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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Improving the Planning of Future Track Interventions Using Digital Tools</title>
      <link>https://trid.trb.org/View/2671828</link>
      <description><![CDATA[This paper proposes a methodology to capitalise on the recent advances in technology to efficiently estimate the required condition-related track interventions, possession times and their expected costs for a railway network early in the intervention planning process. Having such estimates not only helps track managers effectively plan and allocate resources, but it also enhances the communication between different stakeholders within the intervention planning process, e.g., asset managers, line planners, capacity managers, and network developers. The methodology uses data of different levels of detail, probabilistic discrete state modelling of the condition of components, and component-level intervention strategies. It also uses fault trees to connect potential losses in service with the likelihood of corrective interventions that may occur due to sudden events as a function of the condition of the components. The methodology is used to estimate the required condition-related interventions, possession times and expected costs for a 25 km railway network in Switzerland. The results indicate that the methodology has the potential to help track managers early in the intervention planning process. Once implemented in a digital environment, the methodology will lead to improvements in the efficiency of the planning process, improvement in the timing of preventive interventions and the reduction in corrective intervention costs.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671828</guid>
    </item>
    <item>
      <title>Decision Support for Predictive Maintenance Planning on the Freight Corridor From Rotterdam to Genoa</title>
      <link>https://trid.trb.org/View/2671093</link>
      <description><![CDATA[The central task of any rail infrastructure manager is to maintain the track properly and make it available for operation. To achieve this, maintenance management must meet both safety and economic challenges. The assessment criteria to be applied for network maintenance are defined in regulations and standards. In detail, the considered limit values for the superstructure are defined by the standard EN13848 [1] at European level and by DB Group Guideline (KoRil) 821.2001 [2] at the national level. On this basis, the condition assessment of the superstructure is determined by the measured values obtained during prescribed regular inspections (e.g., of the track geometry). Due to the nature of the underlying optimization problem, which is characterized by a high complexity due to the individual development of the condition of each 25 m segment, the diversity of the machine types, the variation on the effect of maintenance and the large range of possible train free time windows an exact solution of the resulting linear mixed-integer program would require a high numerical effort and thus an infeasible computation time. Therefore, we present a heuristic solution approach to solve the described optimization problem with sufficient accuracy in a significantly lower runtime. The solution approach is based on a branch and bound algorithm, where the local optimization is limited by the respective machine characteristic.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671093</guid>
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    <item>
      <title>Applicable Conditions for Polyurea Resin Spraying Method for Water Leakage and Uneven Tunnel Lining Surfaces</title>
      <link>https://trid.trb.org/View/2675873</link>
      <description><![CDATA[Falling pieces of tunnel lining hitting a train could cause an accident. Regular tunnel maintenance to prevent this is therefore essential. Repairs are planned and carried out in areas where there is a potential risk of spalling. A method called the “polyurea resin spraying method,” has been developed to prevent spalling by spraying tunnel linings with polyurea resin. This paper presents the results of a study carried out to extend the applicability of this method and some application examples: the effect of surface moisture during application on the adhesion strength of the resin, long-term durability in the presence of leaking water, adhesion strength of the resin on an uneven surface, and results of its application in a real tunnel.]]></description>
      <pubDate>Mon, 08 Jun 2026 08:38:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675873</guid>
    </item>
    <item>
      <title>Geotechnical Infrastructure Observation and Auscultation Manual</title>
      <link>https://trid.trb.org/View/2668472</link>
