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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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    <item>
      <title>Airport runway roughness evaluation using TCP-InSAR technology</title>
      <link>https://trid.trb.org/View/2643571</link>
      <description><![CDATA[Runway roughness evaluation plays a vital role in maintaining operational efficiency and guiding maintenance planning for airport ground operations. Conventional measurement methods often suffer from limited spatial coverage, low efficiency, and significant operational disruptions. This study presents an Interferometric Synthetic Aperture Radar (InSAR) approach for runway roughness assessment that achieves wide-area, efficient, and non-intrusive evaluation. The Temporarily Coherent Point InSAR algorithm effectively mitigates specular reflection from runway surfaces, delivering high-precision time-series vertical displacement data. Subsequently, the Nonlinear PCA-Co Kriging method integrates InSAR observations with levelling Digital Elevation Model data, followed by interpolation and resampling to create a high-resolution 3D runway elevation model. From this model, longitudinal profiles of the runway centerline and 6 meters east/west were extracted for Boeing Bump Index (BBI) calculation. Results demonstrate that the Nonlinear PCA-Co Kriging method surpasses traditional Original Kriging and Co-Kriging techniques in detecting elevation changes. The computed Boeing Bump Index shows excellent agreement with vehicle-mounted measurements (minimum correlation coefficient of 0.94, maximum mean absolute error of 0.025 BBI, standard deviation of 0.028 BBI), validating the method's reliability for runway roughness monitoring.]]></description>
      <pubDate>Sat, 17 Jan 2026 11:41:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643571</guid>
    </item>
    <item>
      <title>Implementation of Multitemporal Synthetic Aperture Radar for Ground Hazard Risk Monitoring on Railway Right of Way</title>
      <link>https://trid.trb.org/View/2620575</link>
      <description><![CDATA[Railway transportation has been the backbone of national economies worldwide. When geohazards occur and damage the network, they affect railway operations, resulting in delays and detrimental social and economic effects. A potential tool for monitoring the vast network for geohazards is satellite-based radars. Interferometric synthetic aperture radar (InSAR) may be used to study a wide range of geophysical phenomena. Its ability to study geohazards is frequently constrained by several challenges stemming from adverse atmospheric effects and wave scattering associated with site conditions and terrain characteristics. The authors have developed the framework of a monitoring system that uses satellite radar imagery analysis for identifying geohazard-prone locations through continuous monitoring of large regions. This paper discusses one implementation of multitemporal InSAR techniques that includes the new concept of a “Rolling SAR Image Stack.” In addition, it introduces three postprocessing techniques that enable the detection of critical locations where geohazard failures may initiate along the railway right of way before an event takes place. A site characterization and classification guide is introduced to facilitate the selection of the most effective SAR analysis method for monitoring the area of interest. The guide considers on-site conditions affecting the quality and availability of radar data. This paper summarizes the investigations, methodologies, and approaches that led to the development of the workflow of the proposed monitoring system and demonstrates the ability of the proposed monitoring framework to identify critical locations of geohazard failure potential through implementation case studies.]]></description>
      <pubDate>Fri, 07 Nov 2025 11:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620575</guid>
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    <item>
      <title>A radar corner reflector-InSAR (CRI) technique refined by transfer learning-based air temperature identification for deformation monitoring of low-coherence bridge</title>
      <link>https://trid.trb.org/View/2593626</link>
