ANN (artificial neural network) is a huge architecture which has the capability of generalizing and learning from the data which is given in the form of exercises and examples from the humans. This ANN gives us a meaningful solution to problems if the input data isn’t correct or incomplete from the previous examples and experiences. This property makes ANN an outstanding tool for solving complex engineering problems.
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2. Structural & Foundation Analysis
3. CPM & BIM
5. Construction Technology (Career Building Course)
7. Construction Project Management
8. Building Information Modelling
11. ETABS Software
Actually, the elements that process the things in neural networks are similar to the connection between neurons and the human brain. The strategy of developing a neural network for this compressive strength of the concrete is to train the ANN on the basis of results obtained from a series of experiments using the same material. If the results from the experiment have enough data which is relevant to the material behaviour then the ANN will have sufficient data to predict the behaviour for incomplete input. The trained ANN can also give approximate results related to the same material.
The compressive strength of the concrete is one of the most important and major mechanical property of the concrete which is generally measured after concreting and curing the cubes for 28 days. The compressive strength is influenced by many factors some of them are aggregate size, aggregate quality, the grade of cement, water-cement ratio, water content. Unfortunately, the equations to find the compressive strength are not yet available. The existing codes can only give target mean strength. And also, the strength cannot be found if we use other materials like fly-ash, superplasticizer, silica fume, etc…
Knowledge of Concrete Technology.
Knowledge of Testing concrete cubes.
Testing apparatus for compressive strength (CTM or UTM)
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Predicting the compressive strength approximately without wasting any materials and not actually performing the experiment.
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