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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Communicating the Benefits of a Maintenance Budget Overhaul to Legislators: The Shock Factor of Inaction</title>
      <link>https://rip.trb.org/View/2768406</link>
      <description><![CDATA[Budget overruns can force the Kentucky Transportation Cabinet (KYTC) to delay routine maintenance like guardrail repair, pothole patching, and mowing, particularly if the agency has to dedicate more funding than expected to winter and emergency responses. Although deferred maintenance does not immediately take assets out of service, repeated delays degrade infrastructure condition. How delays affect user safety is not well understood, easily quantified, nor grasped by the public. Identifying preservation needs, calculating maintenance returns on investment, and developing objective measures that can underpin maintenance budget allocations will all help to close these knowledge gaps. However, they must be complemented by the use of proven communications strategies that vividly convey to legislators and the public the long-term risks and costs of deferring routine maintenance.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:04:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768406</guid>
    </item>
    <item>
      <title>Guidelines for Maintenance of Uncrewed Aircraft Systems Used in Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2714359</link>
      <description><![CDATA[The use of uncrewed aircraft systems (UAS), commonly known as drones, has increased exponentially in various industries due to their versatility and cost-effectiveness. However, as the use of UAS in commercial operations under Federal Aviation Administration (FAA) regulations continues to expand, ensuring the safety, reliability, and airworthiness of the UAS fleet becomes increasingly important. Maintaining a diverse fleet of UAS from multiple platforms, used across various industries, necessitates a comprehensive maintenance optimization strategy to address potential risks, ensure regulatory compliance, and maximize operational efficiency. Research is needed to help state departments of transportation (DOTs) manage UAS maintenance activities to enhance the design, maintenance, inspection, and construction of transportation infrastructure.

OBJECTIVE: The objective of this project is to develop guidelines for state DOTs for comprehensive maintenance of UAS to promote safe, reliable, and cost-effective operation. The guidelines shall include minimum maintenance standards and schedules adaptable to multiple UAS platforms and operational demands that can support streamlined maintenance management approaches.  ]]></description>
      <pubDate>Mon, 15 Jun 2026 16:45:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2714359</guid>
    </item>
    <item>
      <title>Innovative Approaches to Maintenance Funding for Active Transportation Infrastructure on State Highways Research</title>
      <link>https://rip.trb.org/View/2712169</link>
      <description><![CDATA[Currently, no substantial detailed advice or research exists in the leading national work on active transportation maintenance or active transportation policy on innovative maintenance funding strategies. More robust resources do exist for capital expenditures and new projects. Current funding maintenance recommendations generally do not go beyond mentioning that consistent funding is good and having a plan is good. Good public policy requires more detailed thought and specific guidance, which are especially critical during the planning and scoping phases to build maintainability in from project initiation.

Research is needed to address this gap and build from recently completed maintenance research. A comprehensive list, an analysis of funding sources and their constraints, and a cost-to-benefits comparison will provide the necessary groundwork for an informed assessment of innovative research strategies and enable good case studies to be identified. Focusing on ongoing maintenance needs, guidance for planning and scoping phases, and guidance on how active transportation maintenance fits into the larger transportation context would help ensure that these strategies work for current maintenance teams and result in better new projects coming into maintenance obligations.

The objective of this research is to identify innovative funding strategies for active transportation facility maintenance on state highways. Appropriately maintained facilities provide road safety, economic development, and land value benefits that could be factored into strategies. The research will address how these strategies align with ongoing maintenance needs for these facilities and provide guidance on maintenance in the planning and scoping of projects.]]></description>
      <pubDate>Tue, 09 Jun 2026 12:38:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712169</guid>
    </item>
    <item>
      <title>Road Network Restoration after Major Disruptions</title>
      <link>https://rip.trb.org/View/2447123</link>
      <description><![CDATA[This project develops practical optimization methods for selecting, sequencing, and scheduling restoration actions for disrupted road networks based on incomplete and gradually improving information. Road networks may be severely damaged by events such as hurricanes and earthquakes, and prompt restoration is often necessary for the resumption of emergency services, other essential services, and normal activities.

The proposed methods employ artificial intelligence heuristics such as genetic algorithms and particle swarm algorithms to optimize the schedules of restoration tasks. A hybrid optimization approach combines fast traffic assignment with microscopic simulation to refine solutions. The methods are designed to start with incomplete, uncertain information and adapt dynamically as additional data becomes available from weather forecasts, work crews, and the public. The project also develops methods for pre-planning purposes, including preparing effective restoration plans based on estimated probabilities of disruptions and their consequences.

