KEYWORDS: Modal analysis, System identification, Data processing, Data acquisition, Detection and tracking algorithms, Damage detection, Structural health monitoring, Signal processing, Sensors, Diagnostics, Bridges, Buildings, Roads
Structural Health Monitoring (SHM) using dynamic characteristics of structures is crucial for early damage detection. Damage detection can be performed by capturing and assessing structural responses. Instrumented structures are monitored by analyzing the responses recorded by deployed sensors in the form of signals. Signal processing is an important tool for the processing of the collected data to diagnose anomalies in structural behavior. The vibration signature of the structure varies with damage. In order to attain effective damage detection, preservation of non-linear and non-stationary features of real structural responses is important. Decomposition of the signals into Intrinsic Mode Functions (IMF) by Empirical Mode Decomposition (EMD) and application of Hilbert-Huang Transform (HHT) addresses the time-varying instantaneous properties of the structural response. The energy distribution among different vibration modes of the intact and damaged structure depicted by Marginal Hilbert Spectrum (MHS) detects location and severity of the damage. The present work investigates damage detection analytically and experimentally by employing MHS. The testing of this methodology for different damage scenarios of a frame structure resulted in its accurate damage identification. The sensitivity of Hilbert Spectral Analysis (HSA) is assessed with varying frequencies and damage locations by means of calculating Damage Indices (DI) from the Hilbert spectrum curves of the undamaged and damaged structures.
KEYWORDS: Data modeling, Mathematical modeling, Clouds, Structural health monitoring, Data mining, Data processing, Damage detection, Signal processing, Computer simulations, Data storage, Double positive medium, Feature selection, Feature extraction, Machine learning
Recently, data-driven models for Structural Health Monitoring (SHM) have been of great interest among many researchers. In data-driven models, the sensed data are processed to determine the structural performance and evaluate the damages of an instrumented structure without necessitating the mathematical modeling of the structure. A framework of data-driven models for online assessment of the condition of a structure has been developed here. The developed framework is intended for automated evaluation of the monitoring data and structural performance by the Internet technology and resources. The main challenges in developing such framework include: (a) utilizing the sensor measurements to estimate and localize the induced damage in a structure by means of signal processing and data mining techniques, and (b) optimizing the computing and storage resources with the aid of cloud services. The main focus in this paper is to demonstrate the efficiency of the proposed framework for real-time damage detection of a multi-story shear-building structure in two damage scenarios (change in mass and stiffness) in various locations. Several features are extracted from the sensed data by signal processing techniques and statistical methods. Machine learning algorithms are deployed to select damage-sensitive features as well as classifying the data to trace the anomaly in the response of the structure. Here, the cloud computing resources from Amazon Web Services (AWS) have been used to implement the proposed framework.
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