Effect of Graphene on physical & mechanical Properties of Concrete and optimization using Machine Learning.
Budget: ₹600 – ₹1,500 INR
I need a full-length (60–65 pages) B.Tech final-year project report on the effect of graphene on the physical and mechanical properties of concrete, finished with a machine-learning–based optimisation section that relies on Neural Networks. I will supply two key items the moment we start:
1. The official department report template that fixes every margin, heading level, font style and size.
2. A core research paper whose data, figures and experimental details must be mirrored accurately in the report.
Structure and writing must follow the template word-for-word where formatting is concerned. Content must be paraphrased well enough to keep overall plagiarism below 10 % (I run Turnitin). The technical chapter on optimisation should demonstrate how you train, validate and test a neural-network model to predict—or ideally optimise—strength and durability metrics based on graphene dosage and mix parameters. Feel free to employ Python, Keras or TensorFlow; include plots, hyper-parameter tables and model-performance metrics (MAE, RMSE, R²) in the report itself.
Deliverables I expect:
• Editable Word document (fully formatted, 60–65 pages).
• All original figures, tables and Python notebooks/scripts.
• A brief “how-to-run” note for the ML code.
• Turnitin (or equivalent) plagiarism report screenshot showing <10 %.
I’m working to an ASAP schedule, so let me know your earliest realistic turnaround. When you respond, a short note on your relevant experience with concrete materials research and neural-network modelling is enough—I’m skipping lengthy proposals. Once we agree, I’ll share the template and source paper so you can dive straight in.
1. The official department report template that fixes every margin, heading level, font style and size.
2. A core research paper whose data, figures and experimental details must be mirrored accurately in the report.
Structure and writing must follow the template word-for-word where formatting is concerned. Content must be paraphrased well enough to keep overall plagiarism below 10 % (I run Turnitin). The technical chapter on optimisation should demonstrate how you train, validate and test a neural-network model to predict—or ideally optimise—strength and durability metrics based on graphene dosage and mix parameters. Feel free to employ Python, Keras or TensorFlow; include plots, hyper-parameter tables and model-performance metrics (MAE, RMSE, R²) in the report itself.
Deliverables I expect:
• Editable Word document (fully formatted, 60–65 pages).
• All original figures, tables and Python notebooks/scripts.
• A brief “how-to-run” note for the ML code.
• Turnitin (or equivalent) plagiarism report screenshot showing <10 %.
I’m working to an ASAP schedule, so let me know your earliest realistic turnaround. When you respond, a short note on your relevant experience with concrete materials research and neural-network modelling is enough—I’m skipping lengthy proposals. Once we agree, I’ll share the template and source paper so you can dive straight in.