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    <title>Transport Research International Documentation (TRID)</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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>STGAN: Spatial-Temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction</title>
      <link>https://trid.trb.org/View/2591211</link>
      <description><![CDATA[Pavement distress, manifested as cracks, potholes, and rutting, significantly compromises road integrity and poses risks to drivers. Accurate prediction of pavement distress deterioration is essential for effective road management, cost reduction in maintenance, and improvement of traffic safety. However, real-world data on pavement distress is usually collected irregularly, resulting in uneven, asynchronous, and sparse spatial-temporal datasets. This hinders the application of existing spatial-temporal models, such as DCRNN, since they are only applicable to regularly and synchronously collected data. To overcome these challenges, we propose the Spatial-Temporal Graph Autoregression Network (STGAN), a novel graph neural network (GNN) model designed for accurately predicting irregular pavement distress deterioration using complex spatial-temporal data. Specifically, STGAN integrates the temporal domain into the spatial domain, creating a larger graph where nodes are represented by spatial-temporal tuples and edges are formed based on a similarity-based connection mechanism. Furthermore, based on the constructed spatiotemporal graph, we formulate pavement distress deterioration prediction as a graph autoregression task, i.e., the graph size increases incrementally and the prediction is performed sequentially. This is accomplished by a novel spatial-temporal attention mechanism deployed by the proposed STGAN model. Utilizing the ConTrack dataset, which contains pavement distress records collected from different locations in Shanghai, we demonstrate the superior performance of STGAN in capturing spatial-temporal correlations and addressing the aforementioned challenges. Experimental results further show that STGAN outperforms baseline models, and ablation studies confirm the effectiveness of its novel modules. Our findings contribute to promoting proactive road maintenance decision-making and ultimately enhancing road safety and resilience.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591211</guid>
    </item>
    <item>
      <title>Investigating the Potential of Predicting Field Performance of Commercially Available High Early Strength Materials for Concrete Deck Repair Using Experimental Data</title>
      <link>https://trid.trb.org/View/2657943</link>
      <description><![CDATA[High early strength (HES) repair materials have been increasingly used in repair projects across the United States due to their rapid strength gain, minimizing road closure and traffic disturbance. This study aimed to use statistical analysis to investigate the potential of using available experimental data of different material properties to predict the long-term field performance of HES materials. Data were collected from the literature and repair inspections across Nebraska. The collected data were represented by the rate of degradation and the normalized crack length. In addition, a moment-curvature analysis was performed to investigate potential reasons for the early deterioration of HES repairs. The results indicated a potential to predict long-term field performance based on the rate of degradation of HES materials using experimental compressive strength data. The coefficient of determination (R2) of the 1-h compressive strength data plotted against the rate of degradation can be as high as 0.726. The setting time experimental data can be used to predict the long-term field performance based on the normalized crack length, with an R2 of 0.5827 for the final setting time plotted against the normalized crack length. The results reported based on the statistical analysis of experimental testing data against the rate of degradation and the normalized crack length are highly sensitive to the size of the dataset, which raises the need for long-term evaluation of installed HES repairs. The outcomes of this study will contribute to the research concerning the long-term performance of HES materials by providing insights into their relationship with different, experimentally tested mechanical and durability properties.]]></description>
      <pubDate>Tue, 17 Feb 2026 10:30:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657943</guid>
    </item>
    <item>
      <title>Maintenance record-enriched machine learning model for pavement international roughness index prediction</title>
      <link>https://trid.trb.org/View/2643693</link>
      <description><![CDATA[Advances in machine learning techniques have enhanced pavement-management systems, enabling better proactive and preventive maintenance and rehabilitation (M&R) practices. The Federal Highway Administration's (FHWA) long-term pavement performance (LTPP) program's continuous efforts to develop a rich pavement performance database have resulted in a significant data bank of various pavement sections comprising climate, traffic, distress, and pavement structural properties. Past literature has leveraged this data to develop and train intelligent models for pavement performance predictions, and popular among these works is the use of numerical variables as predictors of pavement conditions. Typical numerical variables include quantitative distress values, pavement structure characteristics, and climatic and traffic loading data. Even though M&R activities have shown significant and proven contributions in rejuvenating pavement structures, previous studies have not investigated the possibility of integrating raw textual maintenance data logs into machine-learning model training. In this study, the authors demonstrate that