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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>
    <image>
      <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> 
Developing a Web Interface for Pipe Deterioration Models
</title>
      <link>https://rip.trb.org/View/2762052</link>
      <description><![CDATA[Pipeline infrastructure is critical to energy transportation and urban utility systems, yet aging pipelines are increasingly vulnerable to corrosion, material degradation, and environmental damage. Traditional integrity assessment relies on periodic inspection and offline analysis, providing only discrete snapshots of system condition and failing to capture the continuous, uncertain evolution of deterioration, leaving maintenance and risk-mitigation decisions reactive rather than predictive.
This project develops a dynamic probabilistic deterioration model and an interactive web-based decision-support interface. The predictive engine is a Dynamic Bayesian Network (DBN) constructed in the GeNIe modeling environment, mapping causal dependencies among pipeline condition factors (coating degradation, soil corrosivity, operating parameters) and defining time-slice transitions to capture corrosion evolution. Conditional probability tables are parameterized from historical inspection records, empirical corrosion models, and structured expert elicitation. The DBN is exported and embedded in a backend inference module that applies Bayesian updating to user inputs and computes posterior risk probabilities in real time. An interactive frontend translates the probabilistic forecasts into intuitive time-series risk curves and visual dashboards supporting 'what-if' scenario analyses.
The result is a fully functional web-based framework bridging probabilistic engineering modeling and practical pipeline integrity management. Operators and field engineers can integrate inspection data, simulate risk trajectories, forecast deterioration trends, optimize maintenance schedules, and mitigate corrosion risk before structural failures occur — shifting asset management from reactive to proactive, reducing costly emergency repairs, extending infrastructure lifespan, and mitigating risks to public safety and ecological health. Deliverables include the calibrated DBN model, open-source web interface code, implementation guidelines, a technical report, and peer-reviewed publication.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:19:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762052</guid>
    </item>
    <item>
      <title>Quantum Bayesian Networks for Cyber-Physical Threat Prediction in Pipeline SCADA Systems</title>
      <link>https://rip.trb.org/View/2762051</link>
      <description><![CDATA[Pipeline supervisory control and data acquisition (SCADA) systems are cyber-physical systems whose expanding connectivity to enterprise networks and remote services has broadened the cyber-attack surface. Intrusions can propagate beyond digital networks to manipulate physical processes, producing operational disruption, safety hazards, environmental damage, and financial loss. Predicting such cross-domain threats is difficult because attacks are rare and governed by complex causal dependencies, and classical sampling-based inference scales poorly under rare-evidence conditions.
This project constructs a Bayesian network representing causal relationships among cyber-attacks, physical impacts, and sensor observations in a pipeline SCADA environment, informed by a hardware-in-the-loop pipeline testbed with conditional probability tables derived from operational data and engineering judgment. After establishing a classical inference baseline for posterior estimation under normal and rare-evidence queries, the network is encoded as a quantum circuit that preserves its causal structure. Three quantum inference families are implemented and evaluated: quantum rejection sampling, quantum likelihood weighting, and a variational Born-machine approach for posterior attack-probability estimation. Methods are benchmarked on a noiseless simulator and on superconducting quantum hardware, with comparison across accuracy, sample efficiency, and circuit-depth requirements.
Expected outputs include a structured probabilistic model of cyber-physical threat propagation, a quantum-circuit implementation suited to near-term hardware, a comparative evaluation report, open-source code, and peer-reviewed publications. Findings will clarify where quantum inference can improve sampling efficiency for rare-event queries in critical infrastructure security and where current hardware constrains deployment, informing risk-assessment workflows for pipeline operators, the Pipeline and Hazardous Materials Safety Administration (PHMSA), and state DOTs, and laying groundwork for quantum-enhanced digital twin and resilience frameworks.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:17:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762051</guid>
    </item>
    <item>
      <title>AI-Enabled Spatio-Temporal Risk Assessment and Decision Support for Pipeline Infrastructure Preservation</title>
      <link>https://rip.trb.org/View/2732468</link>
