Abstract

Infrastructure systems play a pivotal role in developing the economy and public services, which positively affects the quality of life of the communities. Thus, it is of paramount importance to investigate the current infrastructure capacity, assess its capability to sustain the anticipated disruptions, then plan the necessary recovery strategies to reduce their detrimental significance and increase their resilience. The growing decline in roads condition has recently grasped the attention of numerous researchers and practitioners regarding road resiliency during its life-cycle. 62.6% of roads in Canada are in good condition, according to Canada Infrastructure Report (2016). Nevertheless, with current investment rates, significant road networks will suffer a decline in their condition and will be vulnerable to sudden failure (FCM 2016). On the other side, the current situation in the U.S is inferior, where roads are in poor condition, classified as grade D, and not to mention the insufficient investment required to maintain road networks (ASCE, 2017).
Accordingly, this research tackles pavement resilience from an asset management perspective where; it highlights the fact that infrastructure should maintain its resiliency during its life-cycle to maintain a minimum acceptable Level of Service (LOS). The main objective of this research is to develop a resilience-based asset management framework for pavement maintenance and rehabilitation (M&R). The proposed methodology involves a set of sequential steps as follows; 1) define infrastructure resilience, 2) investigate resilience-related indicators in the same dimension of resilience definition, 3) develop a resilience-based asset management model for M&R decisions, 4) optimize the attained M&R plan for short and long-term decisions, and 5) formulate a resilience index. First, resilience is defined based on a comprehensive review of the previous literature and targeting an integrated definition that combines both asset management and resilience concepts. Then, resilience-associated indicators are investigated based on the predefined resilience definition, and the different indicators are later classified and modeled for a pavement network.
The resilience-based asset management model is carried out through the development of five components; 1) a central database of asset inventory that includes numerous data that would serve as input for the proposed model, 2) a pavement condition and level of service (LOS) assessment models that encompass the different effects of climatic conditions on pavement condition, surface, and structural conditions, and LOS, 3) regression modeling of the effect of Freeze-Thaw on pavement and investigation of flooding effect on both pavement surface and structural conditions, 4) financial and temporal models for recovery/intervention actions are formulated through computational models that account for the intervention costs and time, then link them to the later used optimization model, and 5) an optimization model to formulate the mathematical problem for the proposed resilience assessment approach and integrate the formerly-mentioned components. The utilized optimization model employs a single objective that relies on a combination of meta-heuristic rules. Genetic algorithms are utilized as an innovative idea that formulates the mathematical denotation for the proposed resilience definition. Principle Components Analysis (PCA) is used and manipulated as a novel method to establish resilience indicators’ weights and compute the resilience index. A PCA framework is developed based on optimization model output to generate the required weights for the desired resilience index. This model offers dynamic resilience indicators’ weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems, where it differs during their lifecycle with the change in maintenance and rehabilitation plans, systems retrofit, and the occurring disruptive events throughout their life-cycle.
The proposed model serves as an initial step toward providing more resilient municipal infrastructures. The model emphasizes that recovery plans should follow proactive measures to adapt to sudden or unforeseen events rather than just adopting a reactive approach, which deals with the sudden events after their occurrence. This pavement resilience assessment framework is also beneficial for asset management experts. M&R plans would not only target enhancing or restoring pavement condition or LOS but also incorporate the implementation of proper recovery strategies for both regular and extreme events into the M&R plan while taking the natural deterioration and aging effects into account. Two case studies were undertaken to demonstrate the effectiveness of the proposed methodology.