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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>A new assessment method for safety status of circular tunnels considering the integration of analytical models with displacement data</title>
      <link>https://trid.trb.org/View/2659667</link>
      <description><![CDATA[The performance status of circular Tunnel Boring Machine tunnels predominantly relies on qualitative evaluations using limited indicators, which fail to fully utilize high-volume field data from modern detection technologies, resulting in suboptimal specificity and reliability of the analysis outcomes. This study proposed a new assessment method for safety status of circular tunnels considering the integration of analytical models with displacement data. The assessment method fully considered the multisource data including the geological information, tunnel dimensions, actual lining displacements, lining reinforcement, and nonlinear constitutive relationships of the lining materials. The internal forces and stresses of the circular tunnel were calculated by the analytical models. Numerical modeling validation confirmed the model’s reliability. The applicable scope of the assessment method was clarified through the parameter sensitivity analyses. A safety evaluation index and its classification derived from the assessment method were subsequently established. The research findings reveal that the correlation coefficients (between test values and analytical values) of lining displacement, bending moment and axial force are more than 98 %, 97 % and 86 % respectively under the shallow and deep buried tunnel scenarios, which verified the reliability of the integration between analytical models and displacement data. The mechanical behaviors of a circular tunnel are influenced by factors such as the geological types, tunnel dimensions, lining displacements, lining reinforcement and lining material types. The ratio of lining stress to its yield strength (stress-strength ratio) ultimately determines the safety of the tunnel. It is worth mentioning that the safety state of the lining ring cannot be identified separately by the ovality of displacement, which should be determined jointly by the major and minor axes of the elliptical deformation. According to parameter sensitivity analysis, the order of sensitivity influence on the stress-strength ratio is: structural displacement > lining thickness > tunnel diameter > lining strength > soil lateral pressure coefficients. The assessment method synergizes with detection technology advancements, which provides theoretical foundations for predicting the mechanical performance of service tunnels.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659667</guid>
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    <item>
      <title>Improving the Efficiency of Road Construction Projects through Building Information Modeling (BIM) for Infrastructure</title>
      <link>https://trid.trb.org/View/2640273</link>
      <description><![CDATA[Infrastructure projects, particularly road development, are critical and substantial financial investments in modern societies. Consequently, industry and academia collaborate to advance the methodologies in designing and implementing such projects, leveraging contemporary technologies such as building information modeling (BIM). However, the use of BIM in road projects faces several barriers, such as inefficiencies in checking models. To address that, in this paper, the authors developed a new BIM-based model checking method for road projects, based on invariant signatures of architecture, engineering, and construction (AEC) objects and industry foundation classes (IFC) standards. The focus is on two primary categories: pavement and drainage. Due to the inherent difficulties of ensuring quality assurance (QA) in the IFC model, the developed BIM-based workflow aims to establish a systematic QA approach and tool for identifying distinct components within road projects implemented in the IFC format, which offers several advantages. Firstly, it provides comprehensive information regarding the geometry and components of the project, thereby reducing the likelihood of errors and enhancing the effectiveness of conveying the design solution. Secondly, BIM facilitates visualization and virtual representation of the drainage and pavement works, enabling automatic detection and classification of road assets. Lastly, utilizing clash detection within the BIM workflow effectively resolves coordination issues that may arise during the project. The research findings contribute to optimizing time and cost management and enhancing project performance for designers and decision-makers involved in road projects by utilizing BIM and IFC throughout the whole life cycle of such projects.]]></description>
      <pubDate>Tue, 28 Apr 2026 12:18:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640273</guid>
    </item>
    <item>
      <title>Applications of NATM in Garhwal Himalayan Tunnelling based on Field Evidence from Rishikesh–Karnaprayag Railway Tunnel Project</title>
      <link>https://trid.trb.org/View/2690968</link>
