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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7VHVubmVsIGxpbmluZyZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O1R1bm5lbCBsaW5pbmcmcXVvdDsiIC8+PC9maWx0ZXJzPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" 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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Field evaluation of coal-based porous ceramics for acoustic improvement in a traffic tunnel: Reverberation control and speech intelligibility</title>
      <link>https://trid.trb.org/View/2776321</link>
      <description><![CDATA[Traffic tunnels present acoustic challenges because confined geometry and reflective boundaries cause severe reverberation and impair emergency public address systems. Conventional sound-absorbing materials are constrained by tunnel fire safety and durability requirements. Although coal-based porous ceramics offer fire resistance and mechanical strength, their field acoustic effectiveness remains insufficiently validated. This paper assesses coal-based porous ceramics in a 145 m operational traffic tunnel (8.0 m wide, 5.0 m high) using impedance-tube characterization, reverberation-room measurements, field tests, and validated numerical simulations. Field measurements in the untreated tunnel revealed the spatially averaged overall speech transmission index (STI) around 0.34 (“Poor”) and the per-band modulation transfer index (MTI) as low as 0.18 at 500 Hz. After treatment, the 100–5000 Hz band-averaged reverberation time (RT) was reduced by approximately 5.2 s. The sound pressure level at 500 Hz decreased by approximately 11–14 dB at 100 m from the source, while the spatially averaged RT along the tunnel axis was reduced from 8–9 s to about 1.2 s; the spatially averaged overall STI increased to approximately 0.63 (“Good”); and the 500 Hz MTI rose to approximately 0.55. Parametric simulations showed that a 20 mm backing cavity suited space-constrained tunnels, whereas a 60 mm cavity enhanced low-frequency absorption, achieving an approximately 58% RT reduction at 125 Hz. Single-wall treatment provided a material-efficient option, while double-wall treatment was preferable for safety-critical tunnels. The findings demonstrate the acoustic feasibility of porous ceramic panels prepared from coal-based raw materials and provide design guidance for noise control in tunnel infrastructure.]]></description>
      <pubDate>Tue, 22 Sep 2026 15:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2776321</guid>
    </item>
    <item>
      <title>A viscoelastic-plastic composite lining tunnel mechanical analysis model that considers excavation stress release and stratum rheology</title>
      <link>https://trid.trb.org/View/2704255</link>
      <description><![CDATA[During tunnel construction in weak strata, support structure failure is often encountered due to the rheological properties of the ground and the plastic behavior of the rock. Therefore, analytical approaches can be effectively employed to conduct failure analysis of tunnel support systems in such conditions. Therefore, this study proposes a viscoelastic-plastic analytical solution for composite lining tunnels considering excavation stress release and strata rheology. This method incorporates the virtual support pressure theory, classical Maxwell and Kelvin-Voigt creep models, and the Hoek-Brown yield criterion to describe stress release, rheological behavior, and plastic deformation of the surrounding rock. The effectiveness of the analytical method was validated through numerical simulation. The proposed analytical approach can accurately describe the dynamic evolution mechanism of the plastic zone during tunnel excavation in the construction phase, while also effectively analyzing the long-term mechanical behavior of the tunnel. Based on this, the influences of plastic zone parameters, support parameters, and creep parameters on the failure of the support structure were analyzed. The practical value of the method was demonstrated through its application in actual engineering projects. Finally, based on the analysis results, measures to prevent and mitigate support structure failure in weak strata were proposed. This study, proceeding from a theoretical perspective, provides a theoretical basis and guidance for the design and construction of tunnel engineering in weak strata.]]></description>
      <pubDate>Wed, 02 Sep 2026 09:21:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704255</guid>
    </item>
    <item>
      <title>Soil-structure interaction of metro tunnel in layered strata: mechanical mechanism, parameter analysis and field monitoring</title>
      <link>https://trid.trb.org/View/2701543</link>
