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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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    <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>Applications of Aerial LiDAR Datasets for 3D Probabilistic Slope Stability Analysis of Transportation Geotechnical Infrastructure Assets</title>
      <link>https://trid.trb.org/View/2678424</link>
      <description><![CDATA[Annually, several billion dollars are spent to mitigate landslides and rehabilitate structures affected by slope failures. Such instabilities may arise from several factors, including complex slope terrain geometry, varying geomorphology, seasonal groundwater fluctuations, changes in river water levels, prolonged waterlogging, and development of intermittent slickensides. Incorporating these effects into slope stability analysis can be challenging, requiring thorough field investigations and geotechnical assessments, as well as performing 2D and 3D slope stability analysis to accurately estimate reliable factors of safety. This paper presents a case study on stability analysis for a slope adjacent to US Highway 75 in Minnesota. This site is characterized by the interactions between transportation infrastructure and the Sandhill River passing through the region to join the Red River of the North. To analyze the impact of complex slope geometry and its interactions with infrastructure, aerial remote sensing data was obtained to generate a detailed 3D slope geometry, which was further processed to conduct 2D and 3D slope stability analyses using Spencer’s method and probabilistic methods using Monte Carlo Simulations, Latin Hypercube Sampling, and Response Surface Method. The results from deterministic slope stability analysis show that the slope factor of safety was higher when using peak undrained shear strength. In addition, the probability of failure of the slope using 3D and 2D analyses at peak undrained shear strength case was observed to be negligible compared to the case with residual undrained shear parameters. The critical factor of safety values of two hypothetical extreme scenarios for the area were considered to gain an in-depth understanding of the risk posed to the transportation infrastructure. The present paper summarizes the critical role of incorporating 3D slope stability analysis and pore-water pressure conditions to account for boundary effects and complex failure mechanisms.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678424</guid>
    </item>
    <item>
      <title>Project #578 - UTC Safety21: Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides - Phase 3</title>
      <link>https://trid.trb.org/View/2780187</link>
      <description><![CDATA[This report summarizes Phase 3 research findings of the project Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides, sponsored by the National UTC Safety21 program. The work advances an integrated framework for detecting and monitoring slope instability and assessing landslide risk along highways, railroads, and roadways. The studies include geometry-resolved InSAR monitoring and directional forecasting at Haystack Mountain; intervention-aware InSAR and multi-horizon machine learning for the Baltimore CSX Belt Line; a simulation-based CaseBank and machine-learning framework for rapid slope-stability assessment; soil-specific probabilistic rainfall thresholds for landslide initiation in Maryland; physics-informed multi-sensor machine-learning/deep-learning benchmarking and LiDAR monitoring for early warning; laboratory characterization of soils from Maryland landslide-inventory sites; and a national railroad landslide-exposure inventory developed through GIS-based proximity screening.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780187</guid>
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    <item>
      <title>UTC Safety21: Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides - Phase 3</title>
      <link>https://trid.trb.org/View/2778223</link>
      <description><![CDATA[Geologic hazards including slope failures, landslides, mudflows, debris flows, etc. and hydrological hazards related to floods and stormwater surge can be destructive to transportation infrastructure (geotechnical asset) and threaten property and human life along the highway, railroad and road ways. Landslides alone cause thousands of deaths and many billions of dollars in damage every year. 

Morgan State University team proposes a multi-phase (multi-year) project focusing on safety of transportation infrastructure systems by preventing geohazard, specifically slope failure and landslides and minimizing impacts of geohazard. This project will employ an integrated approach of geotechnical and Artificial Intelligence (AI)/Machine Learning methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the DOT SHA and delineating landslides and high-risk areas. 

The objectives (tasks) of the proposal include: (1) with AI/Machine Learning approaches assess the risks of landslides based on soil/rock types, weather conditions, mechanical properties of slope materials, stream gage station flow data, pavement material and design, and the status of existing retaining structures along the selected highway sections, using Maryland as case studies and railroad, (2) identify and map the high-risk areas based on controlling factors such as geometry and mechanical properties of soil or rock, and triggering factors, including gravitational and hydraulic forces, using available survey data, remote sensing and LiDAR and InSAR data and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with agencies and organizations (such as DOT SHA, Federal Railroad Administration), and (4) recommend strategies for reducing the risks of landslides with real-time monitoring for the high-risk areas, and improving the safety of the transportation infrastructure. All the methods and strategies can be transferred to other states or regions with similar geological conditions and engineering configurations. Phase 1 of this project covered task 1 and part of task 2. Phase 2 covers part of task 1 and task 2. Phase 3 will cover task 3 and will also include literature review and summary of landslides along the railroad. This project will primarily complement the ongoing project sponsored by the Maryland DOT (MDOT) SHA (see more information in TRID) led by Zhuping Sheng in collaboration with Carnegie Mellon University (CMU) (Dr. Sean Qian). In this phase the research team will also expand their collaboration with CMU team (Dr. Christoph Mertz) by including technology transfer in photographical images processing to build conceptual models and identify slope failures.   