      <description><![CDATA[Synthetic Aperture Radar Interferometry (InSAR) is a promising tool for long-term deformation monitoring of long-span bridges due to its ability to rapidly acquire surface displacement information under all-weather and all-time conditions. However, its practical application remains limited by three key challenges: (1) the sparse and unstable distribution of detectable permanent scatterers on bridges owing to weak radar backscatter; (2) the propagation of phase errors from satellite orbital data and digital elevation models, which deteriorates the reliability of differential interferometry; and (3) the imprecise estimation of thermal dilation using meteorological station data, which often overlooks spatial variability in air temperature between the bridge and the station. To address these issues, this study developed a novel artificial radar corner reflector (CR)-based InSAR (CRI) technique specifically for single-bridge monitoring, with three key advancements: (1) installation of CRs on the bridge deck to enhance radar backscatter intensity and improve target detectability; (2) compensation for the flat and elevation phases of InSAR measurements by extracting coordinates and elevations from the constructed interferometric image pairs; and (3) development of a transfer learning-based air temperature prediction model for bridge sites to improve the accuracy of thermal dilation phase estimation in InSAR analysis. The CRI technique was applied to a three-span continuous steel arch bridge using the COSMO-SkyMed images from December 2022 to June 2024 to obtain millimeter-scale line-of-sight (LOS) deformation. A correlation analysis between the LOS deformation and air temperature revealed two significant cooling-induced deformation events. A structure-driven decomposition strategy was proposed to recover the bridge’s three-dimensional displacement from LOS measurements, accounting for the influence of bridge bearings on CR motion. The results were validated using Beidou Navigation Satellite System (BDS) observations and then compared with the early warning thresholds specified in relevant bridge health monitoring standards (T/CECS 529–2018) for performance evaluation. The results demonstrate the potential of CRI in advancing the use of InSAR techniques for bridge health monitoring.]]></description>
      <pubDate>Thu, 16 Oct 2025 17:02:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593626</guid>
    </item>
    <item>
      <title>Regional-scale bridge health monitoring: survey of current methods and roadmap for future opportunities under changing climate</title>
      <link>https://trid.trb.org/View/2571979</link>
      <description><![CDATA[Climate-related extreme events are becoming increasingly frequent, posing significant threats to bridges, which are critical components of transportation infrastructure. This paper offers an overview of recent advancements in methodologies and technologies for conducting structural health monitoring (SHM) of bridges over large areas, where deploying sensors on every structure may be cost-prohibitive for local administrations. It specifically examines two approaches that have garnered interest in the past decade: indirect SHM, which involves instrumenting vehicles and analyzing their dynamic responses to infer information about bridges, and satellite interferometric radar data, which offer static displacement measurements for large regions and has recently been exploited for civil SHM purposes. Additionally, it reviews the recent developments in population-based SHM, which facilitates knowledge-sharing among structures with similar characteristics within a population. Through an analysis of the advantages and limitations of these three rapidly developing research areas, the paper outlines future opportunities and lays the roadmap for a comprehensive “regional-scale SHM” approach based on remote and/or crowdsourced data, supported by population-level analyses. Specific topics addressed include strategies for similarity assessment among monitored structures, available data sources, and feature extraction/integration approaches for different scenarios.]]></description>
      <pubDate>Thu, 25 Sep 2025 09:30:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571979</guid>
    </item>
    <item>
      <title>Regional-scale bridge condition monitoring using InSAR displacements and environmental data</title>
      <link>https://trid.trb.org/View/2571972</link>
