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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>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Applications of NATM in Garhwal Himalayan Tunnelling based on Field Evidence from Rishikesh–Karnaprayag Railway Tunnel Project</title>
      <link>https://trid.trb.org/View/2690968</link>
      <description><![CDATA[Geological conditions encountered during the Rishikesh–Karnaprayag railway tunnel construction deviated significantly from contractual predictions across rock classes C2 to B1. Stable B1 rock was absent, while squeezing (C2/C3) and rolling (B3) ground were encountered 4.1 times more than forecast. To address this uncertainty, pull length was modelled using data from over 200 blast rounds. Multiple linear regression correlated pull length to specific drilling, specific charge, and tunnel area. Specific charge decreased from 1.68 kg/m³ in B1 to 0.70 kg/m³ in C2 and further with increasing tunnel area, confirming a conservative, low-energy blasting strategy in poor ground. Rock-class-specific models achieved coefficient of determination (R²) values of 0.18–0.54, with the highest predictability in B3. Monte Carlo simulation (10,000 iterations) quantified prediction uncertainty, yielding 90% prediction intervals ranging from 0.98 to 1.47 m in C2 to 1.95–3.90 m in B1. These intervals provide a risk-informed basis for advance planning. The results demonstrate that New Austrian Tunnelling Method’s observational adaptability, guided by real-time blasting and support calibration, is essential for safe and efficient excavation in geologically unpredictable Himalayan terrain. The models offer field-validated, quantitative guidelines for blast design where conventional assumptions fail.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690968</guid>
    </item>
    <item>
      <title>Physics-Informed Explainable AI and SMOTE-GPC for the Classification of Surrounding Rock Mass in Tunneling</title>
      <link>https://trid.trb.org/View/2533724</link>
      <description><![CDATA[The classification of surrounding rock mass is essential for characterizing rock properties and geological conditions in tunneling engineering. While numerous empirical rock mass classification systems have been proposed (e.g., rock mass rating system, rock structure rating system), they tend to heavily rely on engineers’ experience, which is unfavorable for tunnel construction, particularly in deep-buried and ultralong tunnels. Alternatively, machine learning, i.e., artificial intelligence (AI), methods estimate the classification of the surrounding rock mass using certain readily available rock indices (e.g., volumetric joint count). However, most machine learning models are considered black box models, leading to unexplainable predictions. In addition, employing all measurements of readily available rock indices as input may lead to excessive model complexity and a reduction in generalization performance. In this case, a Gaussian process classification (GPC) approach combined with the synthetic minority oversampling technique (SMOTE), Bayesian framework, and SHapley Additive exPlanations is proposed in this study for the probabilistic classification of the surrounding rock mass and selection of the optimal GPC model based on imbalanced and sparse measurement data. It is worth noting that the proposed method can also provide physics-informed explanations for the prediction and model class selection results and determine the significant input variables for each grade of the surrounding rock mass. A real-life example is employed to illustrate and validate the proposed approach. The results show that the F₁ score of the optimal GPC model reaches 0.93, which is comparable with those of the GPC model with all input variables.]]></description>
      <pubDate>Fri, 18 Apr 2025 09:17:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2533724</guid>
    </item>
    <item>
      <title>Construction and Application of Combination Prediction Model for Tunnel Surrounding Rock Deformation</title>
      <link>https://trid.trb.org/View/2475359</link>
      <description><![CDATA[In the construction of tunnel engineering, the stability of surrounding rock deformation is closely linked with the safety of tunnel engineering. Therefore, to better predict the deformation of tunnel surrounding rock during the construction period, this paper combines the existing gray prediction model and time series model in a nonlinear way, constructs a gray time series combination model, and applies the combination model to the actual tunnel engineering monitoring data to test the effectiveness of its deformation prediction. The results show that the combined model can effectively improve the prediction accuracy compared with a single model, thus realizing better deformation prediction.]]></description>
      <pubDate>Fri, 27 Dec 2024 15:28:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475359</guid>
    </item>
    <item>
      <title>Analysis of rock mass classifications for safer infrastructures</title>
