<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc3IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSIyeWVhcnMiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycyAvPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Automatic Leak Detection in Pipelines Using Thermal Images from Fixed-Wing Drones and Convolutional Neural Networks</title>
      <link>https://trid.trb.org/View/2742205</link>
      <description><![CDATA[This study aims to develop and validate an automated approach for large-scale pipeline leak detection using fixed-wing unmanned aerial vehicles (UAVs) equipped with thermal imaging sensors and convolutional neural networks (CNNs). Pipeline leakages represent a critical challenge for energy infrastructure operators because of their environmental, economic, and safety implications, particularly in large and geographically distributed pipeline networks where ground-based inspections are costly and time-consuming. The proposed methodology integrates long-range thermal image acquisition from fixed-wing UAV flights with a deep-learning-based detection pipeline designed to identify and localize leakage events under real-world operating conditions. Unlike conventional approaches that predominantly rely on rotary-wing platforms or visible-spectrum imagery, the presented framework leverages the extended coverage capabilities of fixed-wing UAVs and the robustness of thermal imaging to improve monitoring efficiency across large areas. The main contributions of this work include the development of an integrated thermal-based leak detection framework tailored for fixed-wing UAV operations, the application of CNNs for automated leak identification and localization, and comprehensive validation under both simulated and real-world conditions. Quantitative evaluation demonstrates reliable performance, achieving a leak localization root mean square error of 0.8989 m on straight pipeline segments and an overall detection accuracy of 81%. The proposed approach is directly applicable for pipeline operators, infrastructure monitoring agencies, and energy companies by enabling early leak identification, reducing inspection costs, and supporting safer and more efficient maintenance planning. Furthermore, the presented framework provides a foundation for future research focused on larger annotated datasets and integration with predictive maintenance systems. All data generated or analysed during this study are included in this published article.]]></description>
      <pubDate>Thu, 06 Aug 2026 09:05:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742205</guid>
    </item>
    <item>
      <title>Identification of gas-liquid flow patterns in horizontal pipes using phase fraction-integrated physics-guided neural network</title>
      <link>https://trid.trb.org/View/2737844</link>
      <description><![CDATA[Accurate identification of gas-liquid two-phase flow patterns in pipelines is a key challenge for optimizing offshore oil and gas transport, marine energy systems, and sustainable maritime operations. This paper integrates the gas-liquid flow pattern transition mechanism with a physics-guided neural network (PGNN) and proposes an identification model for gas-liquid flow patterns in horizontal pipes that combines data fitting with physical constraints. A database of horizontal gas-liquid flow patterns is developed with different pipe diameters (12.7 - 300 mm), gas superficial velocities (0.014 - 200.6 m/s), liquid superficial velocities (0.002 - 8.930 m/s), and liquid viscosities (1.002 - 166 mPa⸱s), comprising 5625 data points. Based on the visualization images of gas-liquid flow in a 65 mm ID horizontal pipe, the flow patterns are classified into annular flow, intermittent flow, dispersed bubble flow, and stratified flow, and the flow pattern data in the database are subsequently reclassified. The predictive abilities of 14 phase fraction correlations are evaluated using an existing flow pattern-phase fraction database, and the correlation proposed by Rouhani is found to have the best coefficient of determination and error control (RRMSE = 10.25%, R² = 0.9). As this correlation serves as the phase fraction physics-guided module, PGNN achieves 97.9% flow pattern identification accuracy on the independent test set, which is superior to other models. The effectiveness of the physics-guided module is quantitatively verified, showing high fitting accuracy (R² > 0.93) for physics-guided quantities. In cases where the flow pattern boundary is blurred and difficult to identify, PGNN maintains an accuracy of 96.88%, surpassing models such as random forest (95.3%) and multilayer perceptron (85.2%).]]></description>
      <pubDate>Wed, 05 Aug 2026 09:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737844</guid>
    </item>
    <item>
      <title>Integrity Assessment of Corroded Subsea Pipelines for Hydrogen-Blended Gas Transportation: A Coupled Experimental and Numerical Approach</title>
      <link>https://trid.trb.org/View/2737522</link>
