Structural health monitoring of the technological university of the philippines - engineering science education program (esep) building on determining vibration using rs4d and seismobuild/
Jeremiah S. De Leon, Joshmel S. Moya, Clarence John B. Rodriguez, Dustin O. Sabile, and Eduardo Lorenzo L. Ylo.--
- Manila: Technological University of the Philippines, 2024.
- xii, 197pages: 29cm.
Bachelor's thesis
College of Engineering.--
Includes bibliographic references and index.
This study developed a structural health monitoring system for the Engineering Science Education Program (ESEP) building at the Technological University of the Philippines Manila. The system utilized a Raspberry Shake 4D (RS4D) sensor to monitor vibrations over 30 days continuously, determining the building's natural frequency and period. Pre-processing techniques were applied to enhance the accuracy and reliability of the acceleration data, followed by dynamic property analysis using the Fast Fourier Transform (FFT). Results were compared with Finite Element Method (FEM) modeling conducted in SeismoBuild. FEM analysis estimated natural frequencies of 2.969 Hz, 3.666 Hz, and 3.85 Hz, with corresponding periods of 0.337 s, 0.273 s, and 0.260 s. However, FFT-processed RS4D data indicated higher natural frequencies of 4 Hz, 7.99 Hz, and 12 Hz, with shorter periods of 0.25 s, 0.125 s, and 0.083 s, reflecting greater stiffness in the actual structure. Discrepancies between FEM predictions and RS4D observations were attributed to factors such as building age, soil conditions, and simplifications in the FEM model. The results confirm the RS4D sensor's effectiveness in structural health monitoring, showing strong alignment with FEM analysis and revealing key insights into the building's dynamic behavior. While online data collection enables real-time monitoring, offline methods provide higher data quality, highlighting inherent trade-offs.
Structural health monitoring Raspberry shake 4d (rs4d) Vibration monitoring