As PI Dr. Sheng will coordinate the efforts in collaboration with MDOT/SHA and advise other faculty and postdoctoral research associates and graduate students to carry out the project under Tasks 1 and 2. Dr. Zhuping Sheng has experience in geohazards assessment and mitigation, geotechnical and water resources engineering.  The team includes Co-PIs, Dr. Oludare Owolabi with experience in transportation engineering and resilient infrastructure and will mainly cover Task 1 and Dr. Yi Liu with experience in geohazards, land subsidence and landslides and geotechnical engineering and will cover Task 2. They have conducted and are currently conducting research supported by MDOT SHA, which provides a strong foundation for future collaboration with the partner MDOT SHA and others for technical transfer. Dr. Kofi Nyarko, Professor of the Electrical and Computer Engineering and Director of Morgan AI/ML program, has extensive experience in AI/ML research and applications, including automating complex systems through computer vision and machine learning as well as scientific/engineering simulation & visualization, predictive visual analytics and will mainly cover Task 3.

The Morgan AI/ML program will provide in-kind support (Dr. Nyarko’s time) and support for one student who will help with sensor tests, calibration and assembling as part of technical transfer at selected sites in consultation with Maryland DOT/SHA. The team will also explore opportunity for summer intern program with both UTC and the AI/ML program's support, which is designed for development of future workforce in transportation safety. Students have participated in and will continue to participate in exchange programs and deployment partner symposium and other activities. Through this project the MSU team will continue to expand collaboration with CMU and other partner institutes via faculty meetings, seminars, national summit, and other venues, which provides great opportunity for professional development.]]></description>
      <pubDate>Thu, 17 Sep 2026 14:52:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2778223</guid>
    </item>
    <item>
      <title>Effects of vertical stress and wetting path on undisturbed granite residual soil and slope failure mode transition</title>
      <link>https://trid.trb.org/View/2692674</link>
      <description><![CDATA[Granite residual soil slopes are highly prone to sliding, representing the most common natural disaster disrupting transportation. The key challenge lies in understanding the time-dependent failure mechanisms governed by coupled hydro-mechanical processes. This study resolves this gap through comprehensive laboratory testing and numerical modeling of undisturbed specimens incorporating vertical stress gradients and wetting paths. Key findings reveal: (1) Vertical stress (0–120 kPa) increases air-entry values by > 200% and reduces moisture variation by 38% in soil–water characteristic curves. This stress-dependency is uniquely observed in undisturbed specimens and has direct implications for slope hydrology under varying overburden; (2) Wetting reduces cohesion hyperbolically from 84.6 kPa to 20.8 kPa, while the effective friction angle remains stable at approximately 29°, indicating that cohesion loss, rather than frictional degradation dominates the wetting-induced strength reduction; (3) Vertical stress gradients induce a fundamental dichotomy in slope failure mechanisms. Shallow low-stress zones exhibit enhanced wetting vulnerability that accelerates linear sliding, whereas deeper high-stress domains accumulate progressive damage culminating in circular failure. This transition is captured through a hydro-mechanically coupled numerical model that integrates the experimentally derived stress-dependent SWCC and suction-cohesion relationship. Crucially, preserving undisturbed soil fabric reveals sequential damage accumulation invisible in remolded samples, while dual-control testing uncovers stress-path dependent weakening previously obscured in conventional analyses. This study established the soil–water characteristics and stress–strain relationship of undisturbed granite residual soil under combined vertical stress gradients and controlled wetting paths, which provide a enhanced understanding of shallow landslide assessment.]]></description>
      <pubDate>Fri, 24 Jul 2026 08:40:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692674</guid>
    </item>
    <item>
      <title>Deformation behavior and mechanisms of geosynthetic reinforced soil corner slopes</title>
      <link>https://trid.trb.org/View/2690896</link>
      <description><![CDATA[This study investigates the complex deformation behavior of geosynthetic reinforced soil (GRS) corner slopes, a critical yet vulnerable element in transportation infrastructure (e.g., highway embankments and flexible abutments), under static loading through integrated experimental and numerical analysis. The research aims to elucidate the unique failure mechanisms and three-dimensional effects inherent in such corner configurations, which are critically important yet not fully addressed in current design practices. Utilizing 3D models constructed with sand backfill, geogrid reinforcement, and non-woven geotextile facing, the backfill was characterized by a hyperbolic stress–strain model and the Mohr-Coulomb failure criterion. Results demonstrate that the slope corner introduces a significant three-dimensional effect, manifesting as a dual role: it provides beneficial passive confinement in the central region but concurrently leads to an uneven displacement distribution and pronounced concentration at the boundaries. The deformation process evolves through four distinct stages: gestation, initial deformation, rapid deformation, and full failure. The failure mechanism progresses from a shallow circular shear zone to a Y-shaped shear surface, ultimately stabilizing as a deep arc-shaped slip surface. A parametric study further demonstrates that a lower soil friction angle and a footing location closer to the facing markedly exacerbate uneven deformations and the three-dimensional distribution characteristics of the displacement field. Conversely, the study concludes that the incorporation of secondary reinforcement is a highly effective measure for mitigating deformations in GRS corner slopes. These findings provide novel insights into the improved design and stability assessment of corner slopes in transportation infrastructure.