      <description><![CDATA[In metro tunnel design, the bedded-beam model (BBM) based on Winkler springs is extensively employed, where the radial subgrade modulus (k) acts as an indispensable parameter for quantifying the soil-structure interaction between the rock mass and tunnel lining. While the anisotropy of k is mainly caused by the presence of joints in the rock mass. The relevant research is insufficient. This research gap restricts the accuracy of tunnel structural analysis and design optimization. To address this critical issue, this study integrates theoretical derivation based on the stress-function variational method with in-situ field monitoring to comprehensively explore the soil-structure interaction mechanism in layered strata. A theoretical model describing the anisotropic distribution of k in orthotropic layered strata was established, and its reliability was verified through comparative analysis with existing analytical solutions and field monitoring data. Meanwhile, a systematic parameter sensitivity analysis was conducted to elucidate the influences of key factors on the radial subgrade modulus. Additionally, field monitoring was performed during tunnel construction to collect real-time stress and displacement responses of the lining structure, and the proposed theoretical model was further validated through engineering application. The results demonstrate that the radial subgrade modulus of orthotropic layered strata exhibits inherent anisotropy, regardless of whether the in-situ stress field is uniform or non-uniform. As the lateral pressure coefficients increase, the radial subgrade modulus exhibits a decreasing trend in the horizontal direction while showing a simultaneous increase in the vertical direction. With the increase of elastic modulus, the radial subgrade modulus in this direction will also gradually increase, and the magnitude of this increase aligns with the theoretical model put forward by Gallerkin. To maximize the potential of rock masses, it is necessary to comprehensively take into account the influence of anisotropy and various factors when choosing a reasonable radial subgrade modulus.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701543</guid>
    </item>
    <item>
      <title>Comprehensive Design and Structural Analysis of Steel-Fiber-Reinforced Concrete Tunnel Linings for Railway Infrastructure</title>
      <link>https://trid.trb.org/View/2754179</link>
      <description><![CDATA[The structural performance and durability of tunnel linings are critical for the safety of modern railway infrastructure. This study presents a comprehensive analysis of steel-fiber-reinforced concrete (SFRC) tunnel linings using a hybrid analytical and data-driven framework. The primary objective is to evaluate the effectiveness of SFRC in enhancing structural behavior under complex loading conditions. An integrated ORCA cyclic generative adversarial monitoring (OCGAM) framework, combining ORCA-based optimization with generative adversarial networks, is proposed to model and predict crack development, stress–strain response, and service life of tunnel linings. The framework incorporates key factors such as train-induced dynamic loads, soil–structure interaction, and seismic effects. Numerical simulations demonstrate that SFRC tunnel linings significantly reduce crack width, improve load-bearing capacity, and enhance durability compared with conventional reinforced concrete. The OCGAM framework further enables efficient prediction of structural responses with higher computational accuracy and reduced error rates than traditional analytical approaches. Although the study is limited to simulation-based validation, the results highlight the potential of combining optimization techniques with machine learning for advanced structural assessment. The proposed methodology provides a valuable tool for the design and monitoring of resilient and sustainable railway tunnel systems, and future work will focus on validation by experiments.]]></description>
      <pubDate>Wed, 19 Aug 2026 09:25:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2754179</guid>
    </item>
    <item>
      <title>Attention-Enhanced Dual-Stage Deep Learning for Automated Tunnel Crack Detection and Quantification</title>
      <link>https://trid.trb.org/View/2684780</link>
      <description><![CDATA[Reliable detection and quantification of tunnel lining cracks are vital for metro infrastructure safety. This study proposes a real-time deep learning framework Enhanced YOLOv8 for crack localization and DeepCrack with transfer learning for geometric quantification. It forms a novel quantification-oriented dual-stage model in which attention-enhanced crack detection explicitly guides subsequent fine-grained segmentation and measurement, thereby enabling robust and real-time tunnel crack evaluation under field conditions. Validated on Nanchang Metro Line 1, the model achieves superior accuracy, with Enhanced YOLOv8 reaching an mAP@0.5 of 95.3% and recall of 91.7%, and the segmentation model maintaining an mAP@0.5 above 0.91, significantly outperforming baseline methods. Field deployment demonstrates robust adaptability, achieving mAP@[0.5:0.95] ≈ 0.70, sustaining speeds over 25 FPS with latency below 40 ms per frame, and manual workload decreases by 70%. This work advances automated tunnel inspection by delivering a high-precision, real-time, and field-validated solution with strong potential for broader infrastructure monitoring applications.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684780</guid>
