State-dependent stochastic models: A general stochastic framework for modeling deteriorating engineering systems considering multiple deterioration processes and their interactions

Abstract For performance analysis of deteriorating engineering systems, it is critical to model and incorporate the various deterioration processes and associated uncertainties. This paper proposes state-dependent stochastic models (SDSMs) for modeling the impact of deterioration on the performance of engineering systems. Within the stochastic framework, the change of the system state variables due to different deterioration processes and their interaction is modeled explicitly. As a candidate model to be used in the framework, a new general age and state dependent stochastic model for gradual deterioration is proposed, and its calibration based on data is also discussed. Once the time-variant system state variables are modeled, proper capacity and demand models that take them as inputs can be adopted to fully capture the impact of deterioration processes on the capacity, demand, and other system performances. The proposed framework is first demonstrated through a simple example, and then is used to model the deterioration of an example reinforced concrete (RC) bridge considering deterioration caused by both corrosion and earthquake including their interaction. The results show the importance of modeling the interaction between different deterioration processes, and also verify the advantages of the proposed framework.

[1]  Mark G. Stewart,et al.  Structural reliability of concrete bridges including improved chloride-induced corrosion models , 2000 .

[2]  Dan M. Frangopol,et al.  Life-cycle cost design of deteriorating structures , 1997 .

[3]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[4]  Dan M. Frangopol,et al.  Risk-Based Maintenance Optimization of Deteriorating Bridges , 2015 .

[5]  Iunio Iervolino,et al.  Reliability of structures to earthquake clusters , 2015, Bulletin of Earthquake Engineering.

[6]  Paolo Gardoni,et al.  Renewal theory-based life-cycle analysis of deteriorating engineering systems , 2014 .

[7]  Paolo Gardoni,et al.  Fragility Increment Functions for Deteriorating Reinforced Concrete Bridge Columns , 2010 .

[8]  Gianpaolo Pulcini,et al.  A continuous-state Markov model for age- and state-dependent degradation processes , 2011 .

[9]  Yaping Wang,et al.  Modeling the Dependent Competing Risks With Multiple Degradation Processes and Random Shock Using Time-Varying Copulas , 2012, IEEE Transactions on Reliability.

[10]  Paolo Gardoni,et al.  Probabilistic model for steel–concrete bond behavior in bridge columns affected by alkali silica reactions , 2014 .

[11]  Mahesh D. Pandey,et al.  Gamma processes and peaks-over-threshold distributions for time-dependent reliability , 2007, Reliab. Eng. Syst. Saf..

[12]  A. Taflanidis,et al.  Sample-based evaluation of global probabilistic sensitivity measures , 2014 .

[13]  Daniel Straub,et al.  Temporal Variability in Corrosion Modeling and Reliability Updating , 2007 .

[14]  Mauricio Sánchez-Silva,et al.  Life-cycle performance of structures subject to multiple deterioration mechanisms , 2011 .

[15]  Hans Beushausen,et al.  Corrosion in cracked and uncracked concrete – influence of crack width, concrete quality and crack reopening , 2010 .

[16]  Dan M. Frangopol,et al.  Two probabilistic life-cycle maintenance models for deteriorating civil infrastructures , 2004 .

[17]  Dan M. Frangopol,et al.  Generalized bridge network performance analysis with correlation and time-variant reliability , 2011 .

[18]  Bruce R. Ellingwood,et al.  MAINTAINING RELIABILITY OF CONCRETE STRUCTURES. II: OPTIMUM INSPECTION/REPAIR , 1994 .

[19]  Paolo Gardoni,et al.  Seismic reliability of deteriorating reinforced concrete (RC) bridges , 2013 .

[20]  Paolo Gardoni,et al.  Simulation-based approach for estimation of stochastic performances of deteriorating engineering systems , 2018 .

[21]  Bruce R. Ellingwood,et al.  Reliability-Based Service-Life Assessment of Aging Concrete Structures , 1993 .

