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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <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>
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    <item>
      <title>Evolution of automated crack, pothole, and roughness detection and assessment for flexible pavements: state-of-the-art review</title>
      <link>https://trid.trb.org/View/2737197</link>
      <description><![CDATA[Road maintenance requires substantial investment in inspection and rehabilitation. Technological advancements have introduced automated methods to enhance pavement distress detection. This review traces the evolution of these technologies, from early systems replacing manual surveys to modern pipelines incorporating both traditional and advanced models. It explores the growing potential and translational gaps of OEM sensing and connected and autonomous vehicle data for pavement monitoring. Key challenges identified in the literature include domain shift, the high labeling burden associated with severity-aware segmentation, inconsistent evaluation protocols and reporting practices, calibration and cross-platform comparability, and operational constraints related to bandwidth, edge computing, privacy, and cost. Promising directions include benchmark-driven evaluation, label-efficient learning, uncertainty-aware multi-sensor fusion, and hybrid edge-cloud pipelines to integrate high-frequency vehicle data into pavement management systems. Overall, the paper aims to identify deployment-ready future directions in automated pavement inspection and contribute to scalable, standardized, and cost-effective solutions for network-level infrastructure management.]]></description>
      <pubDate>Wed, 12 Aug 2026 14:59:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737197</guid>
    </item>
    <item>
      <title>Field Evaluation of High-Performance Cold Mix (HPCM) Products</title>
      <link>https://trid.trb.org/View/2752097</link>
      <description><![CDATA[This research project evaluated the field performance of nine High-Performance Cold Mix (HPCM) products from various producers to determine suitability for inclusion on ARDOT’s Qualified Products List (QPL). The test section was constructed in September 2024 on Hwy 338 (Sweet Home Cutoff/Dixon Road) in south Little Rock. Nine unique HPCM products, both bagged and plant-produced, were installed in simulated potholes and monitored over 12 months. Monthly inspections assessed durability, compaction retention, rutting, and adhesion performance. All nine products demonstrated satisfactory performance with minimal degradation through one full freeze-thaw cycle and a summer season. Based on the results, all tested products were approved for inclusion on the Department’s QPL for HPCM. The research also established evaluation criteria for future product submissions, enabling ARDOT to adopt HPCM materials into maintenance and construction projects without requiring individual project-based approvals.]]></description>
      <pubDate>Wed, 12 Aug 2026 09:58:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752097</guid>
    </item>
    <item>
      <title>A Comprehensive Single-Camera Deep Learning Framework for Detecting Cracks and Potholes, Classifying Severity, and Quantifying Pothole Dimensions</title>
      <link>https://trid.trb.org/View/2752100</link>
      <description><![CDATA[Road distresses, such as potholes and multiple forms of cracking, significantly affect road safety, vehicle performance, and infrastructure quality. This study proposes a cost-effective and scalable framework leveraging monocular cameras and advanced computer vision models for detecting, classifying, and quantifying both cracks and potholes. Monocular cameras, widely available in vehicles as dashcams, provide an affordable alternative to expensive sensors, making this approach accessible in resource-constrained settings. The framework integrates YOLOv7-Tiny and YOLOv8-Nano models for accurate detection of road distresses, followed by severity classification using a ResNet50-based transfer learning model adhering to Indian Roads Congress (IRC) guidelines. For quantifying damage, the Segment Anything Model (SAM) is utilized for precise area estimation, and the Dense Prediction Transformer (DPT) enables depth estimation to calculate pothole volumes. Crack identification and pothole detection are jointly handled within the detection stage, ensuring consistent processing across different distress types. Comprehensive datasets, including manually labeled ground truth measurements, validate proposed system. Results show high detection accuracy, robust severity classification, and strong correlations between automated and ground truth areas and volume estimations, with coefficients of determination of 0.95 and 0.77, respectively. By enabling roadway agencies and municipalities to inspect and prioritize maintenance using low-cost tools, this framework supports scalable, data-driven pavement management even in resource-constrained environments. This study highlights potential of monocular cameras and deep learning for affordable, real-time road distress management, addressing critical gaps in traditional approaches reliant on costly sensors. Future work will focus on enhancing depth estimation accuracy for smaller damages and integrating real-time processing for deployment in resource-constrained environments.]]></description>
      <pubDate>Tue, 11 Aug 2026 10:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752100</guid>
    </item>
    <item>