      <description><![CDATA[Geological conditions encountered during the Rishikesh–Karnaprayag railway tunnel construction deviated significantly from contractual predictions across rock classes C2 to B1. Stable B1 rock was absent, while squeezing (C2/C3) and rolling (B3) ground were encountered 4.1 times more than forecast. To address this uncertainty, pull length was modelled using data from over 200 blast rounds. Multiple linear regression correlated pull length to specific drilling, specific charge, and tunnel area. Specific charge decreased from 1.68 kg/m³ in B1 to 0.70 kg/m³ in C2 and further with increasing tunnel area, confirming a conservative, low-energy blasting strategy in poor ground. Rock-class-specific models achieved coefficient of determination (R²) values of 0.18–0.54, with the highest predictability in B3. Monte Carlo simulation (10,000 iterations) quantified prediction uncertainty, yielding 90% prediction intervals ranging from 0.98 to 1.47 m in C2 to 1.95–3.90 m in B1. These intervals provide a risk-informed basis for advance planning. The results demonstrate that New Austrian Tunnelling Method’s observational adaptability, guided by real-time blasting and support calibration, is essential for safe and efficient excavation in geologically unpredictable Himalayan terrain. The models offer field-validated, quantitative guidelines for blast design where conventional assumptions fail.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690968</guid>
    </item>
    <item>
      <title>Influence of Cement and Lime Treatment On the Resilient Modulus and Permanent Deformation of Weak Subgrade in Flexible Pavement</title>
      <link>https://trid.trb.org/View/2692349</link>
      <description><![CDATA[This research evaluates the treatment of weak subgrade soils (A-4 and A-6 soils according to AASHTO classification), using cement and lime stabilisers to enhance pavement design efficiency. The research investigates the influence of varying the moisture contents and stabiliser percentages, namely 2% and 4% cement, both alone and mixed with 1.5% lime. The characteristics of resilient modulus and permanent deformation have been assessed using repeated load triaxial tests. The findings have shown that moisture content can adversely influence the performance and strength of subgrade soils by decreasing resilient modulus values besides increasing permanent deformation in subgrade soils. However, soil treatment by stabilisers has substantially enhanced the overall mechanical strength, as the resilient modulus increased by 180% for soil type A-4 and 310% for soil type A-6. Finally, the research demonstrates that the type and content of the required stabiliser depend highly on the composition of subgrade soils. A cement treatment is effective in soils with a higher content of sand and silt, while a compound treatment of cement and lime can provide significant mechanical strength for subgrade soils with a higher clay fraction.]]></description>
      <pubDate>Tue, 21 Apr 2026 09:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692349</guid>
    </item>
    <item>
      <title>On the Classification of Vehicle Cut-In Scenarios Severity: Empirical Evidence to Validate the Severity Classification of UN R157</title>
      <link>https://trid.trb.org/View/2686157</link>
      <description><![CDATA[The Fuzzy Safety Model (FSM), developed amongst all to support UN Regulation No. 157 on Automated Lane Keeping Systems (ALKS), provided a novel methodology for distinguishing between avoidable and non-avoidable cases of certain test scenarios for Automated Vehicles (AVs). ALKSs, restricted to highway environments, must avoid any reasonably foreseeable and preventable accident. However, beyond this capability, the FSM may also be an optimal tool to classify the difficulty level of the same traffic scenarios. To validate the FSM's ability to classify preventable scenarios according to their difficulty level, a test campaign was conducted focusing on the critical “cut-in” scenario, where another vehicle changes lanes in front of the ALKS, requiring it to decelerate to avoid a collision. The study demonstrates the feasibility of the required tests and the FSM's effectiveness in categorising preventable cases by difficulty level. Results highlight the model's potential to plan, execute, and analyse cut-in scenarios beyond the scope of UN R157. This contribution supports the impartial assessment of AVs while addressing the challenge of representing diverse and challenging traffic conditions with a limited number of tests. The research results underscore the FSM's broader applicability for improving AV safety testing frameworks.]]></description>
      <pubDate>Fri, 03 Apr 2026 12:13:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686157</guid>
    </item>
    <item>
      <title>A prediction model for identifying rigid-flexible classifications of offshore rock-socketed piles</title>
      <link>https://trid.trb.org/View/2685309</link>
      <description><![CDATA[The classification of the pile as rigid or flexible is crucial for estimating the lateral bearing capacity of pile foundations. Existing rigid-flexible classification models are proposed mainly based on soil foundations and cannot accurately classify the rock-socketed pile (RSP) behavior in rock foundations. To improve the accuracy of the existing models, a 3DEC-based synthetic RSP model calibrated by in-situ test data is adopted to study the effect of slenderness ratio L/D and modulus ratio Eₚ/Eₘ on the rigid-flexible classifications of RSPs in various high and low stiffness-ratio foundations. Based on the analysis of the non-linear F-y curves of RSPs, a prediction model for estimating the critical length Lc of pile which is a key parameter to identify rigid-flexible classifications of RSPs for various rock foundations is proposed. The reliability of the proposed model is further assessed and compared with the existing models through the analysis of 25 numerical data and 6 in-situ and laboratory test data. Results show that the proposed model is more capable of providing accurate estimations of L꜀ for identifying rigid-flexible classifications of RSPs than the existing models.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685309</guid>
