The world is growing and living in cities at a rapid rate as well as in India. This effect of rapid urban development is also evident in the construction industry. However, with urbanization comes the moral obligation to conserve the environment. Concrete has been a popular choice as a building material among public engineers around the world for decades. It is characterized by its excellent performance, long life, and low maintenance costs. To accelerate the rapid growth of the city every year small structures are demolished and new and larger ones are built. These demolished items (most of which are usually concrete) are often discarded in the ground and are no longer used for any purpose. This practice contributes to soil fertility. As the wave of sustainability also has an impact on the construction industry, scientists and engineers around the world are looking for sustainable and recyclable materials.
One such object is Recycled Aggregate Concrete (RAC). With a basic extension setting, it is essential to use computer programs to analyze and design structures. Many commercial systems are accessible for the design of concrete buildings; and also have a high value and offer a limited user license. Every software system has a natural assumption that must be clearly understood before its implementation in construction. It is a good idea to software, as is common in many planning companies, using MS-Excel spreadsheet or other PC programs. One downside to these programs is that they are very expensive. It is not possible to customize/adapt to the requirements. The Python and Neural Network approach has been created as the preferred computer program over different dialects due to its simplicity of programming.
Software to analyze Microstructural properties such as Interfacial Transition Zone (ITZ) width and Compressive Strength has been developed by the author and will be available on Kalinga Server for access. Interfacial Transition Zone (ITZ) width reflects the serviceability and durability of Recycled Aggregate Concrete (RAC). Apart from this, graphs will be generated representing Feature Correlation Heatmap and Compressive strength distribution. Bubble graphs will be generated by the software showing the relationship between Compressive strength and other ingredients like cement, coarse aggregates, and recycled aggregates. A neural network model will be generated along with the model loss graph. Input parameters will be the quantity of cement, sand, coarse aggregates, recycled aggregates, micro silica, and water, Specific gravity of sand, and coarse aggregates. The software will analyze and predict Interfacial Transition Zone (ITZ) width and compressive strength depending upon the input parameters. Such predictions will be helpful to plan mixed design proportions which will save cost and time in actual practice. The software has been programmed using Python language and it accesses online library resources for analysis. Analysis and predictions are based upon Artificial Intelligence using Neural networks. The software will be useful to Academicians, Researchers, Consultants, and Industry persons for mix design proportioning of cement, sand, fine and coarse aggregates along with micro silica for better durability and serviceability of Recycled Aggregate concrete.
This platform will guide pre – post university level students.
Pre University Level – IX –XII grade students when they decide streams and choose their career
Post University level – when A student joins corporate & needs to handle the workplace challenges effectively.
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