Sustainable use of plastic as a bitumen modifier
Budget: $14 – $30 NZD
I have already gathered 251 laboratory results on incorporating waste plastic into bitumen, analysed the full data set with a supervised machine-learning routine in Google Colab, and drafted a literature review. What I now need is a complete, publication-ready project report that follows the specific template I will send you.
Your task is to take everything I supply—the draft review (currently cited in IEEE style) and the Colab notebook—and turn it into a cohesive report with every required heading and sub-heading. Please recast all in-text citations and the reference list into APA 7, weave the existing literature review seamlessly into the template, and expand the Methodology so it documents:
• how the 251 data points were collected and cleaned,
• why the chosen machine-learning model was suitable for predicting bitumen performance, and
• a clear, step-by-step walk-through of the Python code (rewritten and heavily commented so another researcher could reproduce the work without opening the notebook).
The Results and Discussion sections already outline key findings; refine the wording, insert the relevant tables and figures, and ensure the statistical outputs match the narrative. Conclude with practical implications for sustainable pavement engineering and suggestions for future research.
Deliverables: a polished Word document in the template format, the cleaned and annotated Python script, and any graphics or tables exported as high-resolution images ready for insertion.
Your task is to take everything I supply—the draft review (currently cited in IEEE style) and the Colab notebook—and turn it into a cohesive report with every required heading and sub-heading. Please recast all in-text citations and the reference list into APA 7, weave the existing literature review seamlessly into the template, and expand the Methodology so it documents:
• how the 251 data points were collected and cleaned,
• why the chosen machine-learning model was suitable for predicting bitumen performance, and
• a clear, step-by-step walk-through of the Python code (rewritten and heavily commented so another researcher could reproduce the work without opening the notebook).
The Results and Discussion sections already outline key findings; refine the wording, insert the relevant tables and figures, and ensure the statistical outputs match the narrative. Conclude with practical implications for sustainable pavement engineering and suggestions for future research.
Deliverables: a polished Word document in the template format, the cleaned and annotated Python script, and any graphics or tables exported as high-resolution images ready for insertion.
Related categories:
Mechanical Engineering
Machine Learning (ML)
Technical Documentation
Mechanical Design