and development trends

Construction of large dams: problems and development trends

Fuzzy Based Bridge Structural Health Rating of Existing Bridges using Accelerometer Sensors: Case of Zamora Bridge

Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

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