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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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    <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>Mn/DOT's On-Line Geo-Spatial Borehole/Sounding Database Development</title>
      <link>https://trid.trb.org/View/2187854</link>
      <description><![CDATA[Minnesota Department of Transportation (Mn/DOT) has advanced over 20,000 soil borings and CPT soundings in the past 46 years. An electronic database of point data was established in the 1970's. A web-based interface and set of query tools was developed in 2003. An intranet version is being used; the public version is expected to be ready in fall of 2006. With minor changes to the data nomenclature, Mn/DOT's system is expected to be fully compatible with evolving geotechnical management systems being developed by a number of cooperating international agencies. Development of the Mn/DOT system, obstacles, difficulties, and benefits are described.]]></description>
      <pubDate>Thu, 19 Dec 2024 11:44:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2187854</guid>
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    <item>
      <title>GIS Based Quantitative Risk Analysis along Prithvi Highway Road Corridor during Extreme Events</title>
      <link>https://trid.trb.org/View/2190837</link>
      <description><![CDATA[Geographic Information System (GIS) tools are widely used for the hazard mapping of slopes. However, quantitative slope instability mapping for a wide area is not commonly used. This study involves the evaluation of slope instability based on the three dimensional deterministic analysis. Various soil parameters that were measured from the field specimens were distributed according to the petrographic regions and slope instability mapping was prepared with the help of an automatic GIS algorithm. Estimated instability zones are verified with the field mapping and aerial photo interpretation. The study shows that the high hazard zone can increase up to 2.5 times of the current situation during the concurrent occurrence of rainfall and earthquake. The instability maps retrieved though this study can be effectively used for the maintenance of highways.]]></description>
      <pubDate>Fri, 23 Aug 2024 16:53:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2190837</guid>
    </item>
    <item>
      <title>R-Value and Resilient Modulus Prediction Models Based on Soil Index Properties for Colorado Soils</title>
      <link>https://trid.trb.org/View/2113038</link>
      <description><![CDATA[Resilient modulus (Mr), as an important mechanical property of soil, is used for analysis and design of pavements. Mr can properly describe the stress-dependent elastic modulus of soil materials under traffic loading. In addition to the resilient modulus, R-value test is commonly used to measure the strength of subgrade, subbase, and base course materials for use in pavements. Both tests are expensive and time consuming to run, and establishing accurate and reliable correlations between the test results and soil physical properties can save a considerable amount of time and money in testing and analyzing the construction materials properties. An extensive database of systematically conducted resilient modulus and R-value tests along with basic soil properties for Colorado soils was established. This paper presents the prediction equations developed through (a) regression analysis of over 2600 R-value data points and associated soil basic properties for soil types (A-1-a, A-1-b, A-2-4, A-2-6, A-2-7, A-4, A-6, and A-7-6) and (b) regression analysis of over 200 resilient modulus tests and associated soil basic properties for soil types (A-1-b, A-2-4, A-4, and A-6). It is demonstrated that the proposed models predict the R-value and resilient modulus values close to the laboratory measured values for all studied soil types.]]></description>
      <pubDate>Wed, 14 Jun 2023 17:09:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113038</guid>
    </item>
    <item>
      <title>Incorporating Precipitation Data into Geotechnical Asset Management.</title>
      <link>https://trid.trb.org/View/2118359</link>
      <description><![CDATA[In recent years, the implementation of geohazard warning systems based on precipitation has gained increasing attention from government officials, decision-makers, and the general public to improve decision-making for resilience planning and response for storm events. The active publication of several precipitation-based datasets presents an opportunity for integration with spatial LiDAR terrain data, and subsurface soil mapping. The Maryland Department of Transportation State Highway Administration (MDOT SHA) maintains thousands of cut slopes, embankments, rock slopes and bridge approach embankments; and often respond to repair resulting highway slope instability after significant storm events. It is important that unacceptable slope distresses are recognized in a timely manner for appropriate maintenance and repair. Early identification of slope instability can be crucial for improving resilience by reducing and mitigating the risk of infrastructure damage, along with economic and social damages caused by storm events. Notifications of elevated risk would allow MDOT SHA engineers to assess slopes along the transportation network in a targeted manner based on the amount of precipitation recorded daily.  This research will help identify relevant precipitation-based data sources, recommend appropriate thresholds for integration with condition assessment data, and for notifications which to assist MDOT SHA with prioritizing geotechnical field slope conditions inspections.]]></description>
      <pubDate>Tue, 14 Feb 2023 14:55:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2118359</guid>
    </item>
    <item>
      <title>Geological Assessment of the Westbound I-40 Slope Failure, Rockwood, Tennessee</title>
      <link>https://trid.trb.org/View/1989296</link>
      <description><![CDATA[Interstate 40 (I-40) west of Harriman and east of Rockwood, Tennessee, has a history of slope instability dating back to slope failures that occurred during interstate construction in the early 1960s. Arcuate cracks appeared through the westbound lane between Mile Markers (MM) 342.8 and 343.2 following heavy precipitation during the 2018-2019 winter. Detailed geologic mapping revealed the arcuate cracks were tension scars related to a landslide complex containing several failure surfaces spanning nearly 1,500 feet along the westbound lane. Accurate detailed geologic mapping applied early in the project allowed development of a site conceptual model that was used to evaluate the cause of landslide movements, evaluate the areal magnitude of the problem, target the geotechnical investigation, and scope the mitigation alternatives analysis. Detailed geologic mapping conducted on USGS and LiDAR topographic base maps provided a detailed preliminary site model to explain the failure geometry and guided the initial geotechnical investigations. The subsurface and subsequent investigations included sonic and conventional geotechnical boreholes, electrical resistivity imaging (ERI) geophysics, historical aerial photograph review, laboratory analysis, instrument installations (inclinometers, standpipes, and vibrating wire piezometers), rockfall hazard assessment and additional targeted detailed geological mapping. The data were incorporated into a 3D geological model which was used to produced 2D sections used for slope stability analysis. The final geotechnical design included a suite of tiebacks anchored into the Pennington Formation shale and a more competent upper sandstone member. Geotechnical design challenges included a deep failure surface, low strength of shale and a complexity of ground conditions.]]></description>
      <pubDate>Thu, 21 Jul 2022 13:39:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1989296</guid>
    </item>
    <item>
      <title>Machine learning methods to map stabilizer effectiveness based on common soil properties</title>
      <link>https://trid.trb.org/View/1763500</link>
      <description><![CDATA[Most chemical stabilization guidelines for subgrade/base use unconfined compressive strength (UCS) of treated soils as the primary acceptance criteria for selecting optimum stabilizer in laboratory testing. Establishing optimal additive content to augment UCS involves a resource-intensive trial-and-error procedure. Also, samples collected from discrete locations for laboratory trials may not be representative of the overall site. This study aims to minimize the number of laboratory trials and help strategize sampling locations by developing spatial maps of UCS at different treatment levels for lime and cement. These spatial maps were developed using machine-learning techniques, and using a database compiled from various reported studies on lime and cement stabilization of soils in the United States. Supervised learning methods under regression and classification categories were used to quantify and classify UCS values after treatments, respectively. Commonly available soil properties like Atterberg limits, gradation, and organic contents along with treatment type and amount were used as predictors and UCS values as the response. Median R² for the best regression model was 0.75 for lime and 0.82 for cement, while the Correct Prediction Rate (CPR) for the best classification model was 92% for lime and 80% for cement. Results showed that satisfactory predictions could be made regarding stabilizer effectiveness using simple soil information commonly available. Best performing models for cement treatment were selected for generating the spatial maps for two counties in Montana. Soil samples collected from these counties were tested with different cement contents to verify the predictions. The results indicate that the Pearson’s correlation coefficient for the regression model was 0.78 and CPR for the classification model was 92%. The authors hope that this study and future studies like these will increase data-driven-decision-making in geotechnical engineering practices.]]></description>
      <pubDate>Mon, 22 Feb 2021 10:21:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1763500</guid>
    </item>
    <item>
      <title>Probabilistic Analysis of Iceberg Scouring Frequency Based on Repetitive Seabed Mapping, Offshore
Newfoundland and Labrador</title>
      <link>https://trid.trb.org/View/1375534</link>
      <description><![CDATA[Seabed areas shallower than approximately 220 m water depth off eastern Newfoundland and Labrador are subject to infrequent, but damaging impacts from keel-dragging icebergs, necessitating costly protection measures for subsea facilities for offshore development projects. Appropriate measures require an accurate assessment of the iceberg scour risk at a particular location. Repetitive sidescan surveys conducted over three decades provide up to 25-year baseline for assessing seabed iceberg scour frequency in targeted areas of oil and gas basins on Grand Bank. Results provide critical groundtruth and allow calibration of probability models used to assess engineering risk for bottom-founded structures. In contrast to more dynamic sea ice regimes such as the Beaufort Sea where ice ridge keels routinely impact the shallow shelf seabed, the northeastern Canadian shelf is subject to infrequent scouring. Repetitive mapping surveys must encompass broad areas (100s of km²) and decadal time intervals to provide statistically meaningful results. In this paper, the authors discuss efforts to refine the mean scour rates, and to measure the uncertainty in the estimates using Monte Carlo and Bayesian modeling techniques. A mean scour frequency rate of 4.1 x 10⁻⁴ scours/km²/yr is calculated, based on repetitive mapping results to 2003.]]></description>
      <pubDate>Tue, 24 Nov 2015 09:28:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1375534</guid>
    </item>
    <item>
      <title>Semi-Automated Disaggregation of a Conventional Soil Map Using Knowledge Driven Data Mining and Random Forests in the Sonoran Desert, USA</title>
      <link>https://trid.trb.org/View/1306554</link>
      <description><![CDATA[Conventional soil maps (CSM) have provided baseline soil information for land use planning for over 100 years. Although CSMhave been widely used, they are not suitable to meet growing demands for high resolution soil information at field scales. The authors present a repeatable method to disaggregate CSM data into ~30-meter resolution rasterized soil class maps that include continuous representation of probabilistic map uncertainty. Methods include training set creation for original CSM component soil classes from soil-landscape descriptions within the original survey database. Training sets are used to build a random forest predictive model utilizing environmental covariate maps derived from ASTER satellite imagery and the United States Geological Survey (USGS) National Elevation Dataset. Results showed agreement at 70 percent of independent field validation sites and equivalent accuracy between original CSM map units and the disaggregated map. Uncertainty of predictions was mapped by relating prediction frequencies of the random forest model and success rates at validation sites.]]></description>
      <pubDate>Tue, 22 Apr 2014 16:07:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1306554</guid>
    </item>
    <item>
      <title>Multisensor Analysis for Soils Mapping</title>
      <link>https://trid.trb.org/View/1286869</link>
      <description><![CDATA[No abstract]]></description>
      <pubDate>Mon, 27 Jan 2014 09:55:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1286869</guid>
    </item>
    <item>
      <title>Geologic Survey Mapping in the United States</title>
      <link>https://trid.trb.org/View/1279554</link>
      <description><![CDATA[No abstract]]></description>
      <pubDate>Mon, 09 Dec 2013 10:21:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1279554</guid>
    </item>
    <item>
      <title>Mapping Naturally Occurring Hazardous Materials in Oregon: Project Aims to Protect Transportation Personnel and Public Health</title>
      <link>https://trid.trb.org/View/1247181</link>
