Convolutional Neural Network Approach for Vibration-Based Damage State Prediction in a Reinforced Concrete Building

Abstract

Structural health monitoring (SHM) is critical in identifying the degradation of infrastructure systems to ensure structural integrity and safety. Vibration-based SHM approaches, including numerical-physics-based modeling and data-driven strategies, are commonly used to detect damage. This study proposes a method for predicting damage conditions using a hybrid vibration-based approach with convolutional neural networks (CNNs) trained with physics-based data sets. The method is evaluated using a five-story reinforced concrete building that undergoes multiple base excitations, resulting in cumulative damage that affects the building’s stiffness and dynamic responses. A set of damage states is defined based on the structure’s response, and simplified models of the building are used to create a training database for the CNNs. The CNNs are trained on noise-free dynamic responses (i.e., accelerations or displacements) from numerically simulated white noise (WN) sequences and then tested with the appropriate floor response data from different types of base shaking. The accuracy of the models is consistently high, with noise-free acceleration and displacement responses yielding results of 99.9% and 93.9% for numerically simulated WN base excitations, respectively. The accuracy remained high when tested with 30 dB signal-to-noise ratio (SNR) noisy acceleration and displacement responses, with accuracies of 99.9% and 93.8%, respectively, and 100% when using acceleration responses from experimentally measured WN base excitations with a similar SNR. Ambient microtremor acceleration data collected within California’s Central Valley were used to validate the approach for low-amplitude ambient ground vibrations, achieving an accuracy of 86.69% when tested with noisy acceleration responses with the measured microtremors as base shaking. The proposed method has limitations in identifying bordering damage states and reduced accuracy when tested on field data, but overall shows promise for damage state identification and story stiffness reduction analysis. This study presents a new method for detecting damage scenarios in buildings using vibration data and machine learning (ML). We used CNNs, a learning algorithm, to analyze vibrations from a five-story building with varying damage levels. The CNN models were trained with computer simulations and real-world measurements to recognize various damage states. The proposed method proved to be highly accurate and efficient in detecting and associating damage with reductions in floor stiffness, even with noisy data, achieving up to 99.9% accuracy. The study also found that detailed computer models are not necessary for generating training data. A simplified model resembling a real structure is effective, making the method more practical and computationally efficient. This allows the technique to be applied in real-world situations, where limited measurements of the structure’s response can be input into the CNN model to assess if the building has experienced damage over time. In summary, this research shows that CNN models trained with data from simple numerical models can effectively identify damage scenarios in a building using real-world measured vibrations from the building, even during minor shaking.