      <description><![CDATA[Road infrastructure networks have a major social and economic role in the development of countries. During the Operation and Maintenance (O&M) period of these structures, their integrity must be promoted, ensuring the safe circulation of users and the service levels established contractually [1]. In order to fulfill the described goals, Ascendi, with the support of specialist consultants in the area, proposes to develop innovative solutions for the management, conservation and maintenance of road geotechnical structures. The innovation in the methodology of auscultation and observation of walls and slopes (W&S) results from the development and integration of a mobile platform for sustainable management of road structures (SustIMS) [2]. This tool allows monitoring of the evolution of the maintenance and conservation states of W&S, knowledge that allows the Asset Management Team to take preventive decisions regarding the sustainable management of W&S, always aiming at safety in circulation and optimization of maintenance costs.]]></description>
      <pubDate>Tue, 26 May 2026 09:41:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668472</guid>
    </item>
    <item>
      <title>Improving preventive maintenance strategies for enhanced pavement friction restoration performance via explainable machine learning</title>
      <link>https://trid.trb.org/View/2672268</link>
      <description><![CDATA[Maintaining sufficient pavement surface friction is crucial for roadway safety, particularly under wet conditions. Preventive Maintenance (PM) treatments have proven effective in restoring pavement friction levels and reducing the likelihood of crashes. However, capturing the friction deterioration behavior after different PM treatments remains challenging due to complex interactions among traffic, climate, and pavement conditions. This study analyzed pavement friction number (FN) using data from the Long-Term Pavement Performance (LTPP) Special Pavement Study 3 (SPS-3) to evaluate the effectiveness of 4 PM treatments at improving and maintaining high levels of friction. Four machine learning models, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were developed to capture the complex relationships between 10 input features and FN. The SHapley Additive exPlanations (SHAP) was employed to interpret model predictions and quantify the contribution of each input and interaction variables. Among all models, XGBoost achieved the best performance, with an R² of 0.76 and RMSE of 5.46 on the test dataset. Results indicate that slurry seals are the most effective treatment for improving pavement friction over a five-year period, followed by chip seal. The XGBoost model, combined with a cluster-based data splitting strategy, provided the most robust and accurate FN predictions. Sensitivity analysis shows that slurry seal maintains FN above the investigatory level (FN = 40) when Annual Average Daily Truck Traffic (AADTT) is below approximately 2000 trucks/day, but its effectiveness declines sharply beyond this threshold. Additionally, pre-treatment surface condition strongly influences pavement friction performance, with higher International Roughness Index (IRI) values corresponding to degraded texture and lower friction. Finally, the SHAP framework offered interpretable insights into data-driven pavement management decisions. These findings provide transportation agencies with actionable insights to guide treatment selection and optimize maintenance planning for safer pavement management.]]></description>
      <pubDate>Thu, 14 May 2026 14:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672268</guid>
    </item>
    <item>
      <title>Optimized User Experience for Labeling Systems for Predictive Maintenance Applications</title>
      <link>https://trid.trb.org/View/2580264</link>
      <description><![CDATA[The maintenance of rail vehicles and infrastructure plays a critical role in reducing train delays, preventing malfunctions, and ensuring the economic efficiency of rail transportation companies. Predictive maintenance systems powered by supervised machine learning algorithms offer a promising approach by detecting potential failures before they occur, reducing unscheduled downtime, and improving operational efficiency. However, the success of such systems depends heavily on high-quality labeled data, necessitating user-centered labeling interfaces tailored to annotators’ needs for Usability and User Experience. This study introduces a cost-effective predictive maintenance system developed as part of the federally funded research project “DigiOnTrack,” which combines structure-borne noise measurement methods with supervised machine learning to provide monitoring and maintenance recommendations for rail vehicles and infrastructure in rural Germany. The system integrates wireless sensor networks, distributed ledger technology for secure data transfer, and a dockerized container infrastructure hosting the labeling interface and alarming dashboard. Train drivers and workshop foreman were annotators, labeling faults on rail infrastructure and vehicles to ensure accurate predictive maintenance recommendations. The Usability and User Experience evaluation revealed that the locomotive drivers’ interface achieved “Excellent Usability,” while the workshop foreman’s interface was rated as “Good Usability.” These results highlight the system’s potential for seamless integration into daily workflows, particularly regarding labeling efficiency. However, areas such as Perspicuity require further optimization for more data-intensive scenarios. The findings offer actionable insights into the design of predictive maintenance systems and labeling user interfaces, providing a foundation for future guidelines in Industry 4.0 applications, particularly in rail transportation.]]></description>