textual maintenance data logs can be a valuable data source in combination with numerical values for machine learning models, resulting in better pavement-condition prediction outcomes across the pavement service life. Through the integration of textual knowledge, the authors explore the possibility of having a single model capable of predicting pavement performance progression during pre- and post-maintenance deterioration phases and immediate improvement after maintenance is performed. Unlike past studies, the ability to encode textual knowledge offers robustness for applicability to any available maintenance records, even when several maintenance activities are conducted simultaneously. The authors trained the machine-learning models using expanded pavement sections for quality and sufficient training data size. Since the textual data needs to be transformed into representative numerical data for training, the authors fine-tuned a transformer-based model – DistilBERT – with LTPP's maintenance corpus data, fostering domain adaptation of language models in pavement engineering. The authors trained the extreme gradient boosting and artificial neural networks models with and without textual knowledge, and the results were compared using held-out pavement sections. In summary, models trained with added textual maintenance knowledge outperform and achieve greater stability than non-text models.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643693</guid>
    </item>
    <item>
      <title>A Streamlined Approach for Probabilistic Pavement Life-Cycle Performance Prediction Via Physics-Informed Neural Networks</title>
      <link>https://trid.trb.org/View/2628270</link>
      <description><![CDATA[Pavement life cycle management (LCM) is essential for assessing the long-term environmental and economic impacts of roadway infrastructure by means of pavement life cycle assessment (LCA) and life cycle cost analysis (LCCA). However, pavement LCA and LCCA studies frequently overlook the use phase due to the limited availability of performance data. To address this gap, this study proposed a streamlined approach for pavement life-cycle International Roughness Index (IRI) prediction utilizing physics-informed neural networks. Dedicated to pavement LCM, the methodology is built upon input variables that are readily available prior to pavement service, ensuring high prediction accuracy and physical consistency while maintaining computational efficiency. Importantly, the model incorporates the IRI drop following maintenance and rehabilitation (M&R) activities and their subsequent impacts on IRI progression of rehabilitated pavements, capturing critical post-M&R behavior with machine learning (ML). By integrating prior domain knowledge and uncertainty consideration into the IRI progression models, the framework effectively accommodates the variability inherent in pavement deterioration processes, supporting robust probabilistic pavement LCM. Finally, the successful integration of the approach into pavement LCA is demonstrated through multiple case studies, which validate its capability in predicting IRI evolution across various M&R cycles under diverse traffic and climatic conditions. Overall, this research provides a more accessible and robust framework via physics-informed ML, even with minimal yet physically justified priors, for pavement assets management and comprehensive assessment of environmental and economic impacts of roadway infrastructure throughout its service life.]]></description>
      <pubDate>Tue, 20 Jan 2026 09:09:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628270</guid>
    </item>
    <item>
      <title>Predicting pavement cracking performance using laser scanning and geocomplexity-enhanced machine learning</title>
      <link>https://trid.trb.org/View/2588361</link>
      <description><![CDATA[Transport infrastructure is vulnerable to crack formation and deterioration due to aging and repetitive loading. Accurate and timely crack assessment and prediction are crucial for effective road maintenance, but existing studies often rely on individual indicators such as crack types, attributes, and severity, which fail to capture the full complexity of crack deterioration. Furthermore, limited research has explored the long-term impacts of traffic, socioeconomic, and climate changes on crack progression, and existing machine learning (ML) models struggle to explain the contributions of individual predictors due to inherent complexities in such spatial data. This study develops a geocomplexity-enhanced ML (GML) approach to evaluate crack deterioration and predict cracks under various future scenarios in the Wheatbelt region of Australia. The study employs laser-scanning data to generate a novel cracking performance index (CPI) and integrates geocomplexity (GC) measures with random forest models to capture local spatial complexities. Results demonstrate that GML significantly outperforms standard ML models in predicting CPI-based crack deterioration. Crack predictions in future scenarios reveal that in the Wheatbelt region, changes in climate factors over time have a more substantial impact on crack progression than traffic and socioeconomic changes, and without effective maintenance, crack propagation rate will significantly increase. It provides empirical evidence for developing preventive maintenance strategies. The developed methods and findings can support the development of adaptive, climate-resilient infrastructure, and long-term road management strategies, enhancing the sustainability of transport infrastructure.]]></description>
      <pubDate>Tue, 23 Sep 2025 08:59:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588361</guid>
    </item>
    <item>
      <title>Predicting Asphalt Pavement Deterioration Under Climate Change Uncertainty Using Bayesian Neural Network</title>
      <link>https://trid.trb.org/View/2512321</link>
