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    <title>Research in Progress (RIP)</title>
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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>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Unraveling the Causes of Fatal Crashes in the U.S.: A Machine Learning Approach to Safer Roads</title>
      <link>https://rip.trb.org/View/2694440</link>
      <description><![CDATA[This project investigates the underlying causes of fatal traffic crashes in the United States using advanced machine learning (ML) techniques to enhance road safety. Each year, traffic crashes claim over 42,000 lives nationwide, inflicting significant social, economic, and health burdens. Traditional analytical methods have struggled to capture the complex, nonlinear interactions among factors such as driver behavior, vehicle characteristics, roadway design, and environmental conditions. To address this limitation, this project employs data-driven ML models to identify key determinants of fatal crashes and generate actionable insights for evidence-based safety interventions.

The research activities will proceed in four phases. First, comprehensive crash data will be collected from the National Highway Traffic Safety Administration (NHTSA) and integrated across multiple datasets to ensure completeness and consistency. Next, statistical analysis and visualization will be used to identify spatial and temporal trends in crash patterns, revealing geographic disparities and risk concentrations. In the modeling phase, several machine learning algorithms—Balanced Bagging, Balanced Random Forest, and RUSBoost—will be developed and compared against traditional logistic regression models to enhance prediction accuracy in imbalanced datasets. Finally, the top-performing model will be used to assess variable importance and generate policy-relevant recommendations.

OBJECTIVE: The objective of this project is to develop predictive models that accurately identify risk factors associated with fatal crashes and support data-informed decision-making by transportation agencies. The findings will guide targeted interventions such as improved traffic regulations, safer roadway designs, and enhanced vehicle technologies. This research will provide a scalable analytical framework for improving transportation safety and sustainability nationwide.
]]></description>
      <pubDate>Tue, 21 Apr 2026 13:45:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2694440</guid>
    </item>
    <item>
      <title>Real-Time Anomaly Detection System for Signalized Intersections</title>
      <link>https://rip.trb.org/View/2673038</link>
      <description><![CDATA[Automated Traffic Signal Performance Metrices (ATSPMs) rely on high-resolution controller data to support signal operations and maintenance. While existing ATSPM Watchdog tools can identify certain detector malfunctions, they typically rely on static thresholds set across the entire system, limiting their ability to detect issues promptly and adapt to site-specific traffic patterns. This project develops and evaluates a statistical, spatiotemporal anomaly detection framework that leverages historical detector behavior and agreement among neighboring detectors to improve alert timeliness and accuracy. The research will characterize practitioner needs, define normal detector behavior, develop and test statistical detection and classification methods, and quantify benefits relative to static thresholds. Results will inform a roadmap for potential real-time implementation within Virginia Department of Transportation (DOT) systems.]]></description>
      <pubDate>Tue, 24 Feb 2026 10:25:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673038</guid>
    </item>
    <item>
      <title>Obtaining Reliable Pavement Friction Measurements Using Connected Vehicles</title>
      <link>https://rip.trb.org/View/2643034</link>
      <description><![CDATA[Pavement friction is a critical factor influencing vehicle control and stopping distance, particularly under wet conditions, yet current measurement practices rely on infrequent and labor-intensive testing methods. These limitations prevent agencies from identifying hazardous low-friction locations in a timely manner. This project investigates whether connected vehicle sensor data can provide a reliable and continuous alternative for pavement friction monitoring.

The research will validate friction estimates derived from connected passenger vehicles against locked-wheel skid testing and invasive and non-invasive roadway sensors. Data will be collected on multiple pavement types along a test corridor in Massachusetts and analyzed using statistical and machine learning methods to relate vehicle-based friction values to standard skid numbers. The project will develop conversion models and data-integration techniques to enable agencies to incorporate connected vehicle friction data into pavement and safety management systems, supporting proactive maintenance and improved roadway safety.]]></description>
      <pubDate>Thu, 18 Dec 2025 15:09:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643034</guid>
    </item>
    <item>
      <title>Spatio Temporal Graph Learning for Real Time Pedestrian Exposure Estimation</title>
      <link>https://rip.trb.org/View/2640189</link>
      <description><![CDATA[Pedestrian crashes occur infrequently and are often underreported, which makes it difficult for agencies to rely only on crash records when assessing safety. Traditional Safety Performance Functions do not capture short term patterns or local context, and therefore cannot fully represent changes in pedestrian activity. This project will create a new framework that uses spatio temporal graph neural networks combined with statistical modeling to estimate pedestrian exposure across different locations and time periods. The research will draw from computer vision systems, Streetlight data, manual counts, roadway characteristics, land use, and travel related factors to produce high resolution exposure estimates.

The modeling framework will include two tiers. The first tier will use generalized linear mixed models to build a baseline exposure structure, while the second tier will apply deep learning methods to capture spatial spillover effects and temporal variation such as peak periods and seasonal changes. The results will help agencies identify areas with elevated pedestrian activity and evaluate how different roadway or land use conditions influence exposure. These data will support improved pedestrian safety analysis and guide the development of timely, evidence based interventions.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:45:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640189</guid>
    </item>
    <item>
      <title>Safety and Operational Performance Assessment of CFIs and DDIs in Utah</title>
      <link>https://rip.trb.org/View/2632836</link>
      <description><![CDATA[This research project will assess the safety and operational performance of Continuous Flow Intersections (CFIs) and Diverging Diamond Interchanges (DDIs) in Utah. The study will develop Utah-specific Safety Performance Functions (SPFs), Crash Modification Factors (CMFs), and Adjustment Factors (AFs), using Utah Department of Transportation (UDOT) data resources and advanced analytical techniques including statistical modeling, machine learning, and computer vision. The findings will support updates to UDOT design guidelines and planning tools such as CAP-X, SPICE, and ICE.]]></description>
      <pubDate>Thu, 27 Nov 2025 08:54:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2632836</guid>
    </item>
    <item>
      <title>Bear Alert Work Zone Worker Alert Systems via a VR-Based Human-in-the-Loop Simulation</title>
      <link>https://rip.trb.org/View/2611276</link>
      <description><![CDATA[This research develops and evaluates an advanced work zone alert system designed to protect highway workers from errant vehicles, responding to the urgent safety need highlighted by the tragic March 2023 Maryland incident that killed six workers. The core innovation is a specialized alert system that delivers multimodal warnings, combining visual (flashing beacon), auditory (alarm), and haptic (vibrating) signals to notify workers when approaching vehicles pose a threat. The system integrates sensors that detect vehicle trajectories and speeds outside normal work zone parameters, triggering immediate alerts through a worker-worn device that provides simultaneous feedback across multiple sensory channels. Design considerations include weatherproof construction for outdoor reliability, adjustable alert thresholds to minimize false alarms while maintaining sensitivity, and ergonomic form factors that don’t impede worker mobility or job performance. Anticipated use cases span various highway maintenance scenarios including lane closures, shoulder work, and mobile operations where traditional barrier protection proves insufficient. This research first validates the Work Zone Safety Alert System by employing human-in-the-loop virtual reality simulations to rigorously test how workers respond to different alert combinations under realistic distraction conditions, generating empirical evidence to optimize the final system design.  It then validates the system in real-world scenarios, evaluating the responsiveness of the system at live work zones in partnership with the Maryland Department of Transportation’s Highway Safety Office.  This dual evaluation framework measures a broad array of reaction times and compliance behaviors, ensuring the technology serves transportation workforce populations effectively.]]></description>
      <pubDate>Mon, 20 Oct 2025 16:15:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2611276</guid>
    </item>
    <item>
      <title>Supplemental Traffic Count Pilot Project for Establishing a Statewide Count Program in Colorado</title>
      <link>https://rip.trb.org/View/2559639</link>
      <description><![CDATA[To design roadway infrastructure for comprehensive safety of all users, understanding exposure is essential. Current count and volume estimation methods primarily focus on vehicular traffic, leaving a gap in exposure data for other supplemental traffic modes. There is also a lack of guidance on supplemental traffic count programs for optimum coverage across regions. Partial guidance on counting programs is available from sources such as the Federal Highway Administration’s Traffic Monitoring Guide. However, all these documents describe the design of a full-scale supplemental traffic counting program as being dependent on and informed by small scale pilot programs. The proposed project will develop guidance for Colorado Department of Transportation (CDOT) to develop a statewide full scale supplemental traffic count program.

