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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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    <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>Permeability evolution driven by the suffusion process in silica sands under cyclic loading: Mathematical model and experimental verification</title>
      <link>https://trid.trb.org/View/2694618</link>
      <description><![CDATA[This study presents a mathematical model to evaluate the current permeability of a granular soil undergoing suffusion. The proposed model is derived from the Kozeny-Carman equation by introducing the erosion ratio, which is an extension of the Kozeny-Carman equation in erodible soils. Two silica sands with different stabilities are tested to investigate permeability evolution during suffusion and to validate the theoretical model, in which the sand column is subjected to dynamic loads and bottom-up seepage. The test results show that fine particles are eroded by seepage flow, and coarser particles are gradually eroded as the hydraulic gradient increases. The proposed model’s predictions are verified by measuring relative permeability within the erosion zone, demonstrating good predictive capability with coefficients of determination (R2) of 0.86 and 0.85 and root mean square errors (RMSE) of 0.93 and 0.88 for Sand-A and Sand-B, respectively. These results confirm that the model reliably captures the evolution of permeability during suffusion under dynamic conditions. The suspension potential of soil particles increases under dynamic loads, thereby further quantifying the intrinsic effects of flow velocity and particle size.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:07:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694618</guid>
    </item>
    <item>
      <title>Quantitative analysis of the mechanical behavior of granular sands subjected to particle pre-crushing</title>
      <link>https://trid.trb.org/View/2692410</link>
      <description><![CDATA[Particle crushing is a critical mechanism governing the macro-mechanical response of granular materials, yet current research predominantly focuses on thebehavior ofpristine sands, leaving a significant gap in understanding the quantitative effects of stress history. To address this, this study employs drained triaxial shearing tests coupled with high-performance Acoustic Emission (AE) monitoring to capture the multi-scale evolution of silica sands with varying degrees of pre-crushing. The results reveal that pre-crushing proportionally enhances peak shear strength and friction angle while suppressing particle re-crushing, volumetric contraction, andcrushing-induced high-frequency (>100 kHz) AE activities. By establishing quantitative characterization frameworks based on dissipated energy and high-frequency AE signatures, a fundamental transition in energy dissipation pathways from crushing-dominated to frictional rearrangement-dominated modes is deduced. This transition is attributed to the enhanced packing efficiency resulting from the combined influence of particle cushioning and a lower initial void ratio. Furthermore, a robust linear correlation between high-frequency AE hits and the breakage index is validated for both pristine and pre-crushed series.These findings highlight the feasibility of frequency-based AE analysis as a continuous, non-invasive tool for quantifying particle crushing under complex testing conditions.]]></description>
      <pubDate>Thu, 23 Jul 2026 09:14:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692410</guid>
    </item>
    <item>
      <title>Vertical load on embankment-installed rigid culvert buried by cohesionless fill</title>
      <link>https://trid.trb.org/View/2652813</link>
      <description><![CDATA[Researchers had presumed different failure mechanisms for calculating the load on culverts, but the research on summarizing, comparing, and evaluating these failure mechanisms was limited. This paper estimates the failure surface and shear stress along the failure surface by numerical analysis, following a brief summary of the methods for calculating the load on the culvert. From the simulation, three types of failure surfaces, i.e., internal, vertical, and external failure surfaces, were observed in the fill. Among them, the dominant surface depended on the friction angle and height. In addition, the lateral earth pressure coefficient at the vertical and dominant failure surface decreased with the fill height and friction angle, contrary to the assumption that the lateral earth pressure coefficient was only influenced by the fill friction angle. Furthermore, when the external and dominant failure surface was simplified as the vertical failure surface with an equivalent settlement surface (ESS), the vertical earth pressure in the interior fill could be accurately calculated if an appropriate value for the ESS height was chosen.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652813</guid>
    </item>
    <item>
      <title>Enhancing Predictive Accuracy and Interpretability of Small Strain Shear Modulus for Granular Soils Using Machine Learning Models</title>
