• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.113-132
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• 1999

Recently in Korea, application of the soil nailing is gradually extended to the sites of excavations and slopes having various ground conditions and field characteristics. Design of the soil nailing is generally carried out in two steps, The First step is to examine the minimum safety factor against a sliding of the reinforced nailed-soil mass based on the limit equilibrium approach, and the second step is to check the maximum displacement expected to occur at facing using the numerical analysis technique. However, design parameters related to the soil nailing system are so various that a reliable design method considering interrelationships between these design parameters is continuously necessary. Additionally, taking into account the anisotropic characteristics of in-situ grounds, disturbances in collecting the soil samples and errors in measurements, a systematic analysis of the field measurement data as well as a rational technique of the optimum design is required to improve with respect to economical efficiency. As a part of these purposes, in the present study, a procedure for the optimum design of a soil nailing excavation wall system is proposed. Focusing on a minimization of the expenses in construction, the optimum design procedure is formulated based on the genetic algorithm. Neural network theory is further adopted in predicting the maximum horizontal displacement at a shotcrete facing. Using the proposed procedure, various effects of relevant design parameters are also analyzed. Finally, an optimized design section is compared with the existing design section at the excavation site being constructed, in order to verify a validity of the proposed procedure.

• Korean Journal of Agricultural Science
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• v.24 no.2
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• pp.145-155
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• 1997

The large scaled field test by prefabricated vertical drains was performed to evaluate the superiority of vertical discharge capacity for drain materials through compare and analyze the time-settlement behavior with drain spacing and the compression index and consolidation coefficient obtained by laboratory experiments and field monitoring system. 1. The relation of measurement settlement($S_m$) versus design settlement($S_t$) and measurement consolidation ratio($U_m$) versus design consolidation ratio($U_t$) were shown $S_m=(1.0{\sim}1.1)S_t$, $U_m=(1.13{\sim}1.17)U_t$ at 1.0m drain spacing and $S_m=(0.7{\sim}0.8)S_t$, $U_m=(0.92{\sim}0.99)U_t$ at l.5m drain spacing, respectively. 2. The relation of field compressing index($C_{cfield}$) and virgin compression index($V_{cclab.}$) was shown $C_{cfield}=(1.0{\sim}1.2)V_{cclab.}$, But it was nearly same value when considered the error with determination method of virgin compression index and prediction method of total settlement. 3. Field consolidation coefficient was larger than laboratory consolidation coefficient, and the consolidation coefficient ratio($C_h/C_v$) were $C_h=(2.4{\sim}3.0)C_v$. $C_h=(3.5{\sim}4.3)C_v$ at 1.0m and 1.5m drain spacing and increased with increasing of drain spacing. 4. The evaluation of vertical discharge capacity with drain spacing from the results of the consolidation coefficient ratio showed largely superior in case the Mebra drain and Amer drain than other drain materials at 1.0m and 1.5m drain spacing, while the values showed nearly same value in case same drain spacing.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.103-116
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• 2008

In this study, in-situ testing method, Dynamic Cone Penetration Test(DCPT) was presented to establish a new compaction control criteria with using mechanical property like elastic modulus instead of unit weight for field compaction control. Soil chamber tests and in-situ tests were carried out to confirm DCPT tests can predict the designed elastic modulus after field compaction, and correlation analysis among the DCPT, CBR and resilient modulus of sub grade were performed. Also, DCPT test spacing criteria in the construction site was proposed from the literature review. In the result of laboratory tests, Livneh's equation was the best in correlation between PR of DCPT and CBR, George and Pradesh's equation was the best in the predicted resilient modulus. In the resilient modulus using FWD, Gudishala's equation estimates little larger than predicted resilient modulus and Chen's equation estimates little smaller. And KICT's equation estimates the modulus smaller than predicted resilient modulus. But using the results of laboratory resilient modulus tests considering the deviatoric and confining stress from the moving vehicle, the KICT's equation was the best. In the results of In-situ DCPT tests, the variation of PR can occur according to size distribution of penetrate points. So DCPT test spacing was proposed to reduce the difference of PR. Also it was shows that average PR was different according to subgrade materials although the subgrade was satisfied the degree of compaction. Especially large sized materials show smaller PR, and it is also found that field water contents have influence a lot of degree of compaction but a little on the average PR of the DCPT tests.

• Journal of the Korea Concrete Institute
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• v.23 no.6
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• pp.711-722
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• 2011

Beam-column gravity or Intermediate Moment frames subjected to unexpected large displacements are vulnerable when no seismic details are provided, which is typical. Conversely, economic efficiency of those frames is decreased if unnecessary special detailing is applied as the beam and column size becomes quite large and steel congestion is caused by joint transverse reinforcement in beam-column connections. Moderate seismic design is used in Korea for beam-column connections of buildings with structural walls, which are to be destroyed when the unexpected large earthquake occurs. Nonetheless, performance of such beamcolumn connections may be substantially improved by the addition of steel fibers. This study was conducted to investigate the effect of steel fibers in reinforced concrete exterior beam-column connections and possibility for the replacement of some joint transverse reinforcement. Ten half-scale beam-column connections with non-seismic details were tested under cyclic loads with two cycles at each drift up to 19 cycles. Main test parameters used were the volume ratio of steel fibers (0%, 1%, 1.5%) and joint transverse reinforcement amount. The test results show that maximum capacity, energy dissipation capacity, shear strength and bond condition are improved with the application of steel fibers to substitute transverse reinforcement of beam-column connections. Furthermore, several shear strength equations for exterior connections were examined, including the proposed equation for steel fiber-reinforced concrete exterior connections with non-seismic details.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.