The objective of this study is to conduct a small-scale pilot supplemental counting program to test approaches identifying count location/distribution, technology use, and calculation of key statistics that would be available from a well-designed count program (such as crash/fatality rates). The outputs of this research effort would inform CDOT senior management and other practitioners when creating a detailed design and schedule for a full-scale supplemental traffic counting program. For researchers, this will provide a robust data driven framework for identifying supplemental traffic count locations. Furthermore, machine learning and statistical models for estimating and predicting supplemental traffic volumes using open source, readily available data will also be produced as part of this project.

In addition to the aforementioned goals of the project, during multiple preliminary meetings with the CDOT team, the research team identified an additional need—creating a training program for the contractors who will collect the supplemental traffic data in the field. It is understood that, like the established vehicular traffic count program, a mature supplemental count program will rely on contractors for the data. The project team deemed it necessary that any contractor selected to collect supplemental traffic data via bidding would have to take a mandatory standardized training program. The project thus now focuses on contractor training and sample data collection during the training, field data collection post-training for a set of sites with different location characteristics (rural/urban/arterial, etc.), and identifying permanent counter locations based on the data collected, factors identified in the literature, and other available data.]]></description>
      <pubDate>Sun, 01 Jun 2025 14:26:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2559639</guid>
    </item>
    <item>
      <title>Effects of Explosives on Avalanche Frequency and Magnitude</title>
      <link>https://rip.trb.org/View/2540004</link>
      <description><![CDATA[The application of explosives for avalanche control is a widely used avalanche risk mitigation method in North America. It is considered a short-term risk management method in that it acts on immediate avalanche hazard when required during the winter. However, the effects of such practices on seasonal avalanche frequency and magnitude are complex and involve multiple factors. The objective of this study is to challenge the long-held belief that regular pro-active explosives control performed consistently throughout the season leads to more frequent but smaller avalanches and ultimately reduces the frequency of avalanches reaching further into the avalanche path runout zone where transportation corridors are typically located. It will be based on a statistical analysis of avalanche occurrence records and extreme runout estimation from avalanche paths with sufficient records from before and after the implementation of a regular explosives control program. The results are expected to help inform long-term planning decisions pertaining to investments into explosives avalanche control infrastructure.]]></description>
      <pubDate>Fri, 18 Apr 2025 13:03:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2540004</guid>
    </item>
    <item>
      <title>Identifying and Analyzing Pass-by Crashes for the Purpose of Designing Proper Intervention Measures to Mitigate Crashes Involving Rural Population</title>
      <link>https://rip.trb.org/View/2509040</link>
      <description><![CDATA[The United States Census Bureau reports that rural areas cover about 97% of the nation’s land area and are home to about 60 million people. About 19% of the American population lives in the rural area according to the Census Bureau. Although only 19% of the population lives in rural areas more than 70% of the 4 million miles of roadways in the United States are in rural areas. According to the NHTSA (2021) the fatality rate was 1.5 times higher in rural areas than in urban areas of the US. In Florida, the fatality rate per 100 million VMT (Vehicles Miles Travelled) in rural areas and urban areas were 2.06 and 1.64, respectively, giving a rural to urban fatality rate ratio of about 1.3.  
This research focuses on analyzing pass-by crashes in rural areas, particularly in FDOT District 3 (Northwest Florida). The primary goal is to identify trends and factors contributing to these crashes and propose interventions aimed at improving transportation safety for rural populations. By focusing on rural transportation, the study aligns with the broader objective of promoting safety in regions that often lack access to infrastructure and transportation resources. The project will explore innovative machine learning and statistical modeling methods to analyze the complex interactions between drivers’ social characteristics and roadway features that influence the frequency and severity of rural pass-by crashes. The findings will inform the development of countermeasures to mitigate risks posed by transportation systems, particularly for populations who live or commute in rural areas.
Data needed to train the models were sourced from the Florida Traffic Safety Dashboard, FDOT GIS Open Data Hub and US Census, focusing on crash events, roadway characteristics and driver demographics. To classify pass-by crashes, distances between crash locations and the drivers’ home ZIP codes were calculated, with a threshold of 30 miles used to define a pass-by crash. Logistic regression and Random Forest models were used to analyze the factors influencing these crashes, with variables such as functional class, weather conditions, vision obstruction, and type of shoulder playing significant roles in predicting crash likelihood. Preliminary results indicate that certain factors, like severe crosswinds, paved shoulders, and specific road classifications, increase the probability of pass-by crashes. Additional future work will focus on refining the models, incorporating additional demographic and roadway data, and further validating the findings.
]]></description>
      <pubDate>Wed, 12 Feb 2025 17:48:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2509040</guid>
    </item>
    <item>
      <title>Sieve Analysis Procedure to Quantify Reclaimed Asphalt Pavement Binder Availability</title>
      <link>https://rip.trb.org/View/2505725</link>
