Generalized bridge network performance analysis with correlation and time-variant reliability

Abstract A novel approach and framework for the analysis of bridge networks is presented. The goal of the analysis is to assess the life-cycle performance of the network and its time-variant reliability. The proposed approach combines three important features that determine its ability to estimate with accuracy and robustness the reliability of a network along its life-cycle. The first one is that the reliability of the individual bridges is modeled as time-variant, due to the deterioration of their structural components. The network performance analysis is repeated for several time instants, with the relative reliability of the bridges. Therefore, the network performance indicators are also time-variant. In this way, the proposed procedure can be used as a basic tool for maintenance planning at the network level. The second asset is that the proposed framework takes into account complex (i.e. “generalized”) network layouts, not necessarily describable using series, parallel or series-parallel models. In fact, when all the possible traffic flows in a network and all the possible trip origins and destinations are considered, it is not feasible, in general, to model the network with a simple scheme. In the present paper, techniques derived from transportation engineering for the traffic flow distribution and assignment are used. The third feature is that the proposed approach considers a correlation structure among the states (in/out of service) of the various bridges of the network. In fact, bridges associated with the same network are likely to share similar characteristics and external loads. Therefore, a correlation structure for the service state of individual bridges is estimated and implemented in the analysis. A case study involving a transportation network with fourteen bridges is presented as a numerical application.

[1]  David M Levinson,et al.  MULTIMODAL TRIP DISTRIBUTION: STRUCTURE AND APPLICATION , 1994 .

[2]  Barry J. Goodno,et al.  Interdependent Response of Networked Systems , 2007 .

[3]  S.-P. Chang Application of the structural health monitoring system to the long span cable-supported bridges , 2006 .

[4]  David A. Hensher,et al.  Handbook of Transport Modelling , 2000 .

[5]  Paolo Gardoni,et al.  Post-hazard flow capacity of bridge transportation network considering structural deterioration of bridges , 2011 .

[6]  Dan M. Frangopol,et al.  Lifetime Performance Analysis of Existing Steel Girder Bridge Superstructures , 2004 .

[7]  P Albrecht,et al.  Composite Modeling of Atmospheric Corrosion Penetration Data , 1994 .

[8]  J. Wardrop ROAD PAPER. SOME THEORETICAL ASPECTS OF ROAD TRAFFIC RESEARCH. , 1952 .

[9]  Dan M. Frangopol,et al.  Probability-Based Bridge Network Performance Evaluation , 2006 .

[10]  Dan M. Frangopol,et al.  Bridge Maintenance, Safety, Management and Life-Cycle Optimization , 2010 .

[11]  J. G. Wardrop,et al.  Some Theoretical Aspects of Road Traffic Research , 1952 .

[12]  Dan M. Frangopol,et al.  Lifetime Performance Analysis of Existing Prestressed Concrete Bridge Superstructures , 2004 .

[13]  Dan M. Frangopol,et al.  Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria: Life-Cycle Maintenance, Failure, and User Costs , 2006 .

[14]  Stephen Warshall,et al.  A Theorem on Boolean Matrices , 1962, JACM.

[15]  M. Piedmonte,et al.  A Method for Generating High-Dimensional Multivariate Binary Variates , 1991 .

[16]  Zhanmin Zhang,et al.  Network-Level Multi-objective Optimal Maintenance and Rehabilitation Scheduling , 2010 .

[17]  Dan M. Frangopol,et al.  Lifetime Performance Analysis of Existing Reinforced Concrete Bridges. I: Theory , 2005 .

[18]  Matthias Bethge,et al.  Near-Maximum Entropy Models for Binary Neural Representations of Natural Images , 2007, NIPS.

[19]  Leonardo Dueñas-Osorio,et al.  Cascading failures in complex infrastructure systems , 2009 .

[20]  Dan M. Frangopol,et al.  Time-dependent interaction between load rating and reliability of deteriorating bridges , 2004 .

[21]  Dan M. Frangopol,et al.  Rating and Reliability of Existing Bridges in a Network , 2003 .

[22]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[23]  Michael G. McNally,et al.  The Four Step Model , 2007 .

[24]  Mahmoud Mesbah,et al.  Reliability Based Investment Prioritization in Transportation Networks , 2010 .

[25]  Dan M. Frangopol,et al.  BRIDGE RATING AND RELIABILITY CORRELATION: COMPREHENSIVE STUDY FOR DIFFERENT BRIDGE TYPES , 2004 .

[26]  Gustavo A. Cragnolino,et al.  Application of Accelerated Corrosion Tests to Service Life Prediction of Materials , 1994 .

[27]  W Caspari,et al.  Recognizing and reducing vulnerabilities of transportation infrastructure , 2010 .

[28]  Dan M. Frangopol,et al.  Optimal planning of retrofitting interventions on bridges in a highway network , 1998 .

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

[30]  Leonardo Dueñas-Osorio,et al.  Reliability Assessment of Lifeline Systems with Radial Topology , 2011, Comput. Aided Civ. Infrastructure Eng..

[31]  日本道路協会 Specifications for highway bridges , 1984 .

[32]  Dan M. Frangopol,et al.  Lifetime Performance Analysis of Existing Reinforced Concrete Bridges. II: Application , 2005 .

[33]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[34]  R. Machemehl,et al.  Combined traffic signal control and traffic assignment: algorithms, implementation and numerical results , 1998 .

[35]  Mda Thomas,et al.  TESTING THE CHLORIDE PENETRATION RESISTANCE OF CONCRETE: A LITERATURE REVIEW , 1997 .

[36]  Masanobu Shinozuka,et al.  Socio-economic effect of seismic retrofit of bridges for highway transportation networks: a pilot study , 2010 .

[37]  Dan M. Frangopol,et al.  Computational Platform for Predicting Lifetime System Reliability Profiles for Different Structure Types in a Network , 2004 .

[38]  Dan M. Frangopol,et al.  A stochastic computational framework for the joint transportation network fragility analysis and traffic flow distribution under extreme events , 2011 .

[39]  Suzanne P. Evans,et al.  DERIVATION AND ANALYSIS OF SOME MODELS FOR COMBINING TRIP DISTRIBUTION AND ASSIGNMENT , 1976 .

[40]  Dan M. Frangopol,et al.  Time-Dependent Bridge Network Reliability: Novel Approach , 2005 .

[41]  Barry J. Goodno,et al.  Seismic response of critical interdependent networks , 2007 .

[42]  Dan M. Frangopol,et al.  Balancing Connectivity of Deteriorating Bridge Networks and Long-Term Maintenance Cost through Optimization , 2005 .

[43]  A. Gibbons Algorithmic Graph Theory , 1985 .

[44]  Dan M. Frangopol,et al.  On the applicability of random field theory to transportation network analysis , 2010 .