The research team will collaborate with the Maryland State Highway Administration and other agencies to ensure the practical applicability of the methods. Technology transfer activities include journal papers, conference presentations, software with a user manual, a final technical report, and workshops for interested transportation organizations.]]></description>
      <pubDate>Wed, 11 Mar 2026 13:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447123</guid>
    </item>
    <item>
      <title>Inventing an AI-Informed Risk Index to Prioritize Transportation Infrastructure Preservation</title>
      <link>https://rip.trb.org/View/2672766</link>
      <description><![CDATA[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.]]></description>
      <pubDate>Sun, 22 Feb 2026 10:44:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2672766</guid>
    </item>
    <item>
      <title>Development of Data Driven Digital Twin for Enhancing Pavement Performance Prediction in South-Central United States</title>
      <link>https://rip.trb.org/View/2658057</link>
      <description><![CDATA[A comprehensive survey conducted by National Cooperative Highway Research Program (NCHRP) Synthesis 501 revealed that many state departments of transportation (DOTs) update their pavement performance models only every 2 to 5 years, with some agencies updating even less frequently. Such lengthy update cycles mean that the models often fail to reflect recent trends in traffic loading and material performance, leading to outdated forecasts that diminish the accuracy and usefulness of maintenance and rehabilitation planning. The primary objective of this project is to develop a data-driven Digital Twin (DT) framework based on pavement management system data that will regularly update Artificial Intelligence (AI)-based performance models for pavements in Louisiana. This framework aims to help state agencies make smarter, more accurate maintenance decisions while reducing costs over time. The proposed Digital Twin platform will focus on the interstate network in Louisiana, given its importance to the state and its wide implications on mobility and freight movement. The work will be divided into five tasks: (1) collect and preprocess pavement management system data for the interstate network, (2) development of digital twin framework, (3) forecast future pavement conditions in digital twin platform, (4) suggest potential maintenance strategies in the digital twin platform, and (5) prepare final report. The project will address the growing need for innovative approaches that can dynamically integrate diverse datasets, learn from both historical and emerging patterns, and provide transportation agencies with actionable, real-time insights. Digital twin technology offers this dynamic capability by enabling a shift from reactive maintenance toward predictive and proactive strategies.  ]]></description>
      <pubDate>Fri, 23 Jan 2026 13:50:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658057</guid>
    </item>
    <item>
      <title>An AI-Based Reasoning Framework for Proactive Infrastructure Monitoring and Preservation Using Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2655750</link>
      <description><![CDATA[This research proposes the development of a Connected Autonomous Vehicles (CAV)-based Proactive Infrastructure Preserving (CAV-PIP) system to enhance the safety, resilience, and operational efficiency of transportation infrastructure. The system leverages the sensing and communication capabilities of CAVs to enable continuous, real-time detection and reporting of roadway anomalies, such as pavement distress and damaged traffic signage. By fusing multi-modal sensor data and incorporating a retrieval-augmented generation (RAG) framework with large language models (LLMs), the system constructs a dynamic prior knowledge base to reason about infrastructure conditions and recommend context-aware maintenance actions. The project aims to transform current reactive maintenance practices into a data-driven, proactive framework that improves decision-making for transportation agencies. The system will be validated through simulation in the CARLA (Car Learning to Act) environment and supported by curated real-world datasets. Expected outcomes include an integrated detection and reasoning framework, structured maintenance reporting tools, and publicly shareable datasets and software packages. The project's broader impact lies in advancing intelligent infrastructure monitoring technologies, reducing long-term maintenance costs, and contributing to safer and more sustainable transportation systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:01:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655750</guid>
    </item>
    <item>
      <title>Digital Twin and Automation of Pipeline Data for Predictive Maintenance and Risk Analysis incorporating Bayesian network</title>
      <link>https://rip.trb.org/View/2655704</link>
      <description><![CDATA[Pipeline infrastructure is critical for global energy transportation, yet aging systems face increasing degradation risks from corrosion, fatigue, and environmental factors. Recent catastrophic failures have demonstrated severe safety, environmental, and economic consequences of inadequate integrity management, with annual losses reaching billions of dollars. Despite significant advances in inspection technologies, including intelligent inspection tools and sensor networks, three fundamental challenges remain unresolved: lack of interpretability in machine learning approaches, difficulty quantifying uncertainties in defect growth predictions, and the challenge of optimizing maintenance decisions with incomplete information.

