Kaiser and Cumulative Proportion Principal Component Analysis for Temperature Compensation of Vibration in Reinforced Concrete Bridge
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
(2022), pp. 1-6
Ronnie Concepcion
a
,
Febus Reidj G. Cruz
b
,
Joy Carpio
c
,
Argel Bandala
d
,
R-Jay Relano
a
,
Joseph Aristotle de Leon
a
,
Adrian Genevie Janairo
d
,
Marielet Guillermo
d
,
Joenel Galupino
e
,
Marvin Jade Genoguin
f
,
Pocholo James M. Loresco
g
a Department of Manufacturing Engineering and Management, De La Salle University, Manila, Philippines
b School of Electrical, Electronics and Computer Engineering, Mapua University, Manila, Philippines
c Systems Engineering Department, University of California, Berkeley, California, USA
d Department of Electronics and Computer Engineering, De La Salle University, Manila, Philippines
e Department of Civil Engineering, De La Salle University, Manila, Philippines
f Department of Civil Engineering, Eastern Visayas State University, Tacloban City, Philippines
g Electrical and Electronics Engineering Department, FEU Institute of Technology, Manila, Philippines
Abstract: Structural health monitoring (SHM) was developed to provide diagnosis of the state of civil structures, such as bridge and building, throughout their lifespan. The superstructure degrades over time mainly because of the continuous effect of environmental factors including temperature, wind, humidity, and traffic loading. Consequently, this study is concerned in reducing the masking effect of environmental factors, specifically on temperature. Principal component analysis (PCA), a supervised machine learning algorithm, was employed as the embedded statistical treatment for multidimensional reduction of feature matrix data to eliminate the temperature effect. Kaiser’s criterion eliminated data variance of almost 20% that may result to poor data reconstruction. Cumulative proportion (CP) criterion eliminated data variance around 4%, which is a better choice for deciding the number of principal components to eliminate. Thus, the proposed experimental study addressed successful temperature compensation from reinforced concrete bridge vibration data by using PCA and CP criterion.