Structural damage quantification using long short-term memory (LSTM) auto-encoder and impulse response functions

This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected "principal components" to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors.