Optimizing Agricultural Waste By-Products: A Machine Learning Approach for Sustainable Construction Practices

Pradyut Anand (), Surya Dev Singh, Priyam Nath Bhowmik, Veeresh Boya and Shatrudhan Pandey ()
Additional contact information
Pradyut Anand: Madanapalle Institute of Technology & Science
Surya Dev Singh: Madanapalle Institute of Technology & Science
Priyam Nath Bhowmik: Madanapalle Institute of Technology & Science
Veeresh Boya: Madanapalle Institute of Technology & Science
Shatrudhan Pandey: Marwadi University

Circular Economy and Sustainability, 2025, vol. 5, issue 3, 2407-2429

Abstract: Abstract This study explores optimizing concrete mix designs by incorporating agricultural waste by-products to promote sustainability in the construction industry. Data were collected on parameters such as by-product content, compressive strength, tensile strength, sulfate resistance, chloride ion penetration, water-cement ratio, curing duration, and slump. Machine learning models, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, and Artificial Neural Networks, were applied to predict these properties. The models were evaluated using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Sequential Least Squares Quadratic Programming (SLSQP) was used to optimize the mix designs. The study aimed to identify the optimal by-product type and percentage that maximizes strength and performance. Results indicated that Corn Cob Ash (15–20%) is optimal for early strength development, while Palm Oil Fuel Ash, Rice Husk Ash, Sugarcane Bagasse Ash, and Wheat Straw Ash are suitable for mid- and later-stage strength. This research presents a machine learning-based approach to optimizing concrete mixes, reducing the need for extensive experimental testing while advancing sustainable construction practices.

Keywords: Agricultural waste by-products; Machine learning models; Compressive strength; Sequential Least Squares Quadratic Programming (SLSQP); Sustainability (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43615-024-00483-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:circec:v:5:y:2025:i:3:d:10.1007_s43615-024-00483-2

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43615

DOI: 10.1007/s43615-024-00483-2

Access Statistics for this article

More articles in Circular Economy and Sustainability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().