      <description><![CDATA[Subsea pipelines are a cost-effective way to transport offshore wind-generated hydrogen, but corrosion defects generated during long-term operation pose significant safety risks for hydrogen blending transportation. This study explores the safety assessment of subsea pipelines with corrosion defects after hydrogen blending. The coupling mechanism between defect size and hydrogen conditions on pipeline failure pressure is investigated through the integration of experiments and simulations. The experimental results indicate that the hydrogen blending ratio exceeding 30% significantly accelerates the degradation of X80 steel properties. According to finite element analysis, defect dimensions are primary factors governing load-bearing capacity, while the influence of hydrogen concentration is constrained by defect size. It is worth noting that pipelines with smaller defects exhibit higher sensitivity to changes in hydrogen concentration. A predictive formula for ultimate bearing performance is established. It combines the hydrogen blending ratio with defect size, and achieves a good fit. Based on the revised single-defect model, a comprehensive safety evaluation methodology for multiple corrosion defects in submarine pipelines is proposed via the integration of an eXtreme Gradient Boosting (XGBoost)-based interaction coefficient model. The above research can provide a theoretical basis and technical reference for the safety assessment of hydrogen transportation in submarine pipelines.]]></description>
      <pubDate>Wed, 05 Aug 2026 09:12:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737522</guid>
    </item>
    <item>
      <title>Advanced Predictive Modeling of Pipeline Settlement in Rectangular Pipe Jacking Tunneling using Hybrid Deep Learning Techniques: A PSO-LSTM-Self Attention Approach</title>
      <link>https://trid.trb.org/View/2742451</link>
      <description><![CDATA[Nowadays, as computer technology makes quick progress, innovative algorithms like deep learning are getting used more and more in underground engineering and lots of other fields. When working on rectangular pipe jacking tunnel projects, accurately predicting the magnitude of pipeline settlement is really key to keeping the work moving smoothly. But traditional ground settlement prediction methods mainly rely on empirical formulas and numerical simulation software. When applied to tunnels with complex geometries, though, these methods usually don’t work as well as needed. To fix this problem, our study came up with a new model called PSO-LSTM-Self-Attention Mechanism (shortened to PSO-LSTM-SAM), specifically designed to predict pipeline settlement caused by rectangular pipe jacking work. This model takes the data collected from construction monitoring and uses that as the input for time series modeling work. That allows for in-depth analysis of real-time settlement data, and as a result, it can make more precise predictions of long-term pipeline settlement. To verify the effectiveness of the PSO-LSTM-SAM algorithm, the researchers compared its prediction results with those from a conventional LSTM network, an LSTM-SAM network, and a PSO-SVR network. They also checked the model’s performance by looking at pipeline settlement predictions from different monitoring points, using data from the Changsha Railway Transit Line 6 project. The results show that the PSO-LSTM model, with the self-attention mechanism added in, greatly boosts how accurate tunnel settlement predictions are, and the model fits the data better, too. This proves that the PSO-LSTM-SAM model works well: by using the strengths of deep learning, it offers a new way to predict pipeline settlement when building rectangular pipe jacking tunnels.]]></description>
      <pubDate>Mon, 03 Aug 2026 15:57:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742451</guid>
    </item>
    <item>
      <title>Optimal Repair Time for Failure Units of Complex Natural Gas Pipeline Network System Based on Cost and System Supply Reliability</title>
      <link>https://trid.trb.org/View/2695136</link>
      <description><![CDATA[The repair time for failure unit is treated as a static parameter in researches on enhancing the supply security of natural gas pipeline network system, while its dynamic impact on system supply capacity is overlooked. To address this gap, a unit optimal repair time determination method based on cost and system supply reliability is proposed. The costs within unit repair time are analyzed. The total unit cost curve is analyzed. The characteristic of unit state transition is captured by Markov process, a unit reliability calculation model considering cumulative risk is proposed. The mapping relationship between unit repair time and system supply reliability is characterized. An optimization model is established with the objective of minimizing total system cost. Two system supply reliability constraint strategies and unit repair time constraints are proposed. A real pipeline network in China is employed as the application platform. Total system cost is minimized when the system supply reliability is 0.99899. The optimal repair time for compressor station units change significantly as target supply reliability increases, the optimal repair time for pipeline units change slightly. This study can reasonably allocate repair resources, providing theoretical and practical support for decision-makers to formulate scientific and efficient failure response plan.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2695136</guid>