      <description><![CDATA[This article introduces a novel methodology for detecting and classifying anomalies in multiple bridges within a geographical region using satellite-based interferometric synthetic aperture radar displacements and environmental measures. The approach uses subspace alignment to harmonize bridge features, enabling the detection of anomalies based on deviations in one bridge compared to the rest of the population. Simulated and real case studies involving steel railway bridges spanning the Po River in Italy demonstrate the effectiveness of the proposed approach, showcasing its potential for large-scale applications. Moreover, the study explores the transferability of knowledge from simulated data to real-world monitoring scenarios, yielding promising results in classifying real instances using synthetic labels. The proposed approach presents practical benefits for bridge monitoring agencies by providing a cost-effective method for enhancing the resilience and safety of transportation infrastructure.]]></description>
      <pubDate>Thu, 25 Sep 2025 09:30:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571972</guid>
    </item>
    <item>
      <title>Ground Motion Detection in Rural Areas Using Satellite Interferometry</title>
      <link>https://trid.trb.org/View/2187738</link>
      <description><![CDATA[Trials sponsored by the UK Highways Agency and the British National Space Centre are underway to investigate the potential for using satellite interferometric radar techniques to monitor ground displacements along the highway network. The current phase comprises the evaluation of the CATInSAR technique which it is reported can detect displacements down to millimetric scale. This paper provides a summary of satellite radar sensing and processing techniques, describes the background to the CATInSAR trials and presents the data currently available. Full results of the trial will be available to present at the conference.]]></description>
      <pubDate>Wed, 18 Dec 2024 10:56:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2187738</guid>
    </item>
    <item>
      <title>Enhancing Transportation Safety with InSAR Land Subsidence Monitoring</title>
      <link>https://trid.trb.org/View/2475283</link>
      <description><![CDATA[Land subsidence is a gradual downward movement and deformation of the Earth's surface. It is driven by geophysical processes such as sediment compaction, tectonic activity, erosion, and human factors like excessive groundwater extraction, mining, and urban development. This study addresses the urgent need to quantify and mitigate the impacts of land subsidence on transportation infrastructure through an integrated approach utilizing Geographic Information Systems (GIS), Interferometric Synthetic Aperture Radar (InSAR), and the Analytic Hierarchy Process (AHP). Focusing on East Baton Rouge Parish, Louisiana, the research examines areas prone to subsidence from 2017 to 2020, specifically targeting critical infrastructure such as Interstate 10, Interstate 12, and major bridges over the Mississippi River. Using a multi-criteria decision analysis framework through AHP, the study systematically prioritizes factors contributing to subsidence, including soil composition, land use/land cover, groundwater extraction rates, and slope stability, leading to the development of detailed susceptibility maps. Integrating machine learning algorithms further enhances the predictive accuracy of risk assessments and infrastructure planning. 
The following tasks will be performed to achieve the objectives of this study: Task 1: preprocess high-resolution Sentinel-1 SAR datasets for InSAR analysis, which generates detailed deformation fields through interferometric processing and time-series analysis. Task 2: apply AHP to assign weights to various subsidence drivers. Task 3: integrate spatial datasets within GIS to create risk maps. Task 4: validate the risk maps using ground truth data from global navigation satellite system observations and historical subsidence records. Task 5: perform temporal analysis of subsidence trends to forecast future deformation patterns, enabling the development of proactive intervention strategies. Task 6: report and share the results. 
The outcomes of this study include practical susceptibility maps and predictive models, offering valuable insights for transportation and urban planning stakeholders. These tools enhance infrastructure resilience by aiding in maintenance prioritization, optimizing land use, and informing policy decisions, ultimately supporting sustainable development by addressing subsidence risks, ensuring the long-term safety and efficiency of transportation networks, and advancing geospatial and remote sensing methodologies for land deformation studies.
]]></description>
      <pubDate>Fri, 20 Dec 2024 19:40:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475283</guid>
    </item>
    <item>
      <title>Operation of Interferometric SBAS-DInSAR Data for Remote Structural Monitoring of Existing Bridges</title>
      <link>https://trid.trb.org/View/2425422</link>
      <description><![CDATA[Several recent studies have investigated the opportunities of differential interferometry synthetic aperture radar (DInSAR) using satellite data for structural health monitoring (SHM) of civil structures. However, its use in structural engineering is still debated because of the lack of a general understanding of the potential and limitations of the technology for bridge SHM. To overcome this issue, specific methods of data processing and displacement assessment with error quantification need to be developed. The present paper aims at giving an insight into the use of small baseline subset-differential synthetic aperture radar interferometry (SBAS-DInSAR) as a remote-sensing technology for civil infrastructure monitoring combining information at a large scale with those associated with a single bridge. In particular, an operational framework for the selection and processing of a measurement time series representative of the structural behavior of the bridge of interest is designed in a way that fully remote characterization and assessment can be carried out by exploiting web-mapping platforms according to the crowd-sensing paradigm. The workflow, designed consistently with a structural engineering perspective, has been tested with reference to a number of bridges crossing the Tiber River in Rome (Italy), showing that the proposed fully remote monitoring procedure can effectively support satellite data processing and interpretation. Moreover, some issues in view of the general use of satellite data for bridge monitoring emerged from the study. Gaps in the measurement point distribution over the bridge decks are frequently observed over all the monitored area, therefore simplified methodologies aimed at estimating expected displacement ranges of different bridge typologies under serviceability loads have been defined enabling a rational interpretation of the data in the light of the selected radar sensor band of operation. Simplified formulations and charts for service and environmental loads are then presented with a twofold objective: (1) supporting the satellite data elaboration and interpretation by means of easy to manage open-source tools; and (2) providing estimates of the expected operational displacements of healthy structures to guide data interpretation and even algorithms for radar signals processing able to incorporate the response of structures to temperature variations.]]></description>
      <pubDate>Fri, 11 Oct 2024 09:32:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425422</guid>
    </item>
    <item>
      <title>Detection of Rockfall-Prone Areas Through InSAR-SBAS Analysis</title>
      <link>https://trid.trb.org/View/2437175</link>
      <description><![CDATA[Identifying and monitoring rockfall- and landslide-prone areas is crucial for effective risk mitigation. Slope instabilities resulting in rockfalls and landslides along the railroad right of way (ROW) present significant challenges and hazards to railway operations and safety. Geohazard event prediction models combine data collected from monitoring systems with information from other heterogeneous sources and provide an overall assessment of the risk. However, such models do not identify specific areas where an event could initiate under certain conditions with a triggering event. In contrast, Interferometric Synthetic Aperture Radar (InSAR) techniques, such as the Persistent Scatterer InSAR (PSInSAR), have shown great promise in monitoring these events. Yet, PSInSAR requires a constant presence of Persistent Scatterers (PS) or high-coherent points in the area being monitored throughout the observation period, making it less universally applicable. Small Baseline Subset (SBAS) analysis is an alternative to the PSInSAR technique that alleviates the PSInSAR shortcomings and can effectively measure soil surface mobilization in the absence of PS. This work employs the SBAS analysis to identify areas along the railroad ROW that exhibit increased geohazard risk. SBAS is implemented within the framework of “threshold stacking” as an efficient method of filtering SBAS data for rockfall risk localization. The research team implemented the proposed technique in two incidents involving rockfalls that resulted in train derailments: the first incident site is in Sandstone, West Virginia and the second site is in Maupin, Oregon. These two cases validate the proposed approach and demonstrate its effectiveness in identifying high risk areas prone to failure. Furthermore, the case studies underscore how satellite-based monitoring can enhance early warning systems for geohazards and assist disaster mitigation and preparedness efforts.]]></description>
      <pubDate>Tue, 01 Oct 2024 16:53:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2437175</guid>
    </item>
    <item>
      <title>Landslide Susceptibility Mapping for Road Corridors by Using a Combined Interferometry SAR and Machine Learning Techniques</title>