This research develops a Valuation Bayesian Network (VBN) integrated with Digital Twin technology as an automated decision-support tool for pipeline integrity management. The VBN approach provides a principled foundation for uncertainty quantification, data fusion, state estimation, prediction, and maintenance planning while maintaining interpretability. The framework incorporates probabilistic degradation and failure models, logic and relational models, and surrogate models to address key performance indicators, failure risks, and remaining life estimation.

The proposed Digital Twin architecture comprises four interconnected layers. The physical layer encompasses pipeline infrastructure with distributed sensors and expert knowledge. The simulation layer employs VBN-based probabilistic models capturing time-dependent degradation processes, where state variables, including defect depth and material properties, are modeled through Markov transition models with parameters learned from historical inspection records, pipeline failure databases, and expert elicitation. The data fusion layer performs Bayesian updating by integrating inspection data, operational history, and expert knowledge, enabling adaptive parameter calibration to capture site-specific degradation patterns. The decision layer implements maintenance optimization using value of information analysis and supports counterfactual reasoning for intervention scenarios.

This framework achieves full uncertainty propagation from measurement noise through remaining life predictions, ultimately enhancing operational safety by predicting high-risk defect development and enabling timely maintenance interventions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:27:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655704</guid>
    </item>
    <item>
      <title>Leveraging AI for Risk‑Informed Culvert Infrastructure Decision‑Making</title>
      <link>https://rip.trb.org/View/2643021</link>
      <description><![CDATA[Culverts are essential components of roadway and drainage systems, yet many are aging, undersized, and vulnerable to failure during heavy rainfall and flooding. Limited inspection resources and outdated condition data make it difficult for transportation agencies to identify which culverts pose the greatest risk to safety and network performance. This project addresses these challenges by applying artificial intelligence and network analysis to improve culvert condition assessment and maintenance prioritization.

The research focuses on the Deerfield County Watershed, where publicly available culvert condition data are incomplete or outdated. Machine learning models will be developed to predict current culvert condition ratings using historical records and environmental data, with an emphasis on interpretable and physics-informed approaches. These predictions will be integrated with geospatial network simulations to identify culverts whose failure would result in significant connectivity loss, flooding exposure, or service disruption. Targeted field inspections will be conducted to validate model predictions and improve accuracy. The results will support proactive, data-driven culvert management and provide a scalable framework for applying AI to infrastructure risk assessment.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:26:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643021</guid>
    </item>
    <item>
      <title>Evaluating Bat Use of NDOT-managed Transportation Structures with Occupancy Modeling</title>
      <link>https://rip.trb.org/View/2636171</link>
      <description><![CDATA[Bridges, culverts, and other transportation structures sometimes provide roosting habitat for bats. Widespread use of these transportation structures across Nevada may potentially provide bat roosting habitat in areas naturally devoid of karst systems, and it is currently poorly documented how the presence of transportation structures in Nevada may increase the range of cavernicolous roosting bat species across the state. These structures may also supplement natural cave roosting habitat in the region that is sometimes disturbed or negatively impacted. Bridges, culverts, and other transportation structures vary in both design and materials, and, therefore, in their potential attractiveness to bats as roosting structures. Currently, Nevada Department of Transportation (NDOT) manages over 2,000 bridges and 52,000 culverts across Nevada. During a pilot study NDOT conducted in 2024, a subset of bridges and culverts were documented as supporting roosting use by bats. Thoroughly understanding the types of transportation structures that bats are using across Nevada, as well as the species, colony type, and degree of use, will provide NDOT an opportunity to efficiently plan maintenance activities on these structures so that future unexpected bridge and culvert maintenance work delays are minimized.