      <title>Enhancing Pothole Detection with Self-Supervised Learning: A Hybrid Contrastive and Noncontrastive Approach for Label-Efficient Segmentation</title>
      <link>https://trid.trb.org/View/2689746</link>
      <description><![CDATA[Potholes pose a significant challenge to road safety, affecting daily life and economic activities. Prompt and effective pothole management is crucial for mitigating their adverse effects. Machine-learning (ML) methods help in automatic pothole detection and prompt road maintenance. Current ML-based methods for identifying potholes predominantly rely on supervised learning techniques, necessitating extensive manual labeling of data. In practice, it is relatively easy to obtain label-free pothole data. However, the labeling process is costly and time-consuming. This paper introduces a self-supervised representation learning (SSL)-based approach for pothole semantic segmentation (pixelwise pothole detection) from road images, greatly reducing the need for labeled data. We propose a hybrid self-supervised learning–based method for pothole segmentation by linearly combining contrastive and noncontrastive learning. Contrastive learning helps in learning image features that are robust to input variations, and noncontrastive learning aids in maximizing information while reducing the redundancy in learned image representations, resulting in better performance. Our method’s effectiveness was demonstrated through rigorous evaluation using two distinct data sets in cross-domain settings using varying data proportions, broadening its potential utility across different contexts. Additionally, the findings reveal that even with as little as 10% or 50% of the labeled data, our SSL-based hybrid approach outperforms state-of-the-art methods trained on 100% data set. On the Pothole-600 data set, it achieved an 8.03% increase in mean intersection over union (mIoU) with 50% of the data and a 4.32% improvement in mIoU with only 10% of the data, showcasing its efficiency and effectiveness.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689746</guid>
    </item>
    <item>
      <title>Robust Pothole Detection Using YOLOv5 and Optimized Datasets for Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2672459</link>
      <description><![CDATA[This research addresses the issue of potholes as a significant threat to road safety and to vehicular integrity. It proposes a robust methodology for automatic pothole detection using the YOLOv5 deep learning framework, enriched with dataset optimization techniques made possible by Roboflow. The goal is to have very high detection accuracy for potholes, reaching a mean Average Precision of about 90%, for both static images and real-time video streams, by paying close attention to dataset preparation and fine-tuning the hyperparameters. The dataset undergoes extensive preprocessing, including annotation, data augmentation to simulate different environmental conditions, and normalization for increasing model generalizability. The different variants of YOLOv5, namely n, s, m, l, and x, are tested based on their precision, recall, F1-score, and map through standard evaluation metrics. The results indicate high performance of the system in detecting potholes on different road surfaces and lighting conditions. The value of this work is in pushing the edge of intelligent transportation systems by providing a scalable and deployable solution for the automation of road-condition monitoring, with the potential improvements in efficiency and safety in the maintenance of roads.]]></description>
      <pubDate>Fri, 29 May 2026 08:59:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672459</guid>
    </item>
    <item>
      <title>Performance Evaluation and Structural Optimization of Fiber-Reinforced Asphalt Concrete (FRAC) for Pothole Repair and Roadway Resilience</title>
      <link>https://trid.trb.org/View/2696034</link>
      <description><![CDATA[This project investigates the engineering properties and field performance of Fiber-Reinforced Asphalt Concrete (FRAC) specifically optimized for high-durability pothole repair and structural patching.]]></description>
      <pubDate>Sat, 25 Apr 2026 12:33:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696034</guid>
    </item>
    <item>
      <title>Pothole impact analysis of three two-wheeled vehicles</title>
      <link>https://trid.trb.org/View/2688637</link>
      <description><![CDATA[The effects of the impact of a selection of two-wheeled vehicles with a pothole were investigated. The vehicles used were a BMW 1250 cc motorbike, a BigBoy 200 cc delivery-type motorbike, and a dual-suspension mountain bicycle. The vehicles were used with standard specifications, and a datalogger was used to measure instantaneous velocities and accelerations. Three speeds were selected for the motorbikes: 20, 40, and 60 km/h. The findings have provided the necessary insight to confirm that damages to a motorcycle and bicycle rim and tyre from impact with a pothole do not readily occur at typical urban road speeds, given the particular circumstances. No evidence of pothole impact was noted. The resulting forces generated for the particular circumstances appear well within acceptable levels of survivability for the motorcycle and bicycle and for a general rider's capabilities. Some baseline forces are determined; however, further and expansive testing is suggested.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688637</guid>
    </item>
    <item>
      <title>Novel 3D grid for reinforcing asphalt pothole repairs: Design, experiments, and numerical analysis</title>
