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Experimental investigation on mode II fracture performance of old-new concrete

Liu Shuang, Shi Zhenwu, Jiang Tao, Wang Huili

Archives of Civil Engineering

2022

Vol. 68, nr 3

469--480

The old-new concrete interface is the weakest part in the composite structure, and there are a large number of microcracks on the interface. In order to study the mode II fracture performance of the bonding surface of old-new concrete, the effect of planting rebar and basalt fiber is investigated. Nine Z-shaped old-new concrete composite specimens with initial cracks are made. Nine shear fracture load-displacement curves are obtained, and the failure process and interface fracture are discussed. On this basis, the mode II fracture toughness and fracture energy are obtained. The regression equations for fracture toughness and fracture energy are deduced with analysis of variance (ANOVA). The results show that fracture toughness and fracture energy increase with the increase of planting rebar number and basalt fiber content. With the increase of the planting rebar number, mode II fracture toughness and fracture energy increase more significantly. Planting rebar is the major factor for mode II fracture performance.

Gastric histopathology image segmentation using a hierarchical conditional random field

Sun Changhao, Li Chen, Zhang Jinghua, Rahaman Md Mamunur, Ai Shiliang, Chen Hao, Kulwa Frank, Li Yixin, Li Xiaoyan, Jiang Tao

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 4

1535--1555

For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, conditional random field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel hierarchical conditional random field (HCRF) based gastric histopathology image segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work. This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance. Especially, a CNN is trained to build up the pixel-level potentials and another three CNNs are fine-tuned to build up the patch-level potentials for sufficient spatial segmentation information. In the experiment, a hematoxylin and eosin (H&E) stained gastric histopathological dataset with 560 abnormal images are divided into training, validation and test sets with a ratio of 1 : 1 :2. Finally, segmentation accuracy, recall and specificity of 78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.