An Integrated Risk-Based Asset Management Framework for Subway Systems

Subway systems play a vital role connecting thousands of people to different destinations on a daily basis. The Canadian infrastructure report card recommended encouraging infrastructure owners to establish asset-management plans based on rates of deterioration and community service levels. Moreover, the 2013 report card for America’s infrastructure assigned a grade D to transit systems indicating they are in a poor condition with strong risk of failure. A possible solution proposed by the 2013 report card is adopting a comprehensive asset management system to maximize investments in light of the fund scarcity dilemma. The main objective of this research is to develop a risk-based asset management framework for subway networks. The framework works along three interrelated sub-models followed by two main models. A generic subway hierarchy is proposed and risk is assessed using three sub-models; probability of failure, consequences of failure and criticality index. Probability of failure is predicted for different structural elements using inspection reports and Weibull reliability function. Consequences of failure are assessed based on seven criteria along financial, social, and, operational perspectives. A criticality index is introduced to the classical risk equation to assess the functional importance of a station in its location using seven attributes along three main criteria. The Fuzzy Analytical Network Process is employed to analyze experts’ feedback used in the consequences of failure and criticality sub-models. This insures incorporating interdependency between criteria and fuzziness of the analysis. The three sub-models are used as inputs in a fuzzy inference engine to compute the predicted risk index. A set of thirty rules derived from experts through interviews and questionnaires is used to shape the relation between the fuzzy output and input variables. Finally, the second model is developed for a risk-based budget allocation model. The model utilizes the risk index components as objective functions. Decision variables are identified as five generic rehabilitation actions along their cost, time, and percentage improvement. The model provides the recommended rehabilitation action in light of the network total risk index and the available budget per time span. This is the first risk assessment framework proposed in the subway networks domain. Using a network analysis approach, the elements of a risk index are analyzed and aggregated from elements to lines and segments levels. The model revealed probability of failure to be the main driver of a risk index followed by criticality index and last, consequences of failure. Within the expected consequences of failure, social impacts had the highest impact (38%) based on experts’ feedback. The criticality index sub-model revealed station location to be the most important criteria (35%) followed by station nature of use (33%) and finally, station characteristics (32%). A segment of six stations from Montreal subway network is analyzed. The assessment indicates two stations with high risk indices showing the necessity of an intervention action. The budget allocation model prioritizes stations for rehabilitation according to the decision maker’s risk appetite, assumed at 0.6. The revised risk index for STA 4 dropped from 0.821 to 0.521 and the overall segment index dropped to zero. This research presents a basis for evaluating subway infrastructure on a structural and functional basis. It assists authorities to derive an informed rehabilitation decision using a generic and consistent framework. The heuristic decision making process followed by authorities is translated into a detailed framework that can be easily implemented and updated. The presented outline can be equally used for segments or the entire network

