DOI: 10.17603/ds2-39d5-bc15
title: System identification of the frame structure
title: in Camera-based real-time damage identification of building structures through deep learning

description: This model uses frequency domain decomposition as well as the genetic algorithm to identify the modal properties of the 3D 4-story frame structure. The acceleration data obtained from the experimental setup are used as the input to this part of the simulation. As the output, the modal properties including the mode periods, mode shape, and story stiffness of the frame are provided.
description: Real-time Damage Identification (DI) augment smart structures with instant damage information, which is essential to take safety actions and avoid spreading the damage throughout the building structure. Having the severity and location of the damage ready via real-time DI will allow for effective scheduling of preventive measures and action plans to isolate the damage and replace affected elements. Accordingly, real-time DI improves structural safety, especially against extreme events unknown at the design stage. This field needs innovative solutions to overcome the difficulties and limitations of model-based approaches and train supervised machine learning classifiers in the absence of measured damaged data. The present project is an image-based DI methodology using deep neural networks to provide real-time data-driven damage information for structural systems. The video and acceleration data are provided through experiments on a small-scale 3D moment-resisting 4-story frame structure subjected to dynamic loading. The dynamic response signals serve as the inputs to the proposed DI methodology. Two different data acquisition (DAQ) configurations are employed simultaneously to measure the dynamic response data to compare the accuracy: acceleration sensors and video recording. Video processing techniques, including global image thresholding and the Kanade-Lucas-Tomasi algorithm, track the floor levels to obtain displacement, velocity, and acceleration signals from recorded videos. Then, the signals received from either DAQ system are further processed to remove noises. Upon feeding the filtered dynamic response signals to a deep learner, the outputs provide real-time DI describing the damage presence and location. Regularization techniques, including dropout to avoid co-adaptation in hidden layers, resulted in accuracy improvements. The neural network is optimized through 10-fold cross-validation. The lateral stiffness is reduced by changing the cross-section of columns to simulate multiple damage levels in different stories. Accordingly, four-column arrangements for each story are investigated, resembling undamaged state and damaged states with minor, moderate, and major severities. The accuracy results obtained from the test dataset show the promising performance of the proposed non-destructive and model-free methodology for real-time DI. These compelling results can potentially transform the structural health monitoring (SHM) field into a new era by providing cost-effective early damage warning for various structural systems in an engineering context, without the need for complicated structural models and the uncertainties of simplified structural models during operation. By providing real-time data, the proposed DI methodology can also update the control strategy and optimize the vibration mitigation efficiency in smart structures with integrated structural control and health monitoring systems.