Research in Analysis of Asphalt Pavement Performance Evaluation Based on PSO-SVM

2011 ◽  
Vol 97-98 ◽  
pp. 203-207 ◽  
Author(s):  
Ke Zhen Yan ◽  
Zou Zhang
Keyword(s):  
Performance Evaluation ◽  
Asphalt Pavement ◽  
Pso Algorithm ◽  
Condition Index ◽  
Pavement Performance ◽  
Small Samples ◽  
Support Vector ◽  
Regression Problem ◽  
Evaluation Indexes ◽  
Pavement Condition Index

An emerging machine learning technique, the support vector machine (SVM), based on statistical learning theory is very good at analyzing small samples and non-linear regression problem. The particle swarm optimize (PSO) can avoid the man-made blindness and enhance the efficiency and capability in forecasting. In this paper, SVM is applied to establish a model for asphalt pavement performance evaluation, optimized by PSO algorithm. In road engineering, PCI, SSI, SRI and IRI were selected as the asphalt pavement performance evaluation indexes, but it is difficult to get pavement condition index. This paper describes the relationships among the four indicators, and SSI, SRI and IRI were used for establishing the prediction model to forecast PCI based on PSO-SVM. The results show that the method is simple and effective for evaluation of asphalt pavement performance.

scholarly journals DEVELOPMENT OF FUZZY MODELS FOR ASPHALT PAVEMENT PERFORMANCE

2019 ◽  
Vol 41 (1) ◽  
pp. 35626 ◽  
Author(s):  
Sérgio Pacífico Soncim ◽  
Igor Castro Sá De Oliveira ◽  
Felipe Brandão Santos
Keyword(s):  
Correlation Coefficient ◽  
Asphalt Pavement ◽  
Condition Index ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Fuzzy Models ◽  
Straight Line ◽  
Qualitative Variables ◽  
Pavement Condition Index ◽  
Pavement Construction

The objective of this paper was to develop fuzzy models for asphalt pavement performance. The fuzzy logic can convert linguistic or qualitative variables into quantitative values. This feature makes it possible to gather experts’ experience about the knowledge they have on factors that affect the pavement performance and its state condition. Forms developed in an organized way were applied for acquiring the knowledge from experts on pavement construction and maintenance. The variables pavement age, traffic, International Roughness Index (IRI) and Flexible Pavement Condition Index (FPCI) were associated with numerical scales and linguistic concepts such as new, old, light, heavy, good, fair, and poor. From the information obtained through the application of forms, variables were modeled with the aid of software InFuzzy and fuzzy models were developed for IRI and FPCI. For validating the model, a straight line adjustment was used to relate the predicted to the observed data. Also, the corresponding correlation coefficient (r) was calculated and residuals were analyzed. The developed models fitted to observed data and correlation coefficient r = 0.71 and 0.70, respectively. 

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 Evaluating Doctor Performance: Ordinal Regression-Based Approach (Preprint)

2017 ◽  
Author(s):  
Yong Shi ◽  
Peijia Li ◽  
Xiaodan Yu ◽  
Huadong Wang ◽  
Lingfeng Niu
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Real Data ◽  
Absolute Error ◽  
Ordinal Regression ◽  
Gradient Boosting ◽  
Support Vector ◽  
Regression Problem ◽  
Physician Performance ◽  
Prediction Function

BACKGROUND Doctor’s performance evaluation is an important task in mobile health (mHealth), which aims to evaluate the overall quality of online diagnosis and patient outcomes so that customer satisfaction and loyalty can be attained. However, most patients tend not to rate doctors’ performance, therefore, it is imperative to develop a model to make doctor’s performance evaluation automatic. When evaluating doctors’ performance, we rate it into a score label that is as close as possible to the true one. OBJECTIVE This study aims to perform automatic doctor’s performance evaluation from online textual consultations between doctors and patients by way of a novel machine learning method. METHODS We propose a solution that models doctor’s performance evaluation as an ordinal regression problem. In doing so, a support vector machine combined with an ordinal partitioning model (SVMOP), along with an innovative predictive function will be developed to capture the hidden preferences of the ordering labels over doctor’s performance evaluation. When engineering the basic text features, eight customized features (extracted from over 70,000 medical entries) were added and further boosted by the Gradient Boosting Decision Tree algorithm. RESULTS Real data sets from one of the largest mobile doctor/patient communication platforms in China are used in our study. Statistically, 64% of data on mHealth platforms lack the evaluation labels from patients. Experimental results reveal that our approach can support an automatic doctor performance evaluation. Compared with other auto-evaluation models, SVMOP improves mean absolute error (MAE) by 0.1, mean square error (MSE) by 0.5, pairwise accuracy (PAcc) by 5%; the suggested customized features improve MAE by 0.1, MSE by 0.2, PAcc by 3%. After boosting, performance is further improved. Based on SVMOP, predictive features like politeness and sentiment words can be mined, which can be further applied to guide the development of mHealth platforms. CONCLUSIONS The initial modelling of doctor performance evaluation is an ordinal regression problem. Experiments show that the performance of our proposed model with revised prediction function is better than many other machine learning methods on MAE, MSE, as well as PAcc. With this model, the mHealth platform could not only make an online auto-evaluation of physician performance, but also obtain the most effective features, thereby guiding physician performance and the development of mHealth platforms.

