Probabilistic SDDLV method for localizing damage in bridges monitored within one cluster under time-varying environmental temperatures

The stochastic dynamic damage location vector (SDDLV) method has been widely utilized for localizing damage in structures. However, under changing environmental temperatures, its accuracy is greatly reduced, and the method may even lose efficacy. Furthermore, there has been no investigation of how SDDLV could localize damage in all bridges that are simultaneously monitored within one cluster, i.e., several medium- and small-span bridges with the same structural design on a continuous elevated corridor. To address these issues, a probabilistic SDDLV method is proposed in this study. First, unlike the conventional SDDLV method, the damage location vectors (DLVs) are generated using acceleration monitoring data acquired in the same period that are obtained from each pair of identical bridges among all the monitored bridges in one cluster. Second, considering variations in the finite element model (FEM) of bridges with respect to the environmental temperature, the probabilistic FEM is applied to replace the regular definite FEM, which is utilized to calculate the stresses of all the elements of the structures. Third, a method based on the probability characteristics of the damage localization index is presented to determine the thresholds of damage localization. Then, by incorporating hypothesis testing and a cross-validation strategy, the structural damage of all monitored bridges within one cluster is localized. Finally, a numerical simulation example is utilized to verify the effectiveness of the proposed method, and the effect of structural deviations in construction on the damage localization results is analyzed through the monitoring data of actual bridges.

[1]  Shinae Jang,et al.  Full-Scale Experimental Validation of High-Fidelity Wireless Measurement on a Historic Truss Bridge , 2011 .

[2]  Jihong Ye,et al.  Localization and quantification of partial cable damage in the long‐span cable‐stayed bridge using the abnormal variation of temperature‐induced girder deflection , 2018, Structural Control and Health Monitoring.

[3]  D. Bernal.  Flexibility-Based Damage Localization from Stochastic Realization Results , 2006 .

[4]  Gaëtan Kerschen,et al.  Structural damage diagnosis under varying environmental conditions - Part II: local PCA for non-linear cases , 2005 .

[5]  Abdollah Malekjafarian,et al.  Damage detection using curvatures obtained from vehicle measurements , 2017 .

[6]  Luc Chouinard,et al.  Likelihood-based testing of wavelet coefficients for damage detection in beam structures , 2017 .

[7]  Bo Chen,et al.  Field monitoring and numerical analysis of Tsing Ma Suspension Bridge temperature behavior , 2013 .

[8]  Charles R. Farrar,et al.  Comparative study of damage identification algorithms applied to a bridge: II. Numerical study , 1998 .

[9]  Michael Döhler,et al.  Vibration‐based damage localization with load vectors under temperature changes , 2019, Structural Control and Health Monitoring.

[10]  Hoon Sohn,et al.  An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge , 1999 .

[11]  Charles R. Farrar,et al.  Comparative study of damage identification algorithms applied to a bridge: I. Experiment , 1998 .

[12]  C. Farrar,et al.  SYSTEM IDENTIFICATION FROM AMBIENT VIBRATION MEASUREMENTS ON A BRIDGE , 1997 .

[13]  Joan Ramon Casas Rius,et al.  A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions , 2017 .

[14]  Chul-Woo Kim,et al.  A Bayesian approach for vibration-based long-term bridge monitoring to consider environmental and operational changes , 2015 .

[15]  Shinae Jang,et al.  Full-Scale Application of Stochastic DLV Method for Highway Bridge Health Monitoring , 2015 .

[16]  Shirley J. Dyke,et al.  Damage Detection Accommodating Varying Environmental Conditions , 2006 .

[17]  H. Hao,et al.  Long term vibration monitoring of an RC slab: Temperature and humidity effect , 2006 .

[18]  A. Emin Aktan,et al.  Use of Modal Flexibility for Damage Detection and Condition Assessment: Case Studies and Demonstrations on Large Structures , 2006 .

[19]  Billie F. Spencer,et al.  Experimental Verification of the Flexibility-Based Damage Locating Vector Method , 2007 .

[20]  Dionisio Bernal,et al.  Load Vectors for Damage Location in Systems Identified from Operational Loads , 2010 .

[21]  Laurent Mevel,et al.  Robust statistical damage localization with stochastic load vectors , 2015 .

[22]  Changping Li,et al.  Damage cross detection between bridges monitored within one cluster using the difference ratio of projected strain monitoring data , 2021, Structural Health Monitoring.

