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.--

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De Leon, Jeremiah S [author]

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TextPublication details: Manila: Technological University of the Philippines, 2024.Description: xii, 197pages: 29cmContent type:

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BTH TA 145 D45 2024

Dissertation note: College of Engineering.-- Bachelor of science in civil engineering: Technological University of the Philippines, 2024. Summary: 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.

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Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TA 145 D45 2024 (Browse shelf(Opens below))	c.1.	Not for loan		BTH0005715

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Bachelor's thesis

College of Engineering.-- Bachelor of science in civil engineering: Technological University of the Philippines, 2024.

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.

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