Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms

Abstract

Concrete is one of the most popular materials for building all types of structures, and it has a wide range of applications in the construction industry. Cement production and use have a significant environmental impact due to the emission of different gases. The use of fly ash concrete (FAC) is crucial in eliminating this defect. However, varied features of cementitious composites exist, and understanding their mechanical characteristics is critical for safety. On the other hand, for forecasting the mechanical characteristics of concrete, machine learning approaches are extensively employed algorithms. The goal of this work is to compare ensemble deep neural network models, i.e., the super learner algorithm, simple averaging, weighted averaging, integrated stacking, as well as separate stacking ensemble models, and super learner models, in order to develop an accurate approach for estimating the compressive strength of FAC and reducing the high variance of the predictive models. Separate stacking with the random forest meta-learner received the most accurate predictions (97.6%) with the highest coefficient of determination and the lowest mean square error and variance.

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

“…However, in the global space of scientific research and in practice, there is a deficit, expressed in a certain conservatism of the construction industry and the slow pace of the introduction of artificial intelligence methods and other innovative methods in this industry [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

“…Currently, one of the relevant areas among artificial intelligence methods in industrial production is neural networks, which allow one to create systems for predicting output parameters, that is, the operational properties of any products, structures, buildings and structures that depend on the characteristics of the initial components and process parameters. All this shows that the production of concrete can be improved using artificial intelligence methods, as well as the development, training, and use of special neural networks to determine the characteristics of the resulting concrete [ 17 , 18 , 19 , 20 , 21 ]. A brief overview of such methods is presented in Table 1 .…”

“…All the above-mentioned machine learning methods and neural network models have been applied to a wide range of materials, such as heavy concrete without additives [ 9 , 27 , 28 ], concrete with the addition of industrial and agricultural waste ash [ 21 ], slag [ 16 ], eggshell powder [ 12 ], microsilica [ 20 ], recycled concrete aggregate [ 18 , 32 ], ceramic waste [ 11 ], reinforced concrete with the addition of carbon nanotubes/nanofibers, [ 10 ] high-strength concrete [ 23 , 31 ], fiber-reinforced concrete [ 29 ], reinforced concrete (columns, beams, slabs) [ 17 , 19 , 22 , 30 ], and geopolymer concrete [ 13 ], as well as porous cement pastes [ 33 ], soil with cement [ 14 ], and metals [ 15 ].…”

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

“…According to the generation of individual learners, the current ensemble learning methods can be roughly divided into three categories: boosting, bagging and stacking. Ensemble learning integrates several learning devices to achieve better performance than a single learning device [ 30 , 31 , 32 ]. The three kinds of ensemble learning are widely used in concrete structures.…”

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

“…However, the ANN model is connected with several limitations that lead to a low-performance prediction for this model. These limitations include slow learning rate [53,54]. Therefore, this study aims to increase the prediction model's ability by training the ANN using the artificial bee colony (ABC) algorithm.…”