As civil structures age and deteriorate, it becomes crucial to conduct structural health
monitoring (SHM) to ensure safety and timely maintenance. Surface defect detection plays a …

Zero‐shot learning, applied with vision‐language pretrained (VLP) models, is expected to
be an alternative to existing deep learning models for defect detection, under insufficient …

With the advancement of deep learning, data-driven structural damage identification (SDI)
has shown considerable development. However, collecting vibration signals related to …

Timely and reliably predicting shield-position deviation is crucial to ensure the successful
completion of tunneling projects. In this study, a double-path hybrid deep neural networks …

The successful development of deep learning and computer vision techniques has recently
revolutionized structural health diagnosis (SHD) during life-cycle construction, inspection …

The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot
learning”(LSL)—at first glance are quite similar to the long-standing task of class imbalanced …

Large amounts of data are often categorized using different systems. In such cases, few-shot
and unsupervised text classification are the two main approaches for dynamically classifying …

Structural damage detection techniques are gaining widespread attention in construction
engineering and management. However, the scarcity of structural damage samples and the …

Visual understanding of temporary on-site objects is essential for robots and project
management in construction. Implementation of deep learning algorithms is challenging on …

In the real mobile screen production line, the yield of normal products is relatively high, so
some defects are difficult to collect. Besides, samples of emerging defects are difficult to …