Link to Latest Report: Coming Soon
Background:
Many of the nation’s bridges are aging and require urgent attention for rehabilitation or replacement. Traditional bridge prioritization approaches do not account for the broader impacts of bridge failures on access to critical infrastructure or community resilience. This project aims to address the shortcomings of the existing systems by developing a decision-support platform that considers diverse performance metrics, including mobility, and disaster resilience. Various national agencies have identified the need for computational platforms for varied purposes, such as the Natural Hazards Engineering Research Infrastructure (NHERI) Design Safe [1], SimCenter [2], funded by the National Science Foundation (NSF), and Interdependent Networked Community Resilience Modeling Environment (IN-CORE) [3], funded by the National Institute of Standards and Technology (NIST). The purpose of these platforms has been to improve disaster resilience by providing archives of data, state-of-the-art algorithms, and access to high-performance computational resources. However, these efforts have typically limited the use case to researchers instead of practicing professionals. While the proposed project is smaller than these cited efforts, the research focus and targeted user groups differ. The PI and Co-PIs will learn from these projects to implement best practices for data management, algorithmic integration, and long-term survivability. Implementing advanced prioritization methodologies requires data from multiple sources, optimization and statistical prediction techniques, and visualization tools to support decision-making. The platform will assess risk factors on bridges from routine wear to extreme disasters, enabling resilient strategies that strengthen evacuation routes and connectivity to essential services. Designed with a human-centered approach, the platform will prioritize bridge projects that improve safety, and quality of life. An online platform will reduce the time and effort of practical applications, enhance communication, and amplify the impact of other research projects.
Objective:
The objectives of this research include:
- Create a cloud-based platform that integrates multiple data sources for bridge condition assessment.
- Implement predictive algorithms to forecast bridge deterioration and recommend optimal bridge projects.
- Visualize the impact of bridge projects on mobility and resilience.
- Evaluate the post-intervention improvement in transportation network performance.
Scope:
The proposed project includes several tasks:
- Task 1 – Data Collection and Integration:
- The platform will establish a secure, centralized data repository to provide comprehensive and accurate evaluations. Data collection will involve acquiring diverse datasets, including Bridge Infrastructure Data — structural condition reports from the National Bridge Inventory (NBI)[4], state-level inventories, and maintenance history and inspection records. Traffic and Mobility Metrics — Real-time and historical traffic data to understand how bridge conditions affect traffic flow and congestion. Demographic Data — Information from sources such as the U.S. Census Bureau [5] to analyze how bridge projects impact community mobility to reach critical services. Disaster and Resilience Data — Data related to natural disaster risk and historical impact analysis to prioritize bridges critical for evacuation routes and emergency services.
- The platform will establish a secure, centralized data repository to provide comprehensive and accurate evaluations. Data collection will involve acquiring diverse datasets, including Bridge Infrastructure Data — structural condition reports from the National Bridge Inventory (NBI)[4], state-level inventories, and maintenance history and inspection records. Traffic and Mobility Metrics — Real-time and historical traffic data to understand how bridge conditions affect traffic flow and congestion. Demographic Data — Information from sources such as the U.S. Census Bureau [5] to analyze how bridge projects impact community mobility to reach critical services. Disaster and Resilience Data — Data related to natural disaster risk and historical impact analysis to prioritize bridges critical for evacuation routes and emergency services.
- Task 2 – Algorithm Implementation for Bridge Prioritization:
- Advanced machine learning models and optimization algorithms will form the backbone of the platform’s predictive and analytical capabilities. The approach includes: Model Development — Implementing machine learning algorithms to forecast bridge deterioration. These models will be trained and validated using historical data to achieve high predictive accuracy. Optimization Techniques — Utilizing linear programming and other optimization methods to prioritize bridge projects. The algorithms will consider budget constraints, bridge performance, and the criticality of each bridge to overall network connectivity. Scenario Analysis — Simulating various maintenance, repair, and replacement strategies to compare outcomes. For example, the platform will be able to model the impact of deferred maintenance versus proactive interventions, providing insights into each approach’s long-term benefits and costs.
- Advanced machine learning models and optimization algorithms will form the backbone of the platform’s predictive and analytical capabilities. The approach includes: Model Development — Implementing machine learning algorithms to forecast bridge deterioration. These models will be trained and validated using historical data to achieve high predictive accuracy. Optimization Techniques — Utilizing linear programming and other optimization methods to prioritize bridge projects. The algorithms will consider budget constraints, bridge performance, and the criticality of each bridge to overall network connectivity. Scenario Analysis — Simulating various maintenance, repair, and replacement strategies to compare outcomes. For example, the platform will be able to model the impact of deferred maintenance versus proactive interventions, providing insights into each approach’s long-term benefits and costs.
- Task 3 – Visualization and User Interface:
- The platform will feature a user-friendly interface designed for easy interaction and exploration of data. Key features will include GIS Mapping — Integrating Geographic Information System (GIS) technology to present bridge information spatially. This will allow users to visualize the distribution of critical infrastructure and understand the connectivity between bridges and essential services such as hospitals, schools, and police stations. Interactive Dashboards — Using visualization tools like Apache Superset, the platform will display data on bridge conditions, traffic flow disruptions, and the impact of bridge projects. Users will be able to filter and explore data based on geographic location, bridge type, and performance metrics.
- The platform will feature a user-friendly interface designed for easy interaction and exploration of data. Key features will include GIS Mapping — Integrating Geographic Information System (GIS) technology to present bridge information spatially. This will allow users to visualize the distribution of critical infrastructure and understand the connectivity between bridges and essential services such as hospitals, schools, and police stations. Interactive Dashboards — Using visualization tools like Apache Superset, the platform will display data on bridge conditions, traffic flow disruptions, and the impact of bridge projects. Users will be able to filter and explore data based on geographic location, bridge type, and performance metrics.
- Task 4 – Project Improvement Evaluation:
- The final objective is to evaluate the impact of bridge maintenance, repair, and replacement on the broader transportation network and community resilience. This will be achieved through: Impact Analysis — Quantifying the benefits of bridge projects, not only in terms of structural improvements but also in terms of enhanced mobility and safety. The platform will use these analyses to provide recommendations for bridge project planning. Feedback Mechanism — Incorporating a feedback loop that allows users to input the outcomes of actual bridge projects. This data will refine predictive models and improve the platform’s accuracy.
Research Team:
Principal Investigator: Neetesh Sharma, Ph.D.
Co-Principal Investigator: Eren Erman Ozguven, Ph.D., and John Sobanjo, Ph.D.