      <description><![CDATA[Uncertainty in climate change poses challenges in obtaining accurate and reliable prediction models for future pavement performance. This study aimed to develop an advanced prediction model specifically for flexible pavements, incorporating uncertainty quantification through a Bayesian Neural Network (BNN). Focusing on predicting the International Roughness Index (IRI) and rut depth of asphalt pavement, BNN model was applied to different climate regions, using long-term pavement performance (LTPP) data from 1989 to 2021. The Tree-structured Parzen Estimators (TPE) algorithm was used to optimize model hyperparameters. The impact of climate change on IRI and rut depth was analyzed. Results showed that the proposed BNN model surpasses Artificial Neural Network (ANN), providing predictions with confidence intervals that account for uncertainty in climate data and model parameters. Compared to historical climate data, increases in IRI and rut depth were more significant when based on projected climate data. Relying only on historical climate data would underestimate pavement deterioration. Climate change appeared to have a more significant impact on rut depth than on IRI. Rut depth was particularly sensitive to climate change, increasing by more than 40%. Considering the uncertainty, rutting depth could increase by up to 85.6%. This highlights the importance of considering regional differences in climate change when developing reliable prediction models. The main contributions of this study include the quantification of uncertainty, the impact analysis of climate change and regional sensitivity analysis. It helps adapt to future climate change and supports informed decision-making in transportation infrastructure management.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:55:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512321</guid>
    </item>
    <item>
      <title>Assessing and Predicting Damage to the Service Performance of Asphalt Mixtures under Snow-Melting Thermal Fatigue</title>
      <link>https://trid.trb.org/View/2543534</link>
      <description><![CDATA[In winter, when electric heating pavements are melting snow and ice, the asphalt concrete often experiences freeze-thaw cycles that can easily lead to thermal fatigue damage to the pavement. Therefore, in this study, the temperature change state of the asphalt pavement during snow melting was simulated using an indoor heating-cooling cycle test, and the service performance of the asphalt pavement after snow-melting thermal fatigue damage was analyzed. The degree of damage to the service performance of stone mastic asphalt (SMA-16) during the heating-cooling cycle was quantitatively assessed using ultrasonic detection technology, and a prediction model between ultrasonic velocity and the damage coefficient of the service performance of the asphalt mixture was established. The results showed that the heating-cooling cycles caused thermal fatigue damage to the road performance of the asphalt mixture. Compared to the pavement asphalt mixture without snowmelt treatment, after undergoing 20 heating-cooling cycles, the porosity of the asphalt mixture increased by 5.25%, Marshall stability decreased by 3.03%, low temperature splitting strength decreased by 3.90%, and residual stability decreased by 4.41%. After the heating-cooling cycles of the asphalt mixture, the waveform was distorted. As the number of heating-cooling cycles increased, the wave velocity and amplitude gradually decreased. Compared to the traditional bridge decks, the road performance damage coefficient of conductive rubber composite bridge decks increased by no more than 11% after five years of snow-melting service. Although active electrical heating for snow melting may accelerate freeze-thaw damage to the pavement, this can be effectively mitigated by precisely controlling the pavement’s high temperature limits. The research results can provide a basis for predicting the service life of electrically heated snow-melting asphalt pavements and compensate for the shortcomings of the existing electrically heated pavement design system.]]></description>
      <pubDate>Tue, 29 Jul 2025 09:45:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543534</guid>
    </item>
    <item>
      <title>A Fast, Reliable and Practical Method to Predict Wheel Profile Evolution</title>
      <link>https://trid.trb.org/View/2407456</link>
      <description><![CDATA[The reliable prediction of wheel wear can help to reduce maintenance costs. With the help of two common approaches (statistical, contact mechanics based), it is possible to predict wheel profile shapes either quickly and precisely, but for a unique operating situation only, or for varying operating scenarios in a more time-consuming, but often less accurate way because so many, sometimes even unknown, input data are needed. There is no method available for predicting worn wheel profile shapes quickly, accurately, and generally. The hybrid approach presented in this work combines the two state-of-the-art approaches mentioned above in order to exploit their advantages and eliminate their disadvantages. The new method was calibrated and validated on wheel measurement data taken from the field. A good agreement between measurements and predictions was observed when using maximum wheel-rail contact shear stresses as the wear measure in the methodology.]]></description>
      <pubDate>Mon, 28 Jul 2025 08:55:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407456</guid>
    </item>
    <item>
      <title>Gradient Index Profile, a Novel Idea for Predicting Equivalent Conicity</title>
      <link>https://trid.trb.org/View/2407451</link>
