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.

Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a concrete sculpture to address the issue of brittle fracture, and the primary objective of this study was to explore the potential of hybridizing the Grey Wolf Optimizer (GWO) with four robust and intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, and CatBoost, to anticipate the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) for sculptural elements. The optimization of hyperparameters for these techniques was performed using the GWO metaheuristic algorithm, enhancing accuracy through the creation of four hybrid ensemble learning models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, and GWO-CatBoost. A comparative analysis was conducted between the results obtained from these hybrid models and their conventional counterparts. The evaluation of these models is based on five key indices: R², RMSE, VAF, MAE, and bias, addressing an objective assessment of the predictive models’ performance and capabilities. The outcomes reveal that GWO-XGBoost, exhibiting R² values of (0.971 and 0.978) for the train and test stages, respectively, emerges as the best predictive model for estimating the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) compared to other models. Consequently, the proposed GWO-XGBoost algorithm proves to be an efficient tool for anticipating CSFrRNSC.

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.