      <description><![CDATA[This project will develop a new procedure to quantify reclaimed asphalt pavement (RAP) recycled binder availability (RBA) using comparative sieve analysis of RAP and recovered RAP aggregate. Work in Stage 1 will involve ruggedness evaluation to identify how closely the sieve analysis procedural operating factors need to be controlled. A fine RAP sample from Virginia and a comparatively coarse RAP sample from Indiana will be acquired to cover variations in climate, geologic origin, and particle size distribution. Ruggedness testing will be conducted according to ASTM E1169 and ASTM C1067. The experimental factors to be evaluated will include: (1) sample size, (2) washing time, (3) drying temperature, (4) particle separation method, (5) sieving time, (6) asphalt removal method, and (7) unwashed vs. washed sample for aggregate recovery. Statistical analysis will be performed to identify experimental factors with statistically significant effects on RBA. Subsequently, controls for these factors will be established to minimize the impacts of their deviation on the average and variance of RBA results. Stage 2 work will involve an interlaboratory study (ILS) according to ASTM E691 to establish the precision of the RBA results. The study will include a minimum of six participating laboratories from academia, industry, and highway agencies.  Three RAP materials, procured from North Carolina, Virginia, and Indiana with distinct RBA values, will be used. A  protocol outlining the necessary procedures and data reporting for the ILS experiments will be developed for the ILS participants. A webinar will also be organized to educate the ILS participants on the experimental procedure and the ILS protocol. Each lab will execute the required sieve analysis experiments according to the ILS protocol. Statistical analysis of the results will be conducted to define the repeatability (single-operator precision) and reproducibility (multi-laboratory precision) of the RBA results. The research team will work with the ILS participants to develop pilot implementation plans. The final report will include all relevant data, methods, models, and conclusions along with guidance on how to use the sieve analysis procedure in a state DOT.   ]]></description>
      <pubDate>Mon, 03 Feb 2025 22:33:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505725</guid>
    </item>
    <item>
      <title>Connected Vehicle Data for Statewide Seat Belt Use Estimation: Proof of Concept </title>
      <link>https://rip.trb.org/View/2480356</link>
      <description><![CDATA[This project aims to explore the use of Connected Vehicle (CV) data to estimate statewide seat belt use, replacing the current manual observation method mandated by the National Highway Traffic Safety Administration (NHTSA). CV data contains records of seat belt activity that may be leveraged to estimate statewide seatbelt usage. Third-party data providers like Wejo and Geotab collect and anonymize CV data so that it can be used by transportation agencies, researchers, and others. Currently, states must conduct extensive manual seat belt use surveys, which are time-consuming and subject to human error. By leveraging anonymized CV event data, this research will provide a proof of concept for a more efficient, accurate, and scalable method to gather seat belt usage data.
CV data offers the potential to collect seat belt engagement data continuously, providing richer insights without the need for costly manual observations. Data from vehicles like those using OnStar services can track seat belt latch events, which can be analyzed for patterns in seat belt use across various times, road types, and regions. A recent report by the USDOT Office of Highway Policy, Travel Monitoring Surveys division titled “Post event Connected Vehicle Data Exploration- Lessons Learned” (2024), used CV data from Wejo and the USDOT Joint Program Office CV pilot project to investigate the feasibility of several applications including seat belt use monitoring. The CV data used in the USDOT report contained instantaneous records with timestamps of seat belt engagement as latched or unlatched.  The report connected latched/unlatched events to associated trips to examine latched/unlatched behavior in the context of speed, distance, and travel time (time spent under respective latched/unlatched status). 
The proposed work expands on the USDOT study in two ways. First, the USDOT study did not evaluate the correlation between seat belt usage statistics generated from CV data and those of observed data such as that collected by states for NHTSA mandated studies. Second, the USDOT study did not investigate the belt latch chain of events, e.g., belt latch before/after vehicle engaged, etc. This is critical knowledge needed for the potential use of CV event data for seat belt use monitoring.  For the proposed work, we will use survey and technology-based approaches to determine latch event chains. By quantifying seat belt latch chain of event, we will be able to determine the degree to which CV event data for seat belt events can replace or supplement traditional means of seat belt use data collection. 
The project is divided into four main tasks. Task 1: Driver and Passenger Survey: A survey will collect data on the timing of seat belt engagement relative to engine startup, as this affects whether CVs can record seat belt use events in a statistically representative way. Task 2: Technology Review: Alternate technologies, such as Event Data Recorders (EDRs) and hospital records, will be examined for their feasibility in capturing seat belt use data. Task 3: Data Collection and Analysis: Statistical models will be developed to correlate manual seat belt observations with CV data, assessing the representativeness of CV data; Task 4: Cost Feasibility Study: A comparative analysis of the costs associated with manual seat belt surveys versus CV data collection will be performed to evaluate the economic feasibility of implementing CV-based seat belt use monitoring.
]]></description>
      <pubDate>Wed, 01 Jan 2025 16:28:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2480356</guid>
    </item>
    <item>
      <title>Development of a Machine Learning Interpretation Aid to Support Rockfall Hazard Forecasting</title>
      <link>https://rip.trb.org/View/2431168</link>
      <description><![CDATA[Geohazards present a substantial risk to Colorado’s transportation network. While overall geohazard risk can be assessed at the individual site/asset scale, corridor scale, or network scale, individual hazard event occurrences tend to be somewhat “random” in nature. What this means is that responses to individual hazard events occur on an emergency or urgent need basis, with no ability to proactively identify and prioritize mitigation measures based on real-time data. 