      <description><![CDATA[A novel idea for predicting the equivalent conicity is presented, based on the inclinations of the wheel thread at the running circle and the rail profile at top-of-rail. Both the shape of the wheel profiles and the shape of the rail profiles can be acceptable in today’s standards, but together the wheel/rail profile combination can lead to an unacceptable high value of the equivalent conicity, which can make the vehicle unstable. By introducing two gradient indices, one for the wheel and one for the rail, it is possible to separate the equivalent conicity into two parts, which also make it possible to put limit values on wheel and rail profiles separately. The indices are combined into a joint index, GIP. The new GIP index is compared to the equivalent conicity for a large number of worn rail and wheel profiles, and show promising results.]]></description>
      <pubDate>Mon, 28 Jul 2025 08:55:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407451</guid>
    </item>
    <item>
      <title>A Model for Predicting the Evolution of Vertical Vehicle-Track Interaction</title>
      <link>https://trid.trb.org/View/2407450</link>
      <description><![CDATA[Vertical track stiffness and its variation as well as the variation of vertical track geometry along the track are one of the main sources for dynamic wheel-rail contact forces. These dynamic forces contribute to the track settlement. If these local settlements vary along the track, geometric irregularities develop further amplifying the dynamic loading of the track caused by the interaction between the vehicle and track. Here, a simple and fast, physical-based dynamic vehicle-track interaction (VTI) modeling approach is presented for predicting the evolution of vertical track geometries. The settlement model implemented into the VTI model is calibrated using the evolution of peak-to-peak values (track quality parameter) measured in the field over 350 days for a track where only concrete sleeper was used. Using this calibrated settlement model, the physical-based VTI model can predict the evolution of another track quality parameter, standard deviation, for this track section. The model could also describe the different evolution of track geometry quality for another track section where concrete sleeper with Under Sleeper Pads (USP) are used. Finally, the calibrated VTI model is used to assess the track geometry deterioration when the vehicle properties are changed. The efficient, physical-based VTI model can assist in designing and optimizing tracks and in supporting of maintenance activities.]]></description>
      <pubDate>Mon, 28 Jul 2025 08:55:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407450</guid>
    </item>
    <item>
      <title>A comprehensive review of the resilient behaviour of unbound granular pavement materials</title>
      <link>https://trid.trb.org/View/2564398</link>
      <description><![CDATA[Flexible pavements rely heavily on unbound granular materials for their base and subbase layers, where their performance under repetitive loading is critical. This performance is gauged by two main parameters: resilient modulus and permanent deformation, which are influenced by both intrinsic material properties such as gradation, moisture content, and density and external factors such as applied stress and load repetitions. Over time, variations in these elements can lead to diminished strength and increased non-recoverable strain, highlighting the necessity for routine evaluation of the pavement’s resilient behavior to maintain its longevity and durability. Given the practical difficulties of frequent field testing, computational models have emerged as vital tools for simulating the resilient modulus and predicting plastic strain, incorporating diverse influencing factors, and calibrated with lab and in-situ data. This paper delves into the properties affecting the resilient behavior of unbound granular pavement materials, emphasizing that stress level and moisture content significantly impact the resilient modulus, while load cycles and stress level notably influence permanent deformation. Central to this study is the exploration of the integrated effect of these factors on resilient behavior. Additionally, it evaluates the current landscape of computational modeling, showcasing the capabilities of the most used models for predicting these parameters through comparative analysis of existing literature. It suggests that to enhance pavement reliability and durability, models must evolve to include predictions on density and gradation for future improvement. This study further identifies key causes of pavement deterioration, helping develop targeted rehabilitation strategies and select accurate models for predicting resilience, ensuring robust pavement design. Furthermore, this review advances the field by merging new insights on the resilience of unbound granular materials with a critical evaluation of computational models. It introduces fresh perspectives and trends, bridging gaps in earlier reviews and paving the way for future research in pavement engineering.]]></description>
      <pubDate>Thu, 26 Jun 2025 16:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564398</guid>
    </item>
    <item>
      <title>Prediction of pavement friction using Markov chain at the project and network levels</title>
      <link>https://trid.trb.org/View/2487605</link>
