Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

Abstract

The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.

Generalized Two-Dimensional Principal Component Analysis and Two Artificial Neural Networks to Detect Traveling Ionospheric Disturbances Using the Tsunami-Atmosphere-Ionosphere Coupling Mechanism

“…The output was a temporary output in each epoch of the training process using the error backpropagation (EBP) algorithm (Omatu et al, 2018). The principal eigenvalues are also called target outputs (Tuntisukrarom and Cheerarot, 2020), which are the real principal eigenvalues presented in Fig. 3.…”

“…The training data were used to train the BPNN. In neural networks, a training dataset consists of labeled data (Belo et al, 2017) that includes training inputs and target outputs, which were real values in this study (Tuntisukrarom and Cheerarot, 2020). The TEC data were transformed into (i.e., mapped to) the principal eigenvalues (using the concept of mapping the TEC data from one domain to another domain.…”

Prediction of compressive strength of geopolymer concrete using machine learning techniques

“…A total of 631 and 125 data‐points were used for training and testing of the model, respectively. The value of R 2 for the training and testing set of data were 0.9969 and 0.9968, respectively 65 . In addition to this, there are few studies for the prediction of various engineering properties of fly ash‐based GPC 36,66 .…”

“…The value of R 2 for the training and testing set of data were 0.9969 and 0.9968, respectively. 65 In addition to this, there are few studies for the prediction of various engineering properties of fly ashbased GPC. 36,66 The input parameters used for predicting the compressive strength of fly ash-based GPC were the amount of fly ash, water glass solution, SH solution, coarse aggregate, fine aggregate, water, the concentration of sodium hydroxide solution, curing time, and curing temperature.…”

Prediction of Compressive Strengths of Concrete with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

“…Revathy et al[5] reported MAPE values of less than 10% for compressive strength, slump flow, v-funnel and L-box flow, an indication of very good performance. According to Tuntisukrarom and Cheerarot[27] a high accuracy prediction ANN model, developed in MATLAB software, had acceptable error and reported RMSEs of 3.339 for training and 3.4569 for testing, among other model performance evaluations, for prediction of compressive strength behavior of ground bottom ash concrete. In this study, for the 3 dataset categories and the overall, MAPE values were below 6%, MAE…”