C. Ramesh Babu ¹, S. Prabavathy ¹, M. Saranya ¹

Affiliations
1 Department of Civil Engineering, Mepco Schlenk Engineering College, Sivakasi - 626005, Tamil Nadu, IN

Abstract

Background/Objectives: An extensive experimental and analytical investigation was carried out to explore the behaviour of circular concrete filled GFRP tubes (CFFTs) using NSC (Normal Strength Concrete), HSC (High Strength Concrete) and UHSC (Ultra High Strength Concrete). Methods/Statistical Analysis: The experimental study explored the compressive behaviour of 27 CFFTs having three different diameters 75, 100 and 150mm of constant thickness 3mm. Un confined concrete strength up to 98 MPa was achieved under normal curing conditions. Findings: The research findings prove that adequately confined high strength CFFTs can exhibit highly ductile behaviour and they are highly sensitive to the level of confinement. The GFRP tubes with UHSC infill could not maintain the load carrying capacity after attaining initial peak strengths. A newer methodology using shell elements was adapted for simulation of CFFTs in ANSYS and the results agreed with that of the experimental investigation. Applications/Improvements: The investigation explored that specimen size does not have major influence on strain reduction factor. New values of strength and strain enhancement ratios K1 and K2 has been arrived.

Keywords

Analytical, Circular, Concrete, Compressive Behavior, Experimental, GFRP.

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M. Saranya ¹, A. Grace Selvarani ¹

Affiliations
1 Department of Computer Science and Engineering, (PG), Sri Ramakrishna College of Engineering, Coimbatore - 641002, Tamil Nadu, IN

Abstract

The proposed system aims in diagnosing Diabetic Retinopathy (DR), a snag in patients with Diabetes for prolonged periods. MinIMaS algorithm is primarily used for background Extraction and Gaussian Mixture Model (GMM) is used for grouping of lesions. The amount of false positives are minimized by the selection of feature vectors used for classification. The proposed system achieves sensitivity of 89% for grouping of bright lesion and sensitivity of 82% for classifying bright lesion which when compared to existing system (KNN classifier) gives a higher sensitivity value. Similarly MinIMaS algorithm provides 91% accurate results when compared to the existing Haar based wavelet transform (70%) and Highest Average Variation (65%). System aids in detecting severity of Diabetic Retinopthy with improved accuracy for timely treatment of patients.

Keywords

Diabetic Retinopathy, Fundus, Lesion, Screening.

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