Cao, Y.; Li, B.; Xiang, Q.; Zhang, Y. Experimental Analysis and Machine Learning of Ground Vibrations caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization. Sustainability2023, 15, 12772.
Cao, Y.; Li, B.; Xiang, Q.; Zhang, Y. Experimental Analysis and Machine Learning of Ground Vibrations caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization. Sustainability 2023, 15, 12772.
Cao, Y.; Li, B.; Xiang, Q.; Zhang, Y. Experimental Analysis and Machine Learning of Ground Vibrations caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization. Sustainability2023, 15, 12772.
Cao, Y.; Li, B.; Xiang, Q.; Zhang, Y. Experimental Analysis and Machine Learning of Ground Vibrations caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization. Sustainability 2023, 15, 12772.
Abstract
Aiming at the prediction of environmental vibrations induced by elevated high-speed railway, a machine-learning method is developed by combining random forest algorithm and Bayesian optimization, which using the dataset from on-site experiments . When it comes to achieving a rapid and effective prediction of environmental vibration, there is few research on com-parisons and verifications of different algorithms, and neither on parameter tuning and optimi-zation of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations caused by high-speed trains running on bridge, and then the en-vironmental vibration characteristics are analyzed in view of ground accelerations and weighted vibration levels. Subsequently, three machine-learning algorithms of linear regression, support vector machine and random forest are developed by using experimental database, and their prediction performance are discussed. Finally, two optimization models for the hyperparameter set of random forest algorithm are further compared. It turns out that the integrated random forest algorithm has higher accuracy in predicting environmental vibration than linear regression and support vector machine; the Bayesian optimization has excellent performance and high efficiency in achieving efficient and in-depth optimization of parameters, and can be combined with the RF machine learning algorithm to effectively predict the environmental vibrations induced by the high-speed railway.
Copyright:
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