      <link>https://trid.trb.org/View/1897898</link>
      <description><![CDATA[In the construction of land transport infrastructures such as roads, highways, or railways, one of the factors that most determine their design is the characteristics of the terrain through which they run. Additionally, tunnels have become one of the most employed solutions to reduce the environmental impact. The characteristics of the rock mass are a vital point to decide the layout of the tunnel and the construction method to tunnel. However, the rock mass are discontinuous, anisotropic, and heterogeneous media, so their classification and knowledge are necessary for a safer design of these infrastructures.The rock mass is not an industrial material with “pre-established” properties and behaviors, but rather a natural material that needs to be analyzed, understood, and standardized. The need to understand the behavior of the rock mass has led throughout modern history to the use of different standards, which lead to the development of geomechanical classifications, with the aim of establishing a common language that translates the very advanced geological language in the macro and microgeological behavior, which is necessary to translate for applications in civil engineering. In the last decades of the 20th century, and in the present 21st, the efforts in the process of understanding the intact rock and the rock mass has been constantly increasing because a better understanding of the rock mass behavior implies a better development of projects developed in the rock mass. This paper succinctly reviews the history of rock mass classifications, their implications in rock mechanics and their applicability in the definition of behaviors as a function of natural conditions and by human action, and their direct implication in some fields of the transport infrastructures management with regard to the hazard and risks.]]></description>
      <pubDate>Wed, 29 Dec 2021 17:16:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1897898</guid>
    </item>
    <item>
      <title>Uncertainty Analysis in Rock Mass Classification and Its Application to Reliability Evaluation in Tunnel Construction</title>
      <link>https://trid.trb.org/View/1881959</link>
      <description><![CDATA[Creation of underground infrastructures and facilities provides a viable solution to rapid urbanization and population growth with the limited and increasingly congested space on the surface, which has posed a critical challenge to urban population’s demands on the living environment. This includes road and rail transport systems, utility tunnels, water and sewage, parking, storage, and even living quarters. These underground structures are constructed in rock and soil materials, which are not precisely known before excavation. This means that there is intrinsic uncertainty due to the inherently heterogeneous nature of the ground, which can have adverse effects on the design and construction of underground works. Traditional deterministic design methods are based on a limited understanding of this inherent uncertainty, which may result in over- or under- design of underground structures. To address this issue, a systematic assessment of uncertainties in rock mass classification systems has been conducted in this study, in conjunction with a reliability-based approach, to evaluate the stability of underground openings. The rock mass quality Q-system has been used as an example of rock mass classification systems in this study, but the approach can also be applied to other rock mass classifications such as rock mass rating (RMR) and geological strength index (GSI).]]></description>
      <pubDate>Wed, 10 Nov 2021 13:54:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1881959</guid>
    </item>
    <item>
      <title>Dynamic prediction models of rock quality designation in tunneling projects</title>
      <link>https://trid.trb.org/View/1760849</link>
      <description><![CDATA[Machine learning (ML) is becoming an appealing tool in various fields of civil engineering, such as tunneling. A very important issue in tunneling is to know the geological condition of the tunnel route before the construction. Various geological and geotechnical parameters can be considered according to data availability to define tunnels' ground conditions. The Rock Quality Designation (RQD) is one of the most important parameters that are very effective in tunnel geology. This article aims to maximize the prediction accuracy of the RQD parameter along a tunnel route through continuous updating techniques. For this purpose, four ML methods of K-nearest neighbor (KNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) were considered. All the RQD observations along the tunnel route were considered as the models’ inputs. For predicting the RQD status along the entire tunnel route, the ML models use the regression technique. For checking the applicability of the models, the Hamru road tunnel in Iran was used. The models were updated twice to assess the update effect on the results achieved during the tunnel construction. In each prediction phase, all the prediction results were compared using different statistical evaluation criteria and the actual mode. Finally, the comparative tests' findings showed that predictions of the GPR model with R2 = 0.8746/root mean square error (RMSE) = 3.5942101, R2 = 0.9328/RMSE = 2.5580977, and R2 = 0.9433/RMSE = 1.8016325 are generally well-suited to actual results for pre-update, first update, and second update phases, respectively. The updating procedure also leads to prediction models that are more accurate and less uncertain than the previous prediction stage.]]></description>