      <link>https://trid.trb.org/View/2672597</link>
      <description><![CDATA[This study presented a hybrid machine learning (ML) approach for predicting the small-strain shear modulus (G[subscript max]) in granular soils that integrated AdaBoost, Decision Tree, and CatBoost models with the Gorilla Troops Optimization algorithm to improve predictive accuracy and model robustness. The approach addressed key limitations of conventional empirical models and standalone ML models in capturing complex parameter interactions across varying soil conditions. A database of 816 samples was compiled using four key soil parameters: void ratio, confining pressure, coefficient of uniformity, and particle shape descriptor. Among the developed models, CatBoost outperformed the other ML models and empirical correlations available in the literature for G[subscript max] estimation, achieving coefficient of determination (R²) values of 0.986 (training) and 0.994 (testing) with minimal associated errors. To enhance model interpretability and transparency, Shapley additive explanations, partial dependence plots, and individual conditional expectation analyses were applied. The results showed that confining pressure was the most influential predictor, while the particle shape descriptor had the least effect on G[subscript max]. The proposed approach provides a reliable, interpretable tool for engineers, supporting more accurate G[subscript max] estimation and reducing uncertainty in geotechnical design.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672597</guid>
    </item>
    <item>
      <title>Incorporation of a hypoplastic material model for sandy soils into a dynamic ALE formulation suitable for structures subjected to moving loads</title>
      <link>https://trid.trb.org/View/2647979</link>
      <description><![CDATA[Recent research has shown that an Arbitrary Lagrangian Eulerian (ALE) formulation can be leveraged to improve the efficiency of simulating structures subjected to moving loads, such as pavements. However, when modeling pavements, subsoil characteristics are not given much importance recently, and the subsoil is often modeled by simple linear elasticity. In this work, a hypoplastic material model capable of accurately describing the behavior of cohesionless soils, is used to model the subsoil response. Additionally, the calibration of the hypoplastic model to obtain material parameters is described. Further, the logarithmic strain approach to extend this model to finite deformations is detailed, and the incorporation of this material model into a dynamic ALE formulation is explained. Finally, the results of a transient simulation of the pavement response, when subjected to a moving load, are provided.]]></description>
      <pubDate>Tue, 24 Mar 2026 09:09:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647979</guid>
    </item>
    <item>
      <title>DEM investigation on granular soil arching with emphasis on particle size distribution effect</title>
      <link>https://trid.trb.org/View/2644134</link>
      <description><![CDATA[Soil arching is a common load transfer mechanism in geotechnical engineering, which is significantly influenced by soil particle size distribution (PSD). Existing studies have not fully understood the PSD effect, specifically the mean particle size (d50) and coefficient of uniformity (Cu), on the arching evolution and critical height. To this end, this study tries to investigate the PSD effect on the evolution of soil arching using the discrete element method. A series of two-dimensional trapdoor tests were simulated on eight specimens with varying d50 and Cu. The macroscopic responses and microscopic mechanisms were systematically analyzed. Simulations reveal that an increase in d50 or Cu leads to a reduction in the critical arching height. This indicates that coarser and better-graded granular soils promote a more rapid development of soil arching effect, thereby enhancing the initial load-transfer efficiency. At the microscopic level, specimens with larger d50 develop stronger yet sparser force chains and exhibit greater normal contact force anisotropy, while specimens with higher Cu form denser contact networks with larger coordination numbers, resulting in more stable force transmission. The findings of this study strongly suggest that PSD significantly controls the soil arching development process through its governing role in fabric formation and force chain structure. Besides, the implications of this study offer direct relevance for optimizing backfill material design in geotechnical practices, notably in pile-supported embankments and underground excavation projects.]]></description>
      <pubDate>Wed, 25 Feb 2026 09:05:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644134</guid>
    </item>
    <item>
      <title>Cone Penetration Test Correlations for Missouri Soils </title>
      <link>https://trid.trb.org/View/2673279</link>
