Predicting the compressive strength of cellulose nanofibers reinforced concrete using regression machine learning models

Abstract

Cellulose nanofibers (CNFs) are the newly introduced plant-based materials in the construction industry to ensure sustainable development. The use of artificial intelligence (AI) techniques especially machine learning (ML) models has assisted to economized civil engineering. This research aims to determine the compressive strength of cellulose nanofibers reinforced concrete by using supervised regression machine learning techniques for analysis before adopting to utilize. To achieve this task, the machine learning models: Random Forest (RF), Linear Regression (LR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), Ada Boosting Regressor (ABR), K-Neighbor Regressor (KNN), Bagging Regressor (BR), XG Boost Regressor (XGBR), Decision Tree (DT), and Pruned Decision Tree (PDT) were implemented. An experimental-based dataset containing 695 data points was prepared and split into two categories (Training dataset = 70%, Testing dataset = 30%) for the evolution of ML models. There were seven independent variables: cement (kg/m³), water (kg/m³), CNFs (kg/m³), superplasticizer (kg/m³), fine aggregate (kg/m³), coarse aggregate (kg/m³), and age (Day) variables as an input and one dependent variable: compressive strength fc of CNFs reinforced concrete (MPa) as an output. The following metrics were employed to gauge the ability of the model: R², MAPE, MAE, MSE, and RMSE. The findings specified that seven out of ten models (RF, BR, XGBR, DT, GBR, ABR, and KNN) to predict the compressive strength of CNFs concrete had a firm capability (R² >0.72, MAPE ≤ 0.1, and MAE ≤ 5) confirming to the standard of R² value greater than 0.60 and metrics values very less, close to one. According to the sensitivity analysis of Random Forest model, water and cement were the factors with the biggest effects on the prediction of CNFs reinforced concrete, while the smallest effecting variable was coarse aggregate. It was concluded that the RF, BR, and DT were the premier predicting models.

Keywords:

• Artificial intelligence
• machine learning
• Cnfs concrete
• compressive strength
• prediction
• sensitivity analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this study are included in this published article.

Additional information

Funding

This work was supported by the Key R & D Projects in Yunnan Province under Grant 202203AC100004.