PhD Oral Exam - Ahmed Mohammed, Building Engineering
Resilience-Based Asset Management Framework for Pavement Maintenance and Rehabilitation
This event is free
School of Graduate Studies
When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
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 significances and increase their resilience. The growing decline in roads condition has recently grasped the attention of numerous researchers and practitioners towards 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 roads network (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. 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 resilience-based asset management model for intervention decisions, 4) optimize the attained intervention plan for short and long-term decisions, and 5) formulate a resilience index. 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. 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 effect 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 recovery/intervention actions are formulated through computational models that account for the intervention costs and time and 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 and genetic algorithms are utilized as an innovative idea that formulates the mathematical denotation for the proposed resilience definition. Principle Components Analysis (PCA) is utilized and manipulated as a novel method to establish resilience indicators weights and compute 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 towards 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, where intervention 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 intervention plan while taking the natural deterioration and aging effects into account. Two case studies were undertaken to demonstrate the effectiveness of the proposed methodology.