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:10:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690896</guid>
    </item>
    <item>
      <title>Innovative Approaches to Addressing Surficial Slope Failures: The Role of Engineered Earth Armoring Solutions in Enhancing Infrastructure Resiliency</title>
      <link>https://trid.trb.org/View/2678218</link>
      <description><![CDATA[In the field of geotechnical engineering, addressing surficial slope failures is crucial for maintaining the integrity and longevity of infrastructure. As engineers, we are tasked daily with finding the most efficient solutions to the challenges of our projects, and it is critical for us to consider the long-term resiliency of the solutions we utilize. Surficial slope failures, often considered maintenance issues or surface erosion, can lead to larger global failures and costly repairs if left unaddressed. Common repair methods, such as pushing the soil back in place or replacing failed soil with large rock riprap, are not always effective. However, Engineered Earth Armoring Solutions (EEAS) have been successfully used to remediate these failed slopes for over 15 years. An EEAS consists of two components: a High Performance Turf Reinforcement Mat (HPTRM) to cover the slope surface, reinforce vegetation, and control erosion; and an Engineered Earth Anchor to permanently secure the HPTRM and improve the surficial stability of the slope. These components work together as a system, providing complementary benefits. In recent years, it has been found that EEAS can stabilize previously failed slopes and can also be applied during initial construction to increase long-term stability, reduce project risk, and enhance the overall resiliency of steepened slopes. Compared to traditional solutions like rock riprap, EEAS can significantly reduce material costs, carbon footprint, and transportation impacts. This paper will focus on geotechnical research compiled over the past years on the use of EEAS for improving the surficial stability of slopes, as well as case studies demonstrating its effectiveness in both reactive remediation efforts and proactive resiliency improvements.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678218</guid>
    </item>
    <item>
      <title>Enhancing random field-based limit equilibrium analysis of road embankment failures on soft soils using convolutional neural networks</title>
      <link>https://trid.trb.org/View/2676300</link>
      <description><![CDATA[This study presents a probabilistic framework that integrates random field theory with convolutional neural networks (CNNs) to efficiently evaluate the stability of road embankments under both stationary and non-stationary random fields. The spatial variability of undrained shear strength is modelled using random field theory and incorporated into limit equilibrium method. Image-based random field outputs from these analyses are employed to train CNN models designed to predict the factor of safety (FS) against road embankment stability. The proposed approach is validated through a case study of road embankment failures on soft clays, subjected to fluctuating water levels. The results indicate that CNNs can accurately reproduce probabilistic stability outcomes while substantially reducing computational time compared with conventional Monte Carlo simulations (MCSs). Although prediction accuracy decreases slightly under highly non-stationary conditions, the CNN models consistently capture key probabilistic trends and failure probabilities. The findings demonstrate that CNN-based surrogate models provide an effective and computationally efficient alternative for probabilistic geotechnical analysis, with strong potential for application in real-time risk assessment.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676300</guid>
    </item>
    <item>
      <title>Risk Assessment and Treatment in Slope Stability Forensic Engineering</title>
      <link>https://trid.trb.org/View/2187144</link>
      <description><![CDATA[Slope instability phenomena are frequently responsible for considerable losses of both money and lives. In view of above consideration, it is not surprising that slope instability phenomena are rapidly becoming the focus of major scientific research, engineering study and practices, and land-use policy throughout the world. The paramount importance of slope instability management is by and large recognized. Herein lies the guiding principle of the current paper; i.e., to describe slope instability hazard assessment and methods to mitigate the associated risks in an appropriate and effective way. Back analysis of failed slopes is one of the most effective ways to advance our knowledge in the field of slope stability engineering. This paper discusses procedures to back calculate soil shear strength parameters from slope failures and illustrates how these parameters can be subsequently used to design slope remedial works.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2187144</guid>
