Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Abstract

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

“…Therefore, before the prediction of students' learning level, data processing methods or data search algorithms can be used to pre-process the initial dataset, in order to effectively improve the prediction accuracy of the model. This reduces forecast errors due to underfitting or overfitting [35,36]. To further analyze the model's ability to predict students' knowledge level, and evaluate the composite model's ability to improve the model, we performed analyses via a confusion matrix.…”

“…Therefore, before the prediction of students' learning level, data processing methods or data search algorithms can be used to pre-process the initial dataset, in order to effectively improve the prediction accuracy of the model. This reduces forecast errors due to underfitting or overfitting [35,36].…”

Understanding Penetration Attenuation of Permeable Concrete: A Hybrid Artificial Intelligence Technique Based on Particle Swarm Optimization

“…During the preparation of permeable concrete, the interspaces of concrete can be connected by adopting corresponding technical measures [5,6]. Permeable concrete refers to the concrete with internal porosity that is greater than 10%, generally 15% to 30%, and most of the pore diameters are greater than 1 mm, with certain water and air permeability [7][8][9][10][11]. As the organic matter and finegrained impurities block the voids of the permeable concrete, water permeability becomes an urgent problem to be solved in the application [7,12,13].…”