      <link>https://trid.trb.org/View/2654789</link>
      <description><![CDATA[Premature failure of asphalt pothole repairs commonly results from stress concentrations at the interface between the repair material and the surrounding pavement. To address this issue, a novel three-dimensional (3D) grid was developed as a composite reinforcement layer to mitigate interfacial stresses and improve the structural integrity of repaired pavements. Laboratory rutting and shear tests confirmed that the 3D grid significantly improves the rutting resistance and interfacial shear strength of pothole repairs. Finite element simulations were then conducted to compare the 3D grid with a conventional planar geogrid, demonstrating that the 3D configuration provides superior mechanical confinement and stress diffusion. Subsequent parametric analyses were performed to examine the effects of the elastic modulus of repair materials, pothole depth, loading position, base stiffness, and traffic wheel load. The results show that the 3D grid performs optimally in shallow potholes and under sidewall loading. It effectively mitigates edge stress concentrations caused by stiff bases and heavy wheel loads. Considering both cost and reinforcement efficiency, a grid aperture of about 40 mm and a protrusion height ranging from one-third to one-half of the pothole depth are recommended for field application. Finally, the limitations of this study and future research on further improving the 3D grid were discussed. These findings present a practical and cost-effective approach for improving the structural performance and service life of asphalt pothole repairs.]]></description>
      <pubDate>Wed, 01 Apr 2026 11:46:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2654789</guid>
    </item>
    <item>
      <title>Development and Implementation of Uttarakhand State Public Works Department Patch Reporting App</title>
      <link>https://trid.trb.org/View/2678103</link>
      <description><![CDATA[The primary goal of this mobile app is to engage the public as a proactive partner in identifying and reporting issues on state Public Works Department (PWD) roads, particularly potholes or patches, to ensure timely and efficient remedial action. By utilizing the road users themselves as contributors of real-time information, the process of issue identification becomes less dependent on PWD inspections and more driven by public participation. This shift not only enhances the efficiency of problem-solving but also builds a positive image of the PWD through prompt issue resolution. The app allows any road user to send information about potholes, accompanied by site photographs, directly to the relevant PWD division. Through easy, one-time password (OTP)-based registration, users gain access to a system that automatically identifies the concerned division using Geographical Information System (GIS) data, even if the user lacks detailed knowledge of the road or agency responsible. The data received from public is processed by sending to person concerned and repair has been done and the same information is uploaded on site and the complainant within stipulated time. This whole process drastically reduces the time between issue identification and action, compared to conventional methods of reporting.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678103</guid>
    </item>
    <item>
      <title>Hierarchical Vision Transformer-Based Deep Learning Architecture, RGB-D Sensing Fusion, and Multimodal LLM-Based Generative AI Pipeline for Automated Pavement Pothole Segmentation, Quantification, and Repair Recommendations</title>
      <link>https://trid.trb.org/View/2669521</link>
      <description><![CDATA[This paper proposes an automated approach for pavement pothole segmentation, quantification, and repair solution generation. The proposed approach integrates a deep learning (DL) architecture with hierarchical vision transformer (HViT) based on the SAM2-UNet architecture, RGB-D sensing fusion, and a multimodal large language model (LLM). It is designed to automatically (1) perform pothole segmentation on RGB images using a HViT-based DL model that allows multi-scale capturing of pavement pothole features, (2) retrieve comprehensive pothole properties through RGB-D fusion and point cloud processing, and (3) generate pothole-induced pavement repair solutions using multimodal LLM. Experimental results showed that the developed HViT-based DL model (i.e., SAM2-UNet) yields superior performance of pothole segmentation with mDice of 0.965 and mIoU of 0.937, while the integrated LLM can generate reasonable pothole-induced pavement repair recommendations. This paper contributes to the body of knowledge by offering a novel approach to advance automated pavement inspection towards more accurate, intelligent, and actionable direction.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669521</guid>
    </item>
    <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>Investigation of a novel blocked waterborne polyurethane emulsified asphalt mixture for pothole repair: material development, road performance and construction parameters</title>
      <link>https://trid.trb.org/View/2633399</link>