    </item>
    <item>
      <title>A Hybrid Intelligent Framework for the Assessment and Classification of Squeezing Potential of Rocks Around Tunnels</title>
      <link>https://trid.trb.org/View/2675948</link>
      <description><![CDATA[Tunnel squeezing is characterized as a significant degree of distortion in the surrounding rock mass that is typically larger than the designed deformation. The squeezing potential of rocks around tunnels can result in support failures, floor heave, and even flood disasters. In this study, the squeezing potential of rocks around tunnels were estimated by employing a hybrid intelligent framework to improve the performance of a classification algorithm. A total of 139 adjacent rock-squeezing patterns were acquired from places such as China, Nepal, and India to form the empirical basis for this study. The data consists of five influential variables, i.e., strength factor, tunnel depth, rock mass quality index, tunnel equivalent diameter and support stiffness. The mechanism of prediction consisted of three steps. Firstly, factor analysis was utilized to reduce the number of influential variables. The resulting factors were then categorized using k-means clustering. Finally, a random forest algorithm was developed to predict various levels of surrounding rock squeezing potential of rocks around tunnels. The proposed hybrid intelligent framework achieved a strong predictive capability of 96%, contributing to safer and more sustainable tunneling practices by reducing operational risks and improving overall structural stability.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675948</guid>
    </item>
    <item>
      <title>Railway track performance prediction considering track-drainage interdependencies</title>
      <link>https://trid.trb.org/View/2634948</link>
      <description><![CDATA[Effective prediction of infrastructure performance is essential for informed asset management. However, traditional approaches often treat different types of assets in isolation, overlooking critical interdependencies (such as those between track and drainage systems) that significantly influence asset degradation and risk. This paper proposes a hybrid model, BaGTA, that is temporally aware, spatially informed and probabilistically grounded to predict railway track performance while accounting for both uncertainty and inter-asset dependencies. The model was trained and validated on a dataset comprising 6,072 track segments and 31,628 drainage assets across four UK railway routes. We demonstrate that incorporating track-drainage interdependencies improves prediction accuracy in both classification and regression tasks. Specifically, the inclusion of interdependencies reduced the prediction error for the Vertical Settlement Standard Deviation (VSD), which is a key indicator of track performance, by 24.65%. The proposed method not only captures complex spatiotemporal relationships but also quantifies uncertainty in predictions, offering a robust decision-support tool for infrastructure operators. This approach has the potential to transform maintenance strategies by enabling proactive, risk-informed, and cost-effective asset management.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:24:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2634948</guid>
    </item>
    <item>
      <title>Land Cover Classification Using U-Net for Calibration of Rainfall-Induced Slope Susceptibility Maps</title>
      <link>https://trid.trb.org/View/2562222</link>
      <description><![CDATA[Land cover classification is essential in roadway design and environmental conservation. The convolutional neural network (CNN) model named U-Net, with its ability to capture fine-grained spatial details, has been proven particularly effective in performing accurate and efficient classifications from satellite imagery. These advancements are particularly valuable in geophysical hazard assessment, where physics-driven predictive models evaluate rainfall-triggered slope susceptibility and identify slope failure hazards. Existing physics-driven models for predicting rainfall-induced slope susceptibilities often focus on factors like slope geometry, soil characteristics, and precipitation but tend to overlook the influence of land cover features such as vegetation, retaining structures, and wetlands on slope stability. The primary objective of this study is to address this gap by developing an accurate semantic segmentation method for aerial imagery using the U-Net convolutional neural network (CNN) model and integrating it with physics-driven models to account for the influence of land cover on slope stability. The methodology includes preprocessing aerial imagery by segmenting images into 256 × 256 pixel patches, normalizing pixel values for consistency across the data set, and using the U-Net architecture to achieve fine-grained segmentation details. This method integrates with the current physics-driven model by converting the classified image into geospatial formats and conducting overlay analysis on slope susceptibility maps to enhance the accuracy of slope susceptibility levels. The results demonstrate high accuracy and a strong Intersection over Union (IoU), highlighting the U-Net model’s effectiveness in identifying and classifying complex land cover types. By integrating detailed land cover data, this research enhances the predictive accuracy of traditional slope failure predictive models and broadens the applicability of U-Net in geophysical modeling, facilitating the identification of critical slopes and enhancing proactive maintenance strategies.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562222</guid>
