• Geomechanics and Engineering
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• v.14 no.5
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• pp.491-498
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• 2018

The Earth Mechanics Institute (EMI) was established at the Colorado School of Mines (CSM) in 1974 to develop innovations in rock mechanics research and education. During the last four decades, extensive rock mechanics research has been conducted at the EMI. Results from uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), point load index (PLI), punch penetration (PP), and many other types of tests have been recorded in a database that has been unexamined for research purposes. The EMI database includes over 20,000 tests from over 1,000 different projects including mining and underground construction, and analysis of this database to identify relationships has been started with preliminary results reported here. Overall, statistically significant correlations are identified between bulk density and mechanical strength properties through UCS, BTS, PLI, and PP testing of sedimentary, igneous, and metamorphic rocks. In this paper, bulk density is considered as a surrogate metric that reflects both mineralogy and porosity. From this analysis, sedimentary rocks show the strongest correlation between the UCS and bulk density, whereas metamorphic rocks exhibit the strongest correlation between UCS and PP. Data trends in the EMI database also reveal a linear relationship between UCS and BTS tests. For the singular case of rock coral, the database permits correlations between bulk density of the core versus the deposition depth and porosity. The EMI database will continue under analysis, and will provide additional insightful and comprehensive understanding of the variation and predictability of rock mechanical strength properties and density. This knowledge will contribute significantly toward the increasingly safe and cost-effective geostructures and construction.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.03a
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• pp.25-40
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• 2008

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2000.09a
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• pp.223-228
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• 2000

In this study, RFD(Rock Blasting Design) program was developed to perform easily on plans of rock blasting. This program has abilities as follows, that is. the test blasting plan, the bench blasting plan, and the blasting vibration analysis. The value of geological property and blasting constants was offered by database, input value of variety constants repeatedly is planned out, faster and easier. And a value of input constant may be used by user for necessity.

• Tunnel and Underground Space
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• v.18 no.5
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• pp.317-327
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• 2008

About 70% of domestic land is mountainous and the construction of many geotechnical structures is inevitable for building transportation networks across region. Many geotechnical surveys, including rock physical and mechanical tests, are performed during construction. Thus study is a basic research for establishing database of physical and mechanical properties in domestic rocks, and analyzing the rock mechanical relationships between 2,000 rock properties obtained from laboratory tests in KOLAS. For the construction of useful database, systematic management, based on the standard information as well as reliable data accumulation, is required.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.43-51
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• 2008

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

• Tunnel and Underground Space
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• v.11 no.3
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• pp.206-216
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• 2001

Many failures in cut slopes occur during and following road construction. Failures are caused, in part, by a lack of understanding of the characteristics of rock mass including its geologic structure. The stability of rock slopes is closely related to factors that include the type of rock, development of geological structures, weathering, characteristics of rock, and the shape of the geologic features. Therefore, it is very important to consider these characteristics of rock mass in the evaluation of rock slope stability. In spite of investigation from many slope failures, these information data were not systematically stored and not efficiently utilized. In this study, a Database system named DB/SLOPE was developed using Oracle for systematic management of cut slopes. The developed database system can be used to estimate of slope stability and to predict of slope failure.

• Tunnel and Underground Space
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• v.10 no.3
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• pp.469-474
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• 2000

In this study, RBD(Rock Blasting Design) program was developed to perform easily on plans of rock blasting. This program has abilities as follows, that is, the test blasting plan, the bench blasting plan, and the blasting vibration analysis. The value of geological property and blasting constants was offered by database, input value of variety constants repeatedly is planned out, faster and easier. And a value of input constant may be used by user for necessity.

• Earthquakes and Structures
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• v.16 no.6
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• pp.705-714
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• 2019

Seismic assessment of underground structures is one of the challenging problems in engineering design. This is because there are usually many sources of uncertainties in rocks and probable earthquake characteristics. Therefore, for decreasing of the uncertainties, seismic response of underground structures should be evaluated by sufficient number of earthquake records which is scarcely possible in common seismic assessment of underground structures. In the present study, a practical risk-based approach was performed for seismic risk assessment of an unsupported tunnel. For this purpose, Incremental Dynamic Analysis (IDA) was used to evaluate the seismic response of a tunnel in south-west railway of Iran and different analyses were conducted using 15 real records of earthquakes which were chosen from the PEER ground motion database. All of the selected records were scaled to different intensity levels (PGA=0.1-1.7 g) and applied to the numerical models. Based on the numerical modeling results, seismic fragility curves of the tunnel under study were derived from the IDA curves. In the next, seismic risk curve of the tunnel were determined by convolving the hazard and fragility curves. On the basis of the tunnel fragility curves, an earthquake with PGA equal to 0.35 g may lead to severe damage or collapse of the tunnel with only 3% probability and the probability of moderate damage to the tunnel is 12%.

• Tunnel and Underground Space
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• v.9 no.3
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• pp.214-220
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• 1999

In this study, the validity of items of RMR system is evaluated and the applicability of this system to the data measured in Korean sites if discussed. Database was constructed from 139 sites, which are composed of subways, railway tunnels and road tunnels. These sites are located nationwide. Analysis shows that original classification of Bieniawski is valid although it was derived empirically. But it has considerable rating difference (error) in the result of Korean application. Thus new classification systems of KRMRI and KRMR2 are suggested, which are deduced from the Korean database. The former includes adjusted ratings and the latter adopts two more items. These are deduced by artificial neural network because it is difficult to select \`characteristic value'to estimate rock quality.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.