A convolutional neural network-based full-field response reconstruction framework with multitype inputs and outputs

Structural health monitoring (SHM) systems evaluate the state of the infrastructures by analyzing the monitored responses. As measuring all target responses is difficult to accomplish due to technical or economic limitations, converting other easy-measuring responses to the target one is a popular way. Relative approaches are separated into data-driven and model-driven ones. This paper proposes a deep learning-based framework to reconstruct multitypes of full-field responses. The adopted architecture is a convolutional neural network (CNN) with an autoencoder structure and skip connections. Varied from other data-driven approaches, the training set in this paper is the responses computed by a finite element model (FEM), with which the CNN can learn the full-field mapping relationships among varied response types. Therefore, the proposed framework is data-model-co-driven. In the numerical simulation section, a simply-supported beam and a continuous beam bridge have been adopted to discuss the influence of hyperparameters (training epoch, kernel size, skip connection, and bottleneck size), sensor arrangement, modeling error, and measurement noise, which indicates that the framework applies to the in-field structures. Furtherly, a laboratory experiment has been conducted to validate the framework using a two-span continuous bridge with obvious FEM error. All results have shown that the deep-learning-based response reconstruction algorithms can obtain the training set from not only in-field measurements, but also numerical models to improve the diversity of training data.