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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Acoustic Emission–Based Statistical Modeling of Low-Temperature Damage in Steel Slag and Crumb Rubber–Modified Asphalt Mixture</title>
      <link>https://trid.trb.org/View/2685679</link>
      <description><![CDATA[The utilization of steel slag as aggregate in asphalt pavement construction offers environmental benefits by reducing land resource occupation and substituting natural aggregates, contributing to sustainable development. However, in seasonally frozen regions, asphalt pavements are prone to low-temperature cracking due to freeze-thaw (F-T) cycles, significantly shortening their service life. This study employed acoustic emission (AE) to monitor the semicircular bending failure process in real time for steel-slag and crumb rubber–modified asphalt mixtures (SAMs) conditioned in aqueous and saline (8 % sodium chloride) environments. By coupling AE parameters (ring count, energy, b-value) with mechanical responses, we quantitatively characterized the multiscale damage evolution from crack initiation to interfacial failure. A Weibull-based statistical damage model was developed to describe deterioration under combined environmental and mechanical effects. Results show that as F-T cycles increase (0, 5, 10, 20), the fracture energy of SAM decreases by 20 to 50 %, with saline conditioning causing 14 % greater degradation than water conditioning. Rapid increases in AE ring count indicate interfacial debonding, whereas declines in b-value mark the transition from microcrack accumulation to macrocrack dominance. The proposed model effectively captures nonlinear damage evolution under F-T and mechanical loading, providing a framework for designing crack-resistant, durable steel slag asphalt pavements in cold regions.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:39:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685679</guid>
    </item>
    <item>
      <title>Model Updating Strategy for Asphalt Mix Material Parameter Identification Using Static Semi-circular Bending Test</title>
      <link>https://trid.trb.org/View/2671485</link>
      <description><![CDATA[One of the major distresses that directly affects the serviceability and quality of flexible pavement structures is cracking, which may occur in various forms such as longitudinal, transverse, and a combination of both that can extend over the width of the pavement to create hazardous conditions for the road users. Fracture energy is one of the most significant parameters used in evaluating the potential for cracking to occur in an asphalt material. The fundamental principles of fracture mechanics illustrate that the crack initiates at the vicinity of the crack tip, if the energy stored near it exceeds the cracking resistance (Gauthier, G., & D. A. Anderson 2006). Researchers have performed laboratory tests such as disc shaped compaction, indirect diametral tensile, single edged notched beam, and semicircular bending (SCB) test to investigate the cracking problem in asphalt mixtures, particularly to study crack resistance and fracture. It is noteworthy that SCB test is popular due to simplicity in testing, repeatability, and ease of data processing (Nsengiyumva 2015). Numerical studies using the finite element method (FEM) have been used to evaluate cracking in pavements with complex microscopic morphology in order to compute the fracture parameters such as stress intensity factor, fracture energy and crack length, which govern crack propagation. However, modeling the crack propagation mechanism is difficult through FEM since the mesh does not align with the direction of crack propagation, thus, requiring remeshing of the geometry for every load step. As a solution, Extended Finite Element Method (XFEM) was developed, which is an improvement over FEM that involves the concepts of fracture mechanics to estimate the crack growth (Yazid et al. 2009).]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671485</guid>
    </item>
    <item>
      <title>Efficient Pavement Crack Monitoring for Road Life Cycle Management</title>
      <link>https://trid.trb.org/View/2671016</link>
      <description><![CDATA[Road pavements are vital for transportation infrastructure, yet they deteriorate over time due to traffic loads and environmental factors, resulting in cracks and damage. This paper introduces an innovative method for crack detection on road pavements using digital imagery. Our approach incorporates geo-localization, annotates, characterizes, and quantifies crack severity. This empowers experts to monitor crack progression, a critical element in pavement management. The methodology allows for seamless result comparison and augments existing techniques, aiding in condition assessment and conservation strategy determination. Timely detection of cracks enables proactive maintenance, preventing structural degradation, and ensuring user safety and comfort. Leveraging deep learning and open-source frameworks like TensorFlow and QGIS, our approach automates road pavement image analysis and crack identification, providing a cost-effective, accessible solution for crack detection. This research offers significant advantages in resource efficiency and accessibility, especially in areas without regular manual inspections or dedicated vehicles, thereby enhancing road pavement monitoring and maintenance.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671016</guid>
    </item>
    <item>
