Story drift and damage level estimation of buildings using relative acceleration responses with multi‐target deep learning models under seismic excitation

Damage detection is one of the primary purposes of structural health monitoring to inform catastrophic risks of structures right after extreme loadings such as earthquakes and hurricanes. In structural design codes, story drifts are considered as an indicator to estimate the damage states of structures. For instance, when the story drift ratios achieve 0.2-0.4%, light damage may be present in a building. In addition, the remaining stiffness ratios can also reveal the damage levels of a structure. Previous studies have shown that structural stiffness changes can affect the frequency responses of structures, for example, changing the locations of poles in frequency response functions. In this research, two multi-target neural network models are developed to concurrently estimate story drifts and remaining stiffness ratios using floor accelerations under seismic excitation. One of the multi-target neural network models focuses on developing a physics-guided loss function with a parallel model combination. Meanwhile, the other neural network model sequentially integrates two deep learning approaches by transfer learning. For example, the long short-term memory units estimate story drift responses from floor accelerations. Then, the short-time Fourier transform layers of floor accelerations yield the remaining stiffness ratio estimation. The proposed models are numerically investigated and experimentally verified. As a result, both models can estimate story drift and remaining stiffness ratio using the proposed neural network models.