Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.

[1]  Hui Li,et al.  Modal identification of bridges under varying environmental conditions: Temperature and wind effects , 2009 .

[2]  Piotr Omenzetter,et al.  Application of time series analysis for bridge monitoring , 2006 .

[3]  You-Lin Xu,et al.  Temperature effect on vibration properties of civil structures: a literature review and case studies , 2012 .

[4]  Hong Zhang,et al.  Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting , 2014, J. Appl. Math..

[5]  Carlos Soares,et al.  A Meta-Learning Method to Select the Kernel Width in Support Vector Regression , 2004, Machine Learning.

[6]  Filippo Ubertini,et al.  System identification of a super high-rise building via astochastic subspace approach. , 2011 .

[7]  Zhibin Lin,et al.  Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection , 2017, KSCE Journal of Civil Engineering.

[8]  Irwanda Laory,et al.  Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects , 2018, Structural Health Monitoring.

[9]  H. Tran-Ngoc,et al.  Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm , 2018, Sensors.

[10]  Xijun Ye,et al.  Modal identification of Canton Tower under uncertain environmental conditions , 2012 .

[11]  Joachim Selbig,et al.  Non-linear PCA: a missing data approach , 2005, Bioinform..

[12]  Costas Papadimitriou,et al.  Hierarchical Bayesian model updating for structural identification , 2015 .

[13]  Deng Yang,et al.  Structural condition assessment of long-span suspension bridges using long-term monitoring data , 2010 .

[14]  Zhiliang Liu,et al.  Kernel Parameter Selection for Support Vector Machine Classification , 2014 .

[15]  Claudomiro Sales,et al.  Deep principal component analysis: An enhanced approach for structural damage identification , 2018, Structural Health Monitoring.

[16]  Gang Li,et al.  Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine , 2017 .

[17]  Haibei Xiong,et al.  Structural health monitoring of Shanghai Tower during different stages using a Bayesian approach , 2016 .

[18]  Guojin Tan,et al.  Vibration Analysis of Reinforced Concrete Simply Supported Beam versus Variation Temperature , 2017 .

[19]  Xiao Zhang,et al.  Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases , 2019, Sensors.

[20]  Yi-Qing Ni,et al.  Correlating modal properties with temperature using long-term monitoring data and support vector machine technique , 2005 .

[21]  Michael E. Fitzpatrick,et al.  Efficient truss optimization using the contrast-based fruit fly optimization algorithm , 2017 .

[22]  Roger Ghanem,et al.  A novel approach for the structural identification and monitoring of a full-scale 17-story building based on ambient vibration measurements , 2008 .

[23]  Jeong-Ho Kim,et al.  Development of a baseline for structural health monitoring for a curved post-tensioned concrete box–girder bridge , 2009 .

[24]  Wen-Hwa Wu,et al.  Assessment of environmental and nondestructive earthquake effects on modal parameters of an office building based on long-term vibration measurements , 2017 .

[25]  Ka-Veng Yuen,et al.  Ambient interference in long-term monitoring of buildings , 2010 .

[26]  Keith Worden,et al.  Some Recent Developments in SHM Based on Nonstationary Time Series Analysis , 2016, Proceedings of the IEEE.

[27]  Luis Eduardo Mujica,et al.  Structural damage detection using principal component analysis and damage indices , 2016 .

[28]  Yi-Qing Ni,et al.  SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data , 2012 .

[29]  Donato Sabia,et al.  A machine learning approach for the automatic long-term structural health monitoring , 2018, Structural Health Monitoring.

[30]  Xilin Lu,et al.  Operational modal analysis of a high-rise multi-function building with dampers by a Bayesian approach , 2017 .

[31]  Elsa Caetano,et al.  Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges , 2017 .