Chuang Zeng
Papers
1
Total Citations
49
H-Index
1
About
Chuang Zeng is a researcher at the forefront of applying machine learning to civil engineering, with a particular focus on concrete construction quality. His most cited work, "Machine learning-based classification of quality grades for concrete vibration behaviour" (2024), has already garnered 49 citations, highlighting its timely impact on automated quality control in construction. Zeng’s primary research areas include intelligent construction monitoring, structural health assessment, and the integration of data-driven methods into traditional material testing. His major contribution lies in developing classification models that can accurately grade concrete vibration behavior—a critical factor for ensuring structural integrity and durability. By replacing subjective manual inspection with objective, real-time analysis, his work promises to enhance safety and efficiency in large-scale infrastructure projects. Zeng’s achievements demonstrate a rare ability to bridge computational techniques with practical engineering challenges, making his research highly relevant for students and professionals seeking to modernize construction practices. His growing citation record reflects the field’s recognition of his innovative approach, positioning him as a rising voice in smart construction and machine learning applications.
Research Focus
Key Achievements
Top Papers
- 1