• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.649-661
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• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• Korean Journal of Remote Sensing
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• v.18 no.1
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• pp.1-12
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• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.423-437
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• 2022

The objective of the study is to evaluate the feasibility of the dynamic behavior of slope through both 1 g shaking table test and numerical analysis. Accelerometers were installed in the slope model with different types of seismic waves. The numerical analysis (ABAQUS and DEEPSOIL) was used to simulate 1 g shaking table test at infinite boundary. Similar Acceleration-time history, Spectral acceleration (SA) and Spectral acceleration amplification factor (Fa) were obtained, which verified the feasibility of modeling using ABAQUS and DEEPSOIL under the same size. The influence of the size (1, 2, 5, 10 and 20 times larger than that used in the 1 g shaking table test) of the model used in the numerical analysis were extensively investigated. According to the similitude law, ABAQUS was used to analyze the dynamic behavior of large-scale slope model. The 5% Damping Spectral acceleration (SA) and Spectral acceleration amplification factor (Fa) at the same proportional positions were compared. Based on the comparison of numerical analyses and 1 g shaking table tests, it was found that the 1 g shaking table test result can be utilized to predict the dynamic behavior of the real scale slope through numerical analysis.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.137-142
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• 2006

Cost and duration is important factors which directly affect profit therefore must be forecasted correctly to accomplish success of projects. So construction company uses EVMS(Earned Value Management System) to forecast final cost and duration. But previous forecasting model has low accuracy because of its linear forecasting method and can't reflect characteristic of company and project and changes as each progress. This paper presents cost and duration forecasting model using the slope prediction of cost and duration as each progress to reflect the various characteristics of construction industry. EVMS data of 23 road construction projects was used to make up regression analysis equation of slope forecasting model.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.106-117
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• 2021

The purpose of this study is prediction of landslide occurrence reflecting the subsurface flow characteristics within the soil layer in the future due to climate change in a large scale watershed. To do this, we considered the infinite slope stability theory to evaluate the landslide occurrence with predicted soil moisture content by SWAT model based on monitored data (rainfall-soil moisture-discharge). The correlation between the SWAT model and the monitoring data was performed using the coefficient of determination (R²) and the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and, an accuracy analysis of landslide prediction was performed using auROC (area under Receiver Operating Curve) analysis. In results comparing with the calculated discharge-soil moisture content by SWAT model vs. actual observation data, R² was 0.9 and NSE was 0.91 in discharge and, R² was 0.7 and NSE was 0.79 in soil moisture, respectively. As a result of performing infinite slope stability analysis in the area where landslides occurred in the past based on simulated data (SWAT analysis result of 0.7~0.8), AuROC showed 0.98, indicating that the suggested prediction method was resonable. Based on this, as a result of predicting the characteristics of landslide occurrence by 2050 using climate change scenario (RCP 8.5) data, it was calculated that four landslides could occur with a soil moisture content of more than 75% and rainfall over 250 mm/day during simulation. Although this study needs to be evaluated in various regions because of a case study, it was possible to determine the possibility of prediction through modeling of subsurface flow mechanism, one of the most important attributes in landslide occurrence.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• The Journal of Engineering Geology
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• v.20 no.3
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• pp.243-255
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• 2010

The slope design of an open-pit mine must consider economical efficiency and stability. Thus, the overall slope angle is the principal factor because of limited support or reinforcement options available in such a setting. In this study, slope displacement, as monitored by a GPS system, was analyzed for a coal mine at Pasir, Indonesia. Predictions of failure time by inverse velocity analysis showed good agreement with field observations. Therefore, the failure time of an unstable slope can be roughly estimated prior to failure. A GIS model that combines fuzzy theory and the analytical hierarchy process (AHP) was developed to assess slope instability in open-pit coal mines. This model simultaneously considers seven factors that influence the instability of open-pit slopes (i.e., overall slope gradient, slope height, surface flows, excavation plan, tension cracks, faults, and water body). Application of the proposed method to an open-pit coal mine revealed an enhanced prediction accuracy of failure time and failure site compared with existing methods.