Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information.

The deterioration of infrastructure's health has become more predominant on a global scale during the 21st century. Aging infrastructure as well as those structures damaged by natural disasters have prompted the research community to improve state-of-the-art methodologies for conducting Structural Health Monitoring (SHM). The necessity for efficient SHM arises from the hazards damaged infrastructure imposes, often resulting in structural collapse, leading to economic loss and human fatalities. Furthermore, day-to-day operations in these affected areas are limited until an inspection is performed to assess the level of damage experienced by the structure and the required rehabilitation determined. However, human-based inspections are often labor-intensive, inefficient, subjective, and restricted to accessible site locations, which ultimately negatively impact our ability to collect large amounts of data from inspection sites. Though Deep-Learning (DL) methods have been heavily explored in the past decade to rectify the limitations of traditional methods and automate structural inspection, data scarcity continues to remain prevalent within the field of SHM. The absence of sufficiently large, balanced, and generalized databases to train DL-based models often results in inaccurate and biased damage predictions. Recently, Generative Adversarial Networks (GANs) have received attention from the SHM community as a data augmentation tool by which a training dataset can be expanded to improve the damage classification. However, there are no existing studies within the SHM field which investigate the performance of DL-based multiclass damage identification using synthetic data generated from GANs. Therefore, this paper investigates the performance of a convolutional neural network architecture using synthetic images generated from a GAN for multiclass damage detection of concrete surfaces. Through this study, it was determined the average classification performance of the proposed CNN on hybrid datasets decreased by 10.6% and 7.4% for validation and testing datasets when compared to the same model trained entirely on real samples. Moreover, each model's performance decreased on average by 1.6% when comparing a singular model trained with real samples and the same model trained with both real and synthetic samples for a given training configuration. The correlation between classification accuracy and the amount and diversity of synthetic data used for data augmentation is quantified and the effect of using limited data to train existing GAN architectures is investigated. It was observed that the diversity of the samples decreases and correlation increases with the increase in the number of synthetic samples.

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems.

As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated. This paper reviews the recent developments for LiDAR-based structural health monitoring, in particular, for detecting cracks, deformation, defects, or changes to structures over time. In this regard, mobile laser scanning (MLS) and terrestrial laser scanning (TLS), specific to structural health monitoring, were reviewed for a wide range of civil infrastructure systems, including bridges, roads and pavements, tunnels and arch structures, post-disaster reconnaissance, historical and heritage structures, roofs, and retaining walls. Finally, the existing limitations and future research directions of LiDAR technology for structural health monitoring are discussed in detail.