A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Sigmoidal Models for Predicting Pavement Performance Conditions
This study presents an approach to develop sigmoidal family pavement performance models (pavement performance ratings versus pavement age) for a pavement management system (PMS). Pavement condition data collected from windshield surveys oftentimes suffer quality issues stemming from human subjectivity, and pavement age sometimes not being properly reset after a treatment. These issues can be systematically addressed by the proposed approach, and nonlinear sigmoidal family performance models can then be developed using the cleaned condition data. In a case study, this approach was successfully applied to a sample data set extracted from the North Carolina Department of Transportation (NCDOT) PMS. Contour plots developed for the raw data and the cleaned data showed that the data cleansing process was effective. Goodness-of-Fit indicators and cross-validation suggest that the resulting nonlinear sigmoidal models fit the condition data well.
Sigmoidal Models for Predicting Pavement Performance Conditions
This study presents an approach to develop sigmoidal family pavement performance models (pavement performance ratings versus pavement age) for a pavement management system (PMS). Pavement condition data collected from windshield surveys oftentimes suffer quality issues stemming from human subjectivity, and pavement age sometimes not being properly reset after a treatment. These issues can be systematically addressed by the proposed approach, and nonlinear sigmoidal family performance models can then be developed using the cleaned condition data. In a case study, this approach was successfully applied to a sample data set extracted from the North Carolina Department of Transportation (NCDOT) PMS. Contour plots developed for the raw data and the cleaned data showed that the data cleansing process was effective. Goodness-of-Fit indicators and cross-validation suggest that the resulting nonlinear sigmoidal models fit the condition data well.
Sigmoidal Models for Predicting Pavement Performance Conditions
Chen, Don (author) / Mastin, Neil (author)
2015-09-21
Article (Journal)
Electronic Resource
Unknown
Sigmoidal Models for Predicting Pavement Performance Conditions
| British Library Online Contents | 2016
Sigmoidal models for predicting pavement performance conditions
| Online Contents | 2016
Elsevier | 1985
Univariate Sigmoidal Neural Network Approximation
| British Library Online Contents | 2012
Development of Linear Mixed Effects Models for Predicting Individual Pavement Conditions
| Online Contents | 2007