Inventing an AI-Informed Risk Index to Prioritize Transportation Infrastructure Preservation
The proposed project seeks to transform national transportation asset datasets into actionable intelligence for preservation planning. Recognizing the fragmentation between roadway and bridge performance data within the Highway Performance Monitoring System (HPMS) and National Bridge Inventory (NBI), the project introduces an artificial intelligence (AI)-driven framework to systematically connect these datasets and develop a unified risk index. The study will first conduct comprehensive literature and data reviews to identify gaps in cross-asset analysis and assess data quality through spatial joins and validation of key attributes such as average daily traffic (ADT). Using descriptive, prescriptive, and predictive analytics, the research will examine relationships among international roughness index (IRI), bridge condition ratings, and traffic loading to uncover deterioration trends and key predictive features. The research will apply advanced machine learning models to forecast performance and support prioritization under budget constraints. The resulting risk index will provide transportation agencies with an objective method to rank preservation needs. This result will enhance Transportation Asset Management Plans (TAMPs) and ensure data-driven resource allocation. Expected outcomes include a validated analytic framework, cross-asset integration methods, predictive deterioration models, and interactive visualization dashboards to aid decision-making. The project directly supports USDOT's strategic goals of economic strength and global competitiveness by improving asset reliability, minimizing disruptions to freight and passenger mobility, and extending infrastructure service life. Educationally, it will train at least one doctoral student in advanced analytics and risk-based asset management. The research will also integrate the methods and results into graduate coursework and research. Technology transfer activities will disseminate results through academic publications, conference presentations, outreach products, and online tools. Stakeholder engagement will ensure practical adoption. Overall, this project aims to deliver a replicable, scalable decision-support tool to strengthen national transportation resilience and investment efficiency.
Language
- English
Project
- Status: Active
- Funding: $347,906.00
-
Contract Numbers:
69A3552348308
-
Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Center for Transformative Infrastructure Preservation and Sustainability
North Dakota State University
Fargo, North Dakota United States 58108-6050 -
Project Managers:
Tolliver, Denver
-
Performing Organizations:
Upper Great Plains Transportation Institute
North Dakota State University
1320 Albrecht Boulevard
Fargo, ND United States 581052 -
Principal Investigators:
Bridgelall, Raj
- Start Date: 20251216
- Expected Completion Date: 20271215
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
- Source Data: CTIPS-065
Subject/Index Terms
- TRT Terms: Artificial intelligence; Asset management; Data analysis; Maintenance management; Risk assessment
- Identifier Terms: Highway Performance Monitoring System; National Bridge Inventory
- Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;
Filing Info
- Accession Number: 01980786
- Record Type: Research project
- Source Agency: Center for Transformative Infrastructure Preservation and Sustainability
- Contract Numbers: 69A3552348308
- Files: UTC, RIP
- Created Date: Feb 22 2026 10:44AM