Chao Xiang is a dedicated researcher and academic at Hunan University whose work centers on advancing the reliability and resilience of civil and structural systems through innovative monitoring and computational techniques. His research spans Structural Health Monitoring (SHM), damage detection, deep learning applications in engineering, and automated crack segmentation—areas in which he has contributed to modernizing infrastructure assessment and ensuring structural safety. By integrating sensing technologies with data-driven modeling, he focuses on improving the accuracy, speed, and automation of detecting structural anomalies, enabling earlier intervention and reducing the risk of catastrophic failures. His work in SHM involves developing intelligent frameworks that combine traditional engineering principles with cutting-edge machine learning to interpret complex structural behaviors. In damage detection, he explores robust algorithms capable of identifying subtle patterns that may indicate deterioration, while his deep learning research emphasizes training architectures that enhance prediction performance and adaptability across diverse structural scenarios. His contributions to crack segmentation involve advancing computer vision techniques to automatically locate and evaluate cracks in materials, significantly improving inspection efficiency and reliability. Through interdisciplinary collaboration, peer-reviewed publications, and continued innovation, he aims to push the boundaries of structural diagnostics and contribute to the development of safer, smarter, and more sustainable infrastructure.