Long-span suspension bridges are significantly susceptible to windstorm-induced vibrations, leading to critical challenges of field measurements along with multicollinearity and nonlinearity between wind features and bridge dynamic responses. To address these issues, this article proposes an innovative machine learning-assisted predictive method by integrating a predictor selector developed from regularized neighborhood components analysis and kernel regression modeling through a regularized support vector machine adjusted by Bayesian hyperparameter optimization. The crux of the proposed method lies in advanced machine learning algorithms including metric learning, kernel learning, and hybrid learning integrated in a regularized framework. Utilizing the Hardanger Bridge subjected to different windstorms, the performance of the proposed method is validated and then compared with state-of-the-art regression techniques. Results highlight the effectiveness and practicality of the proposed method with the minimum and maximum R-squared rates of 89% and 98%, respectively. It also surpasses the state-of-the-art regression techniques in predicting bridge dynamics under different windstorms.

中文翻译：

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机器学习辅助预测大跨度悬索桥暴风振动响应


大跨度悬索桥极易受到暴风引起的振动的影响，导致现场测量以及风特征和桥梁动力响应之间的多重共线性和非线性面临关键挑战。为了解决这些问题，本文提出了一种创新的机器学习辅助预测方法，通过贝叶斯超参数优化调整的正则化支持向量机，集成从正则化邻域分量分析和核回归建模开发的预测选择器。所提出的方法的关键在于先进的机器学习算法，包括集成在正则化框架中的度量学习、内核学习和混合学习。利用受到不同暴风雨影响的哈当厄尔大桥，验证了所提方法的性能，并与最先进的回归技术进行了比较。结果突出了所提出的方法的有效性和实用性，最小和最大 R 平方率分别为 89% 和 98%。在预测不同暴风雨下的桥梁动力学方面，它还超越了最先进的回归技术。