      <description><![CDATA[To overcome the storage limitations of reactive cold patching materials containing polyurethane prepolymer, this study proposed a blocked waterborne polyurethane emulsified asphalt (BWPU-EA) mixture. The deblocking temperature of blocked waterborne polyurethane (BWPU) and the optimal content of BWPU in emulsified asphalt were determined. Then, the microstructure of BWPU-EA and the modification mechanism were explored. Finally, the road performances of BWPU-EA mixture were assessed and compared with those of the emulsified asphalt mixture. The results indicate that the deblocking temperature of BWPU is 50.6 ℃, and the optimal content of BWPU in emulsified asphalt is 15% in mass. After curing, numerous particles and films are observed in the BWPU. Under the same gradation, the road performances AC-13-BWPU-EA mixture are greater than those of the AC-13-EA mixture. The BWPU-EA mixtures demonstrate excellent storage ability, with their Marshall stability remaining above 2.5 kN even after 60 d of storage.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633399</guid>
    </item>
    <item>
      <title>IT Enabled Innovative Concept for Ensuring Safer Roads with Public Participation in Tamil Nadu</title>
      <link>https://trid.trb.org/View/2652198</link>
      <description><![CDATA[The Namma Salai (in Tamil means "Our Road") App developed by the Highways Department, Government of Tamil Nadu, is a public participatory pothole reporting mechanism to ensure pothole free roads for safe mobility. This article deals about the salient features of the application which is being effectively used by the public to report the road related issues, allocation of complaints to the concerned authorities, the timebound resolution by the Department and the feedback mechanism to get the opinion of the public. Also deals about how smartphone technology can be effectively used to streamline the reporting and resolution mechanism with simple process flow.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:54:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652198</guid>
    </item>
    <item>
      <title>Investigation of Potholes in Concrete Slab Bridges</title>
      <link>https://trid.trb.org/View/2159553</link>
      <description><![CDATA[This paper presents experimental and finite element results to accurately evaluate the effect of potholes on the load distribution and performance of concrete slab bridges. This paper will assess the effect of deterioration in simply-supported, one-span, concrete slab bridges subject to AASHTO HS20 loading. The typical two-lane bridge selected for this study had 40 ft (12.2 m) span length, 34 ft (10.4 m) width, and 2 ft (0.6 m) slab thickness. The finite element method was used to model the concrete slab using rectangular SHELL elements. Three one-fifteenth size scale models of the reinforced concrete slab bridges were constructed and tested in the laboratory. Electric resistance strain gages were mounted on the concrete slabs to monitor strains associated with various positions of HS20 wheel loads. The back two axles of the AASHTO HS20 design truck were positioned in each lane and load superposition was used to find the strain values at mid-span. Results of the reference slab subject to multiple lane loading indicated that the slabs behaved as wide beams with minor variation in longitudinal bending moments across the widths of the slabs. The test results compared well with the FEA results. The AASHTO procedure over-estimated the strain values by about 30% when considering design trucks in the center of each of the two lanes. However, when considering a disabled truck parked on the shoulder and allowing two additional design trucks on the reference slab bridge, then AASHTO procedure gave similar strain values to the experimental and FEA results. The presence of various size potholes in one and/or two of the lanes at mid-span was also investigated subject to two possible loading conditions. This paper can assist engineers in predicting the behavior of reinforced concrete slab bridges.]]></description>
      <pubDate>Sat, 07 Mar 2026 16:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2159553</guid>
    </item>
    <item>
      <title>Improvement Study of Style Steel Wheel Durability in Multiple Pothole Impacts</title>
      <link>https://trid.trb.org/View/2669761</link>
      <description><![CDATA[This study focuses on improving the durability of steel wheel rims subjected to Multiple Pothole which is commonly found in Indian village roads — a critical scenario affecting vehicle safety and wheel lifespan. Initial steel wheel designs often face significant deformation or failure under repeated strikes and resulting in tyre air loss due to wheel bend, prompting the need for enhanced performance standards.In this research, a combination of finite element modelling, experimental impact testing, and material optimization strategies were employed to assess and improve the structural integrity of steel rims. Key parameters such as rim profile geometry & material composition were systematically varied to evaluate their influence on impact resistance. Results demonstrate that strategic design modifications and material enhancements can significantly increase the rim's ability to absorb energy and resist bending without substantial weight penalties. The findings offer practical guidelines for the automotive industry to produce more robust steel wheels, enhancing vehicle safety and reducing warranty costs associated with curb impact damage.The improved steel wheel rim design was validated through double curb impact testing, successfully meeting performance criteria and demonstrating enhanced durability without failure.]]></description>
      <pubDate>Tue, 03 Mar 2026 14:48:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669761</guid>
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