Several recent studies (e.g. Kromer et al., 2015; Kromer et al., 2018; Walton et al., 2023a) have demonstrated that many rockfall events exhibit precursors that are detectable by lidar or photogrammetric monitoring months or even years before the final point of failure. The advantage of such monitoring technologies relative to traditional in-ground monitoring devices is that they can cover large slope areas, meaning multiple source areas or an entire cut slope asset can feasibly be monitored. Accordingly, one can envision a future where large numbers of rock slopes are regularly monitored using remote sensing technologies, and potential emerging hazards are identified, forecast, and mitigated, thus minimizing the number of actual rockfall events that ultimately occur. 

Data collection and processing limitations that only a few years ago made it difficult to implement such monitoring at-scale have now been overcome. However, a new challenge now presents itself – with ever-increasing volumes of monitoring data now available, the process of manually evaluating spatially extensive change detection results to determine which subtle apparent ground movements over time represent actual failure precursors (as opposed to noise or data processing artifacts) has become a major bottleneck for potential failure forecasting efforts. As a result, at present, while some events that exhibit particularly large and long-lasting precursors can be detected and mitigated (e.g. Walton et al., 2023a), the vast majority are not identified prior to failure (e.g. Walton et al., 2023b).
The focus of this research project is to develop a machine learning interpretation aid capable of performing a preliminary screening of a point-cloud-derived change detection result to identify potential areas of interest (i.e. where failure precursors may exist) that merit further detailed consideration by an expert. The development of such an interpretation aid would substantially reduce the effort required to effectively forecast rockfall occurrence, and would increase the reliability with which individual events could be successfully forecasted.  The following research objectives shall be met: (1) develop a rockfall precursor database for Colorado slopes of interest using change detection results from historical monitoring; (2)	explore basic statistical approaches to separate potential precursors from other changes that are not of interest; and (3) test machine learning approaches to identify areas of potential interest/concern meriting manual interpretation for forecasting purposes.


