Abstract
This chapter presents the use of principal component analysis (PCA) to extract and summarize the information contained in data acquired by a structural health monitoring (SHM) system for a high-rise building: the Palazzo Lombardia skyscraper in Milano. The structure is one of the tallest skyscrapers in Italy, and it is the house of the regional government. Due to its strategic relevance, Palazzo Lombardia is equipped with an SHM system composed of clinometers and accelerometers, spread over 5 of the 42 floors of the building, to collect information on the static and dynamic behavior of the structure over time.
In previous works, empirical models were proposed to predict the eigenfrequencies of the first vibration modes of the skyscraper as a function of meaningful predictors, accounting for environmental and operational factors responsible for the structural properties changes, i.e., temperature, sun exposure, and wind direction and speed. From an SHM perspective, the predicted eigenfrequency values can be compared with the ones estimated through the adoption of operational modal analysis. Since the model is tuned on data coming from the healthy structure, a significant discrepancy between predicted and estimated eigenfrequency can point out the presence of damage.
Due to the complexity and size of large-scale buildings, quantifying the influencing variables is a hard task (e.g., the temperature that dramatically changes at different locations of the structure) and, so far, other highly correlated physical quantities have been used in place of direct measurements (e.g., root mean square of the accelerations to account for the effect of wind). However, only a limited number of sensors were used, at the risk of not including other meaningful variables or additional information provided by other positions along the structure.
In this work, a preliminary investigation on the use of PCA is presented, aimed at providing a few synthetic indexes, which are highly correlated with the environmental factors that mostly affect the dynamic behavior of the structure. Through the adoption of the PCA, the dimensionality of the problem can be reduced by considering only a few variables, while retaining as much as possible of the variation present in the entire data set. The potential of the proposed approach is discussed on long-term data coming from the operating monitoring system, presenting a case study in the field of SHM of real large-scale civil structures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
AlHamaydeh, M., Ghazal, A.N.: Structural health monitoring techniques and technologies for large-scale structures: challenges, limitations, and recommendations. Pract. Period. Struct. Des. Constr. 27, 03122004 (2022)
Figueiredo, E., Brownjohn, J.: Three decades of statistical pattern recognition paradigm for SHM of bridges. Struct. Health Monit. 21, 3018–3054 (2022)
Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365, 303–315 (2007)
Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley (2012)
Hou, R., Xia, Y.: Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019. J. Sound Vib. [Internet]. Elsevier Ltd. 491, 115741 (2021) Available from: https://doi.org/10.1016/j.jsv.2020.115741
Zhang, C., Mousavi, A.A., Masri, S.F., Gholipour, G., Yan, K., Li, X.: Vibration feature extraction using signal processing techniques for structural health monitoring: a review. Mech. Syst. Signal Process. [Internet]. Elsevier Ltd. 177, 109175 (2022) Available from: https://doi.org/10.1016/j.ymssp.2022.109175
Sohn, H.: Effects of environmental and operational variability on structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365, 539–560 (2007)
Doebling, S.W., Farrar, C.R., Prime, M.B.: A summary review of vibration-based damage identification methods. Shock Vib. Dig. 30, 91–105 (1998)
Lucà, F., Manzoni, S., Cigada, A., Frate, L.: A vibration-based approach for health monitoring of tie-rods under uncertain environmental conditions. Mech. Syst. Signal Process. Academic Press. 167, 108547 (2022)
Berardengo, M., Busca, G., Grossi, S., Manzoni, S., Vanali, M.: The monitoring of Palazzo Lombardia in Milan. Shock Vib. 2017, 1–13 (2017)
Jolliffe, I.T.: Principal Component Analysis. Springer (2002)
Berardengo, M., Cigada, A., Manzoni, S., Vanali, M.: Design and installation of a permanent monitoring system for Palazzo Lombardia in Milano, Italy. ECCOMAS Congr. 2016 – Proc. 7th Eur. Congr. Comput. Methods Appl. Sci. Eng. 2, 3640–3651 (2016)
Berardengo, M., Lucà, F., Manzoni, S., Vanali, M., Acerbis, D.: Empirical models for the health monitoring of high-rise buildings: the case of Palazzo Lombardia. Conf. Proc. Soc. Exp. Mech. Ser. 8, 169–175 (2021)
Lucà, F., Pavoni, S., Manzoni, S., Vanali, M.: Time reliability of empirical models for the prediction of building parameters: the case of Palazzo Lombardia. Lect. Notes Civ. Eng. 254, 186–194 (2023)
Mujica, L.E., Ruiz, M., Pozo, F., Rodellar, J., Güemes, A.: A structural damage detection indicator based on principal component analysis and statistical hypothesis testing. Smart Mater. Struct. [Internet]. 23, 025014 (2014) Available from: http://stacks.iop.org/0964-1726/23/i=2/a=025014?key=crossref.736dd3b30e828ff467efb3487d04236a
Mojtahedi, A., Lotfollahi Yaghin, M.A., Ettefagh, M.M., Hassanzadeh, Y., Fujikubo, M.: Detection of nonlinearity effects in structural integrity monitoring methods for offshore jacket-type structures based on principal component analysis. Mar. Struct. 33, 100–119 (2013)
Datteo, A., Lucà, F., Busca, G., Cigada, A.: Long-time monitoring of the G. Meazza stadium in a pattern recognition prospective. Procedia Eng. 199, 2040–2046 (2017)
Datteo, A., Lucà, F., Busca, G.: Statistical pattern recognition approach for long-time monitoring of the G. Meazza stadium by means of AR models and PCA. Eng. Struct. 153, 317–333 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Society for Experimental Mechanics, Inc
About this paper
Cite this paper
Lucà, F., Gholibeiki, H., Manzoni, S., Mola, E., Pavoni, S., Vanali, M. (2023). Principal Component Analysis of Monitoring Data of a High-Rise Building: The Case Study of Palazzo Lombardia. In: Madarshahian, R., Hemez, F. (eds) Data Science in Engineering, Volume 10. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-34946-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-34946-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34945-4
Online ISBN: 978-3-031-34946-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)