      <description><![CDATA[Pavement friction studies continue to focus on reducing wet-weather accidents as lack of friction or skid resistance has been linked to a portion of those vehicle accidents on wet pavements. Probabilistic models for predicting pavement friction or skid resistance as a function of surface age, at both the project and the network levels, are presented. The models deploy the discrete-time Markov model to predict the deterioration of both asphalt (AC) and concrete (PC) pavements. A pavement management system can utilise such predictive models for monitoring and rehabilitation of segments with marginal pavement friction. Two case studies are presented to demonstrate the potential uses of the proposed models. A non-homogeneous Markov chain was used to predict the future friction number (FN) for 12 sample projects using an analysis period of up to 10-year with the estimation error being less than (0.001). On the other hand, a homogeneous chain was employed to estimate the future state probabilities for both AC and PC sample networks using a 5-year analysis period, resulting in high correlations between observed and estimated state probabilities. Data used in the studies were obtained from the Long-Term Pavement Performance Program (LTPP) database. The data was treated such that AC friction records were corrected for testing temperature variations and friction data was sorted by type of test tyre used.]]></description>
      <pubDate>Fri, 07 Feb 2025 08:47:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487605</guid>
    </item>
    <item>
      <title>How much road deterioration do heavy vehicles cause on Australia’s sealed flexible pavements?</title>
      <link>https://trid.trb.org/View/2487522</link>
      <description><![CDATA[An evidence-based estimate of the load-related road deterioration due to heavy vehicles on the road network is needed for underpinning heavy vehicle road user charges on Australia’s arterial roads. A network level model for estimating the percentage (%) load-related road deterioration was developed for typical sealed unbound pavements in Australia’s sealed road network. This model was based on a network level roughness deterioration model using observational data from long-term pavement performance (LTPP) and long-term pavement performance maintenance (LTPPM) sites and accelerated loading facility (ALF) experimental data. The model estimates that the % load-related road deterioration varied with changes to the traffic load, pavement strength and an environmental coefficient. This outcome also implies that the roughness deterioration model for sealed granular pavements is likely to be more appropriate for predicting Australia’s sealed granular pavement deterioration than past internationally based roughness deterioration models which were originally based on the deterioration of asphalt bound pavements.]]></description>
      <pubDate>Thu, 06 Feb 2025 10:47:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487522</guid>
    </item>
    <item>
      <title>Pavement Condition Prediction for Communities: A Low-Cost, Ubiquitous, and Network-Wide Approach</title>
      <link>https://trid.trb.org/View/2464653</link>
      <description><![CDATA[Effective prediction of pavement deterioration is critical to forecast infrastructure performance and make infrastructure investment decisions under escalating environmental and traffic change. However, most communities often struggle to undertake such predictive tasks due to limited sensing capacity and lack of granular data. With the pavement condition rating (PCR) data generated from artificial intelligence (AI)-powered computer vision technologies and multiple openly available data sets, the authors propose a low-cost and ubiquitous approach to predict system-level pavement conditions using nine communities across the US as an example. In addition to predicting absolute PCRs as was done in classical models, the authors develop another set of models to predict the change in PCRs over any time increment (i.e., time lapse between a PCR observation and retrofit decision point) and compare the results. The findings showed that the proposed low-cost prediction approach yields results comparable to existing studies, demonstrating its promising application in supporting pavement management. Furthermore, the PCR change model indicates that, besides current PCR, weather, road classification, socioeconomics, and built environment attributes are important to predicting PCR change. The interactive impacts also show salient interactive effects between variables and current PCR, offering suggestions on better allocating the limited resources in pavement maintenance projects. Finally, the proposed model could enhance climate resiliency and transportation equity during the pavement management process.]]></description>
      <pubDate>Mon, 16 Dec 2024 11:59:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2464653</guid>
    </item>
    <item>
      <title>A systematic approach to validating and improving pavement performance model for network-wide long-term investment decision making</title>
      <link>https://trid.trb.org/View/2404160</link>
      <description><![CDATA[The road pavement performance models are typically built in parts over many years with various inputs from analysis of past performance data, industry guidelines and best practices, and staff experiences. Appropriate pavement performance models are critical in assuring that the pavement assets are well maintained under uncertain future conditions and funding levels. This paper presents a novel and systematic approach to validating and improving a pavement performance model system. In commonly used methods, the models are validated using project-level data or comparing against short-term works programs. The standard methods are not suitable for network-wide long-term investment decision-making. This validation approach aims to ensure the model system is fit-for-purpose in predicting road conditions at the network level and estimating future maintenance investment needs. The approach has four validation phases, i.e., model input data and processing, deterioration models, treatment triggers and resets, and complete model system validation using past maintenance and condition data. The model validation philosophy is demonstrated using an actual validation process undertaken at Main Roads Western Australia.]]></description>
      <pubDate>Thu, 18 Jul 2024 10:48:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2404160</guid>
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