]]></description>
      <pubDate>Mon, 16 Sep 2024 09:08:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431168</guid>
    </item>
    <item>
      <title>A Machine Learning and Statistical Analysis Framework for Enhanced Engineer's Estimate Accuracy in Highway Infrastructure Projects, Phase I</title>
      <link>https://rip.trb.org/View/2398009</link>
      <description><![CDATA[State Transportation Agencies (STAs) rely on accurate engineer's estimates for budget allocation and contractor bid evaluation in highway projects. However, recent assessments reveal significant inaccuracies, with up to 25% deviations between engineers' estimates and awarded bids in the Wyoming Department of Transportation (WYDOT) in 2019. These deviations also resonate with similar findings published by other STAs. Challenges persist due to poor data quality and variations in estimating methods. This study aims to evaluate WYDOT's engineer's estimates' accuracy against historical bid data and assess consequences on project performance. Methodologically, a literature review and questionnaire survey will inform quantitative analysis of survey data and statistical analysis of bid tabulation data. By enhancing engineer's estimate accuracy, this research seeks to minimize budget deviations, improve project performance, and promote efficiency and transparency in transportation project planning and execution.]]></description>
      <pubDate>Wed, 26 Jun 2024 12:25:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2398009</guid>
    </item>
    <item>
      <title>Automated Valuation Models to Expedite Right-of-Way Acquisition Processes Within the Federal-aid Program Across the Nation.</title>
      <link>https://rip.trb.org/View/2077933</link>
      <description><![CDATA[This is a proof of concept study of Automated Valuation Models to expedite Right-of-Way Acquisition processes and to identify opportunities to incorporate AVM and AVM tools into Federal-aid program.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077933</guid>
    </item>
    <item>
      <title>Probability of Detection in Corrosion Monitoring with FE-C Coated LPFG Sensors (SN-8)</title>
      <link>https://rip.trb.org/View/1976352</link>
      <description><![CDATA[This project aims to develop two statistical methods for determining the probability of detection in corrosion monitoring using long period fiber gratings (LPFG) sensors with thin Fe-C coating, validate these methods from independent laboratory tests, and determine the steel mass loss at 90% probability of detection and the largest steel mass loss that may miss from a corrosion inspection at 95% lower confidence bounds. The two statistical methods are referred to as the Mass Loss-at-Detection (MLaD) method and the Random-Effects Generalization (REG) method. They will be evaluated in terms of computational efficiency, sensitivity to probability distribution assumptions, and robustness to departure from model assumptions. The one with overall superior performance will be recommended for corrosion monitoring in applications. To achieve the project objectives, three tasks will be planned and executed. First, standard test specimens and experimental designs will be prepared to provide relevant cases to sensors’ field applications in steel reinforced concrete (RC) structures. Second, the proposed statistical methods will be validated to determine the largest mass loss with 90% probability of detection at a 95% lower confidence level. Third and last, the required data, computational efficiency, probability distribution sensitivity and model robustness of the two methods will be compared to guide their selection in practice.

Approach and Methodology: Sensor technologies can potentially improve operation efficiency, cost effectiveness, structural reliability, and inspector safety in bridge asset management. However, their implementation must be proven through statistically-viable laboratory performances and successful field operations. Key to assessing their performance is methods to enable the POD analysis for the sensors used in corrosion monitoring in bridges. Following is a presentation of two methods with illustrative examples in aerospace application.

Overall Objectives: This project will qualify corrosion monitoring as an inspection tool for bridges. The statistical methods developed for critical mass loss are transferrable to evaluating the reliability of other measurement technologies. They can be applied to other structures. Once implemented, these data will make inspection more reliable and cost-effective.

Scope of Work in Year 1: (1) Develop test protocols for corrosion sensor/mass loss combination, (2) Qualify the POD for corrosion monitoring with two statistical methods, (3) Compare the two statistical methods in application scenarios. ]]></description>
      <pubDate>Sun, 05 Jun 2022 14:19:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1976352</guid>
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