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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>Research in Progress (RIP)</title>
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    <item>
      <title>Applications of data science and big data analytics in underground transportation infrastructure (UTI-UTC 02)
</title>
      <link>https://rip.trb.org/View/2543307</link>
      <description><![CDATA[This project focuses on harnessing the power of data science, machine learning (ML), and big data analytics to enhance the construction, operation, and maintenance of underground transportation infrastructure (UTI). By collecting and processing large-scale datasets from tunneling projects—such as TBM performance data, geotechnical records, and operational logs—the research develops predictive models to assess ground conditions, detect anomalies, and forecast potential structural failures. Key objectives include refining data-driven methods for real-time TBM state prediction, designing algorithms to detect defects like cracks or rock incursions, and creating interactive visualization tools to support decision-making. The project emphasizes scalable ML architectures (e.g., deep learning, recurrent neural networks) to improve the resilience, safety, and cost-efficiency of UTI systems. Its outcome serves as a foundation for intelligent tunneling and infrastructure health monitoring frameworks in modern urban environments.
]]></description>
      <pubDate>Wed, 07 May 2025 19:00:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543307</guid>
    </item>
    <item>
      <title>Developing Machine Learning (ML) Techniques to Predict Tunnel Performance and Stability (UTI-UTC 09)
</title>
      <link>https://rip.trb.org/View/2543317</link>
      <description><![CDATA[This project explores the application of machine learning (ML) techniques to enhance predictive capabilities in tunnel performance and stability, particularly focusing on mitigating the risk of collapse during tunnel boring machine (TBM) operations. By leveraging geological and TBM operation data from past tunneling projects, the research develops and trains classification models—including multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF) algorithms—to forecast collapse events with high accuracy. A novel contribution of the project is the introduction of the "influence zone" concept, enabling spatial prediction of collapse-prone regions ahead of excavation. The research demonstrates the feasibility of ML in tunneling safety and paves the way for real-time risk monitoring systems that can alert engineers to unstable zones, thereby improving construction planning, operational safety, and infrastructure reliability.
]]></description>
      <pubDate>Wed, 07 May 2025 18:57:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543317</guid>
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    <item>
      <title>Experimental Investigation of Rockburst Phenomenon in Tunnels Using a True-triaxial Apparatus (UTI-UTC 15)
</title>
      <link>https://rip.trb.org/View/2543410</link>
      <description><![CDATA[This research explores the mechanisms and risk factors associated with rockburst events in tunnel environments through controlled laboratory simulations. Utilizing a true-triaxial apparatus and specially designed analog sandstone specimens, the project replicates high-stress underground conditions to trigger and analyze rockburst phenomena. By integrating acoustic emission sensors, digital image correlation techniques, and advanced stress loading protocols, the study captures fracture initiation, crack propagation, and dynamic energy release processes during tunnel excavation. A miniature tunnel boring machine (TBM) is employed to simulate excavation through stressed rock blocks, enabling visualization and quantification of damage evolution. The findings aim to enhance the understanding of rockburst behavior, inform predictive models, and guide the development of effective monitoring and mitigation strategies to improve safety in deep tunneling projects.
]]></description>
      <pubDate>Wed, 07 May 2025 18:43:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543410</guid>
    </item>
    <item>
      <title>Incorporating Spatial Uncertainty to Advance the Practice of Site-Investigations, Geological-Geotechnical Characterization and TBM Performance Prediction (UTI-UTC 24) 

</title>
      <link>https://rip.trb.org/View/2543418</link>
      <description><![CDATA[This research project addresses the challenges posed by spatial variability and uncertainty in subsurface conditions during tunneling operations. By integrating geostatistical methods with tunneling data, the study aims to enhance geological and geotechnical site characterization and improve the predictive accuracy of tunnel boring machine (TBM) performance. The framework incorporates probabilistic models to quantify and propagate spatial uncertainty across soil and rock interfaces, enabling better-informed decisions during design and construction phases. Case studies involving TBM tunneling projects in Washington, D.C. and Seattle demonstrate the effectiveness of the approach, particularly in forecasting ground condition transitions and identifying geohazards such as karstic voids. The methodology supports more reliable risk assessments, adaptive tunneling strategies, and cost-effective infrastructure delivery through improved data interpretation and uncertainty management.
