• The Journal of Engineering Geology
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• v.18 no.1
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• pp.17-26
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• 2008

In this study, the design method of slope reinforced by the earth retention systems were systematically developed, and the flow chart of design procedure fur each system were constructed to design the slope rationally. The proposed design method is composed of 5 steps such as field condition investigation step, slope design step, landslide occurrence prediction step, slope failure scale estimation step and reinforcement countermeasure selection step. The quantitative standard of slope failure scale was established based on the arrangement of various overseas standards which is estimating the slope failure, and the analysis of slope failure scale which is occurred in the country. The slope failure scale is classified into three categories the small scale of slope failure is less than $150m^3$ of slope failure volume, the middle scale of slope failure is from $150m^3$ to $900m^3$ and the large scale of slope failure is more than $900m^3$. The earth retention system could be selected by the proposed slope failure scale based on the slope failure volume. Meanwhile, the design methods of earth retention system such as piles, soil nails and anchors were developed. The optimal countermeasure for slope stability could be proposed using above design methods.

• Journal of the Korean Wood Science and Technology
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• v.37 no.5
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• pp.465-473
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• 2009

Three failure theories were studied to evaluate the wood strength variation with grain slope. Maximum stress theory, Tsai-Hill theory and Hankinson formula were presented to hypothesize the failure of wood according to grain slope to loading direction. Red pine and Japanese larch were used as materials to simulate failure strength prediction with grain slope. Calculation of strength results was that the strength of wood drops rapidly between parallel to grain orientation (0 degree) and 15 degree grain orientation. The strength of wood with grain orientation were somewhat different at small grain angles among failure theories, and this tendency was due to tension and compression distinction, and shear accounting in each theories. For the above 45 degree grain orientation, the predicted failure strength of wood with grain variation were very close in each failure theories and were useful in assessing failure strength of wood. The applicable these theories should be considered that the wood has different behavior in tension and compression, and this lead to different strength at small grain angles in each theories. Furthermore, reconsideration is needed to assess the failure strength of wood at small grain angles in Hankinson formula and further studies are necessary to accounting for shear behavior at small grain angles.

• Journal of Korean Society of Forest Science
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• v.88 no.2
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• pp.240-248
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• 1999

On the basis of data obtained from five forest roads collapsed due to a heavy rainfall of 1995 in Chunchon, Kangwon-do, this study was carried out to evaluate and predict the fill slope failure of forest roads with four factors of forest road structure and those of location condition by using Quantification theory(II). The results were summarized as follows ; In the structure factors of forest road, the fill slope failure was mainly occurred in longitudinal gradients less than $2^{\circ}$ or more than $4^{\circ}$, distance of surface-flow longer than 80m, fill slope length greater than 6m, and fill slope gradients steeper than $35^{\circ}$. In the factors of location condition, the failure was mainly occurred in ridge portion of road position, weathered rock and soft rock of constituent material, slope gradients in the range from $35^{\circ}$ to $45^{\circ}$, and concave and convex of longitudinal slope forms. The priority order for factors influencing on fill slope failure was ranked by fill slope length, constituent material, road position, and so on. And the rate of correct discrimination by analysis of fill slope failure was estimated at the high prediction of 86.5%.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.335-346
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• 2022

Buckling failure is a typical slope instability mode that should be paid more attention to. It is difficult to provide systematic guidance for the monitoring and management of such slopes due to unclear mechanism. Here we examine buckling failure as the potential instability mode for a slope above a railway tunnel in southwest China. A comprehensive model test system was developed that can be used to conduct buckling failure experiments. The displacement, stress, and strain of the slope were monitored to document the evolution of buckling failure during the experiment. Monitoring data reveal the deformation and stress characteristics of the slope with different slipping mass thicknesses and under different top loads. The test results show that the slipping mass is the main subject of the top load and is the key object of monitoring. Displacement and stress precede buckling failure, so maybe useful predictors of impending failure. However, the response of the stress variation is earlier than displacement variation during the failure process. It is also necessary to monitor the bedrock near the slip face because its stress evolution plays an important role in the early prediction of instability. The position near the slope foot is most prone to buckling failure, so it should be closely monitored.

