scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 268-277 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

scholarly journals Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 9 (9) ◽  
pp. 1761 ◽  
Author(s):  
Zhifang Wang ◽  
Shutao Wang ◽  
Deming Kong ◽  
Shiyu Liu
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Methane Concentration ◽  
Mean Squared Error ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Squared Error ◽  
Svm Model ◽  
Methane Detection

Methane, known as a flammable and explosion hazard gas, is the main component of marsh gas, firedamp, and rock gas. Therefore, it is important to be able to detect methane concentration safely and effectively. At present, many models have been proposed to enhance the performance of methane predictions. However, the traditional models displayed inevitable shortcomings in parameter optimization in our experiment, which resulted in their having poor prediction performance. Accordingly, the improved chicken swarm algorithm optimized support vector machine (ICSO-SVM) was proposed to predict the concentration of methane precisely. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum due to its characteristics, so the ICSO algorithm was developed. The formula for position updating of the chicks of the ICSO is not only about the rooster of the same subgroup, but also about the roosters of other subgroups. Therefore, the ICSO algorithm more easily avoids falling into the local extremum. In this paper, the following work has been done. The sample data were obtained by using the methane detection system designed by us; In order to verify the validity of the ICSO algorithm, the ICSO, CSO, genetic algorithm (GA), and particle swarm optimization algorithm (PSO) algorithms were tested, and the four models were applied for methane concentration prediction. The results showed that he ICSO algorithm had the best convergence effect, relative error percentage, and average mean squared error, when the four models were applied to predict methane concentration. The results showed that the average mean squared error values of ICSO-SVM model were smaller than other three models, and that the ICSO-SVM model has better stability, and the average recovery rate of the ICSO-SVM is much closer to 100%. Therefore, the ICSO-SVM model can efficiently predict methane concentration.

scholarly journals Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation

2019 ◽  
Vol 259 ◽  
pp. 02007 ◽  
Author(s):  
Amir Falamarzi ◽  
Sara Moridpour ◽  
Majidreza Nazem ◽  
Reyhaneh Hesami
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Preventive Maintenance ◽  
Mean Squared Error ◽  
Study Data ◽  
Transport Infrastructure ◽  
Coefficient Of Determination ◽  
Ride Quality ◽  
Support Vector ◽  
Svm Model

Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA- SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model.

scholarly journals Predicting the Shear Strength of Reinforced Concrete Beams Using Support Vector Machine

2019 ◽  
Vol 2 (2) ◽  
pp. 74-95
Author(s):  
Cindrawaty Lesmana
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Reinforced Concrete Beams ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Technical Literature ◽  
Depth Ratio ◽  
Svm Model ◽  
Wide Range

A wide range of machine learning techniques have been successfully applied to model different civilengineering systems. The application of support vector machine (SVM) to predict the ultimate shearstrengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in thispaper. An SVM model is built trained and tested using the available test data of 175 RC beamscollected from the technical literature. The data used in the SVM model are arranged in a format ofnine input parameters that cover the cylinder concrete compressive strength, yield strength of thelongitudinal and transverse reinforcing bars, the shear-span-to-effective-depth ratio, the span-toeffective-depth ratio, beam’s cross-sectional dimensions, and the longitudinal and transversereinforcement ratios. The relative performance of the SVMs shear strength predicted results were alsocompared to ACI building code and artificial neural network (ANNs) on the same data sets.Furthermore, the SVM shows good performance and it is proved to be competitive with ANN modeland empirical solution from ACI-05.

scholarly journals Atterberg Limits Prediction Comparing SVM with ANFIS Model

2017 ◽  
Vol 2 (1) ◽  
pp. 20
Author(s):  
Mohammad Murtaza Sherzoy
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Liquid Limit ◽  
Plasticity Index ◽  
Atterberg Limits ◽  
Support Vector ◽  
Plastic Limit ◽  
Anfis Model ◽  
Svm Model

Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy inference Systems (ANFIS) both analytical methods are used to predict the values of Atterberg limits, such as the liquid limit, plastic limit and plasticity index. The main objective of this study is to make a comparison between both forecasts (SVM & ANFIS) methods. All data of 54 soil samples are used and taken from the area of Peninsular Malaysian and tested for different parameters containing liquid limit, plastic limit, plasticity index and grain size distribution and were. The input parameter used in for this case are the fraction of grain size distribution which are the percentage of silt, clay and sand. The actual and predicted values of Atterberg limit which obtained from the SVM and ANFIS models are compared by using the correlation coefficient R2 and root mean squared error (RMSE) value.  The outcome of the study show that the ANFIS model shows higher accuracy than SVM model for the liquid limit (R2 = 0.987), plastic limit (R2 = 0.949) and plastic index (R2 = 0966). RMSE value that obtained for both methods have shown that the ANFIS model has represent the best performance than SVM model to predict the Atterberg Limits as a whole.

scholarly journals Influence of wood-derived biochar on the compactibility and strength of silt loam soil

2017 ◽  
Vol 31 (2) ◽  
pp. 149-155 ◽  
Author(s):  
Ahmed Ahmed ◽  
Yvan Gariepy ◽  
Vijaya Raghavan
Keyword(s):  
Shear Strength ◽  
Moisture Content ◽  
Bulk Density ◽  
Penetration Resistance ◽  
Liquid Limit ◽  
Optimum Moisture Content ◽  
Silt Loam ◽  
Plastic Limit ◽  
Density Maximum ◽  
Loam Soil

Abstract Biochar is proven to enhance soil fertility and increase crop productivity. Given that the influence of biochar on soil compaction remains unclear, selected physico-mechanical properties of soil amended with wood-derived biochar were assessed. For unamended silt loam, the bulk density, maximum bulk density, optimum moisture content, plastic limit, liquid limit, and plasticity index were 1.05 Mg m-3, 1.69 Mg m-3, 16.55, 17.1, 29.3, and 12.2%, respectively. The penetration resistance and shear strength of the unamended silt loam compacted in the standard compaction Proctor mold and at its optimum moisture content were 1800 kPa and 850 kPa, respectively. Results from amending the silt loam with 10% particle size ranges (0.5-212 μm) led to relative decreases of 18.1, 17.75, 66.66, and 97.4% in bulk density, maximum bulk density, penetration resistance, and shear strength, respectively; a 26.8% relative increase in optimum moisture content; along with absolute increases in plastic limit, liquid limit, and plasticity index of 5.3, 13.7, and 8.4%, respectively. While the biochar-amended silt loam soil was more susceptible to compaction, however, soil mechanical impedance enhanced.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

Shear Strength Monitoring System Based on Measurement of Shear Wave Velocity and Moisture Content

2014 ◽  
Vol 635-637 ◽  
pp. 750-754
Author(s):  
Peng Hu ◽  
Qing Li ◽  
Yi Wei Xu ◽  
Nan Ying Shentu ◽  
Quan Yuan Peng
Keyword(s):  
Shear Strength ◽  
Moisture Content ◽  
Shear Wave ◽  
Monitoring System ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Bender Elements ◽  
Strength Measurement ◽  
Soil Shear Strength ◽  
The Relationship

Expound the importance of soil shear strength measurement at mudslide hidden point to release the loss caused by the disaster, explain the relationship between shear wave velocity, moisture content and shear strength, design the shear strength monitoring system combining the shear wave velocity measured by Piezoelectric bender elements and moisture content.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

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