In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing model/s to predict strength of concrete with an acceptable performance.
Carbonation is one of the critical durability issues in reinforced concrete structures in terms of their structural integrity and safety and may cause the fatal deterioration and corrosion of steel reinforcement if ignored. Many researchers have performed a considerable number of studies to predict the carbonation of concrete structures. However, it is still challenging to predict the carbonation depth or carbonation coefficient, as they depend on various factors. Therefore, creating a model that can learn from available data using Data Driven Techniques (DDT) is a step forward in this research field. This study provides new approaches to predict the carbonation coefficient of concrete through Model Tree (MT), Random Forest (RF) and Multi-Gene Genetic Programming (MGGP) approaches. With 827 case studies, the predicted models can be seen as a function of a set of conditioning factors, which are statistically significant in explaining the carbonation mechanism. The results obtained through MT, RF and MGGP were compared with those obtained through Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs) and Genetic Programming (which were previously developed). The results reveal that the MT, RF and MGGP perform better than the previous models. Moreover, the MT technique displays its output in terms of series of equations, RF as multiple trees and MGGP in form of a single equation, which are more user-friendly and applicable in practice.
Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.
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