Geodesy

Engineering

Philosophy

Pure mathematics

Field (mathematics)

Data mining

Computer science

Grid

Geology

Structural health monitoring

Novelty detection

Mathematics

Geography

Autoencoder

Artificial intelligence

Structural engineering

Novelty

Deep learning

Theology

Artificial neural network

Unsupervised learning

Convolutional neural network

Machine learning

Computer-Aided Civil and Infrastructure Engineering

The demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data‐driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field‐acquired data. However, the majority of unsupervised learning SHM research focuses on detecting damage in simple structures or components and possibly low‐resolution damage localization. In this study, an unsupervised learning, novelty detection framework for detecting and localizing damage in large‐scale structures is proposed. The framework relies on a 5D, time‐dependent grid environment and a novel spatiotemporal composite autoencoder network. This network is a hybrid of autoencoder convolutional neural networks and long short‐term memory networks. A 10‐story, 10‐bay, numerical structure is used to evaluate the proposed framework damage diagnosis capabilities. The framework was successful in diagnosing the structure health state with average accuracies of 93% and 85% for damage detection and localization, respectively.

Large‐scale structural health monitoring using composite recurrent neural networks and grid environments