An Integrated Risk-Based Asset Management Framework for Subway Systems

Subway systems play a vital role connecting thousands of people to different destinations on a daily basis. The Canadian infrastructure report card recommended encouraging infrastructure owners to establish asset-management plans based on rates of deterioration and community service levels. Moreover, the 2013 report card for America’s infrastructure assigned a grade D to transit systems indicating they are in a poor condition with strong risk of failure. A possible solution proposed by the 2013 report card is adopting a comprehensive asset management system to maximize investments in light of the fund scarcity dilemma. The main objective of this research is to develop a risk-based asset management framework for subway networks. The framework works along three interrelated sub-models followed by two main models. A generic subway hierarchy is proposed and risk is assessed using three sub-models; probability of failure, consequences of failure and criticality index. Probability of failure is predicted for different structural elements using inspection reports and Weibull reliability function. Consequences of failure are assessed based on seven criteria along financial, social, and, operational perspectives. A criticality index is introduced to the classical risk equation to assess the functional importance of a station in its location using seven attributes along three main criteria. The Fuzzy Analytical Network Process is employed to analyze experts’ feedback used in the consequences of failure and criticality sub-models. This insures incorporating interdependency between criteria and fuzziness of the analysis. The three sub-models are used as inputs in a fuzzy inference engine to compute the predicted risk index. A set of thirty rules derived from experts through interviews and questionnaires is used to shape the relation between the fuzzy output and input variables. Finally, the second model is developed for a risk-based budget allocation model. The model utilizes the risk index components as objective functions. Decision variables are identified as five generic rehabilitation actions along their cost, time, and percentage improvement. The model provides the recommended rehabilitation action in light of the network total risk index and the available budget per time span. This is the first risk assessment framework proposed in the subway networks domain. Using a network analysis approach, the elements of a risk index are analyzed and aggregated from elements to lines and segments levels. The model revealed probability of failure to be the main driver of a risk index followed by criticality index and last, consequences of failure. Within the expected consequences of failure, social impacts had the highest impact (38%) based on experts’ feedback. The criticality index sub-model revealed station location to be the most important criteria (35%) followed by station nature of use (33%) and finally, station characteristics (32%). A segment of six stations from Montreal subway network is analyzed. The assessment indicates two stations with high risk indices showing the necessity of an intervention action. The budget allocation model prioritizes stations for rehabilitation according to the decision maker’s risk appetite, assumed at 0.6. The revised risk index for STA 4 dropped from 0.821 to 0.521 and the overall segment index dropped to zero. This research presents a basis for evaluating subway infrastructure on a structural and functional basis. It assists authorities to derive an informed rehabilitation decision using a generic and consistent framework. The heuristic decision making process followed by authorities is translated into a detailed framework that can be easily implemented and updated. The presented outline can be equally used for segments or the entire network

An Integrated Risk-Based Asset Management Framework for Subway Systems

Subway systems play a vital role connecting thousands of people to different destinations on a daily basis. The Canadian infrastructure report card recommended encouraging infrastructure owners to establish asset-management plans based on rates of deterioration and community service levels. Moreover, the 2013 report card for America’s infrastructure assigned a grade D to transit systems indicating they are in a poor condition with strong risk of failure. A possible solution proposed by the 2013 report card is adopting a comprehensive asset management system to maximize investments in light of the fund scarcity dilemma. The main objective of this research is to develop a risk-based asset management framework for subway networks. The framework works along three interrelated sub-models followed by two main models. A generic subway hierarchy is proposed and risk is assessed using three sub-models; probability of failure, consequences of failure and criticality index. Probability of failure is predicted for different structural elements using inspection reports and Weibull reliability function. Consequences of failure are assessed based on seven criteria along financial, social, and, operational perspectives. A criticality index is introduced to the classical risk equation to assess the functional importance of a station in its location using seven attributes along three main criteria. The Fuzzy Analytical Network Process is employed to analyze experts’ feedback used in the consequences of failure and criticality sub-models. This insures incorporating interdependency between criteria and fuzziness of the analysis. The three sub-models are used as inputs in a fuzzy inference engine to compute the predicted risk index. A set of thirty rules derived from experts through interviews and questionnaires is used to shape the relation between the fuzzy output and input variables. Finally, the second model is developed for a risk-based budget allocation model. The model utilizes the risk index components as objective functions. Decision variables are identified as five generic rehabilitation actions along their cost, time, and percentage improvement. The model provides the recommended rehabilitation action in light of the network total risk index and the available budget per time span. This is the first risk assessment framework proposed in the subway networks domain. Using a network analysis approach, the elements of a risk index are analyzed and aggregated from elements to lines and segments levels. The model revealed probability of failure to be the main driver of a risk index followed by criticality index and last, consequences of failure. Within the expected consequences of failure, social impacts had the highest impact (38%) based on experts’ feedback. The criticality index sub-model revealed station location to be the most important criteria (35%) followed by station nature of use (33%) and finally, station characteristics (32%). A segment of six stations from Montreal subway network is analyzed. The assessment indicates two stations with high risk indices showing the necessity of an intervention action. The budget allocation model prioritizes stations for rehabilitation according to the decision maker’s risk appetite, assumed at 0.6. The revised risk index for STA 4 dropped from 0.821 to 0.521 and the overall segment index dropped to zero. This research presents a basis for evaluating subway infrastructure on a structural and functional basis. It assists authorities to derive an informed rehabilitation decision using a generic and consistent framework. The heuristic decision making process followed by authorities is translated into a detailed framework that can be easily implemented and updated. The presented outline can be equally used for segments or the entire network