      <pubDate>Wed, 27 Jan 2021 09:56:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1760849</guid>
    </item>
    <item>
      <title>Blasting-Induced Vibration Response of the Transition Section in a Branching-Out Tunnel and Vibration Control Measures</title>
      <link>https://trid.trb.org/View/1724880</link>
      <description><![CDATA[Blasting-induced vibration during the excavation of transition section in a branching-out tunnel causes damage and hence affects the safety and stability of the supporting structure and surrounding rock. To examine the effects of excavation and blasting of the transition section in the posterior tunnel on the supporting structures of the anterior tunnel, the influences of the blasting-induced vibration in the posterior tunnel on the anterior tunnel were analyzed under different surrounding rock levels, excavation techniques, distances from explosive source, and net spans. This method was performed by combining numerical simulation with blasting-induced vibration monitoring according to the construction characteristics of the transition section in a branching-out tunnel of a highway. A control technique was investigated to assure the safety and stability of the anterior tunnel during the excavation and blasting of the posterior tunnel. Results demonstrate that (1) the vibration velocity peak behind the blasting excavation surface of the tunnel is higher than that in front. These results suggest paying much attention in monitoring vibration velocity within 10 m behind the excavation surface. (2) The blasting-induced vibration velocity peak on the spandrel at the side that faces the blasting in the anterior tunnel is 2.0–2.5 times than that at the side behind the blasting. Moreover, the blasting-induced vibration velocity peak on the haunch at the side that faces the blasting in the anterior tunnel is 6-7 times than that at the side behind the blasting. (3) Instead of the full-face excavation method, the use of center cross diagram (CRD) technique or side wall pilot tunnel method is suggested for the excavation of surrounding rocks of IV-level, V-level, and III-level with a net span smaller than 3 m. (4) Vibration control measures, such as double wedge-shaped cut blasting and floor blast-hole staged detonation, were adopted by designing and optimizing blasting parameters (e.g., total explosives, maximal segment explosive quantity, detonation order, and detonation interval) in posterior tunnel. According to the test, the blasting-induced vibration velocity peak, which is monitored in the anterior tunnel, can be controlled within 10 cm/s to assure the safety and stability of the supporting structure and surrounding rocks of the tunnel.]]></description>
      <pubDate>Thu, 20 Aug 2020 14:03:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1724880</guid>
    </item>
    <item>
      <title>Physical and Mechanical Properties of Gypsum-Like Rock Materials</title>
      <link>https://trid.trb.org/View/1717670</link>
      <description><![CDATA[In the process of tunnel construction, gypsum rock is often encountered, and the volume of gypsum rock expands when encountering water, which is easy to cause the occurrence of rock fall, collapse, and other disasters, bringing serious challenges to the safe construction of the tunnel. Therefore, in this paper, four groups of samples under different moisture content are tested by ultrasonography, uniaxial compression, conventional triaxial compression, Brazilian splitting, X-ray diffraction, and SEM, and then the physical and mechanical properties of gypsum rock are studied, and the conclusion is as follows: the density of the water saturated sample, and the longitudinal wave velocity of the natural sample are the highest. Both the water saturation and dehydration conditions have a weakening effect on the remolded sample of high-strength gypsum powder. The peak intensity of the sample gradually increases with the increase of confining pressure, and the relationship between the peak intensity and confining pressure of the sample conforms to the Coulomb strength criterion. After high-temperature dehydration, the sample showed obvious plastic softening characteristics. The cohesion and internal friction angle of the sample are closely related to the water content. The cohesion is the largest in the 45°C dehydrated sample, the internal friction angle is the smallest in the saturated sample, whereas