      <title>A lightweight road crack detection model based on multilevel thresholding image segmentation</title>
      <link>https://trid.trb.org/View/2684441</link>
      <description><![CDATA[Automatic pavement crack detection is vital for road infrastructure maintenance. However, existing deep learning models fail to effectively balance detection accuracy with model complexity. To tackle this challenge, a novel lightweight multilevel thresholding segmentation crack detection (MTSCrack) model is proposed in this paper. MTSCrack employs a newly devised procedure adopting pre-processing to achieve a lightweight design. Pre-processing is performed by a multilevel thresholding image segmentation method. Multilevel thresholding segmentation decreases the complexity of input data by quantizing an image into K binary planes. To practically perform multilevel thresholding segmentation, a multilevel thresholding segmentation algorithm with low computational cost is proposed. K binary planes are fused by a convolutional network employing encoder-decoder structure to generate a binary crack prediction map as output. Since the complexity of the input data is low, the encoder and the decoder can adopt simple and lightweight architectures. The convolutional network contains only 0.19M parameters. Thorough performance evaluation experiments were performed. 8 was employed for K. Data split ratios of 1.1:1 and 3:1 were employed on Crack500 dataset and DeepCrack dataset, respectively. Image size of 256 × 256 was employed in most of the experiments. No augmentation was employed. Experimental results indicate that the overall performance of MTSCrack outperforms existing lightweight crack detection models and is comparable to heavyweight models. MTSCrack achieves F1-score of 0.820 and 0.756 on DeepCrack and Crack500, respectively. In addition, MTSCrack achieves computational complexity of 2.63G FLOPs and inference speed of 180 FPS on RTX 3090. MTSCrack was deployed on Jetson TX2 and Jetson XAVIER NX and achieved inference speeds of 15 FPS and 35 FPS on Jetson TX2 and Jetson XAVIER NX, respectively.]]></description>
      <pubDate>Tue, 30 Jun 2026 10:21:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684441</guid>
    </item>
    <item>
      <title>A Conditional Diffusion-Based Crack Segmentation Model: ConditionCrack Segmentation</title>
      <link>https://trid.trb.org/View/2617837</link>
      <description><![CDATA[Pavement cracks pose a direct threat to the safety of transportation systems. ConditionCrack Segmentation (CCS), a pavement crack segmentation model based on conditional diffusion, is proposed in this study. The model progressively removes noise from a random Gaussian distribution, and it is guided by image conditions to generate crack predictions. The model comprises two components: the image encoder and the map decoder. As the image encoder, the combination of MiT and FPN, aggregates multi-scale features and uses the encoded feature map as a conditional input to supplement and constrain noised labels. The map decoder uses stacked transformer layers, reducing the number of parameters in the denoising network and enhancing the ability of the decoder to model both the local details of fractures and global sequences. The model introduces Classifier-Free Guidance (CFG) and a Sampling Drift Calibration Module (SDCM) to adaptively calibrate the decoder, ensuring the effectiveness of the conditional inputs. The proposed CCS approach achieves the highest mIoU on all three evaluated public datasets (mIoU = 84.46 on CRACK500, mIoU = 89.17 on DeepCrack, and mIoU = 79.47 on CFD), outperforming both mainstream semantic segmentation models and pavement crack segmentation models using diffusion models. Moreover, the proposed CCS model achieves 32 FPS, providing a practical real-time detection system framework after performing structural optimization.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617837</guid>
    </item>
    <item>
      <title>Laboratory and field performance of polymer-modified asphalt mixes with high RAP content in a pilot project</title>
      <link>https://trid.trb.org/View/2710250</link>
      <description><![CDATA[The interaction among polymers, aged binder from reclaimed asphalt pavement (RAP), and recycling agents (RA) is still a concern for the asphalt industry. Therefore, laboratory and field performance of six plant-produced polymer-modified (PM) mixes containing up to 40% RAP and RA included in a pilot project were evaluated in this study. The base binder performance grade (PG) specified for the control mix with no RAP/RA for this study was PG64–28M. A softer base binder (PG58–34M) was used for all other RAP mixes to reduce the need for RA dosage. In this study, the RA dosages for the RAP mixes were selected during the job mix formula (JMF) by restoring the high PG of the extracted binders containing RAP compared to the control mix. The extracted binder test results obtained during construction indicated that a softer binder might have been used for the control mix with no RAP instead of the specified PG64–28M binder. The laboratory AC mix results indicated a somewhat similar performance for the RAP mixes. The stiffer RAP mixes showed greater strain sensitivity in the flexural fatigue test than the softer mixes. The mainline mix with 23% RAP and no RA exhibited slightly lower fracture cracking resistance than other mixes. The mixes with RAP also showed higher rutting resistance in the laboratory than the control mix with no RAP. The comparison between the extracted binder and mix frequency sweep results indicated that both had similar rankings and trends, indicating good diffusion of the RAP and virgin binders, except for the mix with 40% RAP and RA. The field survey conducted after 34 months (2.8 years) of service indicated that all test sections (with and without RAP/RA) are in very good condition.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:20:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2710250</guid>
