• The Journal of Engineering Geology
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• v.21 no.4
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• pp.361-367
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• 2011

The uniaxial compressive strength (UCS) is an important factor in the design and construction of surface and underground structures. However, the method employed to measure UCS is time consuming and expensive to apply in the field. Therefore, we developed a model to estimate UCS based on a few properties using linear regression analysis, which is a statistical method. To develop the model, valid factors from the test results were selected from a correlation analysis using a statistical program, and the model was formulated by linear regression based on the relationships among factors. UCS estimates derived from the model were compared with the results of UCS tests, to assess the reliability of the model. The relationship between rock properties and UCS indicates that the factors with the greatest influence on UCS are point load strength and shape facto r. The UCS values obtained using the model are in good agreement with the results of the UCS test. Therefore, the developed model may be used to estimate the UCS of rocks in regions with similar conditions to those of the present study area.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.18-25
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• 2020

The unconfined compressive strength of lightweight treated soils strongly depends on mixing ratio. To characterize the relation between various LTS components and the unconfined compressive strength of LTS, extensive studies have been conducted, proposing normalized factor using regression models based on their experimental results. However, these results obtained from laboratory experiments do not expect consistent prediction accuracy due to complicated relation between materials and mix proportions. In this study, deep neural network model(Deep-LTS), which was based on experimental test results performed on various mixing conditions, was applied to predict the unconfined compressive strength. It was found that the unconfined compressive strength LTS at a given mixing ratio could be resonable estimated using proposed Deep-LTS.

• Journal of the Korean Geotechnical Society
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• v.27 no.1
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• pp.17-24
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• 2011

The characteristics of flow and strength of CLSM depend on the combination ratio including the fly ash, pond ash, cement, water quantity and etc. However, it is very difficult to draw the mechanism about the flow, strength and the mixing ratio of each components. Therefore, the method of calculation drawing the flow about the component ratio of CLSM and compression strength value is needed for the valid practical use of CLSM. To verify the efficiency of artificial neural network, new data which were not used for establishing the model were predicted and compared with the results of laboratory tests. In this research, it was used to evaluate the learning efficiency of the artificial neural network model and the prediction ability by changing the node number of hidden layer, learning rate, momentum, target system error and hidden layer. By using the results, the optimized artificial neural network model which is suitable for a flow and compressive strength estimate of CLSM was determined.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.4
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• pp.383-399
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• 2020

Optimizing the spacing of the disc cutter is a key element in the design of the TBM cutter head, which determines the drilling performance of the TBM. The full-scale linear cutting test is known as the most reliable and accurate test for calculating the spacing of the disc cutter, but it has the disadvantage of costly and time-consuming for the full-scale experiment. In this study, through the numerical analysis study based on the discrete element method, the tendency between Specific Energy-S/P ratio according to uniaxial compression strength and penetration depth of rock was analyzed, and the optimum spacing of 17-inch disc cutter was derived. To examine the appropriateness of the numerical analysis model, the rolling force acting on the disc cutter was compared and reviewed with the CSM model. As a result of numerical analysis for the linear cutting test, the rolling force acting on the disc cutter was analyzed to be similar to the rolling force derived from the theoretical formula of the CSM model. From the numerical analysis on 5 UCS cases (50 MPa, 70 MPa, 100 MPa, 150 MPa, 200 MPa), it is found that the range of the optimum spacing of the disc cutter decreases as the rock strength increases. And it can be concluded that 80~100 mm of disc cutter spacing is the optimum range having minimum specific energy regardless of rock strength. This tends to coincide with the optimal spacing of previously reported disk cutters, which underpins the disk cutter spacing calculated through this study.

• Journal of the Korean Geotechnical Society
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• v.19 no.1
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• pp.209-220
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• 2003

Modification of general Hoek and Brown criterion is carried out to estimate the shaft resistance of drilled shaft socketed into rock mass. Since the general Hoek-Brown criterion can consider the in-situ state of the rock mass, the proposed method, estimating the unit shaft resistance of drilled shafts based on the Hoek-Brown criterion, has increased flexibility compared to other methods exclusively considering uniaxial compressive strength of intact rocks. The proposed method can form the upper and lower bounds, and most culled data (from 21 pile load tests) from the literature can be found between these two bounds. A comparison between the estimated and observed unit shaft resistances shows quite a good correlation even with crude assumptions for the input parameters. The best-fit line drawn from this analysis shows that at the lower strength of intact rocks (up to 10MPa), Horvath and Kenney's equation shows a good correlation with the measured values, and fur strong rocks Rosenberg and Journeaux's equation provides a close estimation with colleted data. The results of parametric studies for GSI and confining stress show that the normalized unit shaft resistance increases with these two factors. In addition, coefficient of the equational form of the estimation can vary with GSI and confining stresses.

• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Tunnel and Underground Space
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• v.17 no.5
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• pp.411-427
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• 2007

Reliability in tunnel analysis is necessary to accomplish technically sound design and economical construction. For this, a thorough understanding of the construction procedure including the ground-support interaction has to be obtained. This paper describes a proper modelling technique to simulate the behavior of the steel fiber reinforced shotcrete (SFRS) which maintain the supporting capability in post-failure regime. The additional supporting effect of the steel support was also verified by 3-D analyses and a new load distribution factor were proposed. The use of the plastic moment limit (PML) alone can eliminate the occurrence of the awkwardly high tensile stress in the shotcrete and can successfully model the post-peak ductile behavior of the SFRS. But with this method, moment is limited whenever the stress caused by moment reaches tensile strength of the shotcrete irrespective of the stress by axial force. Therefore, it was necessary to find a more comprehensive method which can reflect the influence of the moment and axial force. This can be accomplished by the proper use of "liner element" which is the built-in model in FLAC. In this model, the peak and residual strength as well as the uniaxial compressive strength of the SFRS can be specified. Analyses were conducted with these two models on the 2-lane road tunnels excavated in class IV and V rock mass and results were compared with the conventional elastic beam model. Results showed that both models can reflect the fracture toughness of the SFRS which could not be accomplished by the elastic beam model.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.