    </item>
    <item>
      <title>Hydrogen Related Issues at Hydrogen Transport via Existing Gas Pipelines</title>
      <link>https://trid.trb.org/View/2579445</link>
      <description><![CDATA[One of the primary technical challenges in repurposing gas pipelines for hydrogen transport is ensuring material compatibility. In this study, the influence of absorbed hydrogen on the stressed state of the metal is investigated, and the role of hydrogen-induced stress in damage to pipeline steel is analysed. Hydrogen-induced stress in steel specimens preloaded in the elastic and plastic region is assessed using the developed technique. A change in the stress state of the steel specimen as a response to hydrogen charging and desorption depended on the type of preliminary deformation in the elastic or plastic region and hydrogen charging intensity. The role of hydrogen in the implementation of strain ageing of steels, as well as in the formation of pores and delaminations during long-term operation, is considered.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579445</guid>
    </item>
    <item>
      <title>Statistical Analysis of Corrosion Losses of the Main Oil Pipelines Linear Part</title>
      <link>https://trid.trb.org/View/2579443</link>
      <description><![CDATA[The article analyzes the recurrence of defects and damages on main oil pipelines, in particular corrosion, and the problem of ensuring the reliability of steel structures and pipelines used in oil and gas complexes. It has been found that the most common and dangerous operational damage to the linear parts of main oil pipelines is corrosion damage, which has the character of cavities with random depth and length, as well as random placement along the length of the pipeline. A reliable means of detecting corrosion damage is in-line diagnostics, which allows measuring the depth of each corrosion cavity with high accuracy and determining its location along the pipeline length. The length of corrosion damage is represented as a random variable with a logarithmically normal distribution law, independent of the random variable of corrosion depth. The preferred probabilistic model is the sequence of maximum corrosion depth values, which provides a probabilistic description of corrosion damage sufficiently accurate for reliability assessment and is consistent with the accepted repair technology, in which entire sections of the pipeline are replaced in severely damaged areas. Using the statistical analysis, the corrosion effects were studied at each of the selected sites as a result of diagnostics inside the pipe, and attention is drawn to the need to research and improve scientific, technical and technological developments in the field of corrosion and mechanical resistance of metal structures. The results obtained are an important contribution to the development of technical and design measures to improve the efficiency and reliability of oil and gas complexes.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579443</guid>
    </item>
    <item>
      <title>A theoretical study on lateral impact damage of submarine pipeline considering seabed flexibility</title>
      <link>https://trid.trb.org/View/2721163</link>
      <description><![CDATA[Untrenched submarine pipelines lying directly on the seabed are vulnerable and can be damaged by the lateral impact of falling objects, which could lead to significant economic losses and even environmental contamination in case of rupture and oil leakage. This study presents assessment of submarine pipeline damage subjected to lateral impact considering the effect of seabed through theoretical method. A theoretical model is established based on the virtual work principle, wherein the local indentation and global bending of pipeline adopting a string-on-plastic foundation model and classical beam theory respectively and the effect of seabed on energy absorption capacity is integrated via linear spring theory, thereby establishing an analytical framework that incorporates seabed flexibility into impact damage assessment. The accuracy of the new theoretical model was validated by comparing with previous research findings and numerical simulation results, confirming the relationship between impact force and dent depth under the effect of seabed flexibility. Additionally, a parametric analysis was conducted to assess pipeline damage by accounting for various factors. The model provides a new prediction method for accurately considering the effect of soil on the impact damage of untrenched submarine pipelines, offer significant insights into the design and protection of submarine pipelines.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721163</guid>
