Over the last two decades, the integration of big data and deep learning technologies has
demonstrated remarkable effectiveness across various domains of civil engineering, leading …

Monitoring of modal frequencies under an unsupervised learning framework is a practical
strategy for damage assessment of civil structures, especially bridges. However, the key …

Environmental effects may lead to cracking, stiffness loss, brace damage, and other
damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) …

Point cloud can provide rich 3‐D geometric information of structures, and it has been widely
investigated for generating digital functional models, such as finite element (FE) model and …

Damage identification in post-earthquake reinforced concrete (RC) structures based on
semantic segmentation has been recognized as a promising approach for rapid and non …

Data-driven damage-detection schemes are usually unsupervised machine-learning models
in practice, as these do not require any training. Vibration-based features are commonly …

Video processing for structural monitoring has attracted much attention in recent years
thanks to the possibility of measuring displacement time histories in the absence of …

Critical global challenges, such as climate change and the insufficient availability of
resources, mean that it is a pivotal time to make cities more intelligent, efficient, and …

Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of
civil infrastructures, typically realized on bridges and viaducts by means of vibration …

Abstract Structure health monitoring systems play a crucial role in understanding the
condition of structures. However, owing to various uncertain factors, sensor data may be …