Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

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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.

Section: Prediction Methods Detailed Approach Featuresmentioning

confidence: 99%

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

Wang,

Zhang,

et al. 2024

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“…Traditional methods often fail to capture the intricate relationships between various input parameters and the resulting mechanical properties. The Ensemble approach, combining random forest (RF), gray wolf optimizer (GWO), and XGBoost algorithms, offers a sophisticated solution by harnessing the strengths of each constituent algorithm, thereby presenting a robust and innovative predictive model [75]. The predictive accuracy achieved through the Ensemble RF-GWO-XGBoost Algorithm not only contributes to the optimization of geopolymer concrete production but also holds the potential to streamline and advance construction practices.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Wang,

et al. 2024

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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.

“…For instance, predictive models constructed using BP neural networks have shown good predictive performance regarding the compressive strength of different types of concrete [33][34][35][36][37]. Additionally, other machine learning methods, besides BP neural networks, have demonstrated a favorable trend in predicting the 28-day compressive strength of concrete [38][39][40][41][42]. With the continuous advancement of deep learning, models for predicting the compressive strength of concrete established using CNNs and improved convolutional neural networks exhibit superior predictive performance compared to traditional machine learning methods [43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Yu,

Zhu,

Xiang

2024

To overcome limitations inherent in existing mechanical performance prediction models for pervious concrete, including material constraints, limited applicability, and inadequate accuracy, this study employs a deep learning approach to construct a Convolutional Neural Network (CNN) model with three convolutional modules. The primary objective of the model is to precisely predict the 28-day compressive strength of pervious concrete. Eight input variables, encompassing coarse and fine aggregate content, water content, admixture content, cement content, fly ash content, and silica fume content, were selected for the model. The dataset utilized for both model training and testing consists of 111 sample sets. To ensure the model’s coverage within the practical range of pervious concrete strength and to enhance its robustness in real-world applications, an additional 12 sets of experimental data were incorporated for training and testing. The research findings indicate that, in comparison to the conventional machine learning method of Backpropagation (BP) neural networks, the developed CNN prediction model in this paper demonstrates a higher coefficient of determination, reaching 0.938, on the test dataset. The mean absolute percentage error is 9.13%, signifying that the proposed prediction model exhibits notable accuracy and universality in predicting the 28-day compressive strength of pervious concrete, regardless of the materials used in its preparation.