the cohesion is the smallest and internal friction is the largest in the high-temperature dehydrated sample. The characteristics of failure for the natural and 45°C dehydrated samples are almost the same and most samples show shear or shear-tensile failure. The shear plane begins at the edge of the end face of the sample and exhibits a typical diagonal shear failure. The high-temperature dehydrated samples are completely broken under uniaxial and triaxial compression conditions. After high-strength gypsum powder was used to make the remolded sample, the calcium sulfate disappeared, the water content increased, and the main mineral components of the natural and saturated samples were the same. After dehydration at 45°C, the sample began to release structural water and generate SiO<sub>2</sub>. After high-temperature dehydration, the hemihydrate gypsum continued to dehydrate and become soluble anhydrous gypsum.]]></description>
      <pubDate>Wed, 22 Jul 2020 14:40:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1717670</guid>
    </item>
    <item>
      <title>ESTIMATION OF FLOOR HEAVE MECHANISM OF ROCK TUNNELS FOCUSED ON THE CHANGE IN WATER CONTENT</title>
      <link>https://trid.trb.org/View/1679769</link>
      <description><![CDATA[Some mountain tunnels in service suffer from floor heave, and some of them require countermeasures. Case analysis and literature survey were conducted in order to clarify the mechanism of floor heave. From these, it was found that the information at the time of construction couldn’t explain the floor heave after completion enough though floor heave and water was closely related to the floor heave. Furthermore, rock tests and a model test were carried out. These results reveal even if there is little spring water at the time of construction, water will be supplied in some way after completion, and that may lead ground deterioration, and sudden change in water content may lead sudden floor heave. Based on these results, the authors have shown some scenarios of floor heave mechanism of the mountain tunnels, focusing on the amount of spring water at the time of construction and the water supply after completion.]]></description>
      <pubDate>Thu, 19 Mar 2020 10:25:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1679769</guid>
    </item>
    <item>
      <title>INFLUENCE OF CHARACTERISTICS OF TRAINING DATA SET ON THE ACCURACY OF ROCK CLASSIFICATION ESTIMATION FOR MOUNTAIN TUNNELS BY ARTIFICIAL NEURAL NETWORK</title>
      <link>https://trid.trb.org/View/1674361</link>
      <description><![CDATA[In mountain tunnel construction projects, discrepancies between the rock classifications specified at the basic design stage and the rock classifications applied at the actual construction stage often cause significant cost overruns. Therefore, it is expected to improve the accuracy of the rock classification estimated based on the preliminary geological survey. From this point of view, the authors proposed a methodology related to rock classification estimation by artificial neural network. However, past studies have been conducted under the condition that available tunnel data sets are limited. In this study, the authors focused on the influence of the increase in training data on the estimation results and examined the applicability of the proposed method. As a result, in addition to the increase of the indexes applied to the data set, it was found that deep learning of various rock states is effective to improve the accuracy of rock classification estimation.]]></description>
      <pubDate>Wed, 26 Feb 2020 10:51:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1674361</guid>
    </item>
    <item>
      <title>Application of Neural Networks in Analyzing of Rock Mass Parameters in Tunnelling</title>
      <link>https://trid.trb.org/View/1373863</link>
      <description><![CDATA[One of the key problems in tunneling is to define realistic parameters for rock mass properties as a basis for successful numerical modelling. The main goal of this problem is how to extrapolate the parameter from the zone of testing to the whole area (volume) that is of interest for interaction analyses of the system rock mass-structure. So it is necessary to find an appropriate way to find intelligent tools to combine data from empirical classification rock mass methods having in mind that there are a lot of variations in statistic values.First step in the procedure is to divide the tunnel length in quasi-homogenous zones, while the second is to define adequate geotechnical and numerical models as a basis for interaction of rock – structures system and stress-strain behaviour of rock massif. Artificial neural networks (ANN) have been found to be powerful and versatile computational tools for many different problems in civil engineering over the past 2 decades. They have proved useful for solving certain types of problems, which are too complex, or too resource-intensive nonlinear problems to tackle using more traditional computational methods, such as the finite element method. ANN-s are intelligent tools, which have gained strong popularity in a large array of engineering applications such as pattern recognition, function approximation, optimization, forecasting, data retrieval, automatic control or classification, where conventional analytical methods are difficult to pursue, or show inferior performance. A review of some problems in tunnelling that were successfully solved by using neural networks is presented in this paper. A general introduction to neural networks (NN), their basic features and learning methods is given. After that, it is possible to use neural networks to solve necessary problems in tunnelling.]]></description>
