Using Artificial Neural Networks Approach to Estimate Compressive Strength for Rubberized Concrete

Abstract

Artificial neural network (ANN) is a soft computing technique that has been used to predict with accuracy compressive strength known for its high variability of values. ANN is used to develop a model that can predict compressive strength of rubberized concrete where natural aggregate such as fine and coarse aggregate are replaced by crumb rubber and tire chips. The main idea in this study is to build a model using ANN with three parameters that are: water/cement ratio, Superplasticizer, granular squeleton. Furthermore, the data used in the model has been taken from various literatures and are arranged in a format of three input parameters: water/ cement ratio, superplasticizer, granular squeleton that gathers fine aggregates, coarse aggregates, crumb rubber, tire chips and output parameter which is compressive strength.The performance of the model has been judged by using correlation coefficient, mean square error, mean absolute error and adopted as the comparative measures against the experimental results obtained from literature. The results indicate that artificial neural network has the ability to predict compressive strength of rubberized concrete with an acceptable degree of accuracy using new parameters.

Prediction of Swelling Parameters of Two Clayey Soils from Algeria Using Artificial Neural Networks

“…The efficacy of the developed models by ANN in this study for predicting swelling parameters of expansive soils is much better than those developed by MLR. This performance has been demonstrated by many geotechnical researches for the estimation of swelling parameters [25,26] and by other civil and environmental engineering works for the estimation of various parameters [38][39][40][41][42][43][44][45].…”

An Improved Animal Migration Optimization Algorithm to Train the Feed-Forward Artificial Neural Networks

“…In this section, to verify the accuracy and robustness of the proposed IAMO and IAMO-MLP algorithms, the experimental studies were carried out on datasets with different difficulty levels and different features. These datasets are 13 benchmark functions, five classification datasets taken from UCI Machine Learning Repository, and a real-world problem taken from [ 45 ]. The specifications of the hardware and software used in the experiments are as follows: Intel(R) Core(TM) i5-3330 3.00 GHz, 4 GB memory, and Microsoft Windows 10.…”

“…To find the optimum value of the compressive strength, the amount of substances added is empirically determined. Recently, some models based on soft computing techniques have been created such as ANN [ 45 , 56 ] to estimate the compressive strength. In this study, the IAMO-MLP algorithm was used to estimate the compressive strength.…”

“…In this study, the IAMO-MLP algorithm was used to estimate the compressive strength. The dataset of the real-world problem was taken from [ 45 ]. It has three attributes (water–cement ratio w/c , superplasticizer sp, and granular squeleton gs ), one output (comprehensive strength fc ), and 112 instances.…”

Effect of Shrinkage Reducing Admixture on Drying Shrinkage of Concrete with Different w/c Ratios

“…The most important studies using artificial neural networks predict compressive strength in different concretes such as self-compacting concrete [ 13 , 14 , 15 , 16 , 17 ]; high-performance concrete [ 13 , 18 ]; recycled aggregate concrete [ 19 , 20 , 21 , 22 ]; cement mortars [ 23 ]; cement mortars containing nano and micro silica [ 24 ]; concrete containing rice husk ash as a partial replacement for cement and reclaimed asphalt pavement as a replacement for aggregates [ 25 ]; concrete under different temperatures [ 15 , 26 , 27 ] and relative humidity [ 15 ]; heavy weight concrete [ 28 ]; laterized concrete [ 29 ]; polymer concrete with various percentages of fly ash [ 30 ]; silica fume concrete [ 31 ]; high-strength concrete [ 32 ]; rubberized concrete [ 33 ]; clinker mortars [ 34 ]; lightweight concrete [ 27 ]; and self-consolidating high-strength concrete containing palm oil fuel ash [ 35 ].…”