      <description><![CDATA[The safe and efficient operation of pipeline systems is essential to the reliability of the United States’ energy supply chain and its integration with maritime and multimodal transportation networks. Pipeline failures can lead to significant disruptions, economic losses, and safety risks, particularly under the influence of aging infrastructure, human factors, and extreme environmental conditions. Building upon prior research, this project proposes to develop an integrated, artificial intelligence (AI)-enabled framework to support the preservation and resilience of pipeline infrastructure within maritime and multimodal transportation systems. The proposed research will have model development, but focuses on validation, system integration, and deployment of decision-support tools. The project will enhance existing spatio-temporal models by incorporating machine learning and explainable artificial intelligence techniques to improve predictive accuracy and interpretability of pipeline system failure risk under varying environmental and operational conditions. Multi-source data will be integrated into a unified analytical platform, including pipeline incident records and environmental datasets. A key innovation of this research is the development of a multimodal infrastructure risk framework that links pipeline systems with maritime transportation components such as ports, inland waterways, and freight corridors. Multi-layer network modeling and scenario-based simulations will be used to evaluate the impacts of infrastructure disruptions on system performance, including energy distribution, freight movement, and resilience under hazardous events. Through the integration of advanced analytics and multimodal system modeling, this project will deliver scalable, interpretable analytical solutions to enhance the safety, reliability, and resilience of pipeline and maritime transportation infrastructure systems.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:53:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732468</guid>
    </item>
    <item>
      <title> Evaluate PVC Water Main Materials in Roadway Projects</title>
      <link>https://rip.trb.org/View/2731924</link>
      <description><![CDATA[Water main breaks within Michigan Department of Transportation (MDOT) R.O.W. pose significant risks to the Department and stakeholders, including complete road
closures, detours, as well as boil water advisories. MDOT is obligated to replace municipal water mains that are impacted by
Road and Bridge projects, typically at Project costs. The Department currently only specifies ductile iron water main (DIWM)
materials within the influence of its roadways. Rising costs of and supply issues with DIWM in recent years have caused
significant project delays. Municipalities are increasingly requesting the use of PVC water main materials within MDOT R.O.W.
to maintain material continuity of their facilities. Allowing use of alternative materials could reduce costs and/or delays to the
Department. MDOT needs data to address Municipal Engineers and Industry questions on the suitability of allowing PVC water
main on MDOT projects. Several factors must be evaluated in comparison to DIWM; the durability and expected design life,
historical leakage and breakage rates, cause of failures, the long-term safety of PVC water main materials on public health,
and life cycle costs. The research must provide data guidance and recommendations on the advantages and disadvantages of
PVC versus DIWM to allow consideration of a change to current policy.]]></description>
      <pubDate>Fri, 17 Jul 2026 14:29:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731924</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>Guide for Design, Installation, and Testing of Cured In-Place Pipe Liners








</title>
      <link>https://rip.trb.org/View/2419748</link>
      <description><![CDATA[State departments of transportation (DOTs) are increasingly using trenchless strategies to rehabilitate and repair aging infrastructure. Performing pipe repairs to existing systems, as compared to pipe replacement, significantly reduces the construction time and maintenance of traffic operations and roadway reconstruction. With reduced construction time and roadway construction, pipe repairs can increase public safety and cost savings to the state DOTs. One of the most common pipe rehabilitation methods is cured in-place pipe (CIPP) liners. CIPP liners are a trenchless technology that provides a method to structurally rehabilitate existing pipes and conduits with minimal impact to the traveling public. The liner consists of a resin-impregnated material that is inserted into the existing damaged host pipe.

Research is needed to substantiate that CIPP liner technology provides the structural characteristics and durability to extend the service life of the asset. 

The objective of this research is to develop a guide for design, installation, and acceptance of CIPP liners for structural rehabilitation of existing pipelines and conduits and test methods for CIPP liner material.  ]]></description>
      <pubDate>Tue, 20 Aug 2024 09:39:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2419748</guid>
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