    </item>
    <item>
      <title>Development of the AI-powered ideal-E* test</title>
      <link>https://trid.trb.org/View/2676099</link>
      <description><![CDATA[The dynamic modulus (E*) of asphalt mixtures is essential for Mechanistic-Empirical (ME) pavement designs but is seldom tested by Departments of Transportation (DOTs) due to the high cost and complexity of traditional E* tests. This study introduces an Artificial Intelligence (AI)-powered IDEAL-E* test that integrates IDEAL cracking tests at various temperatures, finite element analysis, and machine learning. This innovative approach addresses the limitations of existing models that struggle with evolving asphalt compositions. By simplifying the generation of E* data, it facilitates its use in AASHTO Pavement ME design software and is aligned with current practices with IDEAL cracking tests in DOTs’ QA and contractors’ QC labs.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676099</guid>
    </item>
    <item>
      <title>Enhanced pixel-level crack detection using Att-SegCrack: an improved CNN with feature fusion and large receptive field</title>
      <link>https://trid.trb.org/View/2676095</link>
      <description><![CDATA[Deep convolutional neural networks have demonstrated significant advancements in pavement crack detection. Nevertheless, challenges persist in achieving satisfactory performance due to discontinuous crack edges and low background contrast. This paper presents Att-SegCrack, an enhanced encoder-decoder network that addresses these limitations through three key components. First, a simple yet effective feature fusion scheme restores crack details by bilinearly up-sampling encoder features and integrating them with outputs from the penultimate decoder layer, which subsequently serves as input to the final decoding layer. Second, dilated convolutions expand the receptive field to capture comprehensive contextual information for complete crack profiles. Third, the convolutional block attention module enhances crack-background differentiation in low-level features. Evaluations on two benchmark datasets (Crack500 and DeepCrack) demonstrate that our method outperforms other state-of-the-art methods in crack detection performance.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676095</guid>
    </item>
    <item>
      <title>Comparison of different deep learning algorithms in pavement crack detection</title>
      <link>https://trid.trb.org/View/2676094</link>
      <description><![CDATA[Roads, as one of the most important transportation infrastructures, significantly affect every country’s economy. Therefore, Pavement Management Systems (PMSs) were introduced to schedule and execute maintenance tasks to increase roads’ lifecycle and preserve their serviceability. Pavement condition evaluation is a substantial stage in PMS implementation. Modern approaches have recently emerged to help road authorities perform cost-effective and continuous pavement assessments to maintain their road networks more efficiently and with higher productivity. In this paper, using state-of-the-art techniques, the evaluation of specific pavement distresses: longitudinal, transverse, edge, and joint reflection cracking, was investigated. In the first stage of this research, a dataset of images collected from 130,800 m of Tehran’s BRT routes was obtained. Then, a deep learning model using YOLO architecture was applied for pavement cracking detection. The developed model represents a precision of 82.96% for classifying distress types and 71.46% for distress severity.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676094</guid>
    </item>
    <item>
      <title>Pre-microcracking of cement-treated bases to reduce reflective cracks on asphalt pavements: A comprehensive review</title>
      <link>https://trid.trb.org/View/2701584</link>
      <description><![CDATA[Shrinkage-induced cracking in cement-treated base (CTB) contributes to reflective cracks in semi-rigid pavement. Potential solutions include using low-shrinkage CTB materials with enhanced cracking resistance or implementing pre-microcracking to mitigate shrinkage. This paper provides a comprehensive review of pre-microcracking methods, including laboratory- and field-based techniques, approaches for evaluating pre-microcracking severity, and their effects on CTB mechanical properties, self-healing capability, shrinkage behavior, crack resistance, and reflective cracking in asphalt pavements. Four pre-microcracking methods have been identified in the laboratory, including compression, vibration, vibro-compression, and impaction, while vibratory rolling is commonly employed in the pavement field. Existing studies suggest that pre-microcracking should be conducted after two days of curing to reduce the CTB modulus of elasticity by approximately 40%. The damage rate due to pre-microcracking had a significant impact on strength recovery, whereas its timing strongly affected the density of induced microcracks. The pre-microcracking mechanism by vibration and the self-healing capability of CTB after pre-microcracking are explored. More importantly, pre-microcracking has been demonstrated to mitigate shrinkage effects and enhance CTB cracking resistance. Furthermore, this review highlights the potential for primary research to improve the effectiveness of pre-microcracking solutions, such as determining optimal strength thresholds for pre-microcracking and evaluating the feasibility of pre-microcracking in CTB incorporating waste materials that could help reduce shrinkage, improve cracking resistance, and facilitate self-healing mechanisms.]]></description>