Life Cycle Environmental Assessment of Light Steel Framed Buildings with Cement-Based Walls and Floors

The objective of this paper is to apply the life cycle assessment methodology to assess the environmental impacts of light steel framed buildings fabricated from cold formed steel (CFS) sections. The assessment covers all phases over the life span of the building from material production, construction, use, and the end of building life, in addition to loads and benefits from reuse/recycling after building disposal. The life cycle inventory and environmental impact indicators are estimated using the Athena Impact Estimator for Buildings. The input data related to the building materials used are extracted from a building information model of the building while the operating energy in the use phase is calculated using an energy simulation software. The Athena Impact Estimator calculates the following mid-point environmental measures: global warming potential (GWP), acidification potential, human health potential, ozone depletion potential, smog potential, eutrophication potential, primary and non-renewable energy (PE) consumption, and fossil fuel consumption. The LCA assessment was applied to a case study of a university building. Results of the case study related to GWP and PE were as follows. The building foundations were responsible for 29% of the embodied GWP and 20% of the embodied PE, while the CFS skeleton was responsible for 30% of the embodied GWP and 49% of the embodied PE. The production stage was responsible for 90% of the embodied GWP and embodied PE. When benefits associated with recycling/reuse were included in the analysis according to Module D of EN 15978, the embodied GWP was reduced by 15.4% while the embodied PE was reduced by 6.22%. Compared with conventional construction systems, the CFS framing systems had much lower embodied GWP and PE

Modeling subway risk assessment using fuzzy logic

According to the Canadian Urban Transit Association (CUTA 2012), 140 Billion CAD is required to maintain, rehabilitate, and replace subway infrastructure between the years 2010 and 2014. However, transit authorities are faced by a fund scarcity problem which is hindering them from addressing all the network rehabilitation requirements in an efficient manner. The solution according to the 2013 America’s infrastructure report card is to adopt a comprehensive asset management system to maximize investments. This research develops a risk assessment model for subway stations. Probability of failure of different subway elements are developed using Weibull reliability curves. Consequences of failure are measured against three predefined attributes these are financial, operational, and social impacts of failure. Finally, a criticality index measures the respective station criticality derived from its particular size, location in proximity to different attraction types, and, nature of use. A qualitative approach with the help of expert judgment is adopted to integrate the indices using the Fuzzy Analytic Network Process with application to Fuzzy Preference Programming. The three models are integrated into a fuzzy rule based risk index model to compute element and station expected risk index. The output of the model is a comprehensive risk index that can be used to prioritize elements across stations for rehabilitation. The model is verified through an actual case study comparing elements across six stations and computing probability of failure, consequence of failure, criticality and the risk index. This paper illustrates the general framework of the proposed methodology which will help decision makers prioritize stations and elements across stations for rehabilitation based upon their risk index.Non UBCUnreviewedFacultyOthe

Special Issue “Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems”

This Special Issue includes a collection of papers that address the practical applications of GPR to various civil infrastructure systems [...

Multi-Criteria Decision Making Models for Water Pipelines

The deterioration of water pipelines leads to impaired water quality, increased breakage rate, and reduced hydraulic capacity. The planning of maintenance programs for water pipelines is essential to minimize health and safety concerns and ensure an adequate supply of water in a safe, cost-effective, reliable, and sustainable manner. It is essential to assess the performance of water pipelines to assist municipalities in planning inspection and rehabilitation programs for their pipelines. Several models have been developed to assess the condition or performance of water pipelines based on several affecting factors. However, none of them considered the interdependency relationships between the factors. Moreover, some models did not account for the factors' weights uncertainty. This paper presents the development of performance assessment models for water pipelines. The models consider three groups of factors affecting water pipeline performance, namely, physical, environmental, and operational. The models were developed using data collected from questionnaire surveys distributed among water pipeline experts in Qatar. The factors' weights were calculated using four different methods, namely, analytic hierarchy process (AHP), fuzzy AHP (FAHP), analytic network process (ANP), and fuzzy ANP (FANP). The results showed that the FANP is the most accurate method since it incorporates the interdependency and uncertainty into the decision making process.Qatar National Research Fund (QNRF) for this research project under Award No. QNRF-NPRP 4-529-2-193.Scopu