      <description><![CDATA[The objective of this research is to provide MoDOT with Missouri-specific guidance on the use of cone penetration test (CPT) measurements to estimate geotechnical parameters. This work will focus on two areas: (1) Development of Missouri-specific correlations to estimate undrained strengths of Missouri clays from CPT measurements. and (2) Investigation of inconsistencies between standard penetration test (SPT) and CPT derived values for granular soils in Missouri.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:30:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673279</guid>
    </item>
    <item>
      <title>A time-dependent hydration-driven bonded-particle model for simulating strength evolution in cement-stabilised granular soils: Experimental and DEM insights</title>
      <link>https://trid.trb.org/View/2640846</link>
      <description><![CDATA[This study presents a time-dependent bonded-particle Discrete Element Method (DEM) model for simulating the mechanical behaviour of cement-stabilised granular soils, incorporating the coupled effects of cement content, water-to-cement (w/c) ratio, and curing duration. A comprehensive micromechanical experimental program was conducted to quantify the evolution of bond strength—including tensile, compressive, and shear components—under varying mix designs and curing ages. The experimentally derived bond strength functions (closed-form) were implemented into a custom-developed DEM contact model to represent particle bonding behaviour realistically under different curing times. The model was validated through a series of 90 unconfined compressive strength (UCS) tests on cemented soil samples with varying cement contents (1 %, 2 %, and 3 %), w/c ratios ranging from 0.3 to 1.5, and curing periods of 3 to 90 days. The results revealed that bond strength increased exponentially with curing time, with the majority of strength gain occurring within the first 3–21 days. Numerical simulations closely matched experimental results, demonstrating agreement in peak strength, stress–strain behaviour, and failure modes. DEM simulations show 2.4× higher contact force and 48 % less particle displacement from curing (3–90 days) with increasing cement content. Coordination number rose with longer curing time and higher cement content, then dropped 8–13 % near peak and 3.3–4.5 post-peak, indicating bond breakage and cracking. Bond breakage ratio decreases with curing time due to cement hydration; a lower w/c ratio shows faster bond strength improvement over time. The proposed model reliably predicts cemented soil strength evolution, improving early-age and long-term analysis for geotechnical design.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640846</guid>
    </item>
    <item>
      <title>Experimental and Numerical Evaluation on Uplift Performance of Geotextile-Encased Granular Pile Anchor</title>
      <link>https://trid.trb.org/View/2652038</link>
      <description><![CDATA[This study evaluated the uplift performance of granular pile anchors and geotextile-encased granular pile anchors in cohesionless soils through experimental testing and finite element modelling. The investigation focused on the influence of pile length, diameter, embedment ratio, and soil relative density on pullout load capacity. Results showed that an increase in pile length, diameter, embedment ratio, and relative density led to a significant improvement in pullout capacity for both systems. Geotextile encasement enhanced lateral confinement and reduced bulging, resulting in a 20–48% increase in pullout load compared with the unencased pile anchor. As the relative density of the soil increased from 40% to 80%, the pullout capacity of the geotextile-encased system improved by 25–72%, whereas the unencased pile showed a smaller increase of 27–33%. The benefit of encasement became more pronounced at higher embedment ratios, with the pullout load reaching up to 2.2 times that of the granular pile anchor at an embedment ratio of 15. The results indicated that geotextile encasement modified the failure mechanism and improved the efficiency of load transfer. Overall, the geotextile-encased granular pile anchor demonstrated greater stability and cost-effectiveness for resisting uplift forces in cohesionless soils.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:01:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652038</guid>
    </item>
    <item>
      <title>Deep sequence learning architectures for predicting cyclic soil stress-strain behavior: comparative evaluation of LSTM, GRU, TCN, and hybrid models</title>
      <link>https://trid.trb.org/View/2633499</link>
