The casualties due to slope failures such as landslide, rock fall, debris flow etc. are about 24% in total casualties caused by natural disasters for the last 10 years in Korea. And these slope failures in Korea are focused in the season in which typhoon and torrential rain take place. Not much attention, however, have been put into landslide mitigation research. Meanwhile, USN (Ubiquitous Sensor Network) forms the self-organization network, and transfers the information among sensor nodes that have computing technology ability. Accordingly, USN is embossed a social point technology in Korea. Therefore, a lot of study is progressing about practical use of data which was gathered by sensors.The objective of this paper is to design and implement a real-time slope monitoring system using Ubiquitous Sensor Network (USN) technology. For this we develop module that collects and change slope movement data measured by two tiltmeter and a tension wire, store transferred data in database. Also we develop application program that can easily analyze the data. We apply the prototype system to a test site at KICT for testing and analyzing the system's performance.
The concrete pipe laying works are conducted in most of the construction sites. Several automated systems for the pipe laying works have been developed in advanced countries to improve productivity, safety, and quality, and to gain potential savings in costs. They mainly focused upon easy handling and clamping system for the pipes. The major objective of this research is to develop the stewart platform based teleoperated pipe manipulator prototype and to evaluate technical feasibility and productivity analysis of the system.
To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.
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