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

作者：Aftab Anwar, Wenyi Yang, Jing Li, Wang Yanwei, Bo Sun, Muhammad Ameen, Ismail Shah, Chunsheng Li, Zia Ul Mustafa, Yaseen Muhammad · 发表于：Cogent Engineering · 年份：2023 · DOI：10.1080/23311916.2023.2225278 · 被引用次数：14 · 研究领域：Advanced Cellulose Research Studies、Natural Fiber Reinforced Composites、Innovative concrete reinforcement materials

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 ...