Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring

Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.

CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection

The recent advances in the data science field in the last few decades have benefitted many other fields including Structural Health Monitoring (SHM). Particularly, Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) methods for vibration-based damage diagnostics of civil structures has been utilized extensively due to the observed high performances in learning from data. Along with diagnostics, damage prognostics is also vitally important for estimating the remaining useful life of civil structures. Currently, AI-based data-driven methods used for damage diagnostics and prognostics centered on historical data of the structures and require a substantial amount of data for prediction models. Although some of these methods are generative-based models, they are used to perform ML or DL tasks such as classification, regression, clustering, etc. after learning the distribution of the data. In this study, a variant of Generative Adversarial Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with Gradient Penalty (CycleWDCGAN-GP) model is developed to investigate the "transition of structural dynamic signature from an undamaged-to-damaged state" and "if this transition can be employed for predictive damage detection". The outcomes of this study demonstrate that the proposed model can accurately generate damaged responses from undamaged responses or vice versa. In other words, it will be possible to understand the damaged condition while the structure is still in a healthy (undamaged) condition or vice versa with the proposed methodology. This will enable a more proactive approach in overseeing the life-cycle performance as well as in predicting the remaining useful life of structures.

Generative Adversarial Networks for Labeled Data Creation for Structural Monitoring and Damage Detection

There has been a drastic progression in the field of Data Science in the last few decades and other disciplines have been continuously benefitting from it. Structural Health Monitoring (SHM) is one of those fields that use Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) algorithms for condition assessment of civil structures based on the collected data. The ML and DL methods require plenty of data for training procedures; however, in SHM, data collection from civil structures is very exhaustive; particularly getting useful data (damage associated data) can be very challenging. This paper uses 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labeled vibration data generation. Then, implements structural damage detection on different levels of synthetically enhanced vibration datasets by using 1-D Deep Convolutional Neural Network (1-D DCNN). The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage diagnostics of civil structures. Keywords: Structural Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage Detection, 1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial Networks (1-D GAN), Deep Convolutional Generative Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP)

Generative Adversarial Networks for Data Generation in Structural Health Monitoring

Structural Health Monitoring (SHM) has been continuously benefiting from the advancements in the field of data science. Various types of Artificial Intelligence (AI) methods have been utilized for the assessment and evaluation of civil structures. In AI, Machine Learning (ML) and Deep Learning (DL) algorithms require plenty of datasets to train; particularly, the more data DL models are trained with, the better output it yields. Yet, in SHM applications, collecting data from civil structures through sensors is expensive and obtaining useful data (damage associated data) is challenging. In this paper, 1-D Wasserstein loss Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage associated vibration datasets that are similar to the input. For the purpose of vibration-based damage diagnostics, a 1-D Deep Convolutional Neural Network (1-D DCNN) is built, trained, and tested on both real and generated datasets. The classification results from the 1-D DCNN on both datasets resulted to be very similar to each other. The presented work in this paper shows that for the cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP can successfully generate data for the model to be trained on. Keywords: 1-D Generative Adversarial Networks (GAN), Deep Convolutional Generative Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), 1-D Convolutional Neural Networks (CNN), Structural Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage Detection

Generative Adversarial Networks for Labelled Vibration Data Generation

As Structural Health Monitoring (SHM) being implemented more over the years, the use of operational modal analysis of civil structures has become more significant for the assessment and evaluation of engineering structures. Machine Learning (ML) and Deep Learning (DL) algorithms have been in use for structural damage diagnostics of civil structures in the last couple of decades. While collecting vibration data from civil structures is a challenging and expensive task for both undamaged and damaged cases, in this paper, the authors are introducing Generative Adversarial Networks (GAN) that is built on the Deep Convolutional Neural Network (DCNN) and using Wasserstein Distance for generating artificial labelled data to be used for structural damage diagnostic purposes. The authors named the developed model 1D W-DCGAN and successfully generated vibration data which is very similar to the input. The methodology presented in this paper will pave the way for vibration data generation for numerous future applications in the SHM domain.