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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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      <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>
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      <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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