    </item>
    <item>
      <title>Research on ice particle impact erosion in seawater pipelines of polar vessels</title>
      <link>https://trid.trb.org/View/2718450</link>
      <description><![CDATA[Safety risks associated with ice particle impact on seawater pipelines in polar vessels are investigated in this study. The limitations of existing research regarding ice particle fragmentation and the accurate prediction of erosion behavior are addressed. The mechanical properties of ice particles were evaluated using a coupled CFD-DPM approach, along with paint removal experiments and acoustic emission (AE) technology. The mechanisms underlying ice particle breakage and pipeline erosion were analyzed, and the service life of pipelines constructed from different materials was assessed. A quantitative relationship between impact parameters and material damage was also established. The results reveal that pipeline erosion by ice-laden seawater is a complex process involving kinetic energy-induced fragmentation, flow field-driven aggregation, and turbulence-enhanced collisions. Ice particle fragmentation increased the erosion rate by 25%. Among the materials tested, glass-reinforced epoxy (GRE) showed the poorest erosion resistance, with a service life only one-fifth that of metallic materials. The distribution of erosion hotspots was governed by the orientation of the pipeline elbow, while increases in Stokes number, ice particle diameter, and volume fraction intensified material damage. Future work could integrate shipboard monitoring data with machine learning techniques to enhance the prediction of erosion hotspots.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2718450</guid>
    </item>
    <item>
      <title>Drag embedment anchor penetration in layered sands and through cable and pipeline trenches</title>
      <link>https://trid.trb.org/View/2725457</link>
      <description><![CDATA[Cable Burial Risk assessment (CBRA) is undertaken to identify risks to offshore renewable energy cable infrastructure. CBRA assumes uniform soil conditions and does not recognise the effect of the cable installation methods. Centrifuge model testing was undertaken to explore the performance of a shipping anchor (AC-14) when encountering layered sand soil profiles. In addition, anchor interaction with both cable plough trenches and backfilled V-shaped pipeline plough trench routes were investigated. For a specific anchor, in loose over dense sand layers, penetration is stopped with minimal penetration into the underlying dense layer irrespective of the thickness of the loose layer for the anchor size investigated. Testing a recent layered soil CBRA approach indicated that it performed well when inputs were based upon well-characterised model anchor performance. Anchor interaction with vertical cable plough trenches showed limited modification of anchor behaviour. Similar observations were made for the V-shaped backfilled trenches where the anchor approached at 90 or 45° to the trench. When the anchor followed the route of the trench it dived through the trench and backfilled material and into the underlying dense soil. For CBRA methods to improve there is a need for high-quality characterisation of different anchor types in a wider range of soil conditions where realistic installation practices are considered.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725457</guid>
    </item>
    <item>
      <title>Reproducible AI-assisted systematic review pipeline applied to digital twins for bridge predictive maintenance and lifecycle management</title>
      <link>https://trid.trb.org/View/2728214</link>
      <description><![CDATA[The PRISMA 2020-compliant systematic literature review framework is indispensable for evidence synthesis in engineering research; however, it remains resource-intensive, time-consuming, and susceptible to human inconsistency. This paper presents an AI-assisted systematic review pipeline employing a large language model (claude-opus-4-5, Anthropic) comprising ten sequential modules automating the PRISMA 2020-compliant workflow, including title screening, abstract screening, full-text eligibility, data extraction, bibliometric analysis, thematic synthesis, and gap identification, culminating in automatic generation of a structured manuscript template. Five algorithms are formally documented in pseudocode; a single configuration file controls the workflow, enabling the application to new domains without code modification. Applied to digital twins for bridge predictive maintenance and lifecycle management, a Scopus search retrieved 90 records (2020–2026), from which 20 high-quality articles were identified, all receiving HIGH confidence ratings, revealing five thematic clusters and six critical gaps. The pipeline is provided as open supplementary code.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728214</guid>
    </item>
    <item>
      <title>Pipeline Investigation Report: Exelon Corporation (Baltimore Gas and Electric Company) Natural Gas–Fueled Home Explosion, Bel Air, Maryland, August 11, 2024</title>
