Degradation evaluation of lateral story stiffness using HLA-based deep learning networks. (January 2019) - Zhou, Cong; Chase, J. Geoffrey; Rodgers, Geoffrey W.
Highlights: Develop a deep learning network (DLN) for immediate assessment of stiffness losses. Validate the DLN model against a full-scale 3-story real-world building structure. Methods comparison demonstrates the capability of DLN in longer term monitoring. Abstract: Hysteresis loop analysis (HLA) has proven an effective indicator of damage detection in civil engineering structural health monitoring (SHM). In this paper, the histogram of stiffness (HOS) features are extracted from segregated half cycles of hysteresis loops reconstructed from measured response. A deep learning network (DLN) is proposed with the use of the HOS to classify the damage index ( DI ) based on stiffness degradation for damage identification. Training data are obtained using numerical simulations of 30, 000 realistic, randomly created hysteresis loops, including a wide range of typical linear and nonlinear structural behaviours. Performance of the trained DLN model is assessed using both 1800 additional simulated 3-story "virtual" buildings and experimental data from a 3-story full-scale real building. Results are compared to the validated HLA method. Validation on simulated virtual building data yields prediction accuracy for 97.2% and 91.6% samples without and with 10% added noise, respectively. The comparison shows a good match of trend and percentage stiffness drop between DLN and HLA identification with the average difference for all cases within 1.1–4.6%, indicating a good accuracy of the proposed DLN prediction model for real structures. The overall results show its potential to provide a rapid, and real-time alarm or other notice on damage states and mitigation to emergency response using DLN and thus without detailed engineering analysis.