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Using Emerging Technologies to Capture, Process, and Optimize Asset Inventory and Condition Data
Agencies are becoming more reliant on asset inventory and condition data to create a virtual digital twin to the real world assets that exist and change over time. These changes can result from accidents, natural events, maintenance or construction activities. These changes need to be reflected in the digital twin as close to real time as possible to maintain the usefulness and validity of the virtual twin.
The purpose of this proposed research is examine emerging and established technologies used to capture and update changes to these assets in the field and the necessary steps to ensure that these changes are processed and integrated into the authoritative systems in as close to real time as possible to determine the utility of the data, and how to collect, manage, and apply it more effectively.
Literature Search Summary
• NCHRP 23-16: Implementing and Leveraging Machine Learning at State Departments of Transportation: Available here.
• NCHRP 956 “Guidebook for Data and Information Systems for Asset Management” Available here.
• NCHRP 800 “Successful Practices in GIS-based Asset Management” Available here.
• NCHRP 508 “Data Management and Governance Practices” Available here
• NCHRP 491 “Use of Mobile IT Devices in the Field for Design, Construction and Asset Management” Available here
Emerging and current technologies hold the promise of transforming asset data col-lection for transportation asset management such as the use of drones for inspec-tions, LiDAR field data collection, continuous monitoring of real-time sensor data, and more. While the technology has been transforming, MAP-21 and the Fast Act jump started at many agencies in attaining an inventory of infrastructure assets and trans-portation data. At the same time, accessibility and affordability to collect high volumes of asset inventory data, such as LiDAR point cloud data, present the problem of how agencies can visualize and manage such large amounts of data and integrate the many layers for each transportation asset management plan. Now that the need for such data is federally recognized, further research is needed to understand what the latest technologies for asset management can offer an agency as well as how fre-quently that information needs to be captured and optimized.
Research is needed in the following areas:
· Identification of key current and emerging technologies for the capture, extrac-tion, processing and updating of asset inventory and condition data in authori-tative asset management systems
· Examples of current and emerging technologies include: mobile data collection, (iPhone, tablet, laptop), high resolution imagery, mobile LiDAR, machine learn-ing, artificial intelligence, neural networks, internet of things (IoT), nanotechnol-ogy and microelectronics, ground penetrating radar (GPR), and other data col-lection and processing and integrating technologies.
· Address the challenges of the rapid pace of technological advancement and the application of these technologies in a cost effective and practical manner, con-sidering obsolescence, staff expertise, and willingness to adopt new technolo-gies.
· Evaluate the level of extraction detail and frequency interval needed to support TAM at both the state and local levels and how can the condition assessment can be applied to the performance measures of both pavement and non-pavement assets.
· Further investigate what tools are capable of visualizing and presenting data to all stakeholders in various formats (i.e GIS, systems of engagement) with standardized and consistent formats of presentation and interaction.
· Identify best practices for managing these technologies and systems as they work holistically across the agency as cost effective enterprise solutions, includ-ing but not limited to types of expertise and staff resources.
· The research should include use cases of efficient and effective applications of these technologies, processes and systems.
· The research should consider any refinements that would need to occur in net-work level and project level asset management data collection to make the data useful for compliance (i.e. ADA), safety (i.e. bridge clearances) or engineering purposes (i.e. BIM/CIM).
Urgency and Potential Benefits
State and local transportation agencies are rapidly adopting Asset Management prac-tices to optimize infrastructure conditions for the resources available and to meet Fed-eral Transportation Asset Management Planning reporting requirements. To meet these demands there is a profound need to invest in technology and systems to under-stand the fully inventoried condition of various transportation assets and to model the outcomes of various investment strategies.
The potential benefits of this research is to provide insights to decision makers at transportation agencies on how to navigate a world of constantly changing and evolving technology. It will help agencies work better with technology firms. It will provide guidance on how to minimize the impact of technology on staff and make more effec-tive use of taxpayer dollars. It will provide guidance on how to make data in these au-thoritative systems more useful and through visualization make that data more un-derstandable. By making these systems and data collection practices more efficient and effective it will enable transportation officials to make better informed investments in the transportation leading to a more equitable, sustainable and resilient system.
Digital twins of physical roadway assets can model deterioration, forecast needed treatments, archive inspections, and be updated through subsequent field collection. It is like an API that allows multiple applications to interact with the managed data. Implementation of research may still require a need to manipulate with API, understand data interoperability, and account for differentiation between vendors data collection standards. Although vendors have a lot of solutions developed, the research will determine the best approach given the asset type, acceptable LOS, change detection, and general data management regardless of vendor.
Others Supporting Problem Statement
Potential Panel Members
Person Submitting Statement
New York State DOT (retired)