[23]  John T. DeWolf,et al.  Effect of Temperature on Modal Variability of a Curved Concrete Bridge under Ambient Loads , 2007 .

[24]  Erin Santini-Bell,et al.  Fatigue crack detection in welded structural components of steel bridges using artificial neural network , 2021, Journal of Civil Structural Health Monitoring.

[25]  Sung-Han Sim,et al.  Full-scale experimental validation of decentralized damage identification using wireless smart sensors , 2012 .

[26]  D. Bernal.  Load Vectors for Damage Localization , 2002 .

[27]  Daniel H. Felix,et al.  Crack detection in prestressed concrete structures by measuring their natural frequencies , 2018, Journal of Civil Structural Health Monitoring.

[28]  Smriti Sharma,et al.  One-dimensional convolutional neural network-based damage detection in structural joints , 2020, Journal of Civil Structural Health Monitoring.

[29]  Dionisio Bernal,et al.  Sensitivities of Eigenvalues and Eigenvectors from Complex Perturbations , 2012 .

[30]  Laurent Mevel,et al.  Statistical decision making for damage localization with stochastic load vectors , 2013 .

[31]  Yi-Qing Ni,et al.  Formulation of an uncertainty model relating modal parameters and environmental factors by using long-term monitoring data , 2003, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[32]  Dionisio Bernal,et al.  Damage Localization from the Null Space of Changes in the Transfer Matrix , 2006 .

[33]  Eleni Chatzi,et al.  On the use of mode shape curvatures for damage localization under varying environmental conditions: On the use of mode shape curvatures for damage localization under varying environmental conditions , 2018 .

[34]  S. Zhang,et al.  Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data , 2020 .

[35]  Guido De Roeck,et al.  One-year monitoring of the Z24-Bridge : environmental effects versus damage events , 2001 .

[36]  Yang Liu,et al.  Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method , 2020, Sensors.

[37]  Jyrki Kullaa,et al.  Eliminating Environmental or Operational Influences in Structural Health Monitoring using the Missing Data Analysis , 2009 .

[38]  Guido De Roeck,et al.  Damage identification on the Z24-bridge using vibration monitoring analysis , 2000 .

[39]  Siu-Seong Law,et al.  Long-Term Health Monitoring of in-Service Bridge Deck by Covariance of Covariance Matrix of Acceleration Responses , 2015 .

[40]  Ward Heylen,et al.  Structural damage assessment under varying temperature conditions , 2012 .

[41]  Faramarz Khoshnoudian,et al.  Ensemble classification method for structural damage assessment under varying temperature , 2018 .

[42]  Yang Liu,et al.  Probabilistic Baseline of Finite Element Model of Bridges under Environmental Temperature Changes , 2017, Comput. Aided Civ. Infrastructure Eng..

[43]  Hong Hao,et al.  Damage detection in bridge structures under moving loads with phase trajectory change of multi-type vibration measurements , 2017 .

[44]  Vasilis K. Dertimanis,et al.  On the use of mode shape curvatures for damage localization under varying environmental conditions , 2018 .

[45]  Mijia Yang,et al.  Bridge damage localization through modified curvature method , 2016 .

[46]  Shinae Jang,et al.  Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network , 2016 .

[47]  Wei Zheng,et al.  Mitigating effects of temperature variations through probabilistic-based machine learning for vibration-based bridge scour detection , 2020, Journal of Civil Structural Health Monitoring.

[48]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[49]  H. Adeli,et al.  Integrated Genetic Algorithm for Optimization of Space Structures , 1993 .

[50]  Nicholas A J Lieven,et al.  Frequency response function shape-based methods for structural damage localisation , 2009 .

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

[52]  H. J. Salane,et al.  IDENTIFICATION OF MODAL PROPERTIES OF BRIDGES , 1990 .

[53]  Asoke K. Nandi,et al.  Exponent parameter estimation for generalized Gaussian probability density functions with application to speech modeling , 2005, Signal Process..

[54]  Yonghui An,et al.  A degree of dispersion‐based damage localization method , 2016 .

[55]  Laurent Mevel,et al.  Statistical damage localization with stochastic load vectors using multiple mode sets , 2016 .

[56]  Mustafa Gul,et al.  Eliminating Temperature Effects in Damage Detection for Civil Infrastructure Using Time Series Analysis and Autoassociative Neural Networks , 2019, Journal of Aerospace Engineering.