A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations

This article presents an improved Convolutional Neural Network (CNN)-based approach to predict the vibration response of structures under seismic events without installing sensors at different stories. To do so, three different benchmark buildings, including a Single Degree of Freedom (SDOF) system, a two-dimensional three-story steel moment-framed structure, and a three-dimensional three-story steel structure, were used to validate the proposed framework. A suite of 111 ground motion records, which was originally developed by the SAC project and FEMA P695 instruction, was used to generate a generalized dataset. These records were uniformly scaled to 18 different peak ground accelerations, from 0.05 g to 1.7 g, to ensure the generalization of the dataset in terms of seismic intensity. A strengthened CNN was introduced to provide a prediction model capable of estimating the acceleration, velocity, and displacement histories of the buildings under earthquake records. This network receives not only the ground motion records as the input attribute but also catches additional features such as spectral accelerations at 0.2s and 1.0s as RGB values of colourful images. Results indicate that the proposed algorithm is reliable in estimating the response histories of the buildings under linear and nonlinear conditions.