    </item>
    <item>
      <title>Assessment of Soil Liquefaction Potential Prediction Using Synthetic Data and Soft Computing Techniques</title>
      <link>https://trid.trb.org/View/2669709</link>
      <description><![CDATA[Liquefaction prediction using conventional approaches often relies on empirical correlations and involves costly, time-consuming field studies. Using large databases of post-liquefaction observations, machine learning methods have recently been developed to evaluate liquefaction potential. However, the availability of adequate real-world data limits the efficacy of these approaches. This study investigates the efficacy of the deep learning technique, i.e., Conditional Tabular Generative Adversarial Networks (CTGAN), for generating synthetic data. A comparison between original and synthetic data is made based on the absolute log mean, numeric data standard deviation, cumulative sums per feature, a correlation matrix, principal component analysis (PCA), distributional features and p-values from the Kolmogorov–Smirnov (KS) test. It is found that the synthetic data statistically resembles the original, making it viable for developing predictive models. There is a notable increase in the accuracy of liquefaction predictions when using 10,000 synthetic datasets generated from 288 original datasets. The synthetic data outperforms the original datasets across various machine learning methods, including Logistic Regression, Random Forest, SVM, KNN, and Decision Tree, with improvements in liquefaction classification accuracy of 89%, 98%, 92%, 98%, and 98%, respectively.]]></description>
      <pubDate>Wed, 18 Feb 2026 12:00:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669709</guid>
    </item>
    <item>
      <title>Prediction Model for Uniaxial Compressive Strength of Rocks Based on Dual Optimization of Input Parameters and Hyperparameters</title>
      <link>https://trid.trb.org/View/2651390</link>
      <description><![CDATA[The prediction of uniaxial compressive strength (UCS) is crucial for assessing the classification of rock engineering and evaluating rock mechanical properties. Despite various optimization algorithms integrated with support vector regression (SVR) models, the optimal parameter combinations and prediction accuracy of these models are unclear. To address this, four association algorithms were employed to obtain the best input parameters for the SVR model, and nine optimization algorithms were applied for hyperparameter optimization of the UCS prediction model. A comprehensive evaluation of the predicted results was conducted using four evaluation indicators, score analysis, uncertainty analysis, and the Wilcoxon Test with a dataset of 87 rock samples utilized. The results indicated that Is(50) and Brazilian tensile strength were the two parameters most strongly correlated with the UCS and were regarded as input parameters for prediction. The SVR-based ensemble model effectively predicted the UCS. The SVR model based on the Grasshopper optimization algorithm exhibited the best predictive performance, achieving the highest composite score of 32. These findings can influence further studies on optimizing the UCS prediction models and deepen the understanding of algorithm selection, parameter settings, and evaluation measures.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:01:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651390</guid>
    </item>
    <item>
      <title>Data-driven assessment of rigid pavement vulnerability in Texas coastal regions</title>
      <link>https://trid.trb.org/View/2663108</link>
      <description><![CDATA[This research aims to evaluate the vulnerability of rigid pavements in two major coastal districts of Texas (i.e., Beaumont and Houston) spanning about 900 miles using data-driven approaches. Particularly, the study will (1) identify the key factors contributing to rigid pavement distress under dynamic coastal weather conditions, and (2) develop data-driven strategies to enhance the durability and performance of these pavement networks. Multi-source datasets, such as weather, geotechnical, traffic, coastal proximity, and pavement conditions, will be collected and integrated to support this analysis. Weather data, including temperature and precipitation, will be obtained from national and global databases such as NOAA’s National Centers for Environmental Information (NCEI) and NASA Earthdata/GES DISC. Soil classification and geotechnical attributes will be sourced from the NRCS SSURGO (Soil Survey Geographic Database), while coastal proximity data will be derived from Google Earth. Traffic volumes and loading data will be gathered from TxDOT’s Statewide Traffic Analysis and Reporting System (STARS II). Pavement condition metrics, including distress quantity, distress score, condition score, and ride quality, will be extracted from the Texas Department of Transportation (TxDOT)’s Pavement Management Information System (PMIS) and supplemented with satellite imagery. By integrating these datasets, the project will perform statistical and spatial analyses to establish correlations between weather variables, geotechnical conditions, traffic patterns, and pavement performance indicators.]]></description>
