Damage risk assessment of breakwaters under tsunami attack

Turkey was struck by two major events on 17 August and 12 November 1999, named Izmit (M _w = 7.4) and Düzce (M _w = 7.2) earthquakes, respectively. Rubble mound breakwaters in Izmit Bay experienced little damage, as forecasted by the new risk assessment model in which tsunami occurrence risk was included in the damage estimations. In order to determine the occurrence probability of structural damage under design conditions, including the environmental loading parameters of tsunami and storm waves, tidal range and storm surge, the Conditional Expections Monte Carlo simulation was applied in the risk assessment model developed in this study for the Esenköy Fishery Harbour, Turkey. A tsunami was not the key design parameter when compared to storm waves for the main breakwater of the harbour, however, in places with great seismic activity, the tsunami risk should be important depending on the occurrence probability and magnitude of the tsunami.     

A statistical riverine litter propagation model.

A statistical riverine litter propagation (RLP) model based on importance sampling Monte Carlo (ISMC) simulation was developed in order to predict the frequency distribution of certain litter types in river reaches. The model was preliminarily calibrated for plastic sheeting by a pilot study conducted on the River Taff, Wales (UK). Litter movement was predominantly controlled by reach characteristics, such as vegetation overhang and water-course obstructions. These affects were modeled in the simulations, by utilizing geometric distributions of river reaches in the time domain. The proposed model satisfactorily simulated the dosing experiments performed at the River Taff. It was concluded from the preliminary calibrations that, the RLP model can be efficiently utilized to portray litter propagation at any arbitrarily selected river site, provided that the stream flows and reach characteristics are calibrated by representative probability distributions of similar sections. Therefore, the RLP model can be considered as a new statistical technique that can predict litter propagation in river sections.     

浅谈遥感应用领域对卫星影像的要求

卫星遥感影像在国民经济建设诸多行业中的应用日益广泛,这种广泛性使得行业对卫星遥感数据提出了不同的要求。本文主要从空间分辨率、时间分辨率、光谱分辨率和影像质量等方面对其进行阐述。     

Development of an adaptive disturbance rejection system for the rapidly deployable stable platform—Part 2: Controller design and closed loop response

Currently, both military and civilian operations that require at-sea cargo transfers are severely limited by environmental conditions and loading forces that induce vessel motions. To increase the robustness of at-sea cargo transfer to these environmental conditions and loading forces, efforts have recently been made toward an actively controlled, rapidly deployable stable platform (RDSP). The purpose of the research presented here is to implement an output feedback adaptive controller and adaptive disturbance rejection scheme that will mitigate the effect of environmental conditions and reject disturbances caused by various loading situations. Because of the controller's distinct ability to adapt to various operating conditions, anticipate and reject load disturbances of unknown magnitude, and adjust to stay within input saturation constraints, the framework is a good fit for the RDSP. Three missions are considered using a previously developed 3 degree of freedom simulation of a 1/10th scale RDSP prototype. Results show successful mitigation of load disturbances and a significant reduction in pitch motions using a control command that remains within the given amplitude and rate constraints. In the case of cargo transfer operations, the adaptive control system is able to significantly increase the cargo throughput by rejecting the disturbances before they are able to cause large pitching dynamics.     

面向对象有限元方法在岩土工程中的应用

介绍了面向对象的程序设计方法和它与有限元程序相结合的原理和优点,提出了这种方法在岩土工程中的应用实例,并设计了基于Windows 95/98平台的有限无软件。利用面向对象的方法来研究有限元,是对有限元方法新的有益尝试。     

Marine litter prediction by artificial intelligence

Artificial intelligence techniques of neural network and fuzzy systems were applied as alternative methods to determine beach litter grading, based on litter surveys of the Antalya coastline (the Turkish Riviera). Litter measurements were categorized and assessed by artificial intelligence techniques, which lead to a new litter categorization system. The constructed neural network satisfactorily predicted the grading of the Antalya beaches and litter categories based on the number of litter items in the general litter category. It has been concluded that, neural networks could be used for high-speed predictions of litter items and beach grading, when the characteristics of the main litter category was determined by field studies. This can save on field effort when fast and reliable estimations of litter categories are required for management or research studies of beaches--especially those concerned with health and safety, and it has economic implications. The main advantages in using fuzzy systems are that they consider linguistic adjectival definitions, e.g. many/few, etc. As a result, additional information inherent in linguistic comments/refinements and judgments made during field studies can be incorporated in grading systems.