]]></description>
      <pubDate>Wed, 07 May 2025 17:56:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543418</guid>
    </item>
    <item>
      <title>Mechanical Characterizations of Joints in Segmented Tunnel Liners Due to Flexural and Thrust Jack Loading (UTI-UTC 28)
</title>
      <link>https://rip.trb.org/View/2543421</link>
      <description><![CDATA[This research investigates the structural behavior of joints in segmented tunnel liners subjected to flexural and thrust jack loading, which are critical conditions encountered during tunnel construction and operation. The project focuses on quantifying the mechanical response of these joints, particularly under load scenarios simulating bending moments and axial forces applied by tunnel boring machines (TBMs). Experimental testing is conducted on full-scale precast concrete segments, including those from the Chesapeake Bay Tunnel project, to assess parameters such as joint stiffness, rotational capacity, and load-bearing performance. The study is complemented by detailed numerical modeling and analytical evaluations to validate test results and improve segmental design methodologies. The outcomes are expected to inform design guidelines and enhance the durability, safety, and reliability of segmented tunnel systems used in modern underground transportation infrastructure.
]]></description>
      <pubDate>Wed, 07 May 2025 17:37:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543421</guid>
    </item>
    <item>
      <title>Physical model to study tunnel squeezing under true-triaxial stress state (UTI-UTC 30)
</title>
      <link>https://rip.trb.org/View/2543423</link>
      <description><![CDATA[This project develops a novel physical modeling framework to investigate the phenomenon of tunnel squeezing in weak or highly stressed rock masses under true-triaxial stress conditions. Tunnel squeezing—characterized by excessive and time-dependent ground deformation around the tunnel perimeter—poses significant challenges to safe and cost-effective tunnel construction. To simulate this behavior, a miniature tunnel boring machine (TBM) is integrated into a true-triaxial apparatus capable of replicating realistic in-situ stress states. The model allows for controlled excavation in synthetic clay-rich rock analogs and incorporates real-time measurement of displacement, strain, and support system response. Experimental data are complemented with analytical and numerical analyses to evaluate failure mechanisms and the interaction between the TBM, tunnel liner, and surrounding ground. The research aims to provide a deeper understanding of tunnel-ground interactions under squeezing conditions and guide the development of robust tunneling strategies and support systems for use in challenging geological environments.
]]></description>
      <pubDate>Wed, 07 May 2025 17:23:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543423</guid>
    </item>
    <item>
      <title>TBM Tunnel Liner Design Guidelines</title>
      <link>https://rip.trb.org/View/2100885</link>
      <description><![CDATA[This project will develop criteria, guidelines, and recommendations for the design of precast concrete tunneling segments using synthesis, computer simulation, model scale testing, and full-scale testing.]]></description>
      <pubDate>Wed, 18 Jan 2023 11:17:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2100885</guid>
    </item>
    <item>
      <title>Improved Geotechnical Site Characterization Using Measurement While Drilling</title>
      <link>https://rip.trb.org/View/1872015</link>
      <description><![CDATA[The objectives of this research are (1) to provide proof-of-concept for measurement while drilling (MWD) in Illinois, (2) to provide guidance to Illinois Department of Transportation (IDOT) on using MWD to more accurately identify site stratigraphy, (3) to develop methods to obtain Illinois-specific correlations to soil and rock properties using the drilling parameters obtained from the four categories, and (4) to explore use of MWD for design of driven pile and drilled shaft deep foundations. The goals of this research are to unlock MWD as a technique to complement standard technology practices of SPT borings, rock coring, and Rimac testing (as well as complementing emerging technologies such as cone penetrometer test and geophysics in the near future), to expand subsurface investigations to include MWD boreholes alongside current SPT borings, and to improve the reliability of interpreted ground conditions. Using MWD in IDOT practice will provide higher quality geotechnical and geological subsurface data that will reduce the cost of field exploration, design, and construction of IDOT infrastructure and provide drill rig performance information (e.g., drill component damage detection, time-to-repair using machine learning). Specific beneficial outcomes will involve correlating MWD data with SPT N-value equivalents; new tools to distinguish glacial tills, soft weathered rock, and harder competent rock layers; improved rock property data such as recovery, rock-quality designation, rate, and unconfined strength; standardized methods for collecting MWD data and developing boring logs that are gINT compatible.]]></description>
      <pubDate>Thu, 12 Aug 2021 12:19:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1872015</guid>
    </item>
    <item>
      <title>Geotechnical Database, Phase IV</title>
      <link>https://rip.trb.org/View/1768549</link>
      <description><![CDATA[The implementation of HoleBASE for district data was a successful outcome of the Phase III research. The use of HoleBASE is currently limited to shallow subgrade soil surveys and dynamic cone penetrometer data. Therefore, the Department’s deep soil boring and cone penetrometer (CPT) data have not yet benefitted from the implementation of HBSI during the previous phase of research.

