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. 

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.

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.

VALIDITY OF GARBER MODEL IN PREDICTING PAVEMENT CONDITION INDEX OF FLEXIBLE PAVEMENT IN KERBALA CITY

2018 ◽  
Vol 09 (02) ◽  
pp. 139-151
Author(s):  
Hussein Ewadh ◽  
◽  
Raid Almuhanna ◽  
Saja Alasadi ◽  
◽  
...  
Keyword(s):  
Condition Index ◽  
Flexible Pavement ◽  
Pavement Condition ◽  
Pavement Condition Index

scholarly journals An Investigation of Geography and Climate Induced Distresses Patterns on Airfield Pavements at US Air Force Installations

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Lauren K. Sahagun ◽  
Moses Karakouzian ◽  
Alexander Paz ◽  
Hanns de la Fuente-Mella
Keyword(s):  
Air Force ◽  
Condition Index ◽  
Portland Cement Concrete ◽  
Climate Zones ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Western Us ◽  
Us Air Force ◽  
Pavement Condition Index ◽  
Predominant Factor

This study investigated climate induced distresses patterns on airfield pavements at US Air Force installations. A literature review and surveys of Pavement Condition Index indicated that the predominant factor contributing to the development of pavement distress was climate. Results suggested that, within each type of pavement distress, a geographic pattern exists which is strongly correlated to conventional US climate zones. The US Air Force Roll-Up Database, housing over 50,000 records of pavement distress data, was distilled using a process designed to combine similar distresses while accounting for age and size of samples. The process reduced the data to a format that could be used to perform krig analysis and to develop pavement behavior models for runways built with asphalt cement (AC) and Portland cement concrete (PCC). Regression and krig analyses were conducted for each distress type to understand distress behavior among climate zones. Combined regression and krig analyses provided insight into the overall pavement behavior for AC and PCC runways and illustrated which climate zone was more susceptible to specific pavement distresses. Distress behavior tends to be more severe in the eastern US for AC and in the western US for PCC runway pavements, respectively.

Sign in / Sign up

Export Citation Format

Share Document