    </item>
    <item>
      <title>Assessment of Slope Failure Using Advanced Geotechnical Tests: Case Study in Harrisonburg, Louisiana</title>
      <link>https://trid.trb.org/View/2192145</link>
      <description><![CDATA[The failure of a slope along a segment of Highway 8 near Harrisonburg, Louisiana resulted in its partial closure in December 2008. The Louisiana Department of Transportation and Development needed to assess the damage and find a way to remedy the problem. Soil borings were taken and the slope was monitored using a vertical inclinometer. Then the aerial photos were used to evaluate the site conditions and map the progress of slope failure over time. Further, a series of laboratory tests were performed on the samples obtained by boring from the site to determine the stress path followed by the soil which led to the slope failure. Laboratory testing program included conventional unconsolidated undrained (UU) triaxial tests, multistage consolidated undrained (CU) triaxial tests, and Ko-consolidated undrained (CKoU) triaxial lateral extension tests. Using the extensive laboratory test results, the soil design parameters were selected for slope stability analyses. The limit equilibrium analyses showed different results based on parameters obtained from different stress path testing. Further, partial stabilization at the toe of the slope was recommended as the most suitable remedial measure.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:24:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2192145</guid>
    </item>
    <item>
      <title>Evaluation of Critical Shear Strength of Soils in Nebraska Based on Revised CPT</title>
      <link>https://trid.trb.org/View/2675916</link>
      <description><![CDATA[This study evaluated the “Fully Softened Shear Strength (FSSS)” of soils in Nebraska based on a revised Cone Penetration Test (CPT) method. FSSS is a unique strength condition that soils show substantially reduced strength during rainy seasons, eventually causing failure of slopes and other structures. The revised CPT was adapted to inject water at targeted depths, inducing controlled moisture infiltration and capturing real-time soil softening over designated intervals of 30, 60, or 90 minutes. Through tip resistance ( qc) and sleeve friction (f s) measurements under “in-situ” and “wet” conditions, the test effectively identified fully disturbed/low strength zones around the sleeve. Simultaneous sampling at test locations facilitated laboratory characterization of soil index properties. Subsequent direct shear testing under field-moisture conditions and 48 hour submerged conditions confirmed that high-plasticity soils exhibited similar strength loss. Conversely, lower-plasticity soils demonstrated limited magnitude of shear strength reductions. The correlation proposed by Robertson (2009) was employed to estimate undisturbed strength ( s u) and disturbed strength ( s u(d) ) for the revised CPT parameters, and these correlations were compared to laboratory direct shear strength test data. Marked decreases in peak shear strength similar to FSSS were observed at most sites. These outcomes well aligned with Skempton’s (1970) concept of fully softened shear strength, validating the measurement scheme developed in this study. Overall, the revised CP procedure offers a reliable, efficient in-situ method to evaluate fully softened shear strength, facilitating improved assessments of slope stability and soil behavior.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:48:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675916</guid>
    </item>
    <item>
      <title>Deep Learning-Based Automated Slope Condition Assessment Using Satellite Imagery</title>
      <link>https://trid.trb.org/View/2562225</link>
      <description><![CDATA[Manual inspection of highway slopes is labor-intensive and time-consuming, making monitoring and consistently assessing slope conditions difficult. Early indicators of slope failure, such as erosion, vegetation loss, cracks, and water runoff, can significantly affect slope stability and are challenging to detect manually. While traditional methods rely on direct observation, they do not effectively leverage the advancements in satellite imagery and deep learning for consistent and scalable monitoring of slope conditions. The objective of this research is to develop an automated, deep learning-based system using high-resolution satellite imagery to identify early slope failure indicators and improve monitoring accuracy. The methodology utilizes the DeepLabV3+ convolutional neural network (CNN) architecture, augmented with a Modified ResNet101 backbone, Enhanced Channel Attention (ECA) modules, and Atrous Spatial Pyramid Pooling (ASPP) specifically designed to improve feature extraction and classification accuracy. A custom data set of annotated high-resolution slope images, derived from the Landslide4Sense data set, was used for training and validation. The model achieved a validation accuracy of 97.76%, a mean Intersection over Union (IoU) of 87.9%, and a Dice Coefficient of 90.8%, highlighting its capability to detect critical slope failure indicators, such as erosion, cracks, and vegetation loss, with high precision. This approach was applied to analyze slope imagery, detecting erosion, cracks, and vegetation loss with high precision and robustness. Consequently, the results demonstrate the scalability and effectiveness of the method in improving slope condition monitoring across transportation infrastructure. Detecting early signs of slope failure like erosion, cracks, and vegetation changes helps transportation agencies ensure safety, optimize maintenance resources, and prevent infrastructure damage.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562225</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>Resilience assessment of highway roads affected by slope failures induced by mainshock-aftershock sequences: insights from vine copula fragility surfaces</title>
