Structural Performance Model for Subway Networks

The Transit Federal Administration (FTA) reported that transit use increased by 25% between 1995 and 2005 in North America. Current communities are anticipating a high quality of life where people will be able to move freely with an affordable, reliable and efficient public transit. In 2009, the FTA estimated that 15.8 billion USD is needed annually to maintain and 21.6 billion USD is needed to improve the US transit network to satisfactory conditions. Moreover, the Canadian Urban Transit Association (CUTA) estimated that 140 Billion CAD are required for maintaining, rehabilitating and replacing the subway infrastructure between 2010 and 2014. It is apparent that subway management planning is of extreme importance in order to maintain the safety of infrastructure. Subway management plans consist of assessing the structural performance of subway networks, predicting future performance, planning future maintenance and repair policies and optimizing budget allocation. Most transit authorities lack tools/models for assessing the structural performance of subway network. Therefore, the present research assists in developing the SUbway PERformance (SUPER) model, which assesses structural performance of different components in a subway network and develops performance curves of subway components, systems, lines and the entire network. The developed SUPER model performs the following steps in order to achieve the above-mentioned objectives: (1) identifies and studies network hierarchy, (2) performs structural physical, functional and integrated performance assessment at the component level, and (3) constructs performance curves at the component, line and network levels. The SUPER model uses the Analytic Hierarchy Process and Multi-Attribute Utility Theory in order to assess the integrated components’ performance. It also utilizes a reliability-based cumulative Weibull function to construct the performance curves of components. In addition, series/parallel system modeling techniques are adopted to evaluate and construct the performance models of the systems, lines and network. Finally, a software application based upon the SUPER model is developed, entitled the ‘SUPER Model Software’. Data are collected from the Societe de Transport de Montreal (STM) inspection reports and through questionnaires. The questionnaires target transit authority managers and experienced structural engineers in both Canada and the USA. The developed SUPER model is applied to a network segment of the STM subway network. Results show that system deterioration rates are between 2% and 3% per year. The remaining useful service life are predicted to be until the year 2076 for renovated stations, 2030 for tunnels and between 2024 and 2040 for auxiliary structures. This research is relevant to industry practitioners (managers, engineers and field inspectors) and researchers since it develops structural performance assessment models and curves for subway networks.

[1]  Teresa M Adams,et al.  FAULT-TREE MODEL OF BRIDGE ELEMENT DETERIORATION DUE TO INTERACTION , 1997 .

[2]  E. Vesikari,et al.  Durability Design of Concrete Structures , 2004 .

[3]  Dan M. Frangopol,et al.  Optimizing Lifetime Condition and Reliability of Deteriorating Structures with Emphasis on Bridges , 2008 .

[4]  Dan M. Frangopol,et al.  Condition, safety and cost profiles for deteriorating structures with emphasis on bridges , 2005, Reliab. Eng. Syst. Saf..

[5]  Sue Cox,et al.  Safety, Reliability and Risk Management: An Integrated Approach , 1998 .

[6]  Tarek Zayed,et al.  Comparative Analysis of Life-Cycle Costing for Rehabilitating Infrastructure Systems , 2009 .

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

[8]  Samer Madanat,et al.  Optimal infrastructure management decisions under uncertainty , 1993 .

[9]  Bernard Roy,et al.  A programming method for determining which Paris metro stations should be renovated , 1986 .

[10]  S. Setunge,et al.  Use of Markov Chain for Deterioration Modeling and Risk Management of Infrastructure Assets , 2006, 2006 International Conference on Information and Automation.

[11]  Jidong Yang,et al.  Use of Recurrent Markov Chains for Modeling the Crack Performance of Flexible Pavements , 2005 .

[12]  Samer Madanat,et al.  Poisson Regression Models of Infrastructure Transition Probabilities , 1995 .

[13]  P. Thompson,et al.  The Pontis Bridge Management System, Structural Engineering International , 1998 .

[14]  G Pernica,et al.  DETERIORATION OF CONCRETE : SYMPTOMS, CAUSES AND INVESTIGATION , 2000 .

[15]  Dan M. Frangopol,et al.  Probabilistic Life-Cycle Analysis of Deteriorating Structures under Multiple Performance Constraints , 2004 .

[16]  Dan M. Frangopol,et al.  Life-cycle reliability-based maintenance cost optimization of deteriorating structures with emphasis on bridges , 2003 .

[17]  Dan M. Frangopol,et al.  RELIABILITY-BASED LIFE-CYCLE MANAGEMENT OF HIGHWAY BRIDGES , 2001 .

[18]  Michael A. Lacasse,et al.  Towards Standardization of Service Life Prediction of Roofing Membranes , 1999 .

[19]  George Morcous,et al.  Performance Prediction of Bridge Deck Systems Using Markov Chains , 2006 .

[20]  A. K. S. Jardine,et al.  Maintenance, Replacement, and Reliability , 2021 .

[21]  Dimitri A. Grivas,et al.  Method for Estimating Transition Probability in Bridge Deterioration Models , 1998 .

[22]  Tarek Zayed,et al.  Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines , 2008 .

[23]  Matthew G. Karlaftis,et al.  Probabilistic Infrastructure Deterioration Models with Panel Data , 1997 .

[24]  Nabil Semaan,et al.  Subway station diagnosis index (SSDI) : a condition assessment model , 2006 .

[25]  Dulcy M. Abraham,et al.  Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems , 2006 .

[26]  Gerald J. Lieberman,et al.  Introduction to operation research. , 2001 .

[27]  Seosamh B. Costello,et al.  Derivation of Transition Probability Matrices for Pavement Deterioration Modeling , 2006 .

[28]  Tarek Zayed,et al.  Subway Station Diagnosis Index Condition Assessment Model , 2009 .

[29]  S. Rahman Reliability Engineering and System Safety , 2011 .

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

[31]  Samuel H Carpenter,et al.  PAVEMENT PERFORMANCE PREDICTION MODEL USING THE MARKOV PROCESS , 1987 .

[32]  E Lalonde,et al.  A DECISION-SUPPORT METHODOLOGY FOR ASSET MANAGEMENT , 2003 .

[33]  Jorge A Prozzi,et al.  Estimation of Pavement Performance Deterioration Using Bayesian Approach , 2006 .

[34]  Allen R. Marshall,et al.  The PONTIS bridge management system , 1998 .