Search results

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.