Abstract

One-third of Canadian municipal infrastructure is in fair, poor or very poor condition, thus increasing the risk of service disruption (FCM, 2016). This represents a call for major construction works, which cause considerable business closures and increased travel delays. Therefore, the quantification of socio-economic impacts on businesses, residents, and road users due to construction works, has become important for municipalities. However, there is a lack of research on the quantification of socio-economic impacts at macro-level. In previous research efforts, developed quantification models were mostly site-specific, at micro-level, with no clear distinction between proposed categories of impacts. The main objective of this research is to model social costs of municipal infrastructure interventions at macro-level. First, the indicators that constitute tangible and intangible social costs of infrastructure works were identified and studied. Second, models were developed for each indicator to predict social cost variables. Third, non-linear regression models were built on the relationship between social cost variables and predictor variables, using the method of least squares. The following infrastructure assets are included in this research: potable water, wastewater and storm water pipes and roads. This research demonstrates that social cost indicators related to infrastructure works can be modeled by non-linear regression functions, thus enabling to assess social costs at a large scale. Then the social costs were incorporated in a Decision support system to compare three scenarios of renewal policies. The first scenario that is avoiding open-cut road reconstruction until the buried pipe replacement, results in the highest total costs and the best levels of service (LOS). The second scenario that is avoiding pipe replacement until the road reconstruction, results in the lowest total costs and the lowest LOS. This is because of the increase of pipe failures. Finally, the third scenario that is mixing both of the aforementioned strategies, results in the in-between solution with lower total costs and acceptable LOS.