Using Finite Element Method and Support Vector Machine to Evaluate Scour Bridge Condition

2013 ◽  
Vol 330 ◽  
pp. 900-904 ◽  
Author(s):  
Chung Wei Feng ◽  
Hsun Yi Huang
Keyword(s):  
Support Vector Machine ◽  
Finite Element ◽  
Bridge Piers ◽  
Support Vector ◽  
Generic Model ◽  
Bridge Structure ◽  
Safety Level ◽  
Element Simulation ◽  
Proposed Model ◽  
Bridge Failure

Scouring around bridge piers is one of the major reasons for bridge failures and makes disaster since it tends to occur suddenly and without prior warning. However, the mechanism of water flow around the pier structure is complicated, which makes it is very difficult to develop a generic model to evaluate the scour bridge condition and provide a safety level. In this study, an integrated model that combines support vector machine (SVM) and finite element simulation technology is introduced to estimate the scour depth and determinate the safety level of scour bridge by using the natural frequency of the bridge structure. The proposed model in this study provides effective way to have a prior understanding of scour bridge condition and avoid the disaster of bridge failure.

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

scholarly journals Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5018 ◽  
Author(s):  
Kyu-Won Jang ◽  
Jong-Hyeok Choi ◽  
Ji-Hoon Jeon ◽  
Hyun-Seok Kim
Keyword(s):  
Support Vector Machine ◽  
Gas Sensors ◽  
Learning Algorithm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Minimum Concentration ◽  
Industrial Sites ◽  
Combustible Gas ◽  
Proposed Model ◽  
Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

scholarly journals Strenght analysis chassis of UM electric cars using finite element method

2018 ◽  
Vol 204 ◽  
pp. 07017 ◽  
Author(s):  
Mardji ◽  
Andoko ◽  
Dani Prasetiyo
Keyword(s):  
Finite Element ◽  
Finite Element Simulation ◽  
Simulation Analysis ◽  
Equivalent Stress ◽  
Safety Level ◽  
Electric Cars ◽  
Know How ◽  
Electric Car ◽  
Weight Support ◽  
Element Simulation

Chassis on the vehicle serves as the main weight support vehicle. Designing a precise chassis will give optimal results between the safety level and the size of the construction, so that finite element simulation analysis is required to know how strong the chassis sustains the load on it. The purpose of this research is to get the result of chassis simulation on UM electric car when getting the loading by using ANSYS 18.1 software. As for the step this study started from chassis modeling using Autodesk Inventor Professional 2018 software and finite element simulation using static structural feature in software ANSYS 18.1. From the simulation result obtained Equivalent Stress 59,983MPa, Equivalent Elastic Strain 33,25x10-5 mm / mm Total Deformation 2,43mm and safety factor 3,55.

A Method for Characterizing Running-In of Sliding Contacts

2016 ◽  
Vol 681 ◽  
pp. 228-233
Author(s):  
R. Ismail ◽  
M. Tauviqirrahman ◽  
J. Jamari ◽  
D.J. Schipper
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Pressure Distribution ◽  
Sliding Contact ◽  
Wear Depth ◽  
Contact Pressure Distribution ◽  
Sliding Contacts ◽  
Element Analysis ◽  
Element Simulation ◽  
Proposed Model

Although in terms of conservation wear is undesirable, however, running-in wear is encouraged rather than avoided. Running-in is rather complex and most of the studies related to the change in micro-geometry have been conducted statistically. The purpose of this study was to characterize the running-in of sliding contacts using finite element analysis based on measured micro-geometries. The developed model combines the finite element simulation, Archard’s wear equation and updated geometry to calculate the contact pressure distribution and wear depth. Results show that the proposed model is able to predict the running-in phase of sliding contact system.

scholarly journals Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal

2018 ◽  
Vol 5 (5) ◽  
pp. 537 ◽  
Author(s):  
Oman Somantri ◽  
Dyah Apriliani
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Support Vector ◽  
Chi Square ◽  
Proposed Model ◽  
Chi Squared ◽  
The Difference ◽  
Increase In Accuracy

