Probabilistic Baseline of Finite Element Model of Bridges under Environmental Temperature Changes

The material properties and structural stiffness of actual bridges fluctuate with variations in environmental temperature; therefore, it is not appropriate to use a determined finite element model (FEM) as the baseline model for localizing the structural damage of bridges. To address this issue, we proposed the concept of the probabilistic baseline of FEM of bridges under variable environmental temperature, that is, we established reasonable probability distributions of the physical parameters of bridges that are suitable for damage localization with varying environmental temperature. First, a method is presented to obtain the probabilistic baseline of FEM of bridges, which imports cluster analysis into stochastic FEM updating. Unlike the conventional methods, the measured natural frequencies first are classified into different clusters using the Gaussian mixture method (GMM), with each cluster consisting of measured data that satisfy the same Gaussian distribution. Then, the conventional methods of stochastic FEM updating can be conveniently implemented to obtain the probabilistic baseline of FEM for each cluster. Second, for each cluster, the mean values and covariance of the updating parameters are updated in two sequential steps, and a new approach is proposed for determining the initial covariance of the updating parameters. The results of an actual example show that predetermining a reasonable initial covariance for the updating parameters can accurately and efficiently obtain the updated results. Finally, the effectiveness of the presented method is verified through the monitoring data of an actual bridge.

[1]  Hoon Sohn,et al.  Damage diagnosis under environmental and operational variations using unsupervised support vector machine , 2009 .

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

[3]  J. Mottershead,et al.  Interval model updating with irreducible uncertainty using the Kriging predictor , 2011 .

[4]  Yichang Tsai,et al.  Automatic Detection of Deficient Video Log Images Using a Histogram Equity Index and an Adaptive Gaussian Mixture Model , 2010, Comput. Aided Civ. Infrastructure Eng..

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

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

[7]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[8]  Hojjat Adeli,et al.  Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures , 2008 .

[9]  Erin Santini-Bell,et al.  A Two‐Step Model Updating Algorithm for Parameter Identification of Linear Elastic Damped Structures , 2013, Comput. Aided Civ. Infrastructure Eng..

[10]  Hui Wang,et al.  Bayesian Modeling of External Corrosion in Underground Pipelines Based on the Integration of Markov Chain Monte Carlo Techniques and Clustered Inspection Data , 2015, Comput. Aided Civ. Infrastructure Eng..

[11]  B. Peeters,et al.  Vibration-based damage detection in civil engineering: excitation sources and temperature effects , 2001 .

[12]  Hyo Seon Park,et al.  Model Updating Technique Based on Modal Participation Factors for Beam Structures , 2015, Comput. Aided Civ. Infrastructure Eng..

[13]  R. Christensen Plane Answers to Complex Questions: The Theory of Linear Models. , 1997 .

[14]  Piotr Omenzetter,et al.  Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating , 2015, Comput. Aided Civ. Infrastructure Eng..

[15]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  G. De Roeck,et al.  Vibration based Structural Health Monitoring using output-only measurements under changing environment , 2008 .

[17]  Oral Büyüköztürk,et al.  Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building , 2016, Sensors.

[18]  Djamel Bouchaffra,et al.  Genetic-based EM algorithm for learning Gaussian mixture models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jerome Sacks,et al.  Designs for Computer Experiments , 1989 .

[20]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[21]  Richard Craig Van Nostrand,et al.  Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement , 2002, Technometrics.

[22]  Rosario Ceravolo,et al.  Global Sensitivity‐Based Model Updating for Heritage Structures , 2015, Comput. Aided Civ. Infrastructure Eng..

[23]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[24]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[25]  Hojjat Adeli,et al.  Concurrent Structural Optimization on Massively Parallel Supercomputer , 1995 .

[26]  B. AfeArd CALCULATING THE SINGULAR VALUES AND PSEUDOINVERSE OF A MATRIX , 2022 .

[27]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[28]  M. Link,et al.  Updating of Analytical Models — Basic Procedures and Extensions , 1999 .

