Vibration Analysis Of College Of Engineering (Coe) Building At The Technological University Of The Philippines – Manila Using Raspberry Shake 4d And Artificial Neural Network/ Janelle Ann C. Albo, Jonna P. Coronacion, Mark Aldrin I. Gutierrez, Liezel B. Nuñez and Lea Jelizabeth T. Pico.--

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Albo, Janelle Ann C [author]

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TextPublication details: Technological University of the Philippines, Manila.Description: xv, 173 pages. 29 cmContent type:

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BTH TA 145 A43 2024

Dissertation note: College of Engineering.-- Bachelor of Science in Civil Engineering: Technological University of the Philippines, Manila. 2024 Summary: The Structural Health Monitoring (SHM) systems have become essential instruments in the field of civil engineering, significantly improving the efficiency, safety, and dependability of structures. With the use of sensors, it would be possible to continuously monitor the structural integrity of buildings and systems throughout the duration of their service life. This would enable early detection of structural damage based on predicted structural responses. The study would involve gathering and analyzing seismic data from Raspberry Shake 4D (RS4D) to comprehend the College of Engineering (COE) Building's dynamic behavior, training machine learning models to predict acceleration response of the fifth-floor sensor and detect structural vulnerabilities and validating the methodology through comparative analysis with traditional structural assessment methods. The theoretical period obtained from FEM was 0.8560 seconds on mode 1 on 1.168 hertz with 74.42% of mass participation factor while the actual period of the COE building falls under mode 3 which on 0.6250 seconds on 1.60 hertz with 3.00 % mass participation factor based on the Ansys Software. This means that with March to May,seismic activity does not excite the building for it is not close to the resonance period of COE. Machine learning algorithm percentage of accuracy falls on 88.02% after simulation and having an overall R-value= 0.82297. This indicates that the model created in Machine Learning performs strong positive correlation from the predicted to the actual response and it can be said working well and ready for future usage. The study would improve the structural resilience and safety measures of COE Building for future infrastructure assessments and risk mitigation.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TA 145 A43 2024 (Browse shelf(Opens below))	c.1	Not for loan		BTH0005725

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Bachelor's thesis

College of Engineering.-- Bachelor of Science in Civil Engineering: Technological University of the Philippines, Manila. 2024

Includes bibliographic references and index.

The Structural Health Monitoring (SHM) systems have become essential
instruments in the field of civil engineering, significantly improving the efficiency,
safety, and dependability of structures. With the use of sensors, it would be possible to
continuously monitor the structural integrity of buildings and systems throughout the
duration of their service life. This would enable early detection of structural damage
based on predicted structural responses. The study would involve gathering and analyzing
seismic data from Raspberry Shake 4D (RS4D) to comprehend the College of
Engineering (COE) Building's dynamic behavior, training machine learning models to
predict acceleration response of the fifth-floor sensor and detect structural vulnerabilities
and validating the methodology through comparative analysis with traditional structural
assessment methods. The theoretical period obtained from FEM was 0.8560 seconds on
mode 1 on 1.168 hertz with 74.42% of mass participation factor while the actual period
of the COE building falls under mode 3 which on 0.6250 seconds on 1.60 hertz with 3.00
% mass participation factor based on the Ansys Software. This means that with March to
May,seismic activity does not excite the building for it is not close to the resonance period
of COE. Machine learning algorithm percentage of accuracy falls on 88.02% after
simulation and having an overall R-value= 0.82297. This indicates that the model created
in Machine Learning performs strong positive correlation from the predicted to the actual
response and it can be said working well and ready for future usage. The study would
improve the structural resilience and safety measures of COE Building for future
infrastructure assessments and risk mitigation.

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