      <pubDate>Wed, 24 Jun 2026 11:31:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701584</guid>
    </item>
    <item>
      <title>Development of an IDEAL-Low Temperature (IDEAL-LT) Test for Balanced Mix Design and Acceptance in Cold Regions</title>
      <link>https://trid.trb.org/View/2717330</link>
      <description><![CDATA[State departments of transportation (DOTs) in cold regions are increasingly encountering premature pavement failures, including low-temperature transverse and block cracking, despite adherence to existing design standards, construction methods, and material specifications. These cracks form when thermal stress exceeds the asphalt mixture’s strength during severe temperature drops. Thermal stress accumulation is driven by thermally induced strain, which depends on the mixture’s coefficient of thermal contraction (CTC) and relaxation modulus. However, current mix design practices primarily rely on asphalt binder properties such as stiffness and m-value (a parameter indicating the rate at which asphalt binder stiffness changes over time under stress) from the Bending Beam Rheometer while neglecting CTC and mixture strength at low temperatures. This limits the reliability of evaluation of a mixture’s resistance to thermal cracking and its integration into the Balanced Mix Design (BMD) for cold regions. If this failure mechanism is not properly addressed in BMD, premature thermally induced surface cracking can occur even when the mixtures comply with existing standards. Although the Thermal Stress Restrained Specimen Test (TSRST) and the Disk-Shaped Compact Tension [DC(T)] Test could account for mixture resistance to thermal cracking, they involve complex procedures and labor-intensive sample preparation, discouraging their routine use. 

For NCHRP 20-30/IDEA 266, the research team will develop a simple, reliable alternative, the IDEAL-LT test, for evaluating asphalt mixture resistance to thermal cracking that could then be integrated into a comprehensive BMD framework for cold regions. The test will streamline testing by eliminating the need for specimen cutting, coring, notching, and gluing while enabling simultaneous testing of three replicates and improving correlation with field ranking and with established thermal cracking tests, such as the TSRST and ABCD tests. 

The research will involve building and refining a system unit, validating its field performance, and comparing the results with traditional low-temperature cracking tests. The test frame will be modified to test three replicates simultaneously, improving efficiency and reducing variability. The procedure will be standardized for integration into the BMD. Validation will involve selecting relevant field sections in cold regions with varying levels of thermally induced surface distresses and correlating IDEAL-LT test results with observed field performance. Additionally, the IDEA-LT results will be compared with TSRST/DC(T) and binder ABCD test results to confirm its reliability. Minnesota and Ohio DOTs will collaborate in field evaluations.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:33:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717330</guid>
    </item>
    <item>
      <title>Enhancing Sustainability and Durability in Asphalt Pavements: Evaluating the Impact of Low-Carbon Sulfur Polymer Modifiers and Reclaimed Asphalt Pavement</title>
      <link>https://trid.trb.org/View/2714189</link>
      <description><![CDATA[The asphalt industry faces major challenges, including the need for pavements that last longer and are more sustainable, as traditional asphalt production is costly and has a high environmental impact. To address this, researchers have been exploring modified asphalt binders such as rubber, charcoal, and waste cooking oil, which have shown promise in extending the lifespan, resistance to deformation, and durability of asphalt pavements. In addition, governments and industries are investing in the use of recycled and “green” materials to reduce the carbon footprint and environmental degradation of conventional asphalt mixtures. Building on this momentum, this study investigates the performance of a low-carbon modifier consisting of a composite of sulfur, biochar, and waste cooking oil in the conventional hot mix asphalt mixture with 25% reclaimed asphalt pavement (RAP). The modifier was introduced at 10% and 20% by the total weight of the asphalt binder, representing an asphalt mixture with 11.5% and 22.4% reduction in carbon footprint compared to typical asphalt binder, following Park et al. (2024). To understand how these lower-carbon mixtures perform in the real world, researchers used two standard tests: the Indirect Tensile Asphalt Cracking Test (IDEAL-CT) and Hamburg Wheel Tracking Test (HWT), which examined the fracture (cracking) and rutting resistance of the resulting mixtures, respectively. Extended thermal aging and UV aging were applied to study the effect of long-term aging on each scenario. Two types of aging are used, long-term aging following NCHRP (Report 973) and UV aging following Rajib and Fini (2020). The study results showed that introducing the low-carbon modifier led to less reduction in resistance to aging as measured by fracture resistance and rutting durability compared to the control scenario. This means that they maintained stronger resistance to cracking and rutting even after aging while also reducing the carbon footprint of the mixture by up to 22.4%. This research demonstrates that meaningful reductions in the carbon footprint of asphalt pavements can be achieved without compromising long-term structural performance or durability, supporting more sustainable and resilient transportation infrastructure.]]></description>
