Behaviour of hybrid fibre reinforced concrete-filled steel tubular beams and columns

This paper presents the flexural performance of newly developed hybrid fiber reinforced concrete-filled steel tubular sections. The test parametres are fiber volume fraction and fiber hybridation ratio. Initially mechanical properties studied for 10 mono fiber reinforced concrete mixes using steel and Polypropylene fibres with 0.5%, 1.0%, 1.5%, 2.0% and 2.5% volume fraction. Based on the performance optimum fiber dosage was determined in each fiber, with the same volume fraction three different fiber hybridation was developed. Developed hybrid fiber reinforcement concrete, conventional concrete and optimum mono fiber reinforced concrete was used in the concrete-filled steel tubular beams and columns to determine the structural performance. The test results shows that, fiber reinforced concrete-filled steel tubular beams display significant improvement in the flexural performance.  Keywords: CFST, hybrid, fiber reinforcement concrete, flexural behavior, moment-curvature

Thermal decomposition of N-(salicylidene)-L-leucine in static air atmosphere

The thermal degradation of N-(salicylidene)-L-leucine was studied under non-isothermal conditions in air atmosphere. For kinetic analysis, the TG/DTA/DTG data obtained at three different heating rates were processed by Friedman, Kissinger-Akahira-Sunose, Flynn-Wall-Ozawa and Kissinger methods. The analysis indicates a complex reaction process which can be best described by the three dimensional (Ginstling-Brounshtein) model D4

Experimental investigation on flexural performance of functionally graded concrete beams using flyash and red mud

The continuous change in the strength and other properties, environmental problems, hike in cement price, advancement of construction industry makes the usage of alternative materials as Functionally graded materials (FGM) which leads to a new materials on concrete as Functionally graded concrete (FGC), In this present paper, investigation has carried out on the functionally graded concrete by using red mud and also fly ash. In this M20 grade of concrete is used as the conventional concrete in compression zone and M25 with the replacement of cement by red mud and flyash with varying percentage (5%, 10% 15%) respectively in the tension zone. The results indicated that the 10% of the red mud and 10% of the fly ash as the optimum value for the concrete beam and by load deflection curve, it is evident that functionally graded concrete beam has more advantages than ordinary concrete since it has more durability and strength characteristics. Scanning electron microscope analysis was also carried out on the red mud functionally graded concrete and fly ash functionally graded concrete. It clearly indicated the pores present in the materials which tends to increase in strength of the concrete.Keywords: Functionally graded concrete, M20 and M25 grade, Scanning electron microscope, Red mud, Flyash.

Lung cancer prediction and retrieval using multistage hybrid prediction approach

In cancer diagnosis computer-aided Prediction is considered as significant research domain. The generation and processing of lung cancer identification and Prediction is expensive. During past decades, content-based image retrieval (CBIR) technique has been applied in various medical applications. For effective diagnosis of lung cancer radiologist’s requires effective approach for cancer prediction and diagnosis. In this research, for prediction of CT lung images is achieved using hybrid Prediction approach is adopted with integration of logistic regression and Adaboost classifier. Feature extraction and retrieval of lung cancer image applied through optimization technique known as firefly approach for processing several vector features of lung images to derive salient characteristics for achieving best features. Results illustrated that proposed prediction and retrieval approach exhibits significant performance rather than conventional technique. The proposed approach provides classification accuracy of 97% which is significantly higher

Lung Cancer Prediction and Retrieval Using Multistage Hybrid Prediction Approach

In cancer diagnosis computer-aided Prediction is considered as significant research domain. The generation and processing of lung cancer identification and Prediction is expensive. During past decades, content-based image retrieval (CBIR) technique has been applied in various medical applications. For effective diagnosis of lung cancer radiologist's requires effective approach for cancer prediction and diagnosis. In this research, for prediction of CT lung images is achieved using hybrid Prediction approach is adopted with integration of logistic regression and Adaboost classifier. Feature extraction and retrieval of lung cancer image applied through optimization technique known as firefly approach for processing several vector features of lung images to derive salient characteristics for achieving best features. Results illustrated that proposed prediction and retrieval approach exhibits significant performance rather than conventional technique. The proposed approach provides classification accuracy of 97% which is significantly higher