      <pubDate>Tue, 24 Mar 2026 13:08:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580264</guid>
    </item>
    <item>
      <title>Development and performance evaluation of a rapid-curing waterborne epoxy-based sealer for urban pavement preventive maintenance</title>
      <link>https://trid.trb.org/View/2645560</link>
      <description><![CDATA[Preventive maintenance of urban pavements can cause prolonged road closures. To address the need for rapid reopening, this study develops a novel rapid-curing waterborne epoxy-based sealer. The research objectives focus on optimizing the material formulation and evaluating its key performance characteristics. The methodology involves self-developed drying rate and binder bond-strength tests to determine the optimal ratio of waterborne epoxy resin (WER), styrene–butadiene rubber (SBR) emulsion, and cement, through the analysis of variance (ANOVA). Dynamic shear rheology tests were performed to compare the composite emulsified asphalt with other formulations, while Fourier-transform infrared spectroscopy and X-ray diffraction analyses provided microscopic insights into material interactions. Experimental tests measured the reopening time and pavement skid resistance under different construction methods. The findings demonstrate that the three-phase composite modified emulsified asphalt exhibits superior high-temperature performance and optimal curing characteristics. The developed sealer achieves traffic readiness within 40–90 min and exhibits excellent skid resistance when applied with 20-mesh aggregate at 100 % coverage rate. The study recommends the optimized formulation containing 8 % WER, 4 % SBR emulsion, and 4 % cement as a promising solution for urban pavement preventive maintenance, effectively balancing rapid curing with durable performance.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:41:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645560</guid>
    </item>
    <item>
      <title>National Road Research Alliance (Phase-3)</title>
      <link>https://trid.trb.org/View/2678150</link>
      <description><![CDATA[This solicitation is for the continuation of the National Road Research Alliance (NRRA) for another 5 years and to continue to support Veda development to increase efficiency and effectiveness of both efforts. The NRRA exists to strategically implement cooperative pavement research. State agencies, industry, academia, consultants and associations work together to identify problems, complete research projects and implement results. The goal is to help agencies nationwide achieve consistent benefits from real world road research. It also seeks to provide members a forum to discuss issues and an outdoor, real-world laboratory (MnROAD) for evaluating cutting-edge pavement technologies.  The NRRA consists of five project teams: Flexible, Rigid, Geotechnical, Intelligent Construction Technologies, and Preventive Maintenance and is governed by an Executive Committee made up of two representatives from each government agency participating in the study.   Each team activities include prioritization of short and long-term research, development of long-term research test sections at MnROAD and providing input for technology transfer.  


]]></description>
      <pubDate>Fri, 06 Mar 2026 13:10:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678150</guid>
    </item>
    <item>
      <title>Implementation of an AI-based predictive structural health monitoring strategy for bonded insulated rail joints using digital twins under varied bolt conditions</title>
      <link>https://trid.trb.org/View/2625366</link>
      <description><![CDATA[Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy. Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step, particularly for the railway sector, whose safe and continuous operation plays a strategic role in the well-being and development of nations. In this scenario, the benefits of a digital twin of a bonded insulated rail joint (IRJ) with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored. The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint. As bolt preload conditions vary, four structural health classes were identified for the joint. Two parameters, i.e. gap value and vertical displacement, which are strongly correlated with bolt preload, are used in different combinations to train and test five predictive classifiers. Their classification effectiveness was assessed using several performance indicators. Finally, we compared the IRJ condition predictions of two trained classifiers with the available data, confirming their high accuracy. The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle–infrastructure interaction is complex and always time varying.]]></description>
      <pubDate>Wed, 04 Feb 2026 08:32:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625366</guid>
    </item>
    <item>
      <title>A Three-Stage Decision-Making Method Based on Machine Learning for Preventive Maintenance of Airport Pavement</title>
      <link>https://trid.trb.org/View/2553284</link>
