Fatigue damage assessment of orthotropic steel deck using dynamic Bayesian networks

Abstract Current health monitoring of orthotropic steel deck (OSD) subjected to fatigue damage mainly relies on deterministic physical models and the field measurements through inspections and monitoring. Nevertheless, various aleatory (random) and epistemic (lack of knowledge) uncertainties exist due to empirical justifications, model simplifications, inaccurate statistics of model parameter, measurement error, etc. The present study proposes a probabilistic model for fatigue damage diagnosis and prognosis of an OSD through integrating the physical model with field inspections while accounting for the associated uncertainties, using the dynamic Bayesian network (DBN). The DBN is suitable for representation and reasoning under uncertainty in various fields. The proposed framework aims to fulfill two interdependent tasks (1) diagnosis: track the time-dependent variable (i.e., the crack growth) and calibrate the time-independent variables (i.e., the geometric parameters and the multiplier for the crack shape factor); and (2) prognosis: predict the crack growth in the future. The particle filter (PF) is employed as the Bayesian inference algorithm for the established non-Gaussian DBN composed of continuous variables of various distribution types, as well as nonlinear conditional dependence between them. In addition, a Gaussian process (GP) surrogate model is established to relate the output stress response of the physical model (OSD) under the input truck load and model parameters, which is further implemented as the conditional probability distribution (CPD) in the DBN model. Finally, the proposed framework is illustrated by a numerical example of fatigue crack growth on the OSD subjected to truck load.

[1]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[2]  Bruno Castanier,et al.  A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[3]  Sankaran Mahadevan,et al.  Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin , 2017 .

[4]  Sankaran Mahadevan,et al.  Efficient approximate inference in Bayesian networks with continuous variables , 2018, Reliab. Eng. Syst. Saf..

[5]  E. W. C. Wilkins,et al.  Cumulative damage in fatigue , 1956 .

[6]  Jianren Zhang,et al.  Bridge Remaining Strength Prediction Integrated with Bayesian Network and In Situ Load Testing , 2014 .

[7]  Daniel Straub,et al.  Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks , 2009 .

[8]  이근우.  Applying material optimization to fracture mechanics analysis to improve the reliability of the plastic IC package in reflow soldering process , 2006 .

[9]  Matthias W. Seeger,et al.  Gaussian Processes For Machine Learning , 2004, Int. J. Neural Syst..

[10]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[11]  Haikun Wei,et al.  A Gaussian process regression based hybrid approach for short-term wind speed prediction , 2016 .

[12]  Wei Zhang,et al.  Probabilistic fatigue damage assessment of coastal slender bridges under coupled dynamic loads , 2018, Engineering Structures.

[13]  Daniel Straub,et al.  Assessing and updating the reliability of concrete bridges subjected to spatial deterioration – principles and software implementation , 2015 .

[14]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[15]  Matthew Collette,et al.  A dynamic discretization method for reliability inference in Dynamic Bayesian Networks , 2015, Reliab. Eng. Syst. Saf..

[16]  Daniel Straub,et al.  Reliability analysis and updating of deteriorating systems with dynamic Bayesian networks , 2016 .

[17]  Jianjun Qin,et al.  Risk Management of Large RC Structures within Spatial Information System , 2012, Comput. Aided Civ. Infrastructure Eng..

[18]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[19]  A. J. Mcevily,et al.  Crack opening displacement and the rate of fatigue crack growth , 1972 .

[20]  Farid Taheri,et al.  Proposed modifications to the Wheeler retardation model for multiple overloading fatigue life prediction , 2006 .

[21]  Daniel Straub,et al.  Dynamic Bayesian Network for Probabilistic Modeling of Tunnel Excavation Processes , 2013, Comput. Aided Civ. Infrastructure Eng..

[22]  Jeffrey A. Laman,et al.  Fatigue-load models for girder bridges , 1996 .

[23]  Silvia Torresan,et al.  Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective. , 2017, Journal of environmental management.

[24]  Natasha Smith,et al.  Bayesian networks for system reliability reassessment , 2001 .

[25]  Carl E. Rasmussen,et al.  Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..

[26]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .