Takayuki Hirayama

Evaluation of Life Cycle Cost for Wave-dissipating Works Considering Occurrence Probability of High Waves

ABSTRACT Ota, T.; Kawamura, H.; Matsumi Y., and Hirayama, T., 2016. Evaluation of life cycle cost for wave-dissipating works considering occurrence probability of high waves. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 675–679. Coconut Creek (Florida), ISSN 0749-0208. This study deals with the influences of occurrence frequency and the duration of high waves on estimation of life cycle cost (LCC) for wave-dissipating works that consist of wave-dissipating blocks using Monte Carlo simulation. LCC is defined as sum of an initial construction cost and an expected repair cost of wave-dissipating blocks. As analysis results of high waves at four observation stations in NOWPHAS, the occurrence frequency of high waves can be approximated by the Poisson distribution. In the computation of LCC, occurrence frequency of high waves is assumed as once a year or is determined by using the Poisson distribution for comparison. The duration of high waves is given in consideration of its statistical characteristics. The optimum size of block that minimizes LCC agrees in most cases regardless of the occurrence frequency and the duration of high waves, however, the variation in the total repair cost during the in-service period is different depending on the occurrence frequency.

EVALUATION SYSTEM FOR REFLECTION COEFFICIENT OF SEAWALL WITH DAMAGE OF ARMOR ON CROWN AND SLOPE

To introduce the performance design into the decision making problem of the repair or renewal of the damaged armor block on seawall, a performance evaluation system of seawall with the accumulative damage of armor is necessitated. In this study, numerical experiments and model tests are conducted to investigate the reflection coefficient of the seawall under condition of the damage progression of armor blocks on crown and slope of seawall. The regressive prediction properties between the reflection coefficient and the damage parameter could not be recognized from those results. The performance evaluation system is developed using a neural network system, which evaluates the causal relation of the reflection coefficient and the accumulative damage parameter. The neural network with a three-layer calibrated by the numerical experiments can predict the reflection coefficient of seawall under the damage progression of armor layer within about 10% errors.