This proposed phase, Phase IV, will focus on updating Phase 1 to modern platforms that will stand the test of time, and allow, again, Geographic Information System (GIS) display of the data, addressing the connections issues related to previous phases and enhancements to move the database forward.

With the recent acquisition of Keynetix by Bentley, OpenGround Cloud (which is the Cloud-based version of the HoleBASE software implemented in Phase III) will replace gINT. This project will research and connect data during this transition. Additionally, the implementation of the Data Interchange for Geotechnical and Geo-Environmental Specialists (DIGGS) is a goal of the Department. The project will research and assist Section 67 with the implementation of DIGGS to allow for the collection of geotechnical data from retainer consultants and other entities and agencies.

The deep borings and CPTs represent the majority of geotechnical data consumed by the Department and would therefore benefit from a move to an all-in-one database/mapping/database management solution. It appears that the features of HBSI or a similar all-in-one solution would streamline many processes for the Pavements & Geotechnical section as well as the Materials Lab.
]]></description>
      <pubDate>Mon, 08 Feb 2021 15:28:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1768549</guid>
    </item>
    <item>
      <title>DC Resistivity for model Tunnel Boring Machines (TBM) in laboratory environment (UTI-UTC 08)</title>
      <link>https://rip.trb.org/View/1458193</link>
      <description><![CDATA[To apply a geophysical method known as Direct Current (DC) resistivity to a laboratory scale Tunnel Boring Machine (TBM) for the purpose of imaging hazards ahead of tunneling.  The objective is to determine the conditions under which such an approach is viable on a real TBM. Considerations include the highly conductive metal TBM and the location of any anomalous structures ahead of the tunnel face.  Benefits include improved geotechnical hazard prediction ahead of tunneling and reduced operational downtime. ]]></description>
      <pubDate>Mon, 29 Jan 2018 12:20:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1458193</guid>
    </item>
    <item>
      <title>Update the Pile Design by CPT Software to Incorporate Newly Developed Pile-CPT Methods and Other Design Features</title>
      <link>https://rip.trb.org/View/1464356</link>
      <description><![CDATA[The primary objectives of this research project are to collect all available pile load tests database from the Louisiana Department of Transportation and Development (LADOTD) and the corresponding Cone Penetration Test (CPT) soundings and soil borings close to test pile locations, and perform a screening based on soil condition and failure criteria during the load test, to compare between the measured and estimated pile resistance of the collected pile load tests database for all direct CPT methods, and  perform statistical analyses to evaluate/rank the pile-CPT method(s) for use in Louisiana soil, to select, modify and/or develop a new pile-CPT method for use in the design of piles driven in Louisiana soils, re-calibration the resistance factor (ø) for all selected pile-CPT methods, to update the Louisiana Pile Design-Cone Penetration Test (LPD-CPT) software to incorporate the newly selected pile-CPT prediction methods, to update the “LPD-CPT” software to incorporate the effect of scour on soil properties and CPT data, and hence the long-term pile resistance and the pile set-u[ empirical equations into updated “LPD-CPT” software, incorporate the calibrated resistance factors (ø) for pile-CPT methods and pile set-up into the update “LPD-CPT” software in order to design the pile according to Load and Resistance Factor Design (LRFD) design methodology, and update the “LPD-CPT” software to be able to generate synthetic CPT profiles for all piles in the project.]]></description>
      <pubDate>Wed, 12 Apr 2017 10:51:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1464356</guid>
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