The objectives of this research are to: (1) Determine which transportation structures across Nevada are most likely to provide roosting habitat for bats through the development of a predictive model to improve NDOT bridge survey planning efficiency. (2) Complete 500 to 650 visits to transportation structures assuming approximately four culvert visits for every bridge visit (because bridge surveys are generally more time intensive) to determine which surveyed structures show no signs of use, show signs of use, and are actively occupied at the time of survey. (3) Determine the type of colony and seasonality of use for occupied structures. (4) Develop best management recommendations into an estimated 20-page bat management plan plus references and appendices based on survey results and a finalized model. ]]></description>
      <pubDate>Tue, 09 Dec 2025 14:09:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636171</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 57-03. Practices for Monitoring POA-Required Bridges During and After Floods</title>
      <link>https://rip.trb.org/View/2630487</link>
      <description><![CDATA[Background
Scour is the most common cause of bridge failures. Per 23 CFR 650.313, state departments of transportation (DOTs) must perform a scour appraisal for all bridges over water and prepare a documented scour plan of action (POA) for any bridge determined to be scour critical or to have unknown foundations. A POA typically includes a plan for monitoring the bridge during or after flooding to ensure it is safe for traffic or closed if found to be unsafe.
When state DOTs use monitoring as a countermeasure a variety of approaches and techniques are deployed. These approaches can vary depending on factors such as data availability, funding, and resources for monitoring bridges during and after floods. Therefore, a synthesis study documenting state DOT practices for both on- and off-system bridges will provide information on technologies, methodologies, and knowledge gaps related to POAs.
The objective of this synthesis is to document state DOT practices and policies for POA implementation, including monitoring methods, software, instrumentation, and other tools used in these efforts.
Information to be gathered includes (but is not limited to):
Conditions or triggers that initiate on-site monitoring at a bridge; 
List requirements in POAs for on-site bridge monitoring including, observations and measurements during flooding; 
Practices for prioritizing bridges for POA monitoring; including when multiple POA-required bridges are affected by flooding;
Benefits and challenges of POA monitoring and inspection; 
Practices and procedures for emergency and formal bridge closures and reopening;
Written policies and procedures supporting POA implementation;
Procedures for maintaining and updating POA monitoring protocols for individual bridges over time, including updating conditions and triggers for feedback and monitoring;
Units within the DOT responsible for POA development, lifecycle POA management, and monitoring;
 Responsibilities for POA monitoring of non–state-owned bridges;
Roles and responsibilities for flood monitoring and scour monitoring inspections, including post-event;
Use of tools (e.g., GIS-based systems, or other software) that leverage online rainfall or streamflow data to trigger monitoring;
 Practices and equipment used for onsite and remote monitoring;
Qualifications and/or training for personnel performing POA monitoring; and
Use of fixed instrumentation in POA monitoring.
Information will be gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs for the development of case examples. Information gaps and suggestions for research to address those gaps will be identified.
Information sources (partial):
23 CFR 650.313 Inspection procedures. https://www.ecfr.gov/current/title-23/chapter-I/subchapter-G/part-650/subpart-C/section-650.313
]]></description>
      <pubDate>Wed, 26 Nov 2025 16:33:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2630487</guid>
    </item>
    <item>
      <title>AI-Driven Infrastructure Prioritization: Vision-Language Model Framework for Capital Planning</title>
      <link>https://rip.trb.org/View/2620732</link>
      <description><![CDATA[This project develops a scalable, artificial intelligence (AI)-powered framework for automated condition assessment and capital investment prioritization of road and bridge infrastructure across Colorado. Current manual inspection methods are costly, slow, and limited in coverage, often missing early signs of degradation. Leveraging recent advances in Vision-Language Models (VLMs), the project proposes a novel approach that extracts Pavement Condition Index (PCI) and bridge deck ratings from satellite and street-level imagery using VLMs guided by prompt engineering and in-context learning, requiring no retraining, and will be validated against Colorado Department of Transportation (CDOT) inspection records using machine learning model accuracy metrics.

The framework integrates three components: (1) Infrastructure condition (2) network criticality, computed via graph-theoretic metrics and traffic data, and (3) hazard exposure, based on geospatial data for landslides, wildfires, and floods. These layers will be synthesized into a weighted prioritization model to rank road segments for capital upgrades, refined through CDOT expert review.

A web-based geographic information system (GIS) tool will visualize prioritization results, supporting interactive exploration and decision-making. The tool will be pilot-tested with CDOT districts, and outcomes will be disseminated through a final report, peer-reviewed publication, Transportation Learning Network (TLN) webinar, and Colorado LTAP training.

