Abnormal Monitoring Data Diagnosis for Super High-Rise Structures Based on Ensemble Neural Network

Abstract:

To address the inefficiency in manual identification of diverse anomalies in structural health monitoring （SHM） data， a method based on ensemble learning model is proposed to detect the abnormal data in super high-rise building SHM systems. By employing short-time Fourier transform， the time-frequency domain information containing the main structural vibrational modes is extracted and compressed， thus the feature extraction and high-fidelity compression of original data attained. Moreover， a Bagging ensemble strategy is introduced， and multiple training subsets are generated through bootstrap sampling， based on which each individual neural network model is trained independently. By aggregating the prediction results of multiple well-trained models， the precision of anomaly detection is enhanced. Furthermore， the proposed method is applied into the Shanghai Tower SHM system to validate the feasibility and reliability. The results indicate that the diagnosis accuracy reaches 98.8% by the proposed ensemble model-based abnormal data detection method， and high precision and strong robustness of the anomaly SHM data diagnosis are confirmed.