[29]  Anil Kumar,et al.  Identification, Model Updating, and Validation of a Steel Twin Deck Curved Cable‐Stayed Footbridge , 2014, Comput. Aided Civ. Infrastructure Eng..

[30]  Giuseppe Quaranta,et al.  Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements , 2011, Comput. Aided Civ. Infrastructure Eng..

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

[32]  John E. Mottershead,et al.  Parameter selection and covariance updating , 2016 .

[33]  John E. Mottershead,et al.  Stochastic model updating: Part 1—theory and simulated example , 2006 .

[34]  H. Adeli,et al.  Concurrent genetic algorithms for optimization of large structures , 1994 .

[35]  Hojjat Adeli,et al.  Monitoring the behavior of steel structures using distributed optical fiber sensors , 2000 .

[36]  R. L. McGreevy,et al.  Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures , 1988 .

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

[38]  Vincenzo Gattulli,et al.  Output‐Only Identification and Model Updating by Dynamic Testing in Unfavorable Conditions of a Seismically Damaged Building , 2014, Comput. Aided Civ. Infrastructure Eng..

[39]  Oral Büyüköztürk,et al.  Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization , 2015, Comput. Aided Civ. Infrastructure Eng..

[40]  Hong Wang,et al.  An efficient statistically equivalent reduced method on stochastic model updating , 2013 .

[41]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[42]  Raimondo Betti,et al.  A Hybrid Optimization Algorithm with Bayesian Inference for Probabilistic Model Updating , 2015, Comput. Aided Civ. Infrastructure Eng..

[43]  I. Flood,et al.  Neural networks in civil engineering: a review , 2001 .

[44]  John E. Mottershead,et al.  Model Updating In Structural Dynamics: A Survey , 1993 .

[45]  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.

[46]  Sergio Benedetto,et al.  A soft-input soft-output maximum a posteriori (MAP) module to decode parallel and serial concatenated codes , 1996 .

[47]  H. Adeli,et al.  Augmented Lagrangian genetic algorithm for structural optimization , 1994 .

[48]  John E. Mottershead,et al.  Finite Element Model Updating in Structural Dynamics , 1995 .

[49]  Per Christian Hansen,et al.  REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems , 1994, Numerical Algorithms.

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

[51]  Gaëtan Kerschen,et al.  Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis , 2005 .

[52]  T. Simpson,et al.  Use of Kriging Models to Approximate Deterministic Computer Models , 2005 .

[53]  Franco Bontempi,et al.  Genetic Algorithms for the Dependability Assurance in the Design of a Long‐Span Suspension Bridge , 2012, Comput. Aided Civ. Infrastructure Eng..

[54]  LinRen Zhou,et al.  Response Surface Method Based on Radial Basis Functions for Modeling Large-Scale Structures in Model Updating , 2013, Comput. Aided Civ. Infrastructure Eng..

[55]  J. S. Hunter,et al.  The 2 k — p Fractional Factorial Designs , 1961 .

[56]  Michael Link,et al.  Stochastic model updating—Covariance matrix adjustment from uncertain experimental modal data , 2010 .

[57]  Kazuomi Yamamoto,et al.  Efficient Optimization Design Method Using Kriging Model , 2005 .

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

[59]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[60]  Hojjat Adeli,et al.  Optimization of space structures by neural dynamics , 1995, Neural Networks.

[61]  Hojjat Adeli,et al.  Machine Learning: Neural Networks , 1994 .

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

[63]  C. Maresa,et al.  Stochastic model updating : Part 1 — theory and simulated example , 2006 .

[64]  Gene H. Golub,et al.  Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.

[65]  M. Friswell,et al.  Finite–element model updating using experimental test data: parametrization and regularization , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[66]  Farrokh Mistree,et al.  Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization , 2001 .

[67]  R. Fox,et al.  Rates of change of eigenvalues and eigenvectors. , 1968 .

[68]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[69]  Hojjat Adeli,et al.  Distributed Genetic Algorithm for Structural Optimization , 1995 .