      <link>https://trid.trb.org/View/2633487</link>
      <description><![CDATA[Slope failures frequently occur in mountainous regions during earthquakes, posing severe risk to highway roads, public transportation system, and human safety.Despite this, limited research has focused on quantifying the resilience of transportation slopes under sequential mainshock-aftershock (MSAS) events. This study proposes a quantitative framework for evaluating the seismic resilience of highway roads affected by slope failures induced by MSAS sequences. A series of 2D nonlinear dynamic time history analyses were performed in PLAXIS. Real MSAS ground motions were obtained from the PEER database. Vine copula models were employed to construct fragility surfaces that capture complex nonlinear dependencies between intensity measures (IMs) and engineering demand parameters (EDP). These fragility surfaces were integrated with restoration functions to evaluate the resilience of urban and major roads for slope angles of 25°, 35°, and 45°. The results indicate that aftershocks (AS) significantly increase the probability of road failure, with up to a 21% reduction in resilience compared to mainshock (MS)-only cases. Moreover, reducing the slope angle enhances resilience by up to 28% for urban roads and 13% for major roads. The proposed framework provides a robust probabilistic tool for resilience-based seismic risk assessment of transportation infrastructure subjected to MSAS loading.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:52:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633487</guid>
    </item>
    <item>
      <title>Slope Stability Analysis and Risk Assessment for Transportation Infrastructure Using GIS and Simulation</title>
      <link>https://trid.trb.org/View/2562088</link>
      <description><![CDATA[Slope instability is a significant threat to transportation infrastructure, particularly in regions prone to natural hazards. This research aims to develop a comprehensive GIS-based methodology to identify and assess slope instability risks along highways. By integrating high-resolution DEMs, geological maps, hydrological data, and historical landslide inventories, potential slope failure zones can be delineated. Numerical simulations, such as limit equilibrium analysis and finite element modeling, will be used to quantify slope stability under various conditions. This research will enable the prioritization of high-risk sections for targeted mitigation measures, such as slope stabilization techniques and early warning systems. By enhancing the resilience of transportation infrastructure, this research contributes to mitigating the socio-economic impacts of slope failures.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562088</guid>
    </item>
    <item>
      <title>Selecting Appropriate Mitigation Methods for Soil Slope Failures: A Safety and Equity-Centric Approach
</title>
      <link>https://trid.trb.org/View/2628205</link>
      <description><![CDATA[Soil slope failures pose significant risks to both human lives and infrastructure, necessitating effective mitigation strategies. This research proposal aims to investigate the selection of appropriate mitigation methods for soil slope failures, with a dual focus on safety and equity considerations.
The proposed study will employ a multidisciplinary approach, integrating geological, climatic, geotechnical engineering, and socioeconomic perspectives. It will begin with comprehensive review of existing mitigation methods, including slope flattening, stabilization, lightweight fill, and drainage. Subsequently, a systematic evaluation framework will be developed to assess the efficacy of these methods in terms of safety enhancement and equitable distribution of benefits.
Safety considerations will encompass factors such as slope stability and resilience to extreme weather events. Equity considerations will involve analyzing the distribution of risks and benefits among different socioeconomic groups, with a particular emphasis on vulnerable communities disproportionately affected by soil slope failures.
Taking advantage of the collected field slope data for the Kansas Department of Transportation (KDOT) geotechnical asset management system, computational modeling techniques will be employed to quantify the performance of various mitigation strategies under different scenarios. Additionally, stakeholder consultations will be conducted to incorporate local knowledge and community perspectives into the decision-making process.
The anticipated outcomes of this research include a set of guidelines for selecting optimal mitigation methods tailored to specific soil slope failure scenarios, taking into account both safety and equity. By integrating technical expertise with social equity principles, this study seeks to contribute to the development of more resilient and equitable disaster risk reduction strategies for soil slopes. This research will contribute to the knowledge and procedure for selecting optimal mitigation methods of soil slopes to address their safety and community equity. 
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:23:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628205</guid>
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