      <pubDate>Tue, 16 Jun 2026 16:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714189</guid>
    </item>
    <item>
      <title>Tencate-MIRIFI MPV400 Polypropylene Nonwoven Geotextile</title>
      <link>https://trid.trb.org/View/2709291</link>
      <description><![CDATA[This is a pavement preservation project in Great Falls, Montana, involving a cold mill, overlay, and added paving fabric. The contract called for installation of an approximate length of 0.4 miles of the designated paving fabric on prepared milled surface to potentially aid in extending the service life of the pavement. Prior to construction there were 87 visible cracks and in 2021 there were only 14 cracks. The geotextile fabric could be a contributing factor to the decrease in reflective cracking. To go along with that information, 8 of the 14 cracks were in the control section with no geotextile paving fabric. In conclusion for this evaluation, the Tencate-MIRIFI MPV400 Polypropylene Nonwoven Geotextile did not impact the ride or rut but might have reduced the reflective cracking. This report includes photographs from preconstruction (March 2017), construction (August 2017), and site inspections (April 2018, April 2019, March 2020, September 2021, May 2022).]]></description>
      <pubDate>Tue, 09 Jun 2026 10:56:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709291</guid>
    </item>
    <item>
      <title>Evaluation of Spray-On Rejuvenator-Treated Asphalt Concrete Pavements for Stiffness and Cracking Resistance</title>
      <link>https://trid.trb.org/View/2674232</link>
      <description><![CDATA[Asphalt pavements deteriorate over time due to the aging process that increases binder stiffness and brittleness and reduces performance and durability. Rejuvenators, particularly spray-on rejuvenators (SORs), offer an economical and easy-to-implement solution by restoring the maltenes-to-asphaltenes ratio and improving the flexibility of aged asphalt binders. This study focuses on evaluating the changes in stiffness and cracking resistance of asphalt concrete (AC) mixtures by interlaboratory testing of the modified bending beam rheometer (Mod-BBR) test and the circular bending test (CBT). A total of 104 specimens were tested using the Mod-BBR, and 39 specimens were evaluated using the CBT to assess the effectiveness of 12 SORs on an AC test section at the MnROAD test facility. The findings indicated that (i) SORs significantly influence the mechanical properties of AC mixtures and (ii) the performance of different SORs varied significantly, with some demonstrating substantial reduction in creep stiffness and improving fatigue resistance after 2 years of application, whereas others showed limited or no significant effect. In addition, an inverse correlation was observed between the creep stiffness and the fatigue resistance properties such as cracking tolerance index, flexibility index, and cracking resistance index, reinforcing the necessity to adjust the AC stiffness to enhance the durability of the pavements. The study emphasizes the critical importance of selecting suitable SORs and highlights the necessity of regular reapplications to ensure the sustained performance of pavement structures. Additionally, the testing methods showed reliability in evaluating the effectiveness of SOR treatments under different laboratory settings and conditions.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674232</guid>
    </item>
    <item>
      <title>Coupled Analysis on Frost-Heaving Depression Effect of Geosynthetics Drainage Material for Road Pavement</title>
      <link>https://trid.trb.org/View/2113157</link>
      <description><![CDATA[The frost heave of subgrade soil causes cracking and unevenness of pavement structure in cold region, which leads to uncomfortable driving and high maintenance cost. As one of the measures against frost heave damage, it has been considered to mitigate frost heave by draining water from the subgrade soil in road structure since soil moisture plays a critical role in ground heaving. The wicking fabric, a new type of geosynthetics drainage material, has been recently developed to drain water out of pavement and reduce the water content. Previous studies indicated that the wicking fabric has great promise as n effective means to solve the frost-heave-related problems on road systems. The purpose of this study is to verify the effectiveness of the wicking fabric for frost-heaving mitigation under local weather and soil conditions in Hokkaido by conducting the coupled thermo-hydro-mechanical (THM) analysis. The result of analysis indicates that wicking fabric has an excellent ability to drain water in road structure and that wicking fabric is an effective approach to mitigate frost heave.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113157</guid>
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