      <description><![CDATA[The goal of preventative maintenance (PM) decision-making on airport pavements is to deploy the appropriate maintenance countermeasures at the correct time. This paper proposed a three-stage method for maintenance based on machine learning, which further refined the PM decision-making process. First, a pavement maintenance level model was developed using the PCA and PSO algorithm optimized SVM model. The model was then used to separate pavement maintenance into three categories: daily, PM, and major. Second, the DBSCAN and OPTICS were utilized to further divide the PM requirements finely. In order to implement the scientific decision-making of PM, suitable maintenance procedures were ultimately chosen based on the predominant damage kinds of the pavement units. The results showed that, when compared to the original SVM model, the classification accuracy of the PCA-PSO-SVM model was greatly improved, with total accuracy and accuracy of each class increasing by 10%, 41.7%, 4.6%, and 7.8%, respectively. When clustering the airport pavement performance dataset, OPTICS outperformed the DBSCAN technique. Four groups of PM demands were discovered by visualizing the best grouping levels after dimensionality reduction.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:17:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553284</guid>
    </item>
    <item>
      <title>Prediction of pavement maintenance quality and performance indicators using particle swarm optimized gradient boosting decision trees</title>
      <link>https://trid.trb.org/View/2643636</link>
      <description><![CDATA[The rapid global expansion of highway networks has significantly increased maintenance demand. Pavement preventive maintenance is an effective strategy for prolonging pavement lifespan and optimizing budgets. This study developed tree-based models, specifically XGBoost, LightGBM, and CatBoost, with and without Particle Swarm Optimization (PSO), to predict the Pavement Maintenance Quality Index (PQI) and four key performance indicators: Pavement Surface Condition (PCI), Riding Quality Index (RQI), Rutting Depth (RDI), and Skid Resistance (SRI). The models were trained on four years of measured data, incorporating features such as age, lane, direction, section length, average annual climate, and distress and repair area. The Shapley Additive Explanations method was used to evaluate feature importance. The results demonstrated that PSO enhanced the performance of all models, with PSO_CatBoost achieving the highest predictive accuracy. Validation on unseen data from a subsequent year and a separate highway confirmed strong model performance, yielding R² values of 0.7984 and 0.6575 for PQI and 0.9379 and 0.9 for PCI, while the mean absolute percentage error remained below 1%. The findings suggest that the PSO_CatBoost framework could offer a reliable, data-driven method for lane-specific pavement quality prediction, potentially providing maintenance authorities with a practical tool for screening and prioritising preventive interventions.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643636</guid>
    </item>
    <item>
      <title>Multidimensional automated decision support for asphalt pavement maintenance using the BIM-LCA-LCCA method</title>
      <link>https://trid.trb.org/View/2643589</link>
      <description><![CDATA[To provide a comprehensive foundation for maintenance strategy decision-making, this study integrates Building Information Modeling (BIM), Environmental Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to perform automated Net Present Value (NPV) analysis by monetising the multidimensional impacts of various maintenance strategies. A case study on provincial roads in China shows that preventive maintenance strategies, especially those using emerging technologies like Ultra-Thin Overlay (UTO), are more cost-effective than traditional strategies and should be prioritised when conditions allow. Additionally, the study identifies the optimal maintenance frequencies for each strategy: once every 5 years for the UTO strategy, once every 9 years for the Milling and Filling (M&amp;F) of the Surface Course strategy, and once every 7 years for the Hot In-Place Recycling (HIR) strategy. In summary, the BIM-LCA-LCCA approach offers a versatile, efficient, and real-time method for supporting decision-making in pavement maintenance strategies. It is applicable to most mainstream asphalt pavement structures in China and is expected to provide valuable insights for optimising the design and management of future infrastructure projects.]]></description>
      <pubDate>Mon, 05 Jan 2026 14:53:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643589</guid>
    </item>
    <item>
      <title>Ex-Post Evaluation of the Effects of Simple Proactive Repairs with a Deterioration Prediction Model on Steel and Concrete Bridges Considering Sample Dropping Bias</title>
      <link>https://trid.trb.org/View/2607942</link>