    </item>
    <item>
      <title>Bearing Capacity of FRP-Polymer Grouting Composite Reinforcement for Damaged Tunnel Linings Using Dual-Horizon Peridynamics</title>
      <link>https://trid.trb.org/View/2694479</link>
      <description><![CDATA[To address structural safety risks of tunnel linings with combined void and crack defects under complex geological and operational conditions, this study improved the dual‑horizon peridynamics method to support its engineering application in tunnel lining reinforcement. This study developed a high‑fidelity numerical model using a composite damage criterion that incorporated interface bond‑slip behavior between fiber‑reinforced polymer and concrete. The model used coordinate transformation, material property differentiation, and bond force adjustment to represent nonlinear interface interactions and mechanical responses of heterogeneous materials. The tunnel lining and surrounding rock models simulated conditions with different crack depths and void sizes. Comparisons of fiber‑reinforced polymer bonding, polymer grouting, and their combined use showed that polymer grouting filled voids, reduced local stress concentration, and restored the coordinated load‑bearing system. Fiber‑reinforced polymer bonding carried tensile stress, limited crack growth, and improved shear and tensile resistance. The combined method delivered clear synergistic performance. The study balanced computational efficiency and simulation accuracy, clarified damage evolution and reinforcement mechanisms, and provided a reliable reference for practical tunnel lining reinforcement design.]]></description>
      <pubDate>Wed, 24 Jun 2026 11:29:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694479</guid>
    </item>
    <item>
      <title>Rethinking tunnel-soil relative stiffness: insights from interactions between tunnels and strata subjected to ground surcharge</title>
      <link>https://trid.trb.org/View/2683168</link>
      <description><![CDATA[The stiffness difference between tunnels and surrounding strata can lead to relative deformations when subjected to factors such as ground surcharge, significantly influencing the earth pressure on tunnel linings. To address this, the concept of tunnel-soil relative stiffness has been introduced to evaluate these pressures. Existing methods, however, often treat tunnels and surrounding strata as independent entities, thus overlooking their complex interactions. Drawing insights from a recorded experiment, this paper redefines tunnel-soil relative stiffness, now conceptualized as the ratio of natural ground deformation to tunnel deformation subsequent to their interactions. Based on this definition, an analytical formula is derived to calculate the defined relative stiffness. A numerical case study is subsequently conducted to verify and evaluate the effectiveness of the proposed methods. It is found that traditional methods lead to a notable underestimation of tunnel-soil relative stiffness and consequently, the earth pressures on the tunnel linings; in contrast, the proposed method exhibits relatively better accuracy. The intrinsic physical reasons for the enhanced accuracy of the proposed method are discussed. Finally, leveraging the proposed method, insights on enhancing the load-bearing performance of tunnel linings in soft soils are presented, which may provide a valuable reference for the design and maintenance of tunnel linings in soft soil regions.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:53:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683168</guid>
    </item>
    <item>
      <title>Applicable Conditions for Polyurea Resin Spraying Method for Water Leakage and Uneven Tunnel Lining Surfaces</title>
      <link>https://trid.trb.org/View/2675873</link>
      <description><![CDATA[Falling pieces of tunnel lining hitting a train could cause an accident. Regular tunnel maintenance to prevent this is therefore essential. Repairs are planned and carried out in areas where there is a potential risk of spalling. A method called the “polyurea resin spraying method,” has been developed to prevent spalling by spraying tunnel linings with polyurea resin. This paper presents the results of a study carried out to extend the applicability of this method and some application examples: the effect of surface moisture during application on the adhesion strength of the resin, long-term durability in the presence of leaking water, adhesion strength of the resin on an uneven surface, and results of its application in a real tunnel.]]></description>
      <pubDate>Mon, 08 Jun 2026 08:38:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675873</guid>
    </item>
    <item>
      <title>Automatic detection of structural defects in tunnel lining via network pruning and knowledge distillation in YOLO</title>