      <link>https://trid.trb.org/View/2216643</link>
      <description><![CDATA[Landslides comprise about 42% of the entire natural hazards in Indonesia, claiming one-third of the annual economic losses caused by natural disasters. To mitigate their severe impacts, a landslide susceptibility map (LSM) with actual, continuous, and accurate information about landslide occurrences and their likelihood in a particular area is urgently needed. Therefore, this research presents a reliable landslide susceptibility mapping with combined interferometry synthetic aperture radar (In-SAR) and machine learning (ML) techniques. A new framework of LSM by using In-SAR and ML applied to road infrastructure was proposed. The framework begins with the acquisition of a satellite imaging-based digital elevation model (DEM) of a particular road corridor, in which the landslide contributing factors (slopes, natural drainage networks, lithology, and rainfall) were rendered and overlaid by using GIS. Then, the ML was used to rate those contributing factors to the actual landslide occurrences. In a parallel way, the In-SAR was employed to obtain the ground movements in the road corridor from a series of SAR images derived from the Sentinel-1. Interferograms were then generated to produce ground movement maps. By combining the ground movement maps from the In-SAR and landslide ratings maps from the ML, a landslide susceptibility map was created. The applicability of the framework was investigated through a case study of landslide susceptibility in West Sulawesi, Indonesia.]]></description>
      <pubDate>Wed, 24 Jan 2024 16:55:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2216643</guid>
    </item>
    <item>
      <title>High-speed low-coherence interferometry of fuel films in impinging gasoline direct injection sprays</title>
      <link>https://trid.trb.org/View/2287603</link>
      <description><![CDATA[Quantitative experimental characterization of dynamic fuel film deposition processes is critical to developing better understanding and modeling of cold-start behavior in gasoline direct injection engines. Low-Coherence Interferometry (LCI) offers a quantitative fuel film thickness diagnostic that is immune or resistant to major confounding influences that limit other optical diagnostics in engine-relevant environments. This work describes an extension of spectral LCI techniques to high-speeds (10 kHz) that are capable of resolving the dynamics of fuel film deposition and evaporation in impinging gasoline sprays. Two approaches to LCI, Michelson interferometry and Fizeau interferometry, were tested and results demonstrate the superiority of the Fizeau configuration for enclosed vessel experiments. Experiments were performed at cold start conditions in an enclosed spray chamber with a gasoline spray impinging on a transparent wall. High-speed LCI was used to measure fuel film thicknesses and dynamic changes in thickness. The measurements were used to observe variation in film thickness and behavior with changes in system temperature and fuel composition.]]></description>
      <pubDate>Fri, 22 Dec 2023 11:19:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2287603</guid>
    </item>
    <item>
      <title>Division of Engineering Research on Call Services: Task #12 - Change Detection of Inventoried Landslides Using Remote Sensing Techniques</title>
      <link>https://trid.trb.org/View/2310551</link>
      <description><![CDATA[This study evaluates the effectiveness of utilizing remotely sensed three-dimensional (3D) spatial data, including Light Detection and Ranging(lidar) and Interferometric Synthetic Aperture Radar (InSAR) for interpreting change detections of eastern Ohio (Districts 5, 10, and 11) landslides. The performance of lidar methods used by specialty firm Teren were evaluated for their ability to detect 99 inventoried landslides, relative to field inspection surveys. The performance of InSAR methods used by specialty firm EO59 were evaluated for their ability to detect a subset of 15 inventoried landslides, relative to field inspection surveys. Four selected case studies are detailed to showcase the performance of these methods. Ultimately this work is anticipated to support the potential adoption of remote sensing technologies to supplement or substitute current Ohio Department of Transportation (ODOT) landslide inventory field surveys. The research team believes that in the near-term, the integration of lidar-based approaches with the landslide inventory can potentially increase landslide detection reliability and reduce overall costs. InSAR may additionally be considered for either indicating subtle movements for lower risk landslides, or frequently monitoring movements of higher risk landslides.]]></description>
      <pubDate>Thu, 21 Dec 2023 09:21:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2310551</guid>
    </item>
    <item>
      <title>Residual service life prediction for bridges undergoing slow landslide-induced movements combining satellite radar interferometry and numerical collapse simulation</title>
      <link>https://trid.trb.org/View/2221789</link>