      <description><![CDATA[This study presents a comprehensive evaluation of advanced deep learning models for predicting the mechanical behavior of saturated granular soils under undrained cyclic triaxial loading conditions. We compare recurrent (LSTM, GRU), convolutional (TCN), and hybrid architectures (LSTM → GRU, GRU → LSTM, parallel LSTM + GRU) in forecasting deviatoric stress (q) and mean effective stress (p) from short sequences of axial strain and stress history. Experimental data were obtained from high-resolution laboratory tests on saturated sand specimens and processed as a multivariate sequence-to-vector regression task. Model performance was assessed using RMSE, MAE, and R² across a rigorous 5-fold cross-validation scheme with temporal blocking. The parallel LSTM + GRU hybrid consistently outperformed all other architectures, achieving R² values above 0.997 for both stress components, while the TCN delivered competitive accuracy with notably faster convergence. These findings highlight the complementary strengths of recurrent-convolutional hybrids and underscore the potential of deep sequence learning as a robust alternative to traditional constitutive models for capturing complex cyclic soil behavior, including hysteresis effects and stress path evolution under undrained conditions. The exceptional predictive accuracy demonstrated by hybrid architectures suggests their strong potential for integration into real-time geotechnical monitoring systems and digital twin frameworks for infrastructure resilience assessment.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:52:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633499</guid>
    </item>
    <item>
      <title>Additive manufacturing of granular analogue soils: feasibility studies and mechanical characterization</title>
      <link>https://trid.trb.org/View/2622300</link>
      <description><![CDATA[The use of additive manufacturing (AM) technology for advancements in engineering applications has grown in recent years; however, limitations still exist regarding its use in geotechnical and transportation engineering related applications. This study presents the potential of AM advancements in civil engineering applications for creating analogue soils and offers valuable insights into material type, manufacturing methods (i.e., material orientation), and post-manufactured material mechanical properties. A variety of AM technologies and materials were tested to determine the most suitable product for experimental testing and model validation studies. Based on uniaxial compression tests (UCS), the Powder Bed Binder Jetting (PBBJ) gypsum composite and Selective Laster Sintering (SLS) photopolymer are determined as suitable materials, with the gypsum composite preferred for its stiffness and brittle behavior. Print layer orientation influences compressive strength, with a 5% increase observed in vertical orientation compared to horizontal, while static Young’s modulus remains minimally affected. From compression tests on spheres and cylinders, it is observed that the contact Young’s modulus, determined using Hertzian fitting for spheres, is considerably lower than the static Young’s modulus derived from traditional UCS tests. This discrepancy can be attributed to the pronounced surface roughness of the gypsum composite spheres. Additionally, as particle size increases, there is a noticeable decrease in nominal tensile strength. The findings from drained triaxial compression tests on the analogue soil suggest its capability to replicate the response of conventional granular materials under realistic confining stresses. Denser assemblies exhibit higher peak friction angles and volume dilatancy as compared to loose assemblies. For loose assemblies, particularly for the gypsum composite material, inter-particle friction played a significant role in the mobilized (critical state) friction angle. The use of analogue soils is advantageous for laboratory parametric studies and provides a novel means to validate discrete element method (DEM) numerical simulations, which provide further insight on granular material response.]]></description>
      <pubDate>Mon, 05 Jan 2026 09:52:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622300</guid>
    </item>
    <item>
      <title>Estimation of Bearing Capacity of Shallow Foundations on Cohesionless Soil Using Backpropagation Neural Networks and LSSVM Models</title>
      <link>https://trid.trb.org/View/2620684</link>
      <description><![CDATA[This study compared six backpropagation-based artificial neural networks (ANN) and two kernel-based least squares support vector machine (LSSVM) models to obtain an optimal performance model to estimate the ultimate bearing capacity (UBC) of shallow foundations. A database was collected from the literature and utilized to train (78 datasets) and test (19 datasets) each model. For the first time, the effect of multicollinearity of internal friction angle (ɸ), soil unit weight (ɣ), length to width ratio of footing (L/B), footing depth (D), and footing width (B) was analyzed by the variance inflation factor (VIF) method in estimating UBC. The study revealed that the radial basis function (RBF)-based LSSVM achieved an accuracy of over 99% with the least residuals, i.e., root mean square error of 67.2377 kPa, and outperformed the linear (L) kernel-based LSSVM and Levenberg–Marquardt (LM)-based ANN models. A novel relationship plot, i.e., a feature multicollinearity-sensitivity relationship plot, revealed that the RBF_LSSVM model experienced overfitting (= 1.42) due to the weak multicollinearity of B (= 2.38) and D (= 2.02), which contributed 25.612% (for D) and 21.284% (for B), respectively.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:58:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620684</guid>
