200712103

REMOTE SENSING AND GIS BASED PAVEMENT PERFORMANCE PREDICTION MODEL USING

ARTIFICIAL NEURAL NETWORK

UNDER THE GUIDENCE, Dr.C.UDHAYA KUMAR ASST. PROFESSOR IRS, ANNA

UNIVERSITY

BY, DEVI

PRIYADARISINI.K ROLL NO: 200712103

M.E. GEO INFORMATICS

IRS, ANNA UNIVERSITY.

INTRODUCTIONPavement surface are a major component of Infrastructure.

The existing route system has become structurally inadequate.

GIS can add tremendous functionality to a pavement management condition program.

ObjectivesTo map the present condition of the

pavement.To develop pavement performance

predicition model. To incorporate the model with GIS and

create graphical outputs.To validate the model.To predict and map future condition.

Scope of WorkTo monitor and maintain the major

infrastructure asset ,highway.The knowledge of future pavement

performance is essential to PMS.GIS environment to support the

pavement management decision making using several application.

Which would help in predict the pavement performance accurately.

Study AreaLocation : ChennaiStretches : Sadar Patel Road – 3000m

Data to be UsedNon – Spatial Data

Spatial DataStructure of the

pavement.Surface Parameter

CracksPotholesRut DepthSkid Residence

Soil CharacteristicsSlope

Quick bird – 0.6m

SOI Toposheets : 66C4, C8

Calculation of PSIThe PSI value is calculated by using the

following equation : PSI = 20.715 – 6.676 *log (R) – 0.0283 * D

Where, R – Unevenness Index of the pavement

surface D – Total Surface distress.

Pavement Condition Scale

Present PSI Scale Along S.P Road

Present Condition Map

Development of ANN Structure

Comparison of ANN architecture of the model

Comparison of ANN and PSI for PPP for testing set

Comparison of ANN and PSI for PPP for testing set

Predicted PSI value along SP Road

Future condition map

Pavement Condition Distribution Graph

Future Condition Distribution

ConclusionDevelopment and use of PMS using Pavement

attribute database in GIS environment.Different types of operation can be performed.The developed ANN model can be used for

several Pavement Management decision.The ANN model developed gives out better

results than the PPP to the AASHTO panel data.Effective decision making in Pavement

Management System.Analysis and budget allocation for

rehabilitation purpose.

Future RecommendationApplying the PSI and ANN model concept

criterion to setup maintenance priorities, maintenance cost and pavement management programs.

Adapting GPS and GIS based vision systems for the purpose of distresses data collection and measurements.

ReferencesBarry White and Alex Rocie, “GIS and Pavement Management”, Transportation Engineering

ASCE. San Diego, “Development of GIS based Illinois Highway pavement management”, ESRI user

conference. Deva Pratap, Kiran Kumar, “Highway Information System and Management using GIS”, Gis

development. Michael T. Me Nernery and Thomas Row, “GIS need assessment for TxDOT pavement

information system”, US Dept. of Transport.Andres L.Bako, Zoltan Hervath, “Decision Supporting Model for Highway Maintaince”,

Journal of Infrastructure System, ASCE. Gerardo W.Flintish , Randy Dymond and Jhon Collua, “Pavement Management Application

Using GIS”, NCHRP 2005, pp 1-25.Serdal Terzai, “Modeling the pavement serviceability ratio of flexible highway pavement by

ANN”, ELSEVIER, Construction and Building Materials 17 (2007) 577–582

Contd…Mohammed Taleb Obaidat , Sharaf A. Al-kheder,

“Integration of geographic information systems and computer vision systems for pavement distress classification”, ELSEVIER, Construction and Building Materials 20 (2006) 657–672

J.A.Prozji, S.M.Madanat, “Developing of Pavement Performance models combing Experimental field data”, Journal of Infrastructure System, ASCE.

Samer Madana, Jorge A.Prozzi and Michael Han, “Effect of performance model accuracy on optimal pavement design”, Journal of Infrastructure System, ASCE 2006 August.

Abul Hamid Modh Isa, Law Tick Hwa, Dadarcy Mohamed

Ma’soen, “Pavement performance models for federal roads", Journal of Infrastructure System, ASCE, 2007, April

Bosurgi G., “Artificial Neural Networks for Predicting Road Pavement Conditions”, 4th International SIIV Congress – PALERMO (ITALY), 12-14 September 2007.