首页|Study Findings on Machine Learning Published by a Researcher at Le Quy Don Techn ical University (Identification of damage in steel beam by natural frequency usi ng machine learning algorithms)
Study Findings on Machine Learning Published by a Researcher at Le Quy Don Techn ical University (Identification of damage in steel beam by natural frequency usi ng machine learning algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news reporting from Le Quy Don Technical University by NewsRx journalists, research stated, “In recent times, the efficacy of machine l earning (ML) algorithms as tools for forecasting structural damage has become in creasingly evident.” Our news correspondents obtained a quote from the research from Le Quy Don Techn ical University: “However, input data in structural health monitoring predominan tly comprises normal operational states or states with minor deviations from the initial condition, lacking potentially hazardous states. Consequently, creating a realistic dataset for machine learning models to identify structural damage p oses a challenge. If such data were obtainable, it might involve parameters like stress intensity factor range and stress ratio, which are often difficult to me asure within real structures. In this paper, ML models, including Artificial Neu ral Network (ANN), Extreme Gradient Boosting (XGB), and Random Forest (RF), were constructed to predict the locations, widths, and depths of saw-cuts in steel b eams. The prognostications were based on fluctuations in natural frequencies. Th e natural frequencies under various damage scenarios were identified using the F inite Element Method (FEM).”
Le Quy Don Technical UniversityAlgorit hmsCyborgsEmerging TechnologiesMachine Learning
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