Unveiling the potential of an evolutionary approach for accurate compressive strength prediction of engineered cementitious composites

The different human activities in numerous fields of civil engineering have become possible due to recent development in soft computing. As many researchers have widely extended the use of evolutionary numerical methods to predict the mechanical properties of construction materials, it has become necessary to investigate the performance, accuracy, and robustness of these approaches. Gene Expression Programming (GEP) is a method that stands out among these methods as it can generate highly accurate formulas. In this study, two models of GEP are used to anticipate the compressive strength of engineered cementitious composite (ECC) containing fly ash (FA) and polyvinyl alcohol (PVA) fiber at 28 days. The experimental results for 76 specimens, which are made with ten different mixture properties, are taken from the literature to build the models. Considering the experimental results, four different input variables in the GEP approach are used to arrange the models in two modes: sorted data distribution (SDD) and random data distribution (RDD). Prognosticating the compressive strength values based on the mechanical properties of ECC containing FA and PVA will be possible for the models of the GEP method by using these input variables. The comparison between the experimental results and the results of training, testing, and validation sets of two models (GEP-I and GEP-II), each of which has two distinct distribution modes, is done. It is observed that both modes of RDD and SDD lead to responses with the same accuracy (R-square more than 0.9). Nevertheless, the GEP-I (SDD) model was chosen as the best model in this study based on its performance with the validation data set

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response