Influence of mesoscopic pore characteristics on the splitting-tensile strength of cellular concrete through deep-learning based image segmentation

2022 ◽  
Vol 315 ◽  
pp. 125335
Author(s):  
Xin Fang ◽  
Chao Wang ◽  
Heng Li ◽  
Xiaohua Wang ◽  
Sherong Zhang ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Image Segmentation ◽  
Deep Learning ◽  
Splitting Tensile Strength ◽  
Pore Characteristics ◽  
Cellular Concrete

scholarly journals Practical Prediction Models of Tensile Strength and Reinforcement-Concrete Bond Strength of Low-Calcium Fly Ash Geopolymer Concrete

Polymers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 875
Author(s):  
Chenchen Luan ◽  
Qingyuan Wang ◽  
Fuhua Yang ◽  
Kuanyu Zhang ◽  
Nodir Utashev ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Fly Ash ◽  
Bond Strength ◽  
Prediction Models ◽  
Splitting Tensile Strength ◽  
Geopolymer Concrete ◽  
Structural Factors ◽  
Test Results ◽  
Portland Cement Concrete ◽  
Low Calcium

There have been a few attempts to develop prediction models of splitting tensile strength and reinforcement-concrete bond strength of FAGC (low-calcium fly ash geopolymer concrete), however, no model can be used as a design equation. Therefore, this paper aimed to provide practical prediction models. Using 115 test results for splitting tensile strength and 147 test results for bond strength from experiments and previous literature, considering the effect of size and shape on strength and structural factors on bond strength, this paper developed and verified updated prediction models and the 90% prediction intervals by regression analysis. The models can be used as design equations and applied for estimating the cracking behaviors and calculating the design anchorage length of reinforced FAGC beams. The strength models of PCC (Portland cement concrete) overestimate the splitting tensile strength and reinforcement-concrete bond strength of FAGC, so PCC’s models are not recommended as the design equations.

scholarly journals Uterine magnetic resonance image segmentation based on deep learning

2021 ◽  
Vol 1861 (1) ◽  
pp. 012067
Author(s):  
Yu’ang Niu ◽  
Yuanyang Zhang ◽  
Liping Ying ◽  
Hong Li ◽  
Wenbo Chen ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Image

scholarly journals Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andreas M. Weng ◽  
Julius F. Heidenreich ◽  
Corona Metz ◽  
Simon Veldhoen ◽  
Thorsten A. Bley ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Accurate Estimation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation ◽  
Pearson’S Correlation ◽  
Lung Volumes ◽  
Lung Segmentation ◽  
Pearson's Correlation ◽  
Lung Mri

Abstract Background Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. Methods In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. Results The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. Conclusions Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

Res2-UNeXt: a novel deep learning framework for few-shot cell image segmentation

2021 ◽  
Author(s):  
Sixian Chan ◽  
Cheng Huang ◽  
Cong Bai ◽  
Weilong Ding ◽  
Shengyong Chen
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Framework ◽  
Cell Image

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Effect of length of steel fibers of waste tires on splitting tensile strength of concrete

2019 ◽  
Vol 276 ◽  
pp. 01003 ◽  
Author(s):  
Aneel Kumar Hindu ◽  
Tauha Hussain Ali ◽  
Agha Faisal Habib
Keyword(s):  
Tensile Strength ◽  
Splitting Tensile Strength ◽  
Steel Fibers ◽  
Waste Tires ◽  
Concrete Cylinders ◽  
Proper Disposal

The increase in volume of vehicles ultimately increases the number of waste tires. The proper disposal or reutilization of waste tires is a challenge. This study is aimed to utilize the steel fibers of waste tires as reinforcement in concrete. Concrete cylinders were cast with addition of different percentages of steel fibers (0-2%) and length (10-20 mm). The fresh and hard properties of concrete reinforced with different percentages of steel fibers and lengths were observed. It is seen that splitting tensile strength of concrete increased with increase in the length of fiber and with the increase in the percentage of fiber. The inclusion of the fibers in concrete causes the reduction in the workability of concrete.

scholarly journals Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1965
Author(s):  
Raul-Ronald Galea ◽  
Laura Diosan ◽  
Anca Andreica ◽  
Loredana Popa ◽  
Simona Manole ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Diagnostic System ◽  
Region Of Interest ◽  
General Purpose ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Learning Methods ◽  
Study Results