      <description><![CDATA[During periodic inspections of bridges, the inspector’s first-aid works are sometimes implemented to improve safety and enhance preventative maintenance. It is important to evaluate the effectiveness of the inspector’s first-aid works, but since these works are implemented for damage with relatively high deterioration rates, inspection data samples with such damage that did not receive the inspector’s first-aid works cannot be obtained, which results in sample dropping bias in the data. In this study, this problem is solved by using a deterioration prediction model that takes into account the dropped samples. Then, the effect of the inspector’s first-aid works is quantitatively evaluated after incorporating a deterioration control effect of inhibiting the progression of deterioration by comparing the deterioration processes of when the inspector’s first-aid works are implemented and when they are not. Lastly, through an application case study using visual inspection data of an actual highway viaduct, the effect of the inspector’s first-aid works on inhibiting deterioration is quantitatively evaluated, and the timing of implementation of the inspector’s first-aid works is discussed.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:33:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2607942</guid>
    </item>
    <item>
      <title>Effectiveness of Concrete Deck Sealers and Laminates for Chloride Protection of New and In Situ Reinforced Bridge Decks in Illinois</title>
      <link>https://trid.trb.org/View/2617974</link>
      <description><![CDATA[Preventative maintenance of our infrastructure elements is increasingly vital as the resources to repair and replace these elements become scarce. One form of preventative maintenance is to protect structures from damages inflicted from deicing and anti-icing practices. A low-cost preventative maintenance strategy is the use of protective coatings for bridge decks. Bridge deck concrete is often flawed by cracks. These cracks provide ingress for chloride ions to the reinforcement of the deck and structure. In order to prevent the further ingress of chloride ions, sealers and laminates are often considered practical methods of protection. This research project developed a protocol to evaluate concrete sealer and laminate effectiveness in protecting bridge deck concrete from chloride ion ingress. The protocol developed includes criteria for selecting products for evaluation, sample locations, sample depths, duration of study as well as the method of analysis of the chloride ions present in the concrete dust collected. The results demonstrate not only the relative effectiveness of the various sealers and laminates, but also the durability of each product as compared to control structures without a sealer or laminate applied. The durability and cost of the products can be used to develop the relative cost-effectiveness of each product. The cost-effectiveness values were utilized to develop the recommended policy for the use of bridge deck sealers and laminates for preventative maintenance.]]></description>
      <pubDate>Tue, 23 Dec 2025 08:59:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617974</guid>
    </item>
    <item>
      <title>The Effectiveness of Preventive Maintenance in Railways</title>
      <link>https://trid.trb.org/View/2572567</link>
      <description><![CDATA[Railway maintenance is crucial for ensuring system availability, punctuality, safety, and comfort. Though studies have shown that in reality, maintenance is ‘imperfect’-inspections do not reveal all defects and repairs do not return components to as good as new and may even leave them in a worse state, railway maintenance planning is still based on periodic block-type and age-based maintenance policies that take no account of this phenomenon. Moreover, due to funding constraints, adhering to the conceivably optimistic recommended levels of maintenance, which are derived from such planning, is also nearly impossible. Consequently, planners adopt other factors for prioritizing maintenance, albeit in a non-systematic manner. This suggests a need to develop maintenance policies that take into account the imperfect nature of maintenance and any other practical factors, if cost-effectiveness and reduced incidents are to be achieved. As a first step in this direction, this study uses a case of specific switches and crossings (S&C) components on the Swedish railway network: crossings, the tongue device, and the heating element, to assess the level of adherence to existing maintenance standards and the extent to which repairs deter failures i.e., evidence of imperfect maintenance. It was determined that on average, only 56% of S&C errors are fixed within the recommended time, with the heating element being prioritized over the other failures considered. A comparison of the survival rates (time to first failure) before and after repairs of the components provides evidence for imperfect repair of crossings and the tongue device but evidence of minimal repair of the heating element. Overall, timely repair is seen to improve survival. The survival rate of crossings decreases by approximately 0.07% for each day repair is delayed, while it decreases by 0.02% for the tongue device and 0.08% for the heating element. This study also serves to demonstrate a method for assessing the effectiveness of maintenance regimes.]]></description>
      <pubDate>Wed, 17 Dec 2025 09:39:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572567</guid>
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