摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx记者从Le Quy Don技术大学的新闻报道，研究表明，"最近，机器L收益(ML)算法作为预测结构损伤的工具的有效性变得越来越明显。"我们的新闻记者从Le Quy Don Techn University的研究中获得了一句话：“然而，结构健康监测的输入数据主要包括正常运行状态或与初始状态有轻微偏差的状态，缺乏潜在的危险状态。因此，为机器学习模型创建一个真实的数据集来识别结构损伤是一个挑战。如果能够获得这些数据，本文建立了人工神经网络(ANN)、极梯度提升(XGB)和随机森林(RF)等ML模型,对结构的位置、宽度和应力比进行了预测。预测基于固有频率的波动。使用有限元方法(FEM)确定了各种损伤情况下的固有频率。
Abstract
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).”
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