Experimental Study on Local Compression of Concrete-filled Glass Fiber Reinforced Gypsum Wall Panel

2013 ◽  
Vol 671-674 ◽  
pp. 668-673
Author(s):  
Kao Zhong Zhao ◽  
Jian Feng Li ◽  
Feng Wang
Keyword(s):  
Glass Fiber ◽  
Test Results ◽  
Fiber Reinforced ◽  
Wall Panel ◽  
Gypsum Board ◽  
Local Pressure ◽  
Concrete Core ◽  
Glass Fiber Reinforced ◽  
Wall Panels ◽  
Capacity Calculation

The concrete-filled glass fiber reinforced gypsum wall panel is a kind of panel that the inside cavums of the glass fiber hollow gypsum panel is filled with concrete. The experimental results indicate that the concrete-filled glass fiber reinforced gypsum wall panel which has a better performance of the force and can be used to be the bearing wall of a building can form a novel structural system. When the beams supporting the wall panels, the wall panels which under the beams is in local state of compression. It were gained that when the wall panels are in the local compression state , local pressure loads are primarily borne by the concrete core columns and fiber gypsum board will damage in advance through the eighteen experimental wall panel specimens which in local compression. The test results show that the final destruction of the concrete is caused by being crushed and the contribution of the gypsum wall panel to local compression bearing is small. Compressive stress can only spread in the local loading on concrete core columns, cannot be expanded into an adjacent stud. Finally, the local compression bearing capacity calculation formula of the concrete-filled glass fiber reinforced gypsum wall panel is obtained by analysis of the test results.

Experimental Study on Concrete-Filled Glass Fiber Reinforced Gypsum Wall Panel Compression Members

2012 ◽  
Vol 446-449 ◽  
pp. 16-22
Author(s):  
Kao Zhong Zhao ◽  
Feng Wang ◽  
Xiao Feng Bian
Keyword(s):  
Bearing Capacity ◽  
Glass Fiber ◽  
Structural Design ◽  
Thickness Ratio ◽  
Fiber Reinforced ◽  
Wall Panel ◽  
Glass Fiber Reinforced ◽  
Compression Members ◽  
Wall Panels ◽  
Capacity Calculation

The concrete-filled glass fiber reinforced gypsum wall panel is a kind of panel that the inside cavums of the glass fiber hollow gypsum panel are filled with concrete, which can be used as the bearing wall of a building. The influences of eccentricity distance and height to thickness ratio on the bearing capacity of the compression wall panels were studied, and the failure mechanism and bearing capacity of compression wall panels were gained through the experiments of twenty-seven(nine groups) axial compression wall panel specimens and twenty-seven(nine groups) eccentric compression wall panel specimens. The analysis results indicate that the bearing capacity of compression wall panels is obviously affected by the eccentricity distance and height to thickness ratio, and there is a linear relation between bearing capacity and eccentricity distance. The bearing capacity calculation formula of the concrete-filled glass fiber reinforced gypsum wall panel is obtained by regression analysis, which provides reliable gist for structural design of concrete-filled glass fiber reinforced gypsum wall panel buildings.

The effect of fiber-reinforced interleaves on the interlaminar shear strength of continuous glass fiber-reinforced polypropylene laminates

2013 ◽  
Vol 33 (3) ◽  
pp. 221-227
Author(s):  
Li Fang ◽  
Xuwu Li ◽  
Xiaodong Zhou
Keyword(s):  
Shear Strength ◽  
Glass Fiber ◽  
Interlaminar Shear Strength ◽  
Test Results ◽  
Fiber Reinforced ◽  
Short Glass Fiber ◽  
Glass Fiber Reinforced ◽  
Long Glass Fiber ◽  
Interlaminar Shear ◽  
Short Glass

Abstract In this article, polypropylene (PP), short glass fiber-reinforced polypropylene (SFT-PP), and direct long glass fiber-reinforced polypropylene (DLFT-PP) interleaves were added as interleaves between fabrics during laminated molding to improve the interlaminar shear strength (ILSS). The test results showed that the ILSS was obviously improved. Furthermore, DLFT-PP interleaves were preheated to melt the PP before laminated molding and were then immediately placed between two fabrics to make the melted PP enter the gaps of the fabric and more fibers were used to further improve the ILSS. As expected, the ILSS increased.

Cost Comparison of Traditional Glass Fiber Test Methods and Computer Aided Neural Prediction Supported Systems

2018 ◽  
Author(s):  
SADIK ALPER YILDIZEL ◽  
SERDAR CARBAS ◽  
OSMAN TUNCA
Keyword(s):  
Neural Networks ◽  
Reinforced Concrete ◽  
Glass Fiber ◽  
Fiber Reinforced Concrete ◽  
Test Results ◽  
Fiber Reinforced ◽  
Proportional Limit ◽  
Glass Fiber Reinforced ◽  
Computer Aided ◽  
Site Test

Within the complexity of the industrial production strategies, computer aided technologies have been becoming a survival key for company administrators for reducing expenses. Furthermore, new production methods and adaptation of dynamic market requirements force owners to apply computer aided solutions to reduce to production time of goods to the market. Nowadays, prefabricated concrete producers are facing the same problem and trying to apply new solutions to overcome these high costs. In this research, artificial neural networks and traditional glass fiber testing methods were compared to reduce the quality control and assurance processes of prefabricated glass fiber reinforced concrete (GRC) production. 143 different four-point flexural test results of glass fiber reinforced concrete mixes with the varied parameters as temperature, fiber content and slump values were introduced the artificial neural networks models. The proportional limit properties (LOP) of glass fiber reinforced concrete and trained neural network analysis are taken into consideration for comparison. The outcomes of the analysis reflected that there is a strong correlation between the proportional limit of glass fiber reinforced concrete on-site test and the artificial swarm-based optimization algorithm results. Depending on this secure data, on-site test quantities are reduced and checked for cost deduction of traditional test results.

Cost Comparison of Traditional Glass Fiber Test Methods and Computer Aided Neural Prediction Supported Systems

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
SADIK ALPER YILDIZEL ◽  
SERDAR CARBAS ◽  
OSMAN TUNCA
Keyword(s):  
Neural Networks ◽  
Reinforced Concrete ◽  
Glass Fiber ◽  
Fiber Reinforced Concrete ◽  
Test Results ◽  
Fiber Reinforced ◽  
Proportional Limit ◽  
Glass Fiber Reinforced ◽  
Computer Aided ◽  
Site Test

Within the complexity of the industrial production strategies, computer aided technologies have been becoming a survival key for company administrators for reducing expenses. Furthermore, new production methods and adaptation of dynamic market requirements force owners to apply computer aided solutions to reduce to production time of goods to the market. Nowadays, prefabricated concrete producers are facing the same problem and trying to apply new solutions to overcome these high costs. In this research, artificial neural networks and traditional glass fiber testing methods were compared to reduce the quality control and assurance processes of prefabricated glass fiber reinforced concrete (GRC) production. 143 different four-point flexural test results of glass fiber reinforced concrete mixes with the varied parameters as temperature, fiber content and slump values were introduced the artificial neural networks models. The proportional limit properties (LOP) of glass fiber reinforced concrete and trained neural network analysis are taken into consideration for comparison. The outcomes of the analysis reflected that there is a strong correlation between the proportional limit of glass fiber reinforced concrete on-site test and the artificial swarm-based optimization algorithm results. Depending on this secure data, on-site test quantities are reduced and checked for cost deduction of traditional test results.

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