Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

Abstract

Purpose Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA). Design/methodology/approach MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments. Findings ANN shows more accuracy than MLR with an R2-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R2-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing. Originality/value ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete

“…Additional tests attempting to analyze other regression methods, and simplified ANNs eliminating some inputs variables from the study, also gave good results, although the first-designed ANN was shown to obtain the best results. Furthermore, comparing with other studies aiming to obtain predictions of performance of concretes containing recycled aggregates [24,25], the results are similar or better in terms of accuracy, but the heterogeneity of data used in this study is an important factor, since results have been better or equivalent, even using a smaller amount of input data.…”

“…Artificial intelligence methods have been shown to be more accurate than multiple regression models (MLR) in predicting the compressive strength of concrete. For example, Patil et al [25] have recently proposed an MLR to predict the 28-day compressive strength, taking into account the quantity of cement, natural fine aggregates, coarse recycled concrete aggregates, water, water-to-cement ratio, and the following aggregate properties: water absorption, specific gravity, aggregate impact value and aggregate abrasion value, finding R 2 values less than 0.55 in the training phase and less than 0.75 in the test phase, which highlights the invalidity of the MLR method to predict the mechanical properties of concretes. These same authors also proposed an ANN model with a better accuracy than the MLR model.…”

Predicting the Inelastic Response of Base Isolated Structures Utilizing Regression Analysis and Artificial Neural Network

“…For instance, ANN showed a value of 0.94 for R and 322.49 for MSE compared with regression which yielded a value of 0.75 and 1287.27 for R and MSE, respectively. Finally, a similar observation regarding the capability of ANN compared to multiple linear regression in predicting concrete properties was previously reported [46,47]. Similar to previous sections, the Pearson correlation was carried out to determine the significance of the independent variables.…”

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

“…The multiple linear regression and M5P models [ 59 , 60 ] are compared with the GSC-GBRT model in order to more accurately depict the accuracy of the last one. Multiple linear regression and M5P were used to obtain the predictions, and an 8:2 dataset division ratio was used.…”