Within the scope of this thesis, a reliability based risk assessment, based on Monte Carlo simulation was used to analyse the safety levels of Esenk&ouml / y Fishery Harbor main breakwater, Sea of Marmara, Turkey. In the past, in reliability-based risk assessment methodology in Turkey, the design conditions were only wave characteristics, tidal range, storm surge, wave set-up and the structural system parameters. However in this study, the tsunami risk which was considered as a major design parameter is included in the computations. In this study, development of a structural stability criterion in coastal engineering was suggested to achieve a common definition of reliability including the tsunami risk. The model introduced in this study is a practical technique in the reliability-based risk assessment of breakwaters subject to tsunami risk. In order to determine the occurrence probability of design condition, which is a function of storm waves, tidal range, storm surge and tsunami height, the Monte Carlo simulation, was applied. From the reliability-based risk assessment model applied to Esenk&ouml / y Fishery Harbor as a pilot study in Turkey it was found that, inclusion of the tsunami risk increases the failure risk of the structure, and as lifetime of the structure increases, the impact of tsunami risk on the failure mechanism is more reflected. For Esenk&ouml / y Fishery Harbor main breakwater, tsunami was not the key design parameter when compared to storm waves. However, in regions with great seismic activity, tsunami risk may be very noteworthy depending on the frequency and the magnitude of the tsunami.

Tsunamis are characterized as shallow water waves, with long periods and wavelengths. They occur by a sudden water volume displacement. Earthquake is one of the main reasons of a tsunami development. Historical data for an observation period of 3500 years starting from 1500 B.C. indicates that approximately 100 tsunamis occurred in the seas neighboring Turkey. Historical earthquake and tsunami data were collected and used to develop two artificial neural network models to forecast tsunami characteristics for future occurrences and to estimate the tsunami return period. Artificial Neural Network (ANN) is a system simulating the human brain learning and thinking behavior by experiencing measured or observed data. A set of artificial neural network is used to estimate the future earthquakes that may create a tsunami and their magnitudes. A second set is designed for the estimation of tsunami inundation with relation with the tsunami intensity, the earthquake depth and the earthquake magnitude that are predicted by the first set of neural networks. In the case study, Marmara and Aegean regions are taken into consideration for the estimation process. Return periods including the last occurred earthquake in the Turkish seas, which was the izmit (Kocaeli) Earthquake in 1999, were utilized together with the average earthquake depths calculated for Marmara and Aegean regions for the prediction of the earthquake magnitude that may create a tsunami in the stated regions for various return periods of 1-100 years starting from the year of 2004. The obtained earthquake magnitudes were used together with tsunami intensities and earthquake depth to forecast tsunami wave height at the coast. It is concluded that, Neural Networks predictions were a satisfactory first step to implement earthquake parameters such as depth and magnitude, for the average tsunami height on the shore calculations.