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Experimental study on strengthening of R.C beam using glass fibre reinforced composite
K.M. Mini,Rini John Alapatt,Anjana Elizabeth David,Aswathy Radhakrishnan,Minu Maria Cyriac,R. Ramakrishnan 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.50 No.3
This paper reports the influence of number of layers and length of GFRP sheets wrapped onto RCC beams for strengthening. Twelve beams of size 700mm × 150mm × 150mm were cast and tested. Two beams without GFRP and ten beams wrapped in different lay-up patterns with one and two layers of GFRP sheets was subjected to three point loading test and ultrasonic pulse velocity test. Initial crack load, ultimate failure load and types of failure have been observed and noted. Experimental results indicate a significantincrease in initial and ultimate load carrying capacity of GFRP wrapped beams compared to unwrapped beams. The failed control specimen was retrofitted using U wrap scheme and tested under three point loading.
Experimental study on strengthening of R.C beam using glass fibre reinforced composite
Mini, K.M.,Alapatt, Rini John,David, Anjana Elizabeth,Radhakrishnan, Aswathy,Cyriac, Minu Maria,Ramakrishnan, R. Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.50 No.3
This paper reports the influence of number of layers and length of GFRP sheets wrapped onto RCC beams for strengthening. Twelve beams of size $700mm{\times}150mm{\times}150mm$ were cast and tested. Two beams without GFRP and ten beams wrapped in different lay-up patterns with one and two layers of GFRP sheets was subjected to three point loading test and ultrasonic pulse velocity test. Initial crack load, ultimate failure load and types of failure have been observed and noted. Experimental results indicate a significant increase in initial and ultimate load carrying capacity of GFRP wrapped beams compared to unwrapped beams. The failed control specimen was retrofitted using U wrap scheme and tested under three point loading.
CAMP: Community Access MODIS Pipeline
Hendrix, V.,Ramakrishnan, L.,Ryu, Y.,van Ingen, C.,Jackson, K.R.,Agarwal, D. North-Holland ; Elsevier Science Ltd 2014 Future generations computer systems Vol.36 No.-
The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument's land and atmosphere data are important to many scientific analyses that study processes at both local and global scales. The Terra and Aqua MODIS satellites acquire data of the entire Earth's surface every one or two days in 36 spectral bands. MODIS data provide information to complement many of the ground-based observations but are extremely critical when studying global phenomena such as gross photosynthesis and evapotranspiration. However, data procurement and processing can be challenging and cumbersome due to difficulties in volume, size of data and scale of analyses. For example, the very first step in MODIS data processing is to ensure that all products are in the same resolution and coordinate system. The reprojection step involves a complex inverse gridding algorithm and involves downloading tens of thousands of files for a single year that is often infeasible to perform on a scientist's desktop. Thus, use of large-scale resource environments such as high performance computing (HPC) environments are becoming crucial for processing of MODIS data. However, HPC environments have traditionally been used for tightly coupled applications and present several challenges for managing data-intensive pipelines. We have developed a data-processing pipeline that downloads the MODIS swath products and reprojects the data to a sinusoidal system on an HPC system. The 10 year archive of the reprojected data generated using the pipeline is made available through a web portal. In this paper, we detail a system architecture (CAMP) to manage the lifecycle of MODIS data that includes procurement, storage, processing and dissemination. Our system architecture was developed in the context of the MODIS reprojection pipeline but is extensible to other analyses of MODIS data. Additionally, our work provides a framework and valuable experiences for future developments and deployments of data-intensive pipelines from other scientific domains on HPC systems.