Abstracts – Browse Results
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Aroke, O M (2022) Measuring attention, working memory and visual perception to reduce the risk of injuries in the construction industry, Unpublished PhD Thesis, , George Mason University.
Ceran, N (2002) Private participation in infrastructure: A risk analysis of long-term contracts in power sector, Unpublished PhD Thesis, , George Mason University.
Checherita, C D (2009) A macroeconomic analysis of investment under public-private partnerships and its policy implications—the case of developing countries, Unpublished PhD Thesis, , George Mason University.
Gholizadeh, P (2022) Analyzing accidents among specialty contractors: A data mining approach, Unpublished PhD Thesis, , George Mason University.
Hassan, M E (2013) Assessing the impact of lean/integrated project delivery system on final project success, Unpublished PhD Thesis, , George Mason University.
John Samuel, I (2023) A human-centered infrastructure asset management framework using BIM and augmented reality, Unpublished PhD Thesis, , George Mason University.
Li, Y (2023) Integrated multi-stage decision-support for enhanced infrastructure restoration under uncertainty, Unpublished PhD Thesis, , George Mason University.
- Type: Thesis
- Keywords: coordination; decision support; uncertainty; communication; forecasting; infrastructure management; integration; monitoring; resource allocation; scheduling; inspection
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2849766283
- Abstract:
Critical infrastructure systems (e.g., water, transportation, and communication) provide fundamental services to communities. In recent decades, an increasing number of extreme events made infrastructure systems vulnerable and consequently brought severe impacts on community lifelines. To reduce such impacts, efficient infrastructure restoration is highly desired. The two determining phases of infrastructure restoration are the planning phase and the execution phase. In the planning phase, rapid infrastructure damage identification and restoration scheduling are critical to ensuring the efficient execution of restoration operations. In the execution phase, continuous restoration progress monitoring and control is needed for a timely identification of issues. Successful completion of these tasks involves emergency response agencies performing restoration operations under uncertain situations. However, during infrastructure restoration, coordination and communication among these agencies are challenging mainly because they have different roles, responsibilities, and jurisdictions within which they operate. In addition, unanticipated disruptions, changes in restoration tasks, and shifts in response demands further complicate the restoration process.With advanced data collection technologies, an increasing number of data sources (e.g., physical infrastructure information, socioeconomic information, and geographic information) have become available. Although being useful, transforming these data into useful decision support to facilitate rapid infrastructure restoration remains challenging. First of all, due to the unanticipated disruptions and the blocked road access, a valid and comprehensive damage inspection takes time to perform. Thus, only a limited amount of damage inspection data are available immediately following disruptions. Secondly, socially vulnerable communities are less prepared for disruptions. Therefore, it is important to restore the components that have greater impacts on socially vulnerable communities. Furthermore, to adapt to unanticipated disruptions that arise during the actual restoration, agencies need to update plans about task prioritization and resource allocation. As a result, the actual restoration progress deviates from the planned one. Last but not least, large-scale infrastructure damage typically spans across geographical and jurisdictional boundaries. Restoring these damaged components requires collaborative restoration efforts among various agencies, which takes significant communication and coordination efforts. To enable shared situational awareness and facilitate rapid infrastructure restoration, this research aims (1) to integrate geospatial correlation for addressing the data sparsity issue during infrastructure damage identification; (2) to prioritize restoration tasks while considering the socially vulnerable community demand; (3) to quantify the dynamic change of restoration progress during the restoration execution phase; and (4) to create a synchronized integration of various infrastructure restoration stages (damage identification, restoration scheduling, and progress monitoring).This research contributes to the domain of post-disaster infrastructure management by (1) proposing a systematic geospatial correlation-integrated approach for providing a quick spatial estimate of infrastructure damage status with incomplete information; (2) designing an equity-centered restoration scheduling approach that prioritizes restoration tasks while considering community social vulnerability; (3) performing real-time forecasting of infrastructure restoration progress and incorporate the associated uncertainties using Bayesian inference and earned schedule; and (4) establishing a framework that synchronizes various restoration stages. In practice, this research facilitates rapid infrastructure restoration by (1) providing a quick spatial estimate of infrastructure damage status, which greatly alleviates the effort and cost associated with field inspections; (2) generating up-to-date inf astructure restoration progress forecasting, which enables a timely observation of deviations between the actual and planned restoration progresses; (3) automatically recommending restoration task ranking while incorporating the socially vulnerable community demand, which could potentially alleviate the widening of the preexisting socioeconomic disparities; and (4) promoting quick and shared restoration situational awareness among the involved emergency response agencies, which facilitates communication and coordination and helps overcome challenges resulting from fragmented restoration efforts.
Momtaz, M (2023) Damage life cycle analysis for present and future condition assessments using statistical and machine learning techniques, Unpublished PhD Thesis, , George Mason University.
Solomon, T (2021) Change blindness in the construction industry, Unpublished PhD Thesis, , George Mason University.
Zhou, W (2023) Condition state-based decision making in evolving systems: Applications in asset management and delivery, Unpublished PhD Thesis, , George Mason University.