STR-918: STRUCTURAL MODAL IDENTIFICATION USING AN IMPROVED EMPIRICAL MODE DECOMPOSITION

Empirical mode decomposition (EMD) has shown significant promises in signal decomposition of vibration data of civil engineering structures. Owing to its self-adaptive time-frequency decomposition capability, it is widely used in system identification of both linear and nonlinear structures. Unlike EMD which uses only single sensor, multivariate EMD (MEMD) is recently explored as a modal identification tool utilizing multichannel vibration measurements. In this paper, the performance of MEMD is investigated by integrating with another powerful signal separation technique to undertake modal identification under a wide range of applications. The proposed EMD method is validated using a suite of numerical studies

An improved multi-variate empirical mode decomposition method towards system identification of structures

Structural health monitoring (SHM) plays a key role towards condition assessment of large-scale civil structures using modern sensing technology. Once the rich vibration data is collected, important system information is extracted from the data and sub- sequently such information is used for necessary decision making including adopting maintenance, retro tting or control strategies. System identi cation is one of the key steps in SHM where unknown system information of the structures is estimated based on the response measurements. However, depending on excitation characteristics or system behavior, vibration measurements become complicated where traditional methods are unable to accurately analyze the data. In this thesis, Multivariate Empirical Mode Decomposition (MEMD) method is ex- plored to undertake ambient system identi cation of structures using the multi-sensor vibration data. Due to inherent sifting operation of EMD, the traditional MEMD re- sults into mode-mixing that causes signi cant inaccuracy in structural modal identi - cation. In this research, Independent Component Analysis (ICA) method is integrated with the MEMD to alleviate mode mixing in the resulting modal responses. The pro- posed hybrid MEMD method is veri ed using a suite of numerical, experimental and full-scale studies (e.g., a high-rise tower in China and a long-span bridge in Canada) considering several practical applications including low energy modes, closely spaced frequencies and measurement noise in real-life buildings and bridges. The results show signi cantly improved performance of the proposed method compared to the standard EMD method and therefore, the proposed method can be considered as a robust ambient modal identi cation method for exible structures

Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring

The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy