Modal identification of bridges under varying environmental conditions: Temperature and wind effects
Abstract
Numerous investigations have indicated that structural modal parameters are significantly impacted by varying environmental and operational conditions. This phenomenon will cause confusion when conducting modal-based damage detection and model updating. This paper investigates the dependency of modal frequencies, modal shapes and the associated damping ratios on temperature and wind velocity. The nonlinear principal component analysis (NLPCA) is first employed as a signal pre-processing tool to distinguish temperature and wind effects on structural modal parameters from other environmental factors. The pre-processed dataset by NLPCA implies the relationship between modal parameters and temperature as well as wind velocity. Consequently, the artificial neural network (ANN) technique is employed to model the relationship between the pre-processed modal parameters and environmental factors. Numerical results indicate that pre-processed modal parameters by NLPCA can retain the most features of original signals. Furthermore, the pre-processed modal frequency and damping ratios are dramatically affected by temperature and wind velocity. The ANN regression models have good capacities for mapping relationship of environmental factors and modal frequency, damping ratios. However, environmental effects on the entire modal shapes are insignificant. Copyright © 2009 John Wiley & Sons, Ltd.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Modal parameters pre-processed by NLPCA were capable of retaining the majority of the characteristics of the original signals.
“According to numerical results, the modal parameters pre-processed by NLPCA were capable of retaining the majority of the characteristics of the original signals.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [185]
ANN regression models showed fine mapping of the relationship between modal frequency and environmental factors and damping ratios.
“The ANN regression models showed the fine mapping of the relationship between modal frequency and environmental factors and damping ratios.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [185]
Damping ratios and pre-processed modal frequency were influenced by wind velocity and temperature.
“The damping ratios and the pre-processed modal frequency were also influenced by wind velocity and temperature.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [185]
Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883 · see citing works
Methods of conjugate gradients for solving linear systems
Hestenes, M. R., & Stiefel, E. L. (1952) · Journal of research of the National Bureau of Standards · doi:10.6028/jres.049.044 · Cited by 8,142 · see citing works
Nonlinear principal component analysis using autoassociative neural networks
Kramer, M. A. (1991) · AIChE Journal · doi:10.1002/aic.690370209 · Cited by 3,037 · see citing works
Los Alamos National Lab., N. (. S., Doebling, S., USDOE, W., Farrar, C., Prime, M., & Shevitz, D. (1996) · doi:10.2172/249299 · Cited by 2,899 · see citing works
Modal identification of output-only systems using frequency domain decomposition
Brincker, R., Zhang, L., & Andersen, P. (2001) · Smart Materials and Structures · doi:10.1088/0964-1726/10/3/303 · Cited by 1,841 · see citing works
Principal Component Neural Networks: Theory and Applications
Diamantaras, K., & Kung, S. (1996) · Medical Entomology and Zoology · Cited by 882 · see citing works
Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis
Yan, A.-M., Kerschen, G., Boe, P. D., & Golinval, J. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2004.12.002 · Cited by 537 · see citing works
Statistics, Probability and Reliability for Civil and Environmental Engineers
Kottegoda, N. T., & Rosso, R. (1997) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 419 · see citing works
An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge
Hoon Sohn, A., Dzwonczyk, M., Straser, E. G., Kiremidjian, A. S., Law, K. H., & Meng, T. H. Y. (1999) · Earthquake Engineering & Structural Dynamics · doi:10.1002/(sici)1096-9845(199908)28:8 · Cited by 344 · see citing works
