Xiaotian Bai

Papers

Total Citations

106

H-Index

About

Xiaotian Bai is a leading researcher at the intersection of robotics, structural health monitoring, and intelligent control systems. His work focuses on advancing construction robotics through innovative applications of digital twin technology and deep learning for fault diagnosis. Bai's most influential paper, "A review for control theory and condition monitoring on construction robots" (2023, 63 citations), provides a comprehensive framework for integrating robotic automation in building construction, highlighting how these technologies enhance productivity and safety. His pioneering research on "Digital twin‑assisted fault diagnosis system for robot joints with insufficient data" (2022, 33 citations) addresses a critical challenge in construction robotics—performing accurate fault detection when training data is scarce, using virtual models to simulate real-world conditions. More recently, Bai's "A Review of Technical Advances and Applications of Intelligent Inspection Robots in Structural Health Monitoring" (2025, 10 citations) explores how autonomous robots can replace manual inspections for infrastructure like bridges and construction machinery, reducing costs and preventing accidents. With over 100 total citations, Bai's work is shaping the future of safe, efficient, and intelligent construction environments.