      <link>https://trid.trb.org/View/2728157</link>
      <description><![CDATA[On Sunday, August 11, 2024, about 6:48 a.m., a home explosion and fire occurred at 2300 Arthurs Woods Drive in Bel Air, Maryland, resulting in a destroyed home, two fatalities, and three injuries. The explosion also damaged several residences and displaced families. At the time of the explosion, it was daylight and clear; the temperature was 67°F with no precipitation. ​The National Transportation Safety Board (NTSB) determines that the probable cause of the August 11, 2024, home explosion in Bel Air, Maryland, was the Exelon Corporation subsidiary Baltimore Gas and Electric Company’s ineffective response to reports of a suspected natural gas leak, which allowed for a suspected leak, caused by electrical arcing of electrical service lines, collocated in a common trench with a plastic gas service-line pipe and its tracer wire, to be left unrepaired for over 10 hours, resulting in gas leaking from the service-line pipe, migrating to the accident home, and igniting.]]></description>
      <pubDate>Thu, 23 Jul 2026 16:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728157</guid>
    </item>
    <item>
      <title>AI-Enabled Spatio-Temporal Risk Assessment and Decision Support for Pipeline Infrastructure Preservation</title>
      <link>https://trid.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://trid.trb.org/View/2732468</guid>
    </item>
    <item>
      <title>Spatial and Recurrent Event Survival Analysis of Oil Pipeline Incident Hotspots and Regional Longevity Patterns</title>
      <link>https://trid.trb.org/View/2691719</link>
      <description><![CDATA[Oil pipeline infrastructure is crucial to energy security and economic development; however, many systems now operate beyond their intended design lifespan. This study addresses three key limitations in current pipeline incident analysis: the isolated treatment of recurring incidents, inadequate consideration of regional variation, and challenges associated with left-truncated failure data. This study proposes an integrated framework that combines spatial analysis to identify incident hotspots with recurrent event survival analysis to model time-dependent failure risks across regions, using incident data from the Pipeline and Hazardous Materials Safety Administration and network attributes from the US Energy Information Administration (1900–2022). Results show that while 45% of initial failures occur within the first decade of operation, the intervals between subsequent failures progressively shorten, with an average reduction of 14% between successive incidents. A Cox proportional hazards model adapted for left-truncated data quantifies the influence of key risk factors, while a geographically weighted Cox model reveals significant regional variation in the effects of variables such as coating type. This segment-level, spatially explicit framework supports more accurate, region-specific risk assessments and informs targeted maintenance strategies to improve pipeline safety and resilience.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691719</guid>
    </item>
    <item>
      <title>Leakage Localization Algorithm for Buried Gas Pipeline Networks with a Fusion of Dimensionless Theory and Smart Optimization</title>
      <link>https://trid.trb.org/View/2727358</link>
      <description><![CDATA[Accurate localization of leaks in buried gas pipeline networks is pivotal for urban safety and environmental protection. While previous studies have developed dimensionless gas diffusion models for simulating concentration distributions, their potential for leak source inversion and localization remains underutilized. This study proposes an improved leakage localization algorithm integrating a dimensionless diffusion model with a multistrategy improved particle swarm optimization (PSO) framework, termed GPPSO, which is developed by enhancing standard PSO with two key strategies: (1) good point set (GPS)-based initialization to boost global search ability and population diversity; and (2) a periodic oscillation mutation mechanism to mitigate premature convergence and enhance stability. Validated using publicly available high-pressure pipeline leakage experimental data and medium- to low-pressure pipeline network numerical simulation data (with the numerical model verified against experimental results), the algorithm enables leak localization using sensor concentration data, with errors decreasing progressively as monitoring data accumulate. For the medium- to low-pressure simulation scenarios, the proposed GPPSO-based method reduces localization errors by 77.8% and 66.7% compared with ant colony optimization and standard PSO, respectively. Overall, the proposed algorithm achieves significantly higher localization accuracy and lower total errors, verifying its feasibility and providing a reliable and scalable solution for intelligent pipeline leak monitoring.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727358</guid>
    </item>
  </channel>
</rss>