No abstract

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

: The unique mechanical, electrical, thermal, chemical and optical properties of carbon based nanomaterials (CBNs) like: Fullerenes, Graphene, Carbon nanotubes, and their derivatives made them widely used materials for various applications including biomedicine. Few recent applications of the CBNs in biomedicine include: cancer therapy, targeted drug delivery, bio-sensing, cell and tissue imaging and regenerative medicine. However, functionalization renders the toxicity of CBNs and makes them soluble in several solvents including water, which is required for biomedical applications. Hence, this review represents the complete study of development in nanomaterials of carbon for biomedical uses. Especially, CBNs as the vehicles for delivering the drug in carbon nanomaterials is described in particular. The computational modeling approaches of various CBNs are also addressed. Furthermore, prospectus, issues and possible challenges of this rapidly developing field are highlighted.

Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.