Iterative Updating of Digital Twins Using Convolutional Neural Networks: A Framework for Robust Structural Behavior Prediction

This study introduces a novel iterative updating strategy powered by convolutional neural networks (CNNs) within a digital twin framework for informing structural behavior of infrastructure assets. A two-dimensional (2D) cantilever plate is employed as a benchmark case study for the updating strategy within a digital twin framework. Two distinct surrogate models are the main components of the digital twin framework, which form the basis of this work. The first model is formulated as finite element analysis surrogate, and the training dataset is prepared based on traditional finite element analysis. The second model predicts 2D deformation based on real-world surface images, serving as the ground truth, which is trained on pairs of deformed and reference images of surfaces with speckle patterns. This ground truth deformation data bridges the gap between experimental observations (physical twin) and virtual modeling (digital twin). The core novelty of this study lies in the iterative updating process and the introduction of four specialized CNNs, referred to as “calculators,” designed to refine the inputs for the first surrogate model using the ground truth displacement from the second surrogate model. Each calculator addresses a specific task: 1. Predicting loading in the y direction, given geometry, boundary conditions, loading in the x direction, and ground truth displacement. 2. Predicting loading in the x direction, given geometry, boundary conditions, loading in the y direction, and ground truth displacement. 3. Predicting geometry, given boundary conditions, loading, and ground truth displacement. 4. Predicting boundary conditions, given geometry, loading, and ground truth displacement. These calculators operate simultaneously in an iterative framework, using initial estimates of geometry, boundary conditions, and loading as inputs. Their outputs refine the inputs for subsequent iterations, with the process continuing until convergence. The final refined inputs are used by the first model, and their predictions are compared with the ground truth displacement from the second surrogate model to validate the digital twin. The results demonstrate that this novel approach integrating specialized calculators in an iterative updating process contributes to the robustness and accuracy of digital twins. The proposed updating strategy can pave the way for more reliable structural analysis and predictive modeling through digital twin frameworks, inform efficient infrastructure operation and management, and further contribute to the application and development of digital twin and structural health monitoring techniques.