      <description><![CDATA[This paper proposes an innovative approach for structural prognosis of bridges, that combines displacement information obtained by Synthetic Aperture Radar Interferometry (InSAR), applied to satellite data, with structural and collapse analysis performed through numerical modelling. The purpose of this multidisciplinary approach is to recognize potentially critical conditions and predict the time to failure of bridges affected by slow deformation phenomena, such as slow landslide-induced movements. The methodology involves advanced pre- and post-processing techniques for satellite SAR acquisitions, which allow tracking two-dimensional displacement evolution of multi-span bridges with properly defined error bounds, and uses numerical modelling to simulate damage propagation and structural behaviour up to total collapse. The effectiveness of the proposed synergy between remote displacement measurements and numerical collapse analysis is exemplified through the application to the Albiano-Magra Bridge in Italy, which collapsed on April 8th, 2020. The results have allowed to identify the triggering cause of the collapse and have provided an estimate of the residual service life of the bridge, with increasing reliability as the available satellite monitoring period gets longer. The work demonstrates that a combined analysis considering both satellite measurements and numerical modelling can improve the comprehension of the phenomena concerning the structural collapse due to slow deformations and its prevention.]]></description>
      <pubDate>Mon, 28 Aug 2023 09:34:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2221789</guid>
    </item>
    <item>
      <title>Interferometric Synthetic Aperture Radar versus Inclinometer Ground Surface Deformations at Active Landslide</title>
      <link>https://trid.trb.org/View/2137534</link>
      <description><![CDATA[Rockfalls and landslides commonly occur in the Hawaiian islands. When these events occur along a thoroughfare, they can result in the loss of lives, cause significant damage to infrastructure, and force full or partial road closures, resulting in traffic congestion and commuting delays, sometimes for months. The Hawaii Department of Transportation (HDOT) is interested in historic slope movements along its roads and highways, as this history can guide in prioritizing spending on slope remediation. This has prompted HDOT to be interested in the use of interferometric synthetic aperture radar (InSAR) imagery for detecting historical slope movements and identifying areas that might pose concern for landslide or rockfall activity. To provide a proof of concept for the implementation of this technology in Hawaii, an InSAR case study of a landslide on the island of O’ahu was conducted to compare the results with available inclinometer data. Two-dimensional decomposition was implemented for opposing orbits of descending and ascending Sentinel-1 datasets. Overall, the results show that when both ascending and descending datasets are used to derive line-of-sight displacements that are resolved in a direction along the direction of movement (typically perpendicular to the slope contours), InSAR analysis can effectively capture inclinometer trends in areas experiencing relatively no or little displacement over time (<30?mm/year) but the accuracy diminishes in fast-moving slides (=270?mm/year).]]></description>
      <pubDate>Tue, 14 Mar 2023 08:42:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2137534</guid>
    </item>
    <item>
      <title>High-precision monitoring method for airport deformation based on time-series InSAR technology</title>
      <link>https://trid.trb.org/View/2092705</link>
      <description><![CDATA[In order to solve the problems of low efficiency and discrete data that are associated with current airport area deformation monitoring and to realize the long-term and large-scale deformation monitoring of ground objects at airports, this paper proposes a method to monitor deformation in airport using time-series interferometry synthetic aperture radar (InSAR) technology. Based on analysis of the technical principles and data processing methods of persistent scatterer (PS)-InSAR and small baseline subset (SBAS)-InSAR, 89 SAR datasets from two airports located in different areas that cover a three-year period were collected in this study. Using the collected data, data denoising and precision assurance methods, such as data cropping, connection graph generation, geocoding, etc., were researched. The deformation rate map and cumulative deformation variable map were analyzed to study the deformation development laws of the airport area for the different years. The three-year maximum settlement rate of the airport area investigated in this study is less than 70 mm/year, with the deformation rate concentrated between −13.34 mm/year and 14.54 mm/year. The sinking phenomenon is more significant than the uplift phenomenon in the Xianyang Airport area, indicating that the airport’s foundation is relatively stable and concentrated. The settlement area is the junction of the southern cement runway and the D6 taxiway, and the settlement rate exceeds 30 mm/year.]]></description>
      <pubDate>Wed, 22 Feb 2023 09:57:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2092705</guid>
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