Data-driven prediction of critical flutter velocity of long-span suspension bridges using a probabilistic machine learning approach

Abstract

Among the consequences of wind-induced excitation on long-span cable-supported bridges, flutter instability is the most dangerous and can collapse bridge structures. Until now, the flutter velocity of long-span bridges can be computed after conducting an experimental wind tunnel test or by means of computational fluid dynamics (CFD). However, the approaches mentioned above are cost-prohibitive and time-consuming especially when there are many sampling designs to evaluate. Therefore, it is crucial to develop an alternative solution for evaluating the flutter performance of such structures while minimizing the associated cost and computation time without worsening the accuracy of the results. Scholars have proposed machine learning (ML) techniques to address this issue. Unfortunately, some ML techniques do not provide excellent generalization for small datasets and are prone to bias even when the accuracy is relatively high. Besides, conventional ML models do not consider the uncertainty in data. This study proposes a probabilistic machine learning approach to overcome the weaknesses of the conventional ML models. The dataset used to train the proposed model was generated from 73 sampling designs using a fully numerical approach that involved 2D URANS (Unsteady Reynolds-averaged Navier-Stokes) CFD simulations, quasi-steady flutter theory, finite element model, and multi-mode approach for critical flutter velocity computation. The deck cross-section's most influential parameters and the critical flutter velocity constitute the datasets used to build the proposed probabilistic machine learning model. Our finding substantiates that the probabilistic machine learning approach based on hierarchical Bayesian modeling (PML-HBM) overperformed the conventional machine learning models, including extreme gradient boosting regression (XGBR), random forest (RF), and the support vector regression(SVR). Therefore, PML-HBM can accurately predict the critical flutter velocity. This model is an interesting approach to circumvent the time and cost associated with wind tunnel tests and CFD simulations at the earlier design stage of long-span bridge aerodynamic study.