Performance evaluation of low volume roads in India using soft computational approach
By: Meshram, Kundan.
Contributor(s): Kaur, Suneet.
Publisher: Pune NICMAR 2014Edition: Vol.29(2), Apr-Jun.Description: 49-58p.Subject(s): Construction Engineering and Management (CEM)Online resources: Click here In: NICMAR Journal of construction managementSummary: The Government of India is taking up large projects and incurring large expenses for the development of rural roads. A periodic maintenance is essential for these roads. Hence, there is an urgent need of developing a pavement maintenance and management system for low volume roads. An attempt has been made in this paper to develop an Artificial Neural Network (ANN) model and a Multivariate Regression model to determine reasonably accurate Pavement Condition Index for low volume roads in India. Database used for building the model was collected from ten low volume roads of Madhya Pradesh, India, viz. Jhabua District, Indore District and Dhar District. Periodical data included condition of shoulder, effectiveness of surface drainage, MERLIN roughness value and Dynamic Cone Penetrometer (DCP) value of shoulder layer. Distresses like rutting, longitudinal depression in the central portion of each subsection, edge drop off and cracking are measured on these roads. By traffic survey, Commercial Vehicles Per day (CPVD) are determined. The entire data has been collected by the author, between April 2007 and November 2009 before the monsoon and after the monsoon periods.Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database | School of Engineering & Technology (PG) Archieval Section | Not for loan | 2024-0866 |
The Government of India is taking up large projects and incurring large expenses for the development of rural roads. A periodic maintenance is essential for these roads. Hence, there is an urgent need of developing a pavement maintenance and management system for low volume roads. An attempt has been made in this paper to develop an Artificial Neural Network (ANN) model and a Multivariate Regression model to determine reasonably accurate Pavement Condition Index for low volume roads in India. Database used for building the model was collected from ten low volume roads of Madhya Pradesh, India, viz. Jhabua District, Indore District and Dhar District. Periodical data included condition of shoulder, effectiveness of surface drainage, MERLIN roughness value and Dynamic Cone Penetrometer (DCP) value of shoulder layer. Distresses like rutting, longitudinal depression in the central portion of each subsection, edge drop off and cracking are measured on these roads. By traffic survey, Commercial Vehicles Per day (CPVD) are determined. The entire data has been collected by the author, between April 2007 and November 2009 before the monsoon and after the monsoon periods.
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