      <link>https://trid.trb.org/View/2672022</link>
      <description><![CDATA[Cracks in tunnel linings might cause a lack of water tightness, directly affecting the overall stability and durability using an increased risk of corrosion of the rebar. Hence, automatic, timely and accurate detection of cracks is significant to safe operation and maintenance for tunnels. In recent years, Convolution Neural Network (CNN) has achieved success in the field of computer vision. However, the large storage requirement and high computational consumption of the model limit its application. Aiming to overcome these challenges, this study proposes a lightweight target detector YOLOV5-lite, which utilizes transfer learning to construct the YOLO single-stage target detection model and uses the Efficient Intersection over Union (EIoU) in the loss function to optimize its convergence speed. To improve the model efficiency, a Network Pruning algorithm is performed in order to reduce the number of parameters in the model. To compensate for the loss of accuracy caused by the Network Pruning algorithm compressing the network, knowledge distillation algorithm is implemented in this study and fused with the Network Pruning algorithm. This results in a new lightweight modelling framework which has a high computational efficiency enabling deployment in mobile devices as well as high accuracy leading to good detection performance. To illustrate the advantages of the proposed method, two experiments using an extensive evaluation of the YOLOV5 series of models and a comparison with different model tests were made to validate it. In the evaluation of the YOLOv5 series, key findings include: (a) The optimized YOLOV5-loss model, incorporating the EIoU loss function, achieved an impressive crack recognition accuracy of 0.97. This model demonstrated superior capability in detecting fine cracks, particularly in corner regions, with an accuracy exceeding 0.85. The EIoU loss function offers enhanced sensitivity to overlapping regions and more precise boundary localization, which are critical in identifying minute or boundary-ambiguous cracks. (b) The YOLOV5-finetuned model, which underwent network pruning alone, achieved an accuracy of 0.74 but was hindered by significant detection gaps, despite achieving a 50.9% reduction in model size. In contrast, the YOLOV5-lite model, refined using a combination of network pruning and knowledge distillation, maintained a high recognition accuracy of 0.96, with only a negligible 0.01 difference from the optimal YOLOV5-loss model. When compared with five different models, the YOLOv5-lite model demonstrated significant advantages: A substantial reduction in the size of the proposed YOLOV5-lite by 184.1 MB, 659.4 MB and an increase in the number of the transmitted Frames Per Second (FPS) by 6.69 f/s, 13.24 f/s compared with that of YOLOV3, Faster R-CNN, respectively. Overall, the proposed new method consistently achieves high detection performance as well as substantially reducing computational demands, making it well-suited for real-time applications, particularly in mobile and embedded systems.]]></description>
      <pubDate>Wed, 13 May 2026 09:33:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672022</guid>
    </item>
    <item>
      <title>Stress Response Assessment of a Flexible Lining Structure in Tunnel Construction Using a Novel Hybrid Alternative Intelligent Model</title>
      <link>https://trid.trb.org/View/2671971</link>
      <description><![CDATA[Stress response assessment of flexible lining has always been a key problem during the construction and maintenance of tunnels in seismically active areas. There is a lack of reliable and efficient methods for calculating or predicting the maximum principal stress (MPS) of flexible linings in tunnel engineering. In this paper, a novel hybrid intelligent model, called the crayfish optimization algorithm (COA)-based kernel extreme learning machine (KELM) model, is utilized to precisely solve the aforementioned issue, i.e., the COA-KELM model. To further improve model performance, six types of data allocation are designed to train and test the proposed alternative models. Several indices and evaluation methods are utilized to determine the best model. The results show that the trained COA-KELM model achieves the highest prediction accuracy when the ratio of the training set to the test set is 90%:10%. Besides, the results of the parametric investigation and sensitivity analysis indicate that the damping layer thickness (DLT) is the most important feature for MPS prediction. However, simply increasing DLT is not necessarily beneficial for reducing MPS, especially when DLT exceeds 175 mm. In general, this paper provides a new solution based on artificial intelligence models for predicting the stress response of linings in tunnel construction.]]></description>
      <pubDate>Wed, 13 May 2026 09:33:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671971</guid>
    </item>
