Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Abstract

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.

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

“…As depicted in Figure 1, a correlation factor of "1" is observed for the same parameters. When one parameter changes, the other parameter also changes [24,25]. As can be seen from Figure 1, the overall correlation is below 0.3, which means that the selected input parameters are not highly correlated and can be effectively used in the learning and prediction of machine learning models.…”

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

“…A population-based metaheuristic approach called particle swarm optimization iteratively proceeds to discover the best individual particle by optimizing a problem. In actuality, it is appropriate for very large-scale problems and makes very few assumptions about the current problem [118][119][120]. Positions are updated during PSO construction when better positions are discovered using a specific merit function.…”

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

“…To understand the predictive results of the clogging behavior with other research and standard codes, an in-depth comparison was conducted with the previous studies [65,66]. Figure 12 demonstrates the comparison between the proposed model and the previous studies (one is a physical-based prediction model considering the clogging behavior [67,68]; the other is a hybrid machine learning algorithm based on a PSO-SVM model [52]). It can be observed that the prediction results based on the machine learning models are better than the physical model.…”

“…This is because the proposed physical models are more based on idealized assumptions. For example, the connected pore is assumed to be an equally thick permeable pipe [67,68], or the porosity of the concrete specimens is assumed to be consistent in different sections [67,68]. It is easy for the physical model to be inconsistent with the actual seepage when predicting the permeation decay behavior of pervious concrete, which leads to the low prediction reliability of the physical model.…”