BOT projects plays s vital role in development of infrastructure of the country. A BOT projects involves many parties were dispute can probably occur, which when not resolved on time becomes very expensive in terms of finances, personnel, time, and opportunity costs. In order to minimize dispute data is collected from various construction firms with the help of questionnaire survey, which consist of 5 point scale (1-5, 1-very poor influence to 5 very strong influence) were respondents are requested to rate the factors with their degree of involvement in the arousal of disputes. Analysis shall be done by using SPSS software for identification of 20 factors and then developing neural networks model in resolving disputes. In doing so identified factors influencing the arousal of disputes will be fed to artificial neural network and a network building will be done. Various iterations shall help to assess the effect of change in network parameters. Comparison of their results made in the study gives an optimum combination of parameters for effective resolution of disputes using neural network.
We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the vegetables (tomatoes) that had physiological diseases. Initially tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected
deformed tomatoes by applying the variation of vegetable’s diameter. Later, we used a Genetic Algorithm (GA) based artificial neural network (ANN). Experiments show that the above methods can accurately identify vegetables’ shapes and meet requests of classification; the accuracy rate for the identification for vegetables with physiological diseases was up to 100%. [Nature and Science. 2005; 3(2):52-58].
In recent years sustainability in all domains is expected to be implemented, and in Civil Engineering the importance of sustainability has to be addressed with utmost care. Keeping this as main concern there has been an increasing trend in the study of self-healing materials in concrete. Self healing materials have the ability to heal the concrete once crack is formed. These materials are a family of the super family of the so-called “smart materials”, characterized for their ability to retrieve part of the damage caused by mechanical loads (pores, micro cracks, etc.). In the current study the artificial healing in concrete is induced by crystalline admixture. To decrease the cement content in concrete so as to make it environment friendly fly ash is replacing cement by 15%. Nylon fibres are incorporated to improve the tensile properties of concrete. Cracks would be artificially induced in concrete and the recovery mechanism of the cracks in terms of healing would be studied. Also, the recovery in mechanical properties of concrete: Compressive strength split tensile strength after crack healing would be studied so as to know the variation if any in these properties has occurred by using the chemicals.
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