Integration of Topological Data Analysis (TDA) in Structural Health Monitoring (SHM) for Civil Engineering

¹Ugwu Chinyere Nneoma, ¹Ogenyi Fabian C. and ^1,2Val Hyginus Udoka Eze*

¹Department of Publication and Extension Kampala International University Uganda

²Department of Electrical, Telecommunication and Computer Engineering, Kampala International University, Western Campus, Ishaka, Uganda

*Corresponding Author: Val Hyginus Udoka Eze, udoka.eze@kiu.ac.ug, Department of Electrical, Telecommunication and Computer Engineering, Kampala International University, Western Campus, Ishaka, Uganda (ORCID: 0000-0002-6764-1721)

                                                                       ABSTRACT

Structural Health Monitoring (SHM) is critical for ensuring the longevity and safety of civil engineering structures. Traditional SHM techniques often struggle with the high-dimensional, complex data generated by modern sensor technologies. Topological Data Analysis (TDA) offers a robust alternative by capturing the underlying topological features of this data, enabling more accurate and efficient monitoring. This paper explores the integration of TDA into SHM, discussing its theoretical foundations, practical benefits, and challenges. We review recent advancements, including deep learning enhancements and real-world applications, highlighting how TDA can improve damage detection and predictive maintenance. Future research directions are proposed to further the adoption of TDA in SHM, emphasizing the need for real-time, industrial-grade solutions.

Keywords: Structural Health Monitoring, Topological Data Analysis, Civil Engineering, Persistent Homology, Damage Detection, Deep Learning

CITE AS (2024). Ugwu Chinyere Nneoma, Ogenyi Fabian C. and Val Hyginus Udoka Eze Integration of Topological Data Analysis (TDA) in Structural Health Monitoring (SHM) for Civil Engineering. RESEARCH INVENTION JOURNAL OF ENGINEERING AND PHYSICAL SCIENCES 3(1):23-32.