      <pubDate>Tue, 24 Nov 2015 09:28:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1373863</guid>
    </item>
    <item>
      <title>Analysis on 3D the Dynamic Pressure Arch Effect around a Mountain Tunnel</title>
      <link>https://trid.trb.org/View/1356516</link>
      <description><![CDATA[The existence of pressure arch of tunnel surrounding rock is while known in engineering. The idea that this pressure arch in surrounding rock is helpful to maintaining tunnel stability is accepted both in engineering and in research. In this paper, variations of surrounding rock stress are investigated by using PFC3D, a concept of dynamic pressure arch in the surrounding rock is proposed, and then the 3D dynamic pressure arch effect around the tunnel is analyzed. The 3D dynamic pressure arch effect will be helpful to judge tunnel stability, choose excavation method in tunneling, accurately determine the loosed load, economically design advancing reinforcement measures and rock bolts.]]></description>
      <pubDate>Mon, 29 Jun 2015 09:13:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1356516</guid>
    </item>
    <item>
      <title>A NUMERICAL MODELING STUDY OF UNSTABLE FAILURE DURING TUNNEL EXCAVATION</title>
      <link>https://trid.trb.org/View/1325451</link>
      <description><![CDATA[Numerical analysis is often used for examining underground excavations, especially under severe ground conditions where rock failure such as rockburst, swelling, or squeezing may occur. Perfectly plastic material models are most commonly used in determining rock failures around excavations even though most rocks exhibit brittle and softening behavior in their post-peak regime. In this study, brittle, less brittle and perfectly plastic materials are used in the finite difference study with plane strain condition to examine the effect of the brittleness on unstable failure during tunnel excavation. In addition, the influence of tunnel shape during unstable failure is also examined using the most brittle material characteristics. The major conclusions of this study include: (1) The failed area formed in the numerical model is dependent on the brittleness of the material. (2) Unstable failure such as rockburst can be examined using numerical measures including velocity, acceleration and excess energy in the numerical model. (3) Rectangular tunnel shapes lead to the largest releases of excess energy during unstable failure as compared to circular and horse-shoe shaped tunnels.　極端に悪い地山条件におけるトンネル掘削では, 数値解析による検討が行われる場合が多い. この数値解析では, 地山の破壊を考慮した検討が必要不可欠であるが, 多くの地山がポストピーク特性においてひずみ軟化を伴った脆性的な挙動を示すにも関わらず, 完全塑性体と仮定した数値解析モデルが多用されているのが現状である. 本研究では, ポストピーク特性においてひずみ軟化を示す数値解析モデルを用いて, トンネル掘削における地山の挙動を検討した. その結果, トンネル掘削の影響による破壊領域は, 完全塑性モデルを使った場合と比較してひずみ軟化モデルを使用した場合の方が大きく, その広がりは地山の脆性度に依存することが分かった. また, 山はねのような不安定な破壊に関して, ひずみ軟化モデルを使用することでそのメカニズムが検討できる可能性があること等が分かった.]]></description>
      <pubDate>Fri, 31 Oct 2014 10:58:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/1325451</guid>
    </item>
    <item>
      <title>Prediction of Ground Conditions Ahead of an Advancing Tunnel Face by Quantification of Vector Orientation</title>
      <link>https://trid.trb.org/View/1310322</link>
      <description><![CDATA[Observation and measurement of displacements at the crown of the tunnel is an integral part of New Austrian Tunneling Method (NATM) in complex rock masses to verify the construction parameters, support systems and safety requirements. However, the prediction of ground conditions ahead of an advancing tunnel face is also an application, often less explored in the field by site engineers. Literature suggests displacement vector orientation is a good indicator of weak ground or fault/shear zones ahead of the tunnel face. There has been no quantification between the vector orientation and the properties of the weaker ground ahead. An attempt is made to quantify the effect of the properties of the weaker ground on the vector orientation numerically. The model results are initially compared with analytical solutions, followed by benchmarking with the results of other researchers. The factors which affect the vector orientation are the diameter of the tunnel, stiffness ratio of the rocks and in situ stress ratio. The effect of each of these parameters is studied independently. A correlation between the vector orientation and the variation in ground conditions is then established to predict the properties of the weaker ground ahead of the advancing tunnel face.]]></description>
