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A platform for research: civil engineering, architecture and urbanism
Optimization of Infrastructure Rehabilitation Funding Decisions Considering Vulnerability-Based Stochastic Deterioration Modelling
Infrastructure increasing bill, due to failure to fill the investment gap between infrastructure needs and available funds, is still persistent. Many fund-allocation optimization models were developed to find a solution to this everlasting problem. However, the pace of these efforts is not compatible with the fast deterioration of infrastructure due to its vulnerability to exogenous factors that accelerate its deterioration beyond the expected rate. There is a lack of research efforts that have formulated infrastructure vulnerability in the prioritization and fund-allocation algorithm. Accordingly, this research proposes a fund-allocation optimization model that maximizes infrastructure physical performance under budget constraints, considering a new vulnerability-based stochastic deterioration modelling. The model computes first an overall vulnerability index, for each asset in the network, which is function of the attributing factors that may vary from one geographical location to another. The vulnerability index is then incorporated in a Markov-based deterioration behavior modelling to include the vulnerability impact in the fund-allocation algorithm. In this research, the proposed model is applied to a road network as one type of infrastructure to examine its performance. Thus, an empirical study was conducted to capture the exogenous factors that would make roads vulnerable to faster deterioration, including: excessive traffic loading, climate change, neighboring disturbance, etc. Applying the model and comparing it against the existing models, results demonstrated rationality behind the generated funding decisions using the proposed model, and the cumbersome consequences of ignoring vulnerability. Thus, the model can help policy-makers make realistic funding decisions to maintain infrastructure performance.
Optimization of Infrastructure Rehabilitation Funding Decisions Considering Vulnerability-Based Stochastic Deterioration Modelling
Infrastructure increasing bill, due to failure to fill the investment gap between infrastructure needs and available funds, is still persistent. Many fund-allocation optimization models were developed to find a solution to this everlasting problem. However, the pace of these efforts is not compatible with the fast deterioration of infrastructure due to its vulnerability to exogenous factors that accelerate its deterioration beyond the expected rate. There is a lack of research efforts that have formulated infrastructure vulnerability in the prioritization and fund-allocation algorithm. Accordingly, this research proposes a fund-allocation optimization model that maximizes infrastructure physical performance under budget constraints, considering a new vulnerability-based stochastic deterioration modelling. The model computes first an overall vulnerability index, for each asset in the network, which is function of the attributing factors that may vary from one geographical location to another. The vulnerability index is then incorporated in a Markov-based deterioration behavior modelling to include the vulnerability impact in the fund-allocation algorithm. In this research, the proposed model is applied to a road network as one type of infrastructure to examine its performance. Thus, an empirical study was conducted to capture the exogenous factors that would make roads vulnerable to faster deterioration, including: excessive traffic loading, climate change, neighboring disturbance, etc. Applying the model and comparing it against the existing models, results demonstrated rationality behind the generated funding decisions using the proposed model, and the cumbersome consequences of ignoring vulnerability. Thus, the model can help policy-makers make realistic funding decisions to maintain infrastructure performance.
Optimization of Infrastructure Rehabilitation Funding Decisions Considering Vulnerability-Based Stochastic Deterioration Modelling
Saad, Dina (author) / Bahaa, Mohamed (author) / Osman, Hesham (author)
Construction Research Congress 2020 ; 2020 ; Tempe, Arizona
Construction Research Congress 2020 ; 304-313
2020-11-09
Conference paper
Electronic Resource
English
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