<p class="Judul2"><strong>Abstrak</strong></p><p class="Judul2"> </p><p class="Abstrak">Setiap pelanggan pasti menginginkan sebuah pendukung keputusan dalam menentukan pilihan ketika akan mengunjungi sebuah tempat makan atau kuliner yang sesuai dengan keinginan salah satu contohnya yaitu di Kota Tegal. <em>Sentiment analysis</em> digunakan untuk memberikan sebuah solusi terkait dengan permasalahan tersebut, dengan menereapkan model algoritma S<em>upport Vector Machine</em> (SVM). Tujuan dari penelitian ini adalah mengoptimalisasi model yang dihasilkan dengan diterapkannya <em>feature selection</em> menggunakan algoritma <em>Informatioan Gain</em> (IG) dan <em>Chi Square</em> pada hasil model terbaik yang dihasilkan oleh SVM pada klasifikasi tingkat kepuasan pelanggan terhadap warung dan restoran kuliner di Kota Tegal sehingga terjadi peningkatan akurasi dari model yang dihasilkan. Hasil penelitian menunjukan bahwa tingkat akurasi terbaik dihasilkan oleh model SVM-IG dengan tingkat akurasi terbaik sebesar 72,45% mengalami peningkatan sekitar 3,08% yang awalnya 69.36%. Selisih rata-rata yang dihasilkan setelah dilakukannya optimasi SVM dengan <em>feature selection</em> adalah 2,51% kenaikan tingkat akurasinya. Berdasarkan hasil penelitian bahwa <em>feature selection</em> dengan menggunakan <em>Information Gain (IG)</em> (SVM-IG) memiliki tingkat akurasi lebih baik apabila dibandingkan SVM dan <em>Chi Squared</em> (SVM-CS) sehingga dengan demikian model yang diusulkan dapat meningkatkan tingkat akurasi yang dihasilkan oleh SVM menjadi lebih baik.</p><p class="Abstrak"><strong><em><br /></em></strong></p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Judul2"> </p><p class="Judul2"><em>The Customer needs to get a decision support in determining a choice when they’re visit a culinary restaurant accordance to their wishes especially at Tegal City. Sentiment analysis is used to provide a solution related to this problem by applying the Support Vector Machine (SVM) algorithm model. The purpose of this research is to optimize the generated model by applying feature selection using Informatioan Gain (IG) and Chi Square algorithm on the best model produced by SVM on the classification of customer satisfaction level based on culinary restaurants at Tegal City so that there is an increasing accuracy from the model. The results showed that the best accuracy level produced by the SVM-IG model with the best accuracy of 72.45% experienced an increase of about 3.08% which was initially 69.36%. The difference average produced after SVM optimization with feature selection is 2.51% increase in accuracy. Based on the results of the research, the feature selection using Information Gain (SVM-IG) has a better accuracy rate than SVM and Chi Squared (SVM-CS) so that the proposed model can improve the accuracy of SVM better.</em></p>

Open-Source Essential Protein Prediction Model by Integrating Chi-Square and Support Vector Machine

2020 ◽  
Vol 11 (3) ◽  
pp. 38-56
Author(s):  
S. R. Mani Sekhar ◽  
Siddesh G. M. ◽  
Sunilkumar S. Manvi
Keyword(s):  
Support Vector Machine ◽  
Open Source ◽  
Vital Role ◽  
Support Vector ◽  
Chi Square ◽  
Essential Proteins ◽  
Topological Features ◽  
Proposed Model ◽  
Protein Prediction ◽  
Svm Model

Identification and analysis of protein play a vital role in drug design and disease prediction. There are several open-source applications that have been developed for identifying essential proteins which are based on biological or topological features. These techniques infer the possibility of proteins to be essential by using the network topology and feature selection, which can ignore some of the features to reduce the complexity and, subsequently, results in less accuracy. In the paper, the authors have used selenium driver to scrap the dataset. Later, the authors integrated the chi-square method with support vector machine for the prediction of essential proteins in baker yeast. Here, chi-square is a test of dissimilarity used for altering the record, and afterward, the support vector machine is used to classify the test dataset. The results show that the proposed model Chi-SVM model achieves an accuracy of 99.56%, whereas BC and CC achieved an accuracy of 84.0% and 86.0%. Finally, the proposed model is validated using Statistical performance measures such as PPA, NPA, SA, and STA.

scholarly journals Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1433 ◽  
Author(s):  
Kaige Chi ◽  
Bo Pang ◽  
Lizhuang Cui ◽  
Dingzhi Peng ◽  
Zhongfan Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
River Basin ◽  
Partial Correlation ◽  
Climatic Factors ◽  
Vegetation Coverage ◽  
Support Vector ◽  
Partial Correlation Analysis ◽  
Proposed Model ◽  
Yarlung Zangbo River

Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random forest (RF) to predict the Normalized Difference Vegetation Index (NDVI) of the Yarlung Zangbo River Basin and explore its relationship with climatic factors. High-resolution datasets of NDVI and monthly meteorological observation data from 2000 to 2015 were used to calibrate and validate the proposed model. The proposed model was then compared with artificial neural network and support vector machine models, and principal component analysis and partial correlation analysis were also used for predictor selection of artificial neural network and support vector machine models for comparative study. The results show that RF had the highest model efficiency among the compared models. The Nash–Sutcliffe coefficients of the proposed model in the calibration period and verification period were all higher than 0.8 for the five subzones; this indicated that the proposed model can successfully simulate the relationship between the NDVI and climatic factors. By using built-in variable importance evaluation, RF chose appropriate predictor combinations without principle component analysis or partial correlation analysis. Our research is valuable because it can be integrated into water resource management and elucidates ecological processes in Yarlung Zangbo River Basin.

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