      <pubDate>Fri, 27 Jun 2014 17:02:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1310322</guid>
    </item>
    <item>
      <title>Einfluss der Geologie auf die in einem Erkundungsstollen vor der Ortsbrust des Sentvid-Tunnels gemessenen Verschiebungen / Influence of the geological structure on the displacements measured ahead of the Sentvid tunnel face in small diameter exploratory tunnel</title>
      <link>https://trid.trb.org/View/1253176</link>
      <description><![CDATA[Im Autobahnring um die slowenische Hauptstadt Ljubljana wird der zweiroehrige Sentvid-Tunnel mit Verzweigungen und den dadurch bedingten Kavernen aufgefahren. Um die optimalen Standorte der beiden Kavernen zu finden und um geotechnische Modell- und Materialparameter fuer den Entwurf zu erhalten, wurde ein Erkundungsstollen aufgefahren. Einige Details des Erkundungsstollens und die bei dessen Vortrieb gewonnenen Ergebnisse und Erkenntnisse werden beschrieben. Aufgefahren wird der etwa 1.080 Meter lange Sentvid-Tunnel in bergmaennischer Bauweise. Die Ueberdeckung betraegt zwischen circa 100 und 60 Meter ueber den Kavernen. Der Ausbruch erfolgt mit Tunnelbaggern nach den Prinzipien der NOET. Waehrend die Tunnelabschnitte Kalotte, Strosse und Sohle sukzessiv ausgebrochen wurden, mussten fuer die Kavernen mit Spannweiten bis zu 26 Metern und Hoehen bis zu 16 Metern spezielle Ausbruchfolgen mit bis zu 100 Schritten anhand eines dreidimensionalen geotechnischen Modells entwickelt werden. Der Tunnel verlaeuft durch dichtes, schiefriges Sedimentgestein des Karbons. Waehrend mehrerer Verformungsphasen unterlag die Region starken tektonischen Deformationen. Das Gebirge ist dadurch sehr heterogen und anisotrop. Das urspruengliche geologische Modell aus dem Jahre 2002 konnte durch die mit dem Erkundungsstollen gewonnenen Ergebnisse wesentlich verbessert werden. Danach war mit vier verschiedenen Gebirgstypen zu rechnen. Das ueberwiegend zu erwartende Gebirgsverhalten beim Tunnelvortrieb war gepraegt durch ein Entspannungsgleiten entlang der Schieferungen oder entlang von Stoerungsebenen. Zur Minimierung der Einwirkungen des Vortriebs des Erkundungstollens auf das Gebirge, und um den Ausbruch des Haupttunnels nicht zu behindern, wurde der Erkundungsstollen im oberen Bereich des Querschnitts der Hauptroehren angelegt. Die beim Vortrieb vorgenommenen dreidimensionalen Verschiebungsmessungen vor der Ortsbrust innerhalb des Erkundungsstollens und die Verformungsmessungen ausserhalb des Erkundungsstollens werden detailliert beschrieben. Besonders eingegangen wird auf die Theorie und die Praxis der Bestimmung der Verschiebungen vor der Ortsbrust vor dem Auffahren des Haupttunnels. Der Erkundungsstollen wurde mit umfassenden Messeinrichtungen im Rahmen von experimentellen Erkundungen bestueckt. Exemplarisch werden die Ergebnisse fuer einen bestimmten Tunnelabschnitt dargestellt. Die Ergebnisse zeigen, dass die Verformungen sehr stark von der Anisotropie des Gebirges mit den verschiedenen Anisotropieebenen abhaengen; dies betrifft insbesondere die Vorverschiebungen vor der Ortsbrust. Durch die Beobachtung der Spritzbetonschale des Erkundungsstollens konnten rechtzeitig Bereiche mit Sulfatangriff und dem dadurch moeglichen Zerfall des Spritzbetons lokalisiert werden. ABSTRACT IN ENGLISH: The Sentvid tunnel had first been designed as twin tube double-lane tunnel. Later on, the design included the third traffic lanes and connecting ramp tunnels. The underground junctions required the construction of large caverns. A small diameter exploration gallery was excavated in order to find the optimum locations for both caverns in foliated Permo-Carboniferous soft rock conditions and to provide geotechnical model and material parameters for the design. The exploration gallery furthermore enabled displacement measurements ahead of the excavation face of the main tunnel during its construction. The article presents some details of the exploration gallery and the results obtained from it, showing the results of displacement measurements ahead of the tunnel face and comparisons of the observed displacements ahead of the tunnel face with geological conditions. The tunnel construction won international attention due to its complexity and as an example of best practice in the construction of very large excavations in diverse geotechnical conditions. During the decision making process, an international panel of recognized experts from consulting companies and universities was involved. Later on, the panel regularly monitored the progress of tunnelling works and contributed to the decisions during critical steps of the project. The decision to involve international experts with different experiences and approaches permitted the client to keep the construction risks within acceptable limits. (A)]]></description>
      <pubDate>Thu, 20 Jun 2013 12:06:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1253176</guid>
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