Maintenance problem for infrastructures such as bridge, road, airport, etc., attracts keen interest in modern Japan. We have to cope with repair and maintenance for infrastructures with limited financial resources. In airport pavements, repair works are restricted by daily flight operation, so that efficient maintenance planning is required. In this paper, we try to model deterioration of an existing airport runway, which is composed of 100 units. PRI (Pavement Rehabilitation Index) for each unit is obtained by 3 to 8 times of inspection during around 28 years. PRI is an index to provide an objective evaluation of pavement surface condition, with criteria determined for judging the need for rehabilitation work on runway, taxiway, and apron pavements. Many researches and practitioners use PRI for airport pavement maintenance in Japan. The spatial distribution of deterioration in airfield pavement is not discussed in many previous studies. This study discusses deterioration curve for each unit and feature of spatial distribution. The distribution of deterioration based on PRI and the probability of exceedance of each PRI criteria in future are estimated by the proposed method.

Identifying the parameters of a model and rating competitive models based on measured data has been among the most important and challenging topics in modern science and engineering, with great potential of application in structural system identification, updating and development of high fidelity models. These problems in principle can be tackled using a Bayesian probabilistic approach, where the parameters to be identified are treated as uncertain and their inference information are given in terms of their posterior probability distribution. For complex models encountered in applications, efficient computational tools robust to the number of uncertain parameters in the problem are required for computing the posterior statistics, which can generally be formulated as a multi-dimensional integral over the space of the uncertain parameters. Subset Simulation has been developed for solving reliability problems involving complex systems and it is found to be robust to the number of uncertain parameters. An analogy has been recently established between a Bayesian updating problem and a reliability problem, which opens up the possibility of efficient solution by Subset Simulation. The formulation, called BUS (Bayesian Updating with Structural reliability methods), is based the standard rejection principle. Its theoretical correctness and efficiency requires the prudent choice of a multiplier, which has remained an open question. This paper presents a fundamental study of the multiplier and investigates its bias effect when it is not properly chosen. A revised formulation of BUS is proposed, which fundamentally resolves the problem such that Subset Simulation can be implemented without knowing the multiplier a priori. An automatic stopping condition is also provided. Examples are presented to illustrate the theory and applications.

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