دانلود مقاله انگلیسی رایگان:طراحی و اجرای میدانی یک سیستم تشخیص ضربه با استفاده از کمیته های شبکه های عصبی - 2019
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  • Design and field implementation of an impact detection system using committees of neural networks Design and field implementation of an impact detection system using committees of neural networks
    Design and field implementation of an impact detection system using committees of neural networks

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Design and field implementation of an impact detection system using committees of neural networks


    ترجمه فارسی عنوان مقاله:

    طراحی و اجرای میدانی یک سیستم تشخیص ضربه با استفاده از کمیته های شبکه های عصبی


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 120 (2019) 185-196: doi:10:1016/j:eswa:2018:11:005


    نویسنده:

    Jase D. Sitton, Yasha Zeinali, BrettA. Story


    چکیده انگلیسی:

    Many critical societal functions depend on uninterrupted service of civil engineering infrastructure. Rail- roads represent important infrastructure components of the transportation sector and provide both pas- senger and freight services. Railroad bridges over roadways are susceptible to impacts from overheight vehicles and equipment, which may damage bridge girders or supports and must be investigated after each event. One method of monitoring for vehicle-bridge collisions utilizes accelerometers to monitor for abnormal bridge vibrations corresponding to abnormal activity. Passing trains under normal operat- ing conditions frequently produce significant bridge responses that have similar response characteristics to bridge strikes, but do not need to be investigated. This paper presents an expert system which com- prises committees of artificial neural networks trained to interrogate data collected from accelerometers mounted on the bridge, assess the nature of the acceleration signal, and classify the event as either a passing train or a potentially damaging impact. This system is trained using acceleration time histories from accelerometers installed on 8 low-clearance rail bridges; no finite element model simulations were used for network training or data stream creation. The presented system accurately detects and classifies impacts with average impact detection performance ranging from 91–100% with average false positive rates limited to 0.00–0.75%.
    Keywords: Bridge impacts Impact detection | Signal classification | Feature selection | Artificial neural networks


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 1392 کیلوبایت

    قیمت: رایگان


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