Light-weight Self-Compacting Concrete (LWSCC) might be the answer to the increasing construction requirements of slenderer and more heavily reinforced structural elements. However there are limited studies to prove its ability in real construction projects. In conjunction with the traditional methods, artificial intelligent based modeling methods have been applied to simulate the non-linear and complex behavior of concrete in the recent years. Twenty one laboratory experimental investigations on the mechanical properties of LWSCC; published in recent 12 years have been analyzed in this study. The collected information is used to investigate the relationship between compressive strength, elasticity modulus and splitting tensile strength in LWSCC. Analytically proposed model in ANFIS is verified by multi factor linear regression analysis. Comparing the estimated results, ANFIS analysis gives more compatible results and is preferred to estimate the properties of LWSCC.
This paper presents an improved approach for locating and identifying faults for UHV overhead Transmission line by using GA-ANFIS. The proposed method uses one end data to identify the fault location. The ANFIS can be viewed either as a Fuzzy system, neural network or fuzzy neural network FNN. The integration with neural technology enhances fuzzy logic system on learning capabilities are proposed to analyze the UHV system under different fault conditions. The performance variation of two controllers in finding fault location is analyzed. This paper analyses various faults under different conditions in an UHV using Matlab/simulink. The proposed method is evaluated under different fault conditions such as fault inception angle, fault resistance and fault distance. Simulation results confirm that the proposed method can be used as an efficient for accurate fault location on the transmission line.
The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input
parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze
pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output
relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input
parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based
approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy
system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the
performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models
were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of
a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations.
The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will
help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource
consuming.
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