    <item>
      <title>Analysis of dynamic response mechanism and control measures for tunnel crossing mountainous landslides: A shaking table test study</title>
      <link>https://trid.trb.org/View/2663878</link>
      <description><![CDATA[In seismically active mountainous regions, the dynamic response mechanisms and associated control measures of the earthquake–tunnel–landslide system (ETLS) have become a major research focus in the field of geotechnical engineering. In this study, a shock-absorbing layer (SAL) was adopted as a seismic control measure for the tunnel lining, and a physical model was developed in which the tunnel orthogonally undercrosses the main sliding surface of a landslide (TULS), and comparative seismic dynamic tests were subsequently conducted. In addition, the variational mode decomposition–Hilbert transform (VMD-HT) approach was employed to elucidate the seismic damage evolution of the TULS from the perspectives of energy distribution and frequency shift. The results demonstrate that the VMD-HT method exhibits clear advantages in the separation and extraction of the dominant frequencies of seismic signals, enabling precise decomposition of individual signal components. The dynamic failure of the TULS arises from the coupled interaction among the active movement of the sliding mass, the passive resistance of the tunnel, and seismic loading, and is characterized by a distinct four-stage evolutionary process. Among these, the concentration of shear stress along the main sliding surface is identified as the primary trigger for damage to the tunnel lining structure. With the incorporation of the SAL, the dynamic response of the tunnel lining is significantly reduced compared with the unprotected condition. The SAL effectively mitigates the energy impact of high-frequency components of seismic motions on the structure and slows the damage evolution of the tunnel lining.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663878</guid>
    </item>
    <item>
      <title>Artificial-Intelligence-Assisted Uncertainty Quantification and Reliability-Based Optimization of Deep Tunnel Liner Systems in Rock Formation With Time-Dependent Behavior</title>
      <link>https://trid.trb.org/View/2685705</link>
      <description><![CDATA[Deep tunnels excavated in creep-prone rock formations exhibit pronounced time-dependent deformations, which introduce significant uncertainty into the long-term support performance. In this study, an integrated probabilistic framework was proposed to address this challenge by combining Bayesian inference with reliability-based design optimization. The approach first characterized the probabilistic properties of creep rock parameters using time-dependent tunnel convergence data. These uncertainties were then incorporated into the optimization of a tunnel support system composed of multiple liners (including an inner concrete layer covered by a compressible layer). The use of Artificial Neural Networks as surrogate models significantly enhances computational efficiency while maintaining high prediction accuracy for tunnel convergence and liner stress responses over time. Numerical investigations confirmed that the proposed approach reliably captures the uncertainty associated with creep rock parameters and its impact on long-term tunnel behavior. The proposed framework was validated through numerical applications, demonstrating its effectiveness in identifying optimal support designs that met prescribed safety and serviceability criteria (i.e., allowable stress and displacement of the liner).]]></description>
      <pubDate>Wed, 29 Apr 2026 17:05:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685705</guid>
    </item>
    <item>
      <title>A new assessment method for safety status of circular tunnels considering the integration of analytical models with displacement data</title>
      <link>https://trid.trb.org/View/2659667</link>
      <description><![CDATA[The performance status of circular Tunnel Boring Machine tunnels predominantly relies on qualitative evaluations using limited indicators, which fail to fully utilize high-volume field data from modern detection technologies, resulting in suboptimal specificity and reliability of the analysis outcomes. This study proposed a new assessment method for safety status of circular tunnels considering the integration of analytical models with displacement data. The assessment method fully considered the multisource data including the geological information, tunnel dimensions, actual lining displacements, lining reinforcement, and nonlinear constitutive relationships of the lining materials. The internal forces and stresses of the circular tunnel were calculated by the analytical models. Numerical modeling validation confirmed the model’s reliability. The applicable scope of the assessment method was clarified through the parameter sensitivity analyses. A safety evaluation index and its classification derived from the assessment method were subsequently established. The research