Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning

Ehsan Mansouri, Maeve Manfredi and Jong-Wan Hu ()
Additional contact information
Ehsan Mansouri: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Maeve Manfredi: Department of Structural Engineering, Desimone Consulting Engineering Company, New York, NY 10005, USA
Jong-Wan Hu: Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea

Sustainability, 2022, vol. 14, issue 20, 1-17

Abstract: In order to reduce the adverse effects of concrete on the environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically and environmentally sustainable alternative to portland cement. This is accomplished through the utilization of alumina-silicate waste materials as a cementitious binder. These geopolymers are synthesized by activating alumina-silicate minerals with alkali. This paper employs a three-step machine learning (ML) approach in order to estimate the compressive strength of geopolymer concrete. The ML methods include CatBoost regressors, extra trees regressors, and gradient boosting regressors. In addition to the 84 experiments in the literature, 63 geopolymer concretes were constructed and tested. Using Python language programming, machine learning models were built from 147 green concrete samples and four variables. Three of these models were combined using a blending technique. Model performance was evaluated using several metric indices. Both the individual and the hybrid models can predict the compressive strength of geopolymer concrete with high accuracy. However, the hybrid model is claimed to be able to improve the prediction accuracy by 13%.

Keywords: machine learning; green concrete; python; catboost regressor; extra trees regressor; gradient boosting regressor; geopolymer concrete (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/12990/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/12990/ (text/html)

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:gam:jsusta:v:14:y:2022:i:20:p:12990-:d:939009

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().