      <description><![CDATA[Naturally occurring hazardous materials (NOHMs) are easily overlooked in standard environmental assessments and geologic investigations for transportation projects. The Oregon Department of Transportation partnered with the state’s Department of Geology and Mineral Industries to identify the NOHMs of greatest concern, delineate the likely occurrences, and establish how to detect them, to protect the health and safety of agency personnel, construction workers, and the traveling public. The project compiled the NOHM-GIS Information Layer (NGIL), a spatial data map that can be used to devise policies and procedures for dealing with NOHMs.]]></description>
      <pubDate>Thu, 04 Apr 2013 15:33:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1247181</guid>
    </item>
    <item>
      <title>Airphoto Interpretation of Engineering Soils of Fulton County, Indiana</title>
      <link>https://trid.trb.org/View/1219162</link>
      <description><![CDATA[This report completes a portion of a project concerned with the development of county engineering soils maps of the State of Indiana.  This is the 38th report in the series.  The soils mapping of Fulton County was done primarily by airphoto interpretation.  Some soil test data from the previous study of this area are included in the report and generalized soil profiles of the major soil groups are presented on the soils map.  A print of the engineering soils map is included in the report.]]></description>
      <pubDate>Wed, 23 Jan 2013 09:06:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1219162</guid>
    </item>
    <item>
      <title>Automatic Technique for Abstracting Color Descriptions from Aerial Photography</title>
      <link>https://trid.trb.org/View/1219149</link>
      <description><![CDATA[The interpretation of color aerial photography is increasing in all disciplines of engineering and earth sciences.  One of the problems facing the interpreter using color photography is the need for a rapid and automatic method of describing the various colors present of the photography that aid in the interpretation.  This paper describes a simple, rapid and reasonably accurate method for automatically describing the colors present on aerial photography using simple transmission or reflection densitometers.  This method describes the colors in the Munell notation system or by descriptive names based on the ISCC-NBS system.  A graphical method as well as a computer program were developed to determine the color descriptions.]]></description>
      <pubDate>Wed, 16 Jan 2013 10:16:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1219149</guid>
    </item>
    <item>
      <title>Engineering Soils Map of Greene County, Indiana</title>
      <link>https://trid.trb.org/View/1218903</link>
      <description><![CDATA[This report completes a portion of a long-term project concerned with the development of engineering soils maps of the 92 counties in the State of Indiana. The soils mapping of Greene County was done primarily by the analysis of landforms and associated parent materials as portrayed on stereoscopic aerial photographs. Additional information on soils was obtained from publications of the Soil Conservation Service, U.S. Department of Agriculture.  Test data from roadway and bridge projects was obtained from the Indiana Department of Highways.  Generalized soil profiles for the landforms mapped are presented on the engineering soils map, a copy of which is included at the end of the report.  The text of the report supplements the engineering soils map and includes a general description of the study areas, a discussion of the bedrock and glacial geology, descriptions of the landform-parent material areas, and a discussion of the engineering considerations associated with the soils found in each region.]]></description>
      <pubDate>Mon, 31 Dec 2012 17:54:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1218903</guid>
    </item>
    <item>
      <title>Engineering Soils Map of Knox County, Indiana</title>
      <link>https://trid.trb.org/View/1218905</link>
      <description><![CDATA[This report completes a portion of a long-term project concerned with the development of engineering soils maps of the 92 counties in the State of Indiana. The soils mapping of Knox County was done primarily by the analysis of landforms and associated parent materials as portrayed on stereoscopic aerial photographs. Some test data from soil borings were obtained from the Indiana Department of Highways.  Generalized soil profiles for the landforms mapped are presented on the engineering soils map, a copy of which is included at the end of the report.  The text of the report supplements the engineering soils map and includes a general description of the study areas, descriptions of the landform-parent material areas, and a discussion of the engineering considerations associated with the soils found in the county.]]></description>
      <pubDate>Mon, 31 Dec 2012 17:54:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1218905</guid>
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