By integrating AI, geospatial analytics, and network modeling, this project addresses data-driven infrastructure planning needs—enhancing efficiency, reliability, and resilience—while offering a replicable model nationwide.]]></description>
      <pubDate>Mon, 10 Nov 2025 16:24:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620732</guid>
    </item>
    <item>
      <title>A Reinforcement Learning Framework for Dynamic Inland Waterway Maintenance Under Stochastic Shoaling and Annual Budget Allocation</title>
      <link>https://rip.trb.org/View/2620600</link>
      <description><![CDATA[This research proposes a dynamic, data-driven framework for long-term inland waterway maintenance planning that integrates reinforcement learning (RL), and stochastic modeling. Unlike traditional models that assume deterministic sedimentation, known multi-year budgets, and static decision horizons, the research team models shoaling as a stochastic process, budgets as annually realized random variables, and infrastructure deterioration as a gradual, condition-dependent process. The core of the methodology is an infinite-horizon sequential decision model that makes year-by-year dredging and lock maintenance decisions using RL. Dredging is modeled as a continuous decision variable, and policy learning is guided by a custom-designed simulation environment that reflects realistic physical and institutional constraints. The team trains RL agents using Proximal Policy Optimization (PPO). This work addresses the curse of dimensionality that limits conventional optimization techniques by learning generalizable policies rather than enumerating all possible scenarios. By finding the solution across various uncertainty regimes, the team provides both methodological insights and practical guidance for agencies such as the U.S. Army Corps of Engineers. The resulting framework offers a robust and adaptive tool for managing long-term infrastructure investment under uncertainty]]></description>
      <pubDate>Mon, 10 Nov 2025 09:33:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620600</guid>
    </item>
    <item>
      <title>Research and Assessment of Needs for Sign Maintenance</title>
      <link>https://rip.trb.org/View/2593930</link>
      <description><![CDATA[Kentucky Transportation Cabinet (KYTC) District offices need to verify in-house sign/signal installation and maintenance crews are prepared to implement criteria set forth in the new Manual on Uniform Traffic Control Devices (MUTCD). This requires a thorough assessment of each District’s sign and signal crews to identify needs. This evaluation must review crew staffing levels, equipment and material needs, and training schedules. Based on findings of this assessment, KYTC’s sign installation handbook can be updated with best practices for assembling and managing effective sign and signal crews as well as for conducting effective and safe sign maintenance.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:32:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593930</guid>
    </item>
    <item>
      <title>Phase 2: Quantitative Evaluation Process for Improved Rockslope Safety and Reduced Maintenance</title>
      <link>https://rip.trb.org/View/2593928</link>
      <description><![CDATA[Thousands of problematic rock slopes adversely impact Oregon Department of Transportation's (ODOT’s) infrastructure and will soon require substantial mitigation as Oregon’s highway rockcuts near the end of their respective design life. This deterioration is predicted to be further exacerbated with climate change. Many short-term mitigation techniques such as scaling or blasting are very expensive and dangerous as these techniques require personnel to physically scale the cliff, while longer term mitigation solutions (e.g., nailing) are often too cost prohibitive to deploy beyond a few select slopes. Rockfall mitigation efforts are further compounded by the subjective, ad-hoc nature of the mitigation process, which can lead to uncertainty regarding actual slope improvement and predicted improvement duration. It is also uncertain how long—and how effective—mitigation efforts actually are, particularly with respect to the longevity of scaling before the eroding slopes return to a similar or even more precarious state. Further, when rock slopes are not clearly improved with mitigation, clean-up costs for rockfall accumulate for our District maintenance crews. Taken together, the lack of quantitative information regarding how mitigation efforts actually improve the overall stability of rock slopes runs counter to ODOT’s aim for budget efficiency and transparency, with potential negative impacts on an already highly limited rockfall mitigation budget which could then in-turn negatively impact the safety of the traveling public. Quantitative, objective methods that better inform successful rockfall mitigation techniques, maintenance strategies, and overall asset management is needed. Recently, ODOT Research invested in the development of a series of lidar-based tools for quick assessment and monitoring of rockfall events (SPR809). Specifically, SPR809 refined the Rockfall Activity Index (RAI) hazard screening tool, analyzed seismic hazards, and developed a user-friendly Graphical User Interface (GUI) for implementation. RAI is a point cloud-derived, high-resolution, low-cost, morphology-based, objective approach for assessing rock slopes that can objectively and efficiently analyze large geographic regions. RAI may also be used to identify specific locations to target for mitigation by identifying rapidly eroding areas of the slope and precarious overhangs. This proposed research leverages these recently developed tools to further develop a new methodology for verifying rockfall mitigation effectiveness that can also be used to direct future mitigation and maintenance efforts.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:23:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593928</guid>
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