Prediction of Resilient Modulus of Asphalt Pavement Material Using Support Vector Machine

2011 ◽  
Author(s):  
Yan Kezhen ◽  
Honghui Yin ◽  
Huarong Liao ◽  
Likui Huang
Keyword(s):  
Support Vector Machine ◽  
Resilient Modulus ◽  
Asphalt Pavement ◽  
Support Vector ◽  
Pavement Material

scholarly journals A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuancang Wang ◽  
Jing Zhao ◽  
Qiqi Li ◽  
Naren Fang ◽  
Peicheng Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Performance Prediction ◽  
Asphalt Pavement ◽  
Pavement Performance ◽  
Support Vector ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Pavement Performance Prediction ◽  
Grey Relation

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

scholarly journals An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Least Squares ◽  
Classification Accuracy ◽  
Asphalt Pavement ◽  
Support Vector ◽  
Accuracy Rate ◽  
Pothole Detection ◽  
Steerable Filter

This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS-SVM) and the artificial neural network (ANN). Experimental results obtained from a repeated subsampling process with 20 runs show that both LS-SVM and ANN are capable methods for pothole detection with classification accuracy rate larger than 85%. In addition, the LS-SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). Accordingly, the proposed AI approach used with LS-SVM can be very potential to assist transportation agencies and road inspectors in the task of pavement pothole detection.

scholarly journals Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Comparative Study ◽  
Asphalt Pavement ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Approaches ◽  
Pavement Condition

Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.

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