Intelligent automatic operational modal analysis: Application to a suspension bridge

A new paradigm for the automatic output-only modal identification of linear structures under ambient vibrations is presented, namely the intelligent automatic operational modal analysis (i-AOMA). It exploits the covariance-based stochastic subspace (SSI-cov) algorithm for the output-only identification of the modal parameters and its workflow consists of two main phases. Initially, quasi-random samples of the control parameters for the SSI-cov algorithm are generated. Once the SSI-cov algorithm is performed for each sample, the corresponding stabilization diagrams are processed in order to prepare a database for training the intelligent core of the i-AOMA method. This is a machine learning (ML) technique (namely a random forest algorithm) that predicts which combination of the control parameters for the SSI-cov algorithm is able to provide good modal estimates. Afterward, new quasi-random samples of the control parameters for the SSI-cov algorithm are generated repeatedly until a statistical convergence criterion is achieved. If the generic sample is classified as feasible by the intelligent core of the i-AOMA method, then the SSI-cov algorithm is performed. Hence, stable modal results are distilled from the stabilization diagrams and relevant statistics are also computed to evaluate the uncertainty level due to the variability of the control parameters. The proposed i-AOMA method is finally applied to estimate the modal features of a suspension bridge structure, the Hardanger Bridge in Norway, to demonstrate the feasibility of the proposed methodology.