    </item>
    <item>
      <title>A Study on Soil Behavior and Safety in Dynamic Compaction of Clayey Sand with High Fines Content and High Groundwater Table</title>
      <link>https://trid.trb.org/View/2628347</link>
      <description><![CDATA[This study investigated the behavior of clayey sand with high fines content subjected to dynamic compaction using two methods: Finite Element Method (FEM) modeling and cyclic triaxial testing. Researchers developed an axisymmetric numerical model to simulate the dynamic compaction process and calculate acceleration values and Cyclic Stress Ratios (CSR) with distance from impact. The model was verified against field acceleration measurements, achieving an error margin of ± 20%. Cyclic triaxial tests, conducted under 32 kPa (2-m depth) and 53 kPa (4-m depth) confining stresses, subjected samples to CSR values ranging from 0.05 to 0.60. Laboratory tests revealed distinct, stress-dependent deformation patterns: lower confining stress samples exhibited continuous compressive strain (maximum shear strain of 0.65% at CSR = 0.60), while higher confining stress samples transitioned from compressive to dilative strain at values exceeding 0.40. Analysis of shear strain distribution indicated that strains approaching the critical threshold for soil degradation (≈ 5%) occurred in near-surface zones. This finding highlighted a significant operational safety concern regarding equipment placement near impact locations. The integrated approach provided a comprehensive understanding of soil deformation and informed safety considerations during ground improvement.]]></description>
      <pubDate>Wed, 26 Nov 2025 14:13:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628347</guid>
    </item>
    <item>
      <title>Optimize Geogrid Reinforced Foundation Using Multivariate Adaptive Regression Splines Model and Multi-Objective Genetic Algorithm</title>
      <link>https://trid.trb.org/View/2628052</link>
      <description><![CDATA[This study proposed a new approach for optimizing a reinforcement technique using geogrids with wraparound ends for a strip footing resting on a cohesionless sand bed. The proposed method is based on the Multivariate Adaptive Regression Splines (MARS) model and the Multi-Objective Genetic Algorithm (MOGA), which were used to identify the optimal geogrid parameters. MARS was employed to obtain the correlation functions between input parameters related to geogrid installation and output parameters, including ultimate bearing capacity and settlement ratio. The input parameters for geogids included axial elastic stiffness, normalized width of reinforcement, normalized depth of the first layer, normalized vertical spacing between successive layers, normalized vertical length of the wrapping ends, and normalized lap length of the wrapping ends. MOGA was implemented to satisfy optimization conditions and constraints. MOGA results showed that the geogrid parameters proposed by this optimization method effectively maximized bearing capacity, minimized settlement, and minimized reinforcement cost.]]></description>
      <pubDate>Tue, 25 Nov 2025 09:19:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628052</guid>
    </item>
    <item>
      <title>Effect of Scale-Dependent Particle Morphology on the Single-Particle Crushing Behavior of Granular Soil</title>
      <link>https://trid.trb.org/View/2601824</link>
      <description><![CDATA[Granular soil particles display complex, multi-scale morphologies, including overall form, roundness, and surface roughness. Due to the interplay among shape features at different scales, isolating the effect of a single-scale shape characteristic on particle crushing behavior is challenging. To this purpose, a noise-based framework was employed to produce particle series with controlled gradients in a single-scale particle shape indicator. Over 600 single-particle crushing simulations were conducted using an improved bonded particle model capable of capturing realistic particle geometries. The simulation results highlight that particle crushing strength is governed by macro-scale sphericity, and the size effect is mainly influenced by meso-scale roundness, while micro-scale surface roughness has a negligible impact on crushing strength. As sphericity increases, both characteristic strength σ0 and Weibull modulus m increase, indicating improved breakage resistance and reduced variability in crushing strength. The extent of the size effect exhibits a clear negative correlation with roundness, attributed to the contact area between the particle and the loading platens. Regardless of the initial particle shape, crushing strength has a positive correlation with contact area between the particle and loading platens. Finally, a probabilistic model for the characteristic crushing strength, incorporating both particle shape and size, is proposed and demonstrates good agreement with the test data.]]></description>
      <pubDate>Tue, 11 Nov 2025 09:23:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601824</guid>
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