      <pubDate>Thu, 29 Jan 2026 19:58:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663108</guid>
    </item>
    <item>
      <title>Numerical Evaluation and Validation of Ground Deformation as a Function of Characterized Rock Mass and Stress Regime</title>
      <link>https://trid.trb.org/View/2655707</link>
      <description><![CDATA[This study integrated the Rock Mass Rating (RMR) classification system with the Distinct Element Method (DEM) to quantify post excavation deformation in a tunnel associated with the Rishikesh–Karnprayag broad-gauge rail link project in Uttarakhand, India. Block displacement was used as the primary deformation indicator, supplemented by strength-to-stress ratio assessment and principal stress evaluation. RMR classes ranging from poor to good conditions were investigated across three numerical design scenarios, pessimistic, medial, and optimistic, to account for geotechnical uncertainty. Fifty four numerical models were developed with overburden depths varying from 25 m to 500 m, to examine stress induced deformation patterns and associated failure mechanisms. Hierarchical clustering was employed to group geotechnical attributes, while Random Forest, Extreme Gradient Boosting, and Shapley Additive Explanations were utilized to identify critical parameters influencing deformation behaviour. The results demonstrated a systematic decrease in block displacement with an increase in RMR value, demonstrating its efficiency as a deformation predictor. Strength-to-stress ratio assessment and principal stress distributions corroborated these results. This research successfully bridged empirical classification and numerical modelling, providing a transferable framework for deformation assessment in complex geological environments, such as the Himalaya.]]></description>
      <pubDate>Wed, 28 Jan 2026 14:43:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655707</guid>
    </item>
    <item>
      <title>Stabilization of Subgrade Soil Using Sugarcane Fiber</title>
      <link>https://trid.trb.org/View/2562011</link>
      <description><![CDATA[This study uses sugarcane fiber (after the juice had been removed) for subgrade soil stabilization using soil samples from Chennai, India. The sugarcane fiber was prepared by sun drying and oven drying the sugarcane’s outer shell for 24 h each and manually chopping it into 8 to 12 mm pieces with a length-to-diameter ratio ranging between 4 and 24. The soil used for the study was collected locally, and it was a well-graded sand (SW), per the Unified Soil Classification System (USCS). The unconfined compressive strength (UCS), California bearing ratio (CBR), and modified proctor compaction test were performed on the soil samples without fiber and with varying dosages of added fiber. The results show that the CBR and UCS values increased by 2.12 and 2.40 times, respectively, at the optimum fiber content of 2%, compared to the original soil sample.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562011</guid>
    </item>
    <item>
      <title>Classification of road transport earth retaining structures based on improved ConvNeXt V2</title>
      <link>https://trid.trb.org/View/2627401</link>
      <description><![CDATA[An earth-retaining structure (ERS) is a type of wall widely used to support and protect soil, rock, or other earth materials in many parts of transport infrastructure. ERSs are constructed to create grade separation, particularly at inclined locations or where earth cuttings are required. Over 42 types of ERSs exist in the areas under the jurisdiction of Transport for New South Wales (TfNSW). For effective structural health monitoring, it is crucial to classify an ERS according to its type and establish its GPS coordinates and longitudinal length. The classification is necessary because the defects that occur in the ERS depend on its type. This study proposes a deep learning-based framework that leverages ConvNeXt V2 to classify ERS types and capture the relevant data from vehicle-mounted video capturing systems. The framework integrates spatial–temporal image sequences with GPS coordinates and longitudinal length to enhance context-aware recognition. An improved ConvNeXt V2 model is fine-tuned to learn rich visual features of ERS embedded with GPS metadata. The model is trained and evaluated on a dataset comprising diverse ERS classes captured across TfNSW road transport areas. The experimental results demonstrate significant improvements in classification accuracy and robustness. This emphasizes the potential of the proposed system for scalable, automated ERS inventory and condition assessment.]]></description>
      <pubDate>Tue, 20 Jan 2026 09:09:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627401</guid>
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