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Project Details

Author(s) Zhaohua Wang, Georgia Institute of Technology
CTEDD Funding Year 2017 General RFP
Project Status Complete
UTC Funding $58,266.35
End Date August 31, 2018


The local governments (LGs) in the Unitized States are managing 3/4 of total 4 million miles of roadway and more than 1/2 of nearly 600,000 bridges, which are critical transportation infrastructure assets to support the mobility, economy, and homeland security in local communities and the nation as a whole. To maintain the aging transportation infrastructure in the state of good repair under the shrinking budget, the state Departments of Transportation (DOTs) have adopted asset management systems (AMSs) to conduct cost‐effective maintenance, rehabilitation, and reconstruction (MR&R). However, most of LGs still rely on individuals’ knowledge and experience to manage and make decisions on transportation infrastructure MR&R. The lack of a systematic approach for managing transportation infrastructure assets makes it difficult to for an LG to maximize the benefit of the scarce transportation budget and demonstrate accountability to the legislatures, the public, and other stakeholders. To address the above issues, this project is to analyze the underlying factors that hinder LGs from adopting AMS through a comprehensive survey. Then, a multi‐asset management framework for LGs is proposed. The proposed framework extensively considers the LGs’ particularity in organization, workforce, and funding sources. In the meantime, the similarity to the corresponding state DOTs are also considered. Thus, the state DOTs’ resources can be wisely leveraged. To implement the asset inventory, condition assessment, and management, the most up‐to‐date technologies such as 3D sensing, computer vision, crowdsourcing, cloud computing, GIS/GPS, and mobile and web applications are evaluated and incorporated. The MR&R decision‐making process is modeled as a multi‐asset, multi‐facility mathematical programming. Various exact solutions and heuristic approaches are evaluated and tested to solve the large‐scale optimization problem. Finally, a case study is developed based on the practice of a county or a city in Georgia.

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