Sustainable growth has encouraged the utilisation of waste materials in conventional concrete for replacement. This study focuses on concrete produced by partially replacing cement and sand with waste products, like slag and fly ash. The application of these materials in concrete lowers the global energy demand and also saves money on the verge of depletion. These materials provide increased mechanical and durability properties as well as a wide range of advantages, including decreased strain on natural resources, and a lower carbon footprint. Experimental work pertaining to concrete contributes to the waste of resources, time and money. Over the last four decades, the development of methods for seeking optimal mixing proportions has been the focus of research. Several researchers have worked in recent years to establish reliable concrete models of compressive force prediction. The prediction of compressive strength of concrete is therefore an active research area. An alternative approach that used machine learning has recently gained momentum in the field of civil engineering. Machine learning is a soft computing mechanism that embodies the characteristics of the human brain, learns from prior circumstances and adapts without any restrictions to new environments. In this research work, a model has been proposed to predict the compressive strength of concrete comprising slag and fly ash as partial substitutes. The first section encompasses a brief summary of the works done by different researchers in this field, and the factors affecting the compressive strength of concrete. The next segment elaborates upon fuzzy logic

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Increasing cost of fertilizers and subsidies are proving to be a burden on agriculturists as well as the government. Now it’s high time to involve technology so that precise amount of fertilizers could be added in order to obtain maximum yield. Machine learning is an important tool that can be used to predict precise nitrogen, phosphorous and potassium for fertigation. But due to different soil types and different needs of different crops and varieties, it has become difficult to predict the exact amount of fertilizer needed. In this research paper a solution has been drawn to obtain precision in agriculture. Data has been collected from soil reports, soil science institutes and agriculture institutes of India. It has been rearranged into tables and pre-processed for applying machine learning models. Once the models were applied their accuracies have been evaluated using various parameters. Also, the predictions given by our model were compared by already existing recommendations. This work has been done for irrigated wheat growing areas of India and it could be extended to other crops and other areas all over the world.

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.

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