findings reveal that the correlation coefficients (between test values and analytical values) of lining displacement, bending moment and axial force are more than 98 %, 97 % and 86 % respectively under the shallow and deep buried tunnel scenarios, which verified the reliability of the integration between analytical models and displacement data. The mechanical behaviors of a circular tunnel are influenced by factors such as the geological types, tunnel dimensions, lining displacements, lining reinforcement and lining material types. The ratio of lining stress to its yield strength (stress-strength ratio) ultimately determines the safety of the tunnel. It is worth mentioning that the safety state of the lining ring cannot be identified separately by the ovality of displacement, which should be determined jointly by the major and minor axes of the elliptical deformation. According to parameter sensitivity analysis, the order of sensitivity influence on the stress-strength ratio is: structural displacement > lining thickness > tunnel diameter > lining strength > soil lateral pressure coefficients. The assessment method synergizes with detection technology advancements, which provides theoretical foundations for predicting the mechanical performance of service tunnels.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659667</guid>
    </item>
    <item>
      <title>NDT in Civil Engineering: Advancing Crack Analysis and Maintenance Strategies in Highway Tunnels</title>
      <link>https://trid.trb.org/View/2579898</link>
      <description><![CDATA[Tunnel liner cracks significantly threaten structural stability and public safety, necessitating robust inspection and maintenance strategies. A structural health assessment based on tunnel inspection is performed to detect, identify, measure, and assess the severity of such a defect. For this reason, twin highway tunnels were considered, and liner cracks stretching over the vertical wall and roof arch were identified and measured by visual inspection. Then, NDT methods were employed on sound and cracked (cracks greater than 3 mm in width) parts of concrete. Moreover, a significant comparison was made to discern the quality of concrete between cracked and uncracked portions of the tunnel liner. Our findings indicate that cracked areas exhibit significantly lower compressive strength, as the UCS of the cracked liner was measured to be less than 69 N/mm2 and altered ultrasonic properties, suggesting deteriorating structural health. These discrepancies highlight the importance of targeted maintenance strategies to prevent deterioration and potential failure. Furthermore, statistical analysis was performed b/w sound and cracked concrete liner. Finally, specific and targeted repair measures such as surface sealing, epoxy injection, and grouting were recommended based on the severity and characteristics of the observed cracks. These measures could be implemented depending on the need and nature of the cracks. Overall, this research contributes to the field of civil engineering by providing a detailed assessment of tunnel liner defects and reinforcing the role of NDT in effectively managing tunnel infrastructure. The results serve as a valuable reference for enhancing tunnel operation safety protocols and maintenance practices.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579898</guid>
    </item>
    <item>
      <title>Hybrid Diagnostic Framework for Structural Health Assessment of Highway Tunnel Lining</title>
      <link>https://trid.trb.org/View/2579886</link>
      <description><![CDATA[Ensuring the structural integrity of highway tunnel linings is essential for their long-term safety and functionality. This research focuses on improving assessment methods by combining Rebound Hammer tests with Modal Analysis through the use of Finite Element (FE) modelling on concrete tunnel lining. The Rebound Hammer test was performed on five tunnel panel sections to estimate their compressive strength, while Modal Analysis was used to explore the dynamic properties of the tunnel lining, where both results will be verified through the use of FE model simulation. Modal Analysis identified a dominant mode frequency of 29.2 Hz, indicating a reliable dynamic response in the structure. The findings were further validated through FE Model Analysis, where the simulated modal frequency of 32.98 Hz from the compressive strength of 70 MPa obtained from rebound hammer results closely matched the actual measurements. This study demonstrates that integrating these methods provides a more thorough structural health assessment, enabling precise identification of potential defects and informing maintenance strategies. Additionally, the successful use of these techniques highlights the potential of employing accelerometers for similar infrastructure assessments, offering a solid foundation for future studies in structural monitoring.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579886</guid>
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