scholarly journals Monthly Attenuation Prediction for Asphalt Pavement Performance by Using GM (1, 1) Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Limin Tang ◽  
Duyang Xiao
Keyword(s):  
Condition Number ◽  
Asphalt Pavement ◽  
Condition Index ◽  
Small Sample ◽  
Hunan Province ◽  
Pavement Performance ◽  
Actual Performance ◽  
Factors Affecting ◽  
Model Matrix ◽  
Pavement Performance Prediction

Due to the uncertainty and variability of various factors affecting the pavement performance, the change in pavement performance cannot be completely determined. In addition, this uncertainty is not accurately reflected by the pavement performance prediction model. In particular, the gray GM (1, 1) model is very suitable due to it is ability to better predict the existing situation of a domestic asphalt pavement along with the actual performance of a road surface of the “small sample, poor information” gray system. In this regard, the gray GM (1, 1) model is being increasingly used to forecast the performance of an asphalt pavement. When a gray GM (1, 1) model is used to predict the performance of an asphalt pavement, the condition number of the GM (1, 1) model matrix is too large, which, in turn, leads to the deviation of calculation and even wrong results in some cases. This study analyzed the reason for a large condition number of the GM (1, 1) model matrix. Combined with the numerical characteristics of the pavement condition index (PCI) and pavement quality index (PQI), this study focused on the annual, monthly, and daily attenuations of PCI and PQI to the condition number of the GM (1, 1) model matrix. Accordingly, we propose a method to forecast the performance of an asphalt pavement using the monthly attenuation of PCI and PQI. The PCI and PQI in Hunan Province in recent years have been predicted, and the findings reveal that the prediction GM (1, 1) model for the monthly attenuation of PCI and PQI not only effectively lowered the condition number of the matrix but also ensured that the relative error was small.

Research on Relationship between Riding Quality and Pavement Distress of Asphalt Pavement

2012 ◽  
Vol 178-181 ◽  
pp. 1306-1313 ◽  
Author(s):  
Bo Peng ◽  
Lu Hu ◽  
Yang Sheng Jiang ◽  
Liang Yun
Keyword(s):  
Performance Evaluation ◽  
Nonlinear Regression ◽  
Asphalt Pavement ◽  
Theoretical Research ◽  
Pavement Performance ◽  
International Roughness Index ◽  
Pavement Distress ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Linear And Nonlinear

For asphalt pavement performance evaluation, pavement roughness, which is subject to cracks, potholes, road repairs and so on, is a major factor to influence riding quality. Therefore, riding quality is partly correlated with pavement distress, and the relationship can be transformed to that between pavement roughness and distress rate. However, this relationship is not clear, and not reflected in existing evaluation models. Thus, correlation analysis and non-parametric test of independent samples were applied in this paper to find that, international roughness index and pavement distress rate are significantly different due to different grades of roads, then, linear and nonlinear regression were used to analyze the relationships between international roughness index and pavement distress rate for different road grades. Furthermore, original data were processed by logarithmic transformation, radical transformation, exponential transformation and so on, based on which, corresponding relationships were analyzed by linear and nonlinear regression. Finally, best models to describe relationships between international roughness index and pavement distress rate for different road grades were solved out, and corresponding 90% confidence intervals were computed. Research in this paper offers a reference for improving asphalt pavement performance evaluation system and models, which is conducive to further theoretical research and practice.

Use of Time–Temperature Superposition Principle to Create Pavement Performance Master Curves and Relate Pavement Condition Index and International Roughness Index

2021 ◽  
pp. 036119812110049
Author(s):  
Jose R. Medina ◽  
Ali Zalghout ◽  
Akshay Gundla ◽  
Samuel Castro ◽  
Kamil Kaloush
Keyword(s):  
Superposition Principle ◽  
Condition Index ◽  
Pavement Performance ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Roughness Index ◽  
Temperature Superposition ◽  
Pavement Condition Index ◽  
Pavement Distresses ◽  
Time Temperature Superposition Principle

The international roughness index (IRI) is one of the most popular indices to measure pavement roughness. State agencies and cities with plenty of resources often collect IRI and pavement distresses every year or every other year, but some others with fewer resources will collect this information every 3 to 5 years. Collecting IRI is much more affordable than collecting pavement distresses. With this in mind, the objective of this paper was to establish a relationship between IRI and pavement condition index (PCI) using pavement deterioration models for both PCI and IRI based on the concept of time–deterioration superposition similar to the time–temperature superposition principle, and then combine both models to establish this relationship. Additionally, this study was used to establish threshold limits for IRI measurements that can be used as a general reference for pavement condition. Data from the Long-Term Pavement Performance InfoPave was used to perform the analysis for three network samples from Arizona, California, and Wisconsin. This analysis only included flexible pavements. The results from Arizona, California, and Wisconsin showed a good relationship between IRI and PCI using the proposed approach with a coefficient of determination ranging from 0.71 to 0.85. Furthermore, the analysis showed that the change in IRI over time can be related to the change in PCI over time. The general thresholds developed in this study apply to the sections evaluated but the approach can be used to set limits for other networks.

scholarly journals A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1347 ◽  
Author(s):  
Wenting Zhao ◽  
Juanjuan Zhao ◽  
Xilong Yao ◽  
Zhixin Jin ◽  
Pan Wang
Keyword(s):  
Energy Demand ◽  
Pso Algorithm ◽  
Primary Energy ◽  
Small Samples ◽  
Support Vector ◽  
Energy Structure ◽  
Ensemble Model ◽  
Primary Energy Demand ◽  
Energy Demands ◽  
Development Plans

Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

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