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- 저자 : Kiyoyuki Kaito (검색결과 3 건)
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Performance Evaluation of Advanced Pavement Materials by Bayesian Markov Mixture Hazard Model
한대석,Kiyoyuki Kaito,Kiyoshi Kobayashi,Kazuya Aoki 대한토목학회 2016 KSCE JOURNAL OF CIVIL ENGINEERING Vol.20 No.2
Optimized maintenance and management strategies are often emphasized in infrastructure management. Although such strategies serve to facilitate decision making, it is important to recognize that revolutions in asset management can come from improvements to hardware performance rather than managerial techniques. Underlying the managerial solutions, this study focused on the utility of various pavement materials and a special layer to support a performance-oriented asset management plan. This study compared the life expectancy and uncertainties of Stone Mastic Asphalt (SMA), Polymer Modified Asphalt (PMA), Rut-resistant Asphalt (RRA), Porous Asphalt (PA), and conventional hot-mix asphalt (HMA), which are widely introduced in field maintenance works. Problems associated with insufficient data due to short elapsed time and insufficient time-series performance data were mitigated by employing an advanced statistical method, the Markov mixture hazard model applying hierarchical Bayesian estimation. Empirical studies were conducted using historical performance data covering a period of 5 years (2002-2007) from 150 special monitoring sections in the K-Network. The results provide useful information for developing improved specifications for maintenance design and performance-based contracts that may lead to radical reform of infrastructure asset management. The Markov mixture hazard model with hierarchical Bayesian estimation is also a powerful tool for solving critical limitations in the post-evaluation of short-term projects.
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한대석,Kiyoyuki Kaito,Kiyoshi Kobayashi,Kazuya Aoki 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.5
Road agencies provide maintenance work to serve a satisfactory level of road services to the public. However, as time goes on, pavement structure deteriorates for many reasons. Since repeated maintenance work upon deteriorated pavement structures can accelerate the deterioration speed, the pavements require periodic reconstruction work to recover original integrity. However, in the real world, it is difficult to carry out such a high level of maintenance work due to insufficient budgets, and no evidence for a guarantee of better economic efficiency. To support decision making in asset management, this study tries to define changing pavement performance by repeated maintenance work with empirical data. As an analytical tool, mixed hazard model with hierarchical Bayesian estimation method was applied. With the results, a best maintenance scheme on reconstruction timing was suggested by life cycle cost analysis. For the empirical study, a maintenance history data on Korean national highways, accumulated from 1965, was applied. The analysis procedures and results of this paper could be a good reference to build much realistic long-term maintenance strategy and reasonable budget allocation. In addition, the mixed hazard model with the hierarchical Bayesian estimation method is expected to be a useful tool in solving problems with heterogeneous population sampling, and in finding best practice and gaps among competitive alternatives.
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Daeseok Han,Kiyoyuki Kaito,Kiyoshi Kobayashi 대한토목학회 2014 KSCE Journal of Civil Engineering Vol.18 No.7
The heart of asset management systems for road infrastructure is the deterioration forecasting model. It provides the most fundamental information for better asset management. So far, there are many practices to build a reliable forecasting model using inspection data in conjunction with statistical theories. In many applications, however, an inadequate stock of inspection data or difficulty in applying sophisticated statistical methods have often been serious obstacles. As a solution, this paper suggests applying the Bayesian estimation method with a multi-state exponential hazard Markov chain model for simple and reliable deterioration forecasting for infrastructure. The main contents of this paper are an introduction of the model’s framework combining Markov chain, hazard theory, and Bayesian estimation method, and a demonstration of its practical application with an empirical study. The empirical study was conducted with time-series inspection data of pavement from the Korean National Highways. The estimation results from the suggested method would be useful for improving the current pavement maintenance strategy for Korean National Highways. However, the most important message of this paper is that the framework of the Bayesian Markov hazard model could be the best model to use for other civil infrastructure that has gradual changes in condition. The great advantages from the Bayesian estimation method may facilitate development of customized asset management systems.
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