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    Machine learning techniques for structural health monitoring
    (2016)
    Smarsly, Kay
    ;
    Dragos, Kosmas
    ;
    Wiggenbrock, Jens
    Data-driven approaches are particularly useful for computer-supported assessment of civil engineering structures (i) if large quantities of sensor data are available, (ii) if the physical characteristics of the structure are complex to model (or even unknown), or (iii) if the computational efforts are to be reduced. This paper, upon a classificational review of machine learning techniques in structural health monitoring, reports on an embedded machine learning approach for decentralized, autonomous sensor fault detection in wireless sensor networks, facilitating reliable and accurate structural health monitoring. Based on decentralized artificial neural networks, the embedded machine learning approach is applied to perform autonomous detection of sensor faults injected in the acceleration response data collected by a prototype structural health monitoring system. As demonstrated through laboratory tests, the results highlight the ability of the embedded machine learning approach to autonomously detect sensor faults in a decentralized manner, thus enhancing the reliability and accuracy of structural health monitoring systems.
    Publicationtype: Conference Paper
      57
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    Integrated visualization of installation plans and installation costs of fiber optic networks
    (2016)
    Wiggenbrock, Jens
    ;
    Smarsly, Kay
    Due to the increasing use of the Internet, there is a growing demand for expanding fiber optic networks. However, the cost-effective design and installation of fiber optic networks can be labor-intensive. Typically, computer-aided planning is used to support the design of fiber optic networks, i.e. the drafting of cable installation plans and the calculation of installation costs. Cable installation plans are usually illustrated on digital maps, while costs are typically listed separately in cost units. Hence, the detachment of cost calculations from cable installation plans impedes the efficient and accurate analysis and optimization of installation costs. To facilitate the analysis and optimization of installation costs, the cost calculations must be integrated into the installation plans. This paper presents a modular software system for computer-aided fiber optic networks design, enabling automated cost calculations within cable installation plans. Instead of monochrome installation plans, typically employed in digital maps, a coloring schema is used as an indicator of installation costs. The proposed approach is validated through a case study of the design of a fiber optic network for a residential area. The validation case study showcases the automated calculation of an installation plan of a fiber optic network with integrated installation costs. The colored installation plans enable the identification of the most expensive cabling sections within the residential area, facilitating the cost-efficient design of fiber optic networks.
    Publicationtype: Conference Paper
      23
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