[22]  Paolo Gardoni,et al.  Life-Cycle Analysis of Engineering Systems: Modeling Deterioration, Instantaneous Reliability, and Resilience , 2017 .

[23]  Dan M. Frangopol,et al.  A probabilistic computational framework for bridge network optimal maintenance scheduling , 2011, Reliab. Eng. Syst. Saf..

[24]  Michael P. Enright,et al.  Service-Life Prediction of Deteriorating Concrete Bridges , 1998 .

[25]  Paolo Gardoni,et al.  Modeling Structural Degradation of RC Bridge Columns Subjected to Earthquakes and Their Fragility Estimates , 2012 .

[26]  Mark G. Stewart,et al.  Climate change impact and risks of concrete infrastructure deterioration , 2011 .

[27]  Jamie E. Padgett,et al.  Aging Considerations in the Development of Time-Dependent Seismic Fragility Curves , 2010 .

[28]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[29]  Dan M. Frangopol,et al.  Probabilistic models for life‐cycle performance of deteriorating structures: review and future directions , 2004 .

[30]  Mark G. Stewart,et al.  Time-dependent reliability of deteriorating reinforced concrete bridge decks , 1998 .

[31]  Paolo Gardoni,et al.  Time-variant service reliability of post-tensioned, segmental, concrete bridges exposed to corrosive environments , 2010 .

[32]  P. Gardoni,et al.  Effect of seismic degradation on the fragility of reinforced concrete bridges , 2014 .

[33]  Paolo Gardoni,et al.  Evaluating the Source of the Risks Associated with Natural Events , 2011 .

[34]  Paolo Gardoni,et al.  A stochastic framework to model deterioration in engineering systems , 2015 .

[35]  Iunio Iervolino,et al.  Gamma degradation models for earthquake-resistant structures , 2013 .

[36]  P. Gardoni,et al.  Risk analysis of natural hazards : interdisciplinary challenges and integrated solutions , 2016 .

[37]  Terje Haukaas,et al.  Seismic fragility estimates for reinforced concrete bridges subject to corrosion , 2009 .

[38]  Armen Der Kiureghian,et al.  Probabilistic Capacity Models and Fragility Estimates for Reinforced Concrete Columns based on Experimental Observations , 2002 .

[39]  Emilio Bastidas-Arteaga,et al.  Probabilistic lifetime assessment of RC structures under coupled corrosion–fatigue deterioration processes , 2009 .

[40]  Anders Rønnquist,et al.  Global Buckling Reliability Analysis of Slender Network Arch Bridges: An Application of Monte Carlo-Based Estimation by Optimized Fitting , 2017 .

[41]  P. Gardoni,et al.  Performance of RC Columns Affected by ASR. II: Experiments and Assessment , 2015 .

[42]  Bruce R. Ellingwood,et al.  Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants , 1993 .

[43]  Terje Haukaas,et al.  Probabilistic capacity models and seismic fragility estimates for RC columns subject to corrosion , 2008, Reliab. Eng. Syst. Saf..

[44]  Paolo Gardoni,et al.  Empirical Bayes Approach for Developing Hierarchical Probabilistic Predictive Models and Its Application to the Seismic Reliability Analysis of FRP-Retrofitted RC Bridges , 2015 .

[45]  Armen Der Kiureghian,et al.  PROBABILISTIC SEISMIC DEMAND MODELS AND FRAGILITY ESTIMATES FOR RC BRIDGES , 2003 .

[46]  Bruce R. Ellingwood,et al.  Probabilistic methods for assessing current and future performance of concrete structures in nuclear power plants , 2002 .

[47]  Quanwang Li,et al.  Time-dependent reliability of aging structures in the presence of non-stationary loads and degradation , 2015 .

[48]  Bruce R. Ellingwood,et al.  Risk-informed condition assessment of civil infrastructure: state of practice and research issues , 2005 .

[49]  James L. Beck,et al.  Bayesian updating and model class selection of deteriorating hysteretic structural models using recorded seismic response , 2008 .