Long term vibration monitoring of an RC slab: Temperature and humidity effect
Xia, Y., Hao, H., Zanardo, G., & Deeks, A. (2006) · Engineering Structures · doi:10.1016/j.engstruct.2005.09.001 · Cited by 331 · see citing works
Ni, Y., Hua, X., Fan, K. Q., & Ko, J. M. (2005) · Engineering Structures · doi:10.1016/j.engstruct.2005.02.020 · Cited by 305 · see citing works
Mechanical Vibration and Shock Measurements
Broch, J. T. (1980) · Cited by 259 · see citing works
Non-linear PCA: a missing data approach
Scholz, M., Kaplan, F., Guy, C. L., Kopka, J., & Selbig, J. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti634 · Cited by 207 · see citing works
Limitations of nonlinear PCA as performed with generic neural networks
Malthouse, E. C. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.655038 · Cited by 129 · see citing works
Variability of Modal Parameters Measured on the Alamosa Canyon Bridge
Farrar, C. R., Doebling, S. W., Cornwell, P. J., & Straser, E. G. (1997) · University of North Texas Digital Library (University of North Texas) · Cited by 128 · see citing works
Influence of in-service environment on modal parameters
Alampalli, S. (1998) · Cited by 90 · see citing works
Nonlinear principal component analysis to preserve the order of principal components
Saegusa, R., Sakano, H., & Hashimoto, S. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.03.004 · Cited by 63 · see citing works
Aiello, V., Boiero, D., D’Apuzzo, M., Socco, L. V., & Silvestri, F. (2008) · Structural Control and Health Monitoring · doi:10.1002/stc.247 · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Sun, L., Shang, Z., Xia, Y., Bhowmick, S., & Nagarajaiah, S. (2020) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002535 · Cited by 751 · see citing works
Hou, R., & Xia, Y. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2020.115741 · Cited by 674 · see citing works
Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23042204 · Cited by 492 · see citing works
Temperature effect on vibration properties of civil structures: a literature review and case studies
Xia, Y., Chen, B., Weng, S., Ni, Y., & Xu, Y. L. (2012) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-011-0015-7 · Cited by 316 · see citing works
The state of the art in structural health monitoring of cable-stayed bridges
Li, H., & Ou, J. (2015) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-015-0115-x · Cited by 268 · see citing works
Self-citationMoughty, J. J., & Casas, J. R. (2017) · Applied Sciences · doi:10.3390/app7050510 · Cited by 228 · see citing works
Ubertini, F., Comanducci, G., Cavalagli, N., Pisello, A. L., Materazzi, A. L., & Cotana, F. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.05.025 · Cited by 214 · see citing works
Structural health monitoring research under varying temperature condition: a review
Han, Q., Ma, Q., Xu, J., & Liu, M. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00444-x · Cited by 190 · see citing works
Li, S., Li, H., Liu, Y., Lan, C., Zhou, W., & Ou, J. (2013) · Structural Control and Health Monitoring · doi:10.1002/stc.1559 · Cited by 181 · see citing works
Self-citationZinno, R., Haghshenas, S. S., Guido, G., & Vitale, A. (2022) · IEEE Access · doi:10.1109/access.2022.3199443 · Cited by 181 · see citing works
Mao, J., Wang, H., Feng, D., Tao, T., & Zheng, W. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2146 · Cited by 163 · see citing works
State-of-the-art review on advancements of data mining in structural health monitoring
Gordan, M., Sabbagh-Yazdi, S.-R., Ismail, Z., Ghaedi, K., Carroll, P., McCrum, D. P., & Samali, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.110939 · Cited by 155 · see citing works
Erazo, K., Sen, D., Nagarajaiah, S., & Sun, L. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.07.041 · Cited by 137 · see citing works
Wang, Z., Yang, D., Yi, T., Zhang, G., & Han, J. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3073 · Cited by 117 · see citing works
Vibration attenuation of sandwich structures filled with shear thickening fluids
Gürgen, S., & Sofuoğlu, M. A. (2020) · Composites Part B Engineering · doi:10.1016/j.compositesb.2020.107831 · Cited by 99 · see citing works
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
Entezami, A., Sarmadi, H., & Behkamal, B. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110676 · Cited by 93 · see citing works
Structural health monitoring of a cable-stayed bridge with Bayesian neural networks
Arangio, S., & Bontempi, F. (2014) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2014.951867 · Cited by 92 · see citing works
Recent Developments in Damage Identification of Structures Using Data Mining
Gordan, M., Razak, H. A., Ismail, Z., & Ghaedi, K. (2017) · Latin American Journal of Solids and Structures · doi:10.1590/1679-78254378 · Cited by 89 · see citing works
Daneshvar, M. H., & Sarmadi, H. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.114059 · Cited by 88 · see citing works
Abid, S. R., Mussa, F. I., Tayşi, N., & Özakça, M. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.2042 · Cited by 85 · see citing works
Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section
Li, H., Huang, Y., Ou, J., & Bao, Y. (2011) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/j.1467-8667.2010.00686.x · Cited by 82 · see citing works
Self-citationShahsavari, V., Chouinard, L., & Bastien, J. (2016) · Engineering Structures · doi:10.1016/j.engstruct.2016.11.056 · Cited by 82 · see citing works
Ge, C.-X., & Chen, A. (2019) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2019.1625416 · Cited by 81 · see citing works
On the use of mode shape curvatures for damage localization under varying environmental conditions
Shokrani, Y., Dertimanis, V. K., Chatzi, E. N., & N. Savoia, M. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2132 · Cited by 76 · see citing works
Bridge identification and damage detection using contact point response difference of moving vehicle
Zhan, Y., Au, F. T. K., & Zhang, J. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2837 · Cited by 76 · see citing works
Kostić, B., & Gül, M. (2017) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001085 · Cited by 74 · see citing works
A Summary Review of Correlations between Temperatures and Vibration Properties of Long‐Span Bridges
Zhou, G.-D., & Yi, T. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/638209 · Cited by 73 · see citing works
Entezami, A., Sarmadi, H., Behkamal, B., & Mariani, S. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111984 · Cited by 72 · see citing works
Liang, Y., Li, D., Song, G., & Feng, Q. (2018) · Measurement · doi:10.1016/j.measurement.2018.04.034 · Cited by 71 · see citing works
Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder
Rastin, Z., Amiri, G. G., & Darvishan, E. (2021) · Shock and Vibration · doi:10.1155/2021/6658575 · Cited by 70 · see citing works
Lakshmi, K., Rao, A. R. M., & Gopalakrishnan, N. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.1960 · Cited by 63 · see citing works
Operational modal identification of structures based on improved empirical wavelet transform
Xin, Y., Hao, H., & Li, J. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2323 · Cited by 62 · see citing works
Fiber optic health monitoring and temperature behavior of bridge in cold region
Xiao, F., Hulsey, J. L., & Balasubramanian, R. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.2020 · Cited by 57 · see citing works
Reliability assessment of cable-stayed bridges based on structural health monitoring techniques
Li, H., Li, S., Ou, J., & Li, H. (2010) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2010.496856 · Cited by 57 · see citing works
Self-citationKaloop, M. R., & Hu, J. W. (2015) · Shock and Vibration · doi:10.1155/2015/102680 · Cited by 54 · see citing works
Zhang, Y., Kurata, M., & Lynch, J. P. (2016) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0001198 · Cited by 55 · see citing works
Structural health monitoring of suspension bridges with features affected by changing wind speed
Comanducci, G., Ubertini, F., & Materazzi, A. L. (2015) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2015.02.007 · Cited by 52 · see citing works
Sparse Bayesian learning for structural damage detection under varying temperature conditions
Hou, R., Wang, X., Xia, Q., & Xia, Y. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106965 · Cited by 50 · see citing works
Mu, H.-Q., & Yuen, K.-V. (2018) · Measurement · doi:10.1016/j.measurement.2018.08.022 · Cited by 49 · see citing works
Eigenproperties of suspension bridges with damage
Materazzi, A. L., & Ubertini, F. (2011) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2011.08.007 · Cited by 48 · see citing works
Damage detection of a cable-stayed bridge using feature extraction and selection methods
Bisheh, H. B., Amiri, G. G., Nekooei, M., & Darvishan, E. (2019) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2019.1599964 · Cited by 48 · see citing works
Ahmadian, V., Aval, S. B. B., Noori, M. N., Wang, T., & Altabey, W. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107226 · Cited by 46 · see citing works
Entezami, A., Sarmadi, H., & Behkamal, B. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118722 · Cited by 43 · see citing works
Kaloop, M. R., & Li, H. (2013) · Measurement · doi:10.1016/j.measurement.2013.09.046 · Cited by 43 · see citing works
Self-citationPrincipal component analysis for a seismic usability model of unreinforced masonry buildings
Zucconi, M., Sorrentino, L., & Ferlito, R. (2017) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2017.02.014 · Cited by 41 · see citing works
Wang, X., Li, L., Beck, J. L., & Xia, Y. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107563 · Cited by 41 · see citing works
Hwang, D., Kim, S.-J., & Kim, H.-K. (2021) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001761 · Cited by 41 · see citing works
Kaloop, M. R., & Li, H. (2011) · Measurement · doi:10.1016/j.measurement.2011.02.008 · Cited by 40 · see citing works
Self-citationFallahian, M., Ahmadi, E., & Khoshnoudian, F. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-021-00546-0 · Cited by 38 · see citing works
Jang, J., & Smyth, A. W. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2489 · Cited by 37 · see citing works
Damage metrics for masonry bridges under scour scenarios
Scozzese, F., Tubaldi, E., & Dall’Asta, A. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116914 · Cited by 36 · see citing works
Effect of Environmental Conditions on the Modal Response of a 10‐Story Reinforced Concrete Tower
Regni, M., Arezzo, D., Carbonari, S., Gara, F., & Zonta, D. (2018) · Shock and Vibration · doi:10.1155/2018/9476146 · Cited by 36 · see citing works
Dynamic Performance Analysis of the Towers of a Long-Span Bridge Based on GPS Monitoring Technique
Kaloop, M. R., & Hu, J. W. (2016) · Journal of Sensors · doi:10.1155/2016/7494817 · Cited by 35 · see citing works
Xiong, W., Cai, C. S. S., Kong, B., Zhang, X., & Tang, P. (2019) · Journal of Marine Science and Engineering · doi:10.3390/jmse7050121 · Cited by 34 · see citing works
Wang, H., Mao, J. X., Huang, J. H., & Li, A. Q. (2016) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0000856 · Cited by 32 · see citing works
Meng, Q., & Zhu, J. (2018) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001279 · Cited by 31 · see citing works
Generative Adversarial Network for Damage Identification in Civil Structures
Rastin, Z., Ghodrati Amiri, G., & Darvishan, E. (2021) · Shock and Vibration · doi:10.1155/2021/3987835 · Cited by 31 · see citing works
Siringoringo, D. M., & Fujino, Y. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2252 · Cited by 30 · see citing works
Cao, J., Li, C., Lixia, Z., Wu, Q., & Lv, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238513 · Cited by 29 · see citing works
Real-time temperature field and thermal deformation of slab track on cable-stayed bridge
Zhang, Q., Cai, X., Zhang, Y., Wang, T., & Zhong, Y. (2023) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2023.103582 · Cited by 29 · see citing works
Experimental analysis of the thermohygrometric effects on the dynamic behavior of adobe systems
Zonno, G., Aguilar, R., Boroschek, R. L., & Lourenço, P. B. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.02.140 · Cited by 29 · see citing works
Li, S., He, S., Li, H., & Jin, Y. (2017) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001154 · Cited by 27 · see citing works
Self-citationLai, Y., Chen, J., Hong, Q., Li, Z., Liu, H., Lu, B., Ma, R., Yu, C., Sun, R., Demartino, C., et al. (2022) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2022.e01020 · Cited by 26 · see citing works
Damage Detection of Tension Pendulums in Cable-Stayed Bridges Using Structural Frequency Variance
Wu, G., Yi, T., Yang, D., & Li, H. (2020) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001540 · Cited by 26 · see citing works
Self-citationAlves, V., & Cury, A. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2748 · Cited by 25 · see citing works
Liu, H., Wang, X., & Jiao, Y. (2016) · Shock and Vibration · doi:10.1155/2016/4792786 · Cited by 24 · see citing works
Jirawattanasomkul, T., Hang, L., Srivaranun, S., Likitlersuang, S., Jongvivatsakul, P., Yodsudjai, W., & Thammarak, P. (2025) · Resilient Cities and Structures · doi:10.1016/j.rcns.2025.08.001 · Cited by 23 · see citing works
Bridge damage detection from the equivalent damage load by multitype measurements
Li, Y., Sun, L., Zhang, W., & Nagarajaiah, S. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2709 · Cited by 23 · see citing works
Transfer Learning for CNN-Based Damage Detection in Civil Structures with Insufficient Data
Chamangard, M., Amiri, G. G., Darvishan, E., & Rastin, Z. (2022) · Shock and Vibration · doi:10.1155/2022/3635116 · Cited by 23 · see citing works
Operational modal analysis with compressed measurements based on prior information
Zhou, J. L., Kató, B., & Wang, Y. (2023) · Measurement · doi:10.1016/j.measurement.2023.112644 · Cited by 23 · see citing works
Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data
Shan, W., Wang, X., & Jiao, Y. (2018) · Shock and Vibration · doi:10.1155/2018/8072843 · Cited by 23 · see citing works
Model updating and variability analysis of modal parameters for super high‐rise structure
Ye, X., & Chen, B. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4712 · Cited by 22 · see citing works
Mei, L.-F., Yan, W., Yuen, K., & Beer, M. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111767 · Cited by 22 · see citing works
Mechanisms of thermally induced deflection of a long-span cable-stayed bridge
Zhou, Y., Sun, L., & Peng, Z. (2015) · Smart Structures and Systems · doi:10.12989/sss.2015.15.3.505 · Cited by 20 · see citing works
Li, Z., & Chang, C. C. (2011) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0000370 · Cited by 20 · see citing works
Khalel, H. H. Z., Khan, M., & Starr, A. G. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.105865 · Cited by 19 · see citing works
Mu, H., Zheng, Z., Wu, X., & Su, C. (2018) · Journal of low frequency noise, vibration and active control · doi:10.1177/1461348418786520 · Cited by 19 · see citing works
Continuous tracking of bridge modal parameters based on subspace correlations
Yang, X., Yi, T., Qu, C., Li, H., & Liu, H. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2615 · Cited by 18 · see citing works
Self-citationThe Effect of Temperature Variation on Bridges—A Literature Review
Borah, S., Al‐Habaibeh, A., & Kromanis, R. (2021) · Springer proceedings in energy · doi:10.1007/978-3-030-63916-7_26 · Cited by 18 · see citing works
Time-Frequency-Based Bridge Scour Identification by Trend-Change Detection
Xiong, W., & Cai, C. S. S. (2022) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001940 · Cited by 18 · see citing works
Tower Bridge Movement Analysis with GPS and Accelerometer Techniques: Case Study Yonghe Tower Bridge
Kaloop, M. R., & Li, H. (2009) · Information Technology Journal · doi:10.3923/itj.2009.1213.1220 · Cited by 18 · see citing works
Self-citationProbabilistic Damage Detection of Long-Span Bridges Using Measured Modal Frequencies and Temperature
Deng, Y., Li, A., & Feng, D. (2018) · International Journal of Structural Stability and Dynamics · doi:10.1142/s0219455418501262 · Cited by 18 · see citing works
Wang, S., Wang, H., Xu, M., & Guo, J. H. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106655 · Cited by 17 · see citing works
Ribeiro, D. R. F., Leite, J., Meixedo, A., Pinto, N., Calçada, R., & Todd, M. D. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2700 · Cited by 17 · see citing works
Nonlinear data-driven computational models for response prediction and change detection
Derkevorkian, A., Hernandez-Garcia, M. R., Yun, H., Masri, S. F., & Li, P. (2014) · Structural Control and Health Monitoring · doi:10.1002/stc.1673 · Cited by 17 · see citing works
An, B., Gao, L., Xin, T., Xiang, G., & Ji, W. (2019) · IEEE Access · doi:10.1109/access.2019.2959444 · Cited by 16 · see citing works
Modal identification and fatigue behavior of Eynel steel arch highway bridge with calibrated models
Sunca, F., Ergün, M., Altunışık, A. C., Günaydın, M., & Okur, F. Y. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-021-00512-w · Cited by 16 · see citing works
Bocian, M., Nikitas, N., Kalybek, M., Kużawa, M., Hawryszków, P., Bień, J., Onysyk, J., & Biliszczuk, J. (2022) · Archives of Civil and Mechanical Engineering · doi:10.1007/s43452-022-00582-7 · Cited by 16 · see citing works
Prawin, J., & Anbarasan, R. (2020) · Engineering Structures · doi:10.1016/j.engstruct.2020.111552 · Cited by 15 · see citing works
A review on fatigue monitoring of structures
García-Fernández, N., Aenlle, M. L., Álvarez‐Vázquez, A., Muñiz‐Calvente, M., & Pelayo, F. (2023) · International Journal of Structural Integrity · doi:10.1108/ijsi-09-2022-0117 · Cited by 17 · see citing works
Hwang, D., Kim, S.-J., & Kim, H.-K. (2023) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-023-00710-8 · Cited by 15 · see citing works
Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
Fu, S., & Wu, J. (2025) · Applied Sciences · doi:10.3390/app15137201 · Cited by 14 · see citing works
Thermal effect on dynamic characteristics of concrete girder bridges
Wei, X.-C., Zhang, H.-S., & Liu, Y.-F. (2024) · Structures · doi:10.1016/j.istruc.2024.105974 · Cited by 14 · see citing works
Ma, K.-C., Yi, T., Yang, D., Li, H., & Liu, H. (2021) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001636 · Cited by 14 · see citing works
Self-citationMu, H., Shen, J.-H., Zhao, Z., Liu, H.-T., & Yuen, K. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113571 · Cited by 14 · see citing works
Effect of Temperature on the Modal Variability in Short-Span Concrete Bridges
Cho, K., & Cho, J.-R. (2022) · Applied Sciences · doi:10.3390/app12199757 · Cited by 13 · see citing works
Chu, X., Cui, W., Xu, S., Zhao, L., Guan, H., & Ge, Y. (2023) · Structural Control and Health Monitoring · doi:10.1155/2023/6485040 · Cited by 13 · see citing works
Automatic baseline correction of strain gauge signals
Miao, S., Koenders, E., & Knobbe, A. (2014) · Structural Control and Health Monitoring · doi:10.1002/stc.1658 · Cited by 13 · see citing works
Ma, S., Li, J., Hao, H., & Jiang, S.-F. (2017) · Advances in Structural Engineering · doi:10.1177/1369433217717114 · Cited by 13 · see citing works
- Authors
- Li, H., Li, S., Ou, J., & Li, H.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Li, Hui
- Venue
- Structural Control and Health Monitoring
- Publisher
- Hindawi Limited
- Published
- 2009
- Pages
- n/a-n/a
- ISSN
- 1545-2255 (ISSN-L)
- 1545-2263
- OpenAlex
- W1988699504
- Cited by
- 168
- References
- 18
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 13 · 2023: 19 · 2022: 13 · 2021: 17 · 2020: 16 · 2019: 10 · 2018: 19 · 2017: 8 · 2016: 11 · 2015: 6 · 2014: 5 · 2013: 3 · 2012: 4
- FWCI
- 11.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Control and Health Monitoring (publishedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Vibration Analysis
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- temperature effects, wind speed effects, modal frequencies, damping ratio, mode shapes, nonlinear principal component analysis, signal preprocessing, neural network regression, damage detection, model updating
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review