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(A Reliable, Cost-effective Performance Measurement Technology)

Introduction:-

Condition of pavement detection is one of the important tasks for the proper planning of repairs and rehabilitation of the asphalt-surfaced pavements. It is necessary in those situations where unconditioned pavement compromise safety and pavement ride-ability.

ANALYSIS ON QUALITY OF ROADS USING GEOINFORMATICS

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In this,we present a new and rapidly increasing technology GIS & RS , which does not require much work in field and produce good results.

This method deploys image processing and spectral clustering for identification and estimation of potholes .

And also rutting and fatigue cracks on pavement can be identified with 3D scanning.

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What are problems in asphalt-pavements ?

Potholes. Rutting. Fatigue cracking. Raveling. Bleeding. Longitudinal cracking. Edge cracking.

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Why require timely maintenance ?

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Problems In Conventional Methods

Manually gather all data for analysis. Time consuming. Human errors. More effort is required. Labour expensive. Less accuracy of data. Additional effort is required for analysis.

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Innovative Methods

In the present ,swiftly emergent technology is

GIS & RS. In this we have the methods like

3D line laser image technology

and

Unsupervised method. By using these two methods we can identify the

defects in pavements.

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Potholes

Methods to detect potholes

Supervised methods3D reconstructionVibrationVision analysis

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Mobile imaging vehicle

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Case Study(Pot holes)

This study done by EMIR BUZA, SAMIR OMANOVIC, ALVIN HUSEINOVIC fromUniversity of sarajevu in Bosnia.

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Unsupervised Method

The goal of unsupervised method is to

automatically segregate pixels of a remote

sensing image into groups of similar spectral

character.

Classification is done using "clustering" where

classes of pixels are created based on their shared

spectral signatures

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Flowchart

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Results of pothole estimation

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Conclusions

In this case study ,the proposed method was tested on

50 different pothole images and the detection accuracy

has been calculated manually.

On a given set of data, our method identified all

potholes and the surface estimation was 81% accurate.

Thus , it is suitable for rough estimation of potholes,

and it is cost effective because it uses in-expensive

equipment.

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3D line laser image technology

A 3D scanner is a device that analyses a real-world object or environment to collect data on its shape and possibly its appearance. The collected data can then be used to construct digital three-dimensional models.

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Principle

The laser line is directed to the surface at an oblique angle. It incised on the surfaces and creates a visible line. In areas where the object is lower, the beam is slightly shifted. By means of a camera this displacement of the bright laser lines can be determined in the image.

Triangulation

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Working

In driver direction it measures up to 5mm accuracy with vehicle runs with 100kmph

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Evaluation of rut depth

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Case Study (rutting)

This study done by Mr.Feng Li from Georgia

Institute of Technology in Atlanta.

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In field

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Limitations

Missing PointsThe object surface gets out of the measurement range.Occlusion.Dim laser point.

Unseemly PointsEffect of Loose Rocks.Effect of Selected Road Surface Characteristics.Reflective Surface Test.

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Other Errors

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Conclusions Pavement rutting is one of the major asphalt pavement

surface distresses affecting.

Conventional rutting measurement method is still used by

many state Departments of Transportation however, it is

time-consuming, labour-intensive, and dangerous.

With the advance of sensing technology, emerging 3D line

laser imaging technology is capable of collecting high-

resolution 3D range data at highway speed (e.g., 100 km/h).

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Using this advanced sensing technology to

1) process 3D range data.

2) automatically extract 1D rut depth measurements

and 2D/3D rutting characteristics.

3) assess the accuracy and repeatability of rutting

measurements

4) explore more detailed information to support

current and future pavement management decisions,

e.g., network-level condition

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References

Emir Buza, Samir Omanovic, Alvin Huseinovic given there study on “Pothole Detection with Image Processing and Spectral Clustering”

Feng li given his study on “A methodology for characterizing pavement rutting condition using emerging 3d line laser imaging technology”

Yao, M., Yao, X., Yu, W., and Xu, B. (2009). “A Real-time 3D Scanning System for Pavement Rutting and Pothole Detections.” Proceedings of SPIE: Video metrics, Range Imaging, and Applications X, 74470B-74479.

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