Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Abstract

Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges with its intricate cementitious matrix and a vague mix design, where the components and their relative amounts can influence the compressive strength. In response to these challenges, the application of accurate and applicable soft computing techniques becomes imperative for predicting the strength of such a composite cementitious matrix. This research aimed to predict the compressive strength of GePoCo using waste resources through a novel ensemble ML algorithm. The dataset comprised 156 statistical samples, and 15 variables were selected for prediction. The model employed a combination of the RF, GWO algorithm, and XGBoost. A stacking strategy was implemented by developing multiple RF models with different hyperparameters, combining their outcome predictions into a new dataset, and subsequently developing the XGBoost model, termed the RF–XGBoost model. To enhance accuracy and reduce errors, the GWO algorithm optimized the hyperparameters of the RF–XGBoost model, resulting in the RF–GWO–XGBoost model. This proposed model was compared with stand-alone RF and XGBoost models, and a hybrid GWO–XGBoost system. The results demonstrated significant performance improvement using the proposed strategies, particularly with the assistance of the GWO algorithm. The RF–GWO–XGBoost model exhibited better performance and effectiveness, with an RMSE of 1.712 and 3.485, and R2 of 0.983 and 0.981. In contrast, stand-alone models (RF and XGBoost) and the hybrid model of GWO–XGBoost demonstrated lower performance.

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

“…Cementing materials Fly ash [23,61] and ground granulated blast-furnace slag (GGBS) content [8,61] Activator design parameters Na 2 SiO 3 [7] and NaOH [7] content; molarity of NaOH [7] Concrete design parameters of the fine aggregate, gravel (4/10 mm), and gravel (10/20 mm) content and water/solid ratio [18,19,36] Table 3. The range of the input variable.…”

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

“…The confusion matrix is an effective tool for evaluating classifier performance. Its main function is to compare the difference between the model classification results and the actual data, so as to realize the accuracy of the model classification results [34]. Through images, the misclassified and correctly classified data are visually represented in order to effectively determine the prediction accuracy of the model, as shown in Table 2.…”

Evolutionary Algorithms for Strength Prediction of Geopolymer Concrete

“…However, the GEP-based model was considered ideal and robust because it provided a simple mathematical formulation and a higher generalization capability compared with others. Recently, Zhang et al [21] proposed a hybrid RFR-GWO-XGBoost algorithm for predicting CS of GPC. The results were compared with stand-alone RFR and XGBoost models to display the supremacy of the proposed methodology.…”

“…The main conclusions for the ST models are in the following: prg [4] = sqrt(prg[0]); prg [5] = prg [3] + prg [2]; prg [6] = x [3]; prg [7] = x [6]; prg [8] = sqrt(prg [4]); prg [9] = sqrt(prg[0]); prg [10] = x [5]; prg [11] = prg [6] + prg [10]; prg [12] = x [1]; prg [13] = x [4]; prg [14] = prg [6] + prg [13]; prg [15] = prg [2] − prg [13]; prg [16] = prg [13] − prg [15]; prg [17] = prg [12] − prg [14]; prg [18] = prg [16] − prg [17]; prg [19] = prg [15]/prg [8]; prg [20] = prg [17] − prg [1]; prg [21] = x [5]; prg [22] = prg [11]/prg [9]; prg [23] = prg [7] − prg…”