• Journal of Forest and Environmental Science
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• v.14 no.1
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• pp.145-156
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• 1998

On the basis of data obtained from 5 forest roads(Backyang, Byongatae, Saorang, Bukyu and Dangrim forest road) collapsed under a heavy rainfall in Chunchon, Kangwondo, this study was carried out to predict the cutting slope failure of forest road by using Quantification theory(II). The results were summarized as follows; The cutting slope failure was chiefly occurred by correlated action of road structure, vegetation and topographical factors. The cutting slope failure predicted by partial correlation coefficients and range values was characterized by longer than 8m of cutting slope length, depper than 2.5m of soil depth, between $30^{\circ}$ and $50^{\circ}$ of original ground slope gradient, absence of vegetation coverage on cutting slope, and greater than $60^{\circ}$ of cutting slope gradient. And the rate of correct discrimination by analysis of cutting slope failure was 90.1%.

• Journal of the Korean GEO-environmental Society
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• v.19 no.9
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• pp.29-36
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• 2018

Several studies have been conducted on monitoring system and automatic measuring instruments to prevent slope failure in advance in Korea and overseas. However, these studies have quite complex structure. Since most of the measurement systems are installed on the slope surface, the researches are carried on the measurement system that detects sign of slope collapse in advance and alerts are still unsatisfactory. In this study, slope collapse experiments were carried out to understand the slope failure mechanism according to rainfall conditions. The water content and displacement behavior at the early stage of the slope failure were analyzed through the measurement of the ground displacement and water content. The results of this study can be used by local government as a basic data for the design of slope failure alarm system to evacuate residents in case of slope failure or landslide due to heavy rainfall.

• Tunnel and Underground Space
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• v.16 no.6 s.65
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• pp.451-466
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• 2006

Analysis of slope behavior concerning the structural characteristics of field rock mass can be processed by virtue of borehole information of joint orientation and position acquired from DOM drilled core. Anticipated sliding potential of pre-failed rock slope is analyzed and the regional slope instability is investigated by inspecting the hazardous joints and blocks the traces of which is projected on the cut-face. Cross section has been set at the center of rock slope and the traces of both joints and tetrahedral blocks, which potentially can induce the slope failure, are drawn to investigate the failure modes and the triggering mechanism. Automated monitoring system has been established to measure the slope movement and especially, inclinometer has been installed inside DOM borehole to analyze the slope movement by considering the internal rock structure. Algorithms for predicting the slope failure time have been reviewed and the significance of heavy rainfall on the slope behavior has been investigated.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.489-509
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• 2024

Slope stability analysis and prediction are of critical importance to geotechnical engineers, given the severe consequences associated with slope failure. This research endeavors to forecast the factor of safety (FOS) for slopes through the implementation of six distinct ML techniques, including back propagation neural networks (BPNN), feed-forward neural networks (FFNN), Takagi-Sugeno fuzzy system (TSF), gene expression programming (GEP), and least-square support vector machine (Ls-SVM). 344 slope cases were analyzed, incorporating a variety of geometric and shear strength parameters measured through the PLAXIS software alongside several loss functions to assess the models' performance. The findings demonstrated that all models produced satisfactory results, with BPNN and GEP models proving to be the most precise, achieving an R² of 0.86 each and MAE and MAPE rates of 0.00012 and 0.00002 and 0.005 and 0.004, respectively. A Pearson correlation and residuals statistical analysis were carried out to examine the importance of each factor in the prediction, revealing that all considered geomechanical features are significantly relevant to slope stability. However, the parameters of friction angle and slope height were found to be the most and least significant, respectively. In addition, to aid in the FOS computation for engineering challenges, a graphical user interface (GUI) for the ML-based techniques was created.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• Journal of the Korean GEO-environmental Society
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• v.17 no.3
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• pp.27-35
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• 2016

Even if the various data analyzing methods were suggested to examine the measured slope behaviors, it is difficult to find methods or procedures for connecting the analyzed results of slope stability and measured slope data. This research suggests the analyzing methods combing the stability analysis and measured data based on progressive failure of slope. Slope failure analysis by time degradation were calculated by strength parameters composed of strength reduction coefficients, also which were compared to the measured data according to the variations of safety factor and displacement of slopes. The accumulated displacement curve were shown as 3rd degree polynomials by suggested procedures, which was the same as before researches. The reverse displacement velocity curves were shown as linear function for prediction of brittle slope failures, also they were shown as 3rd degree polynomials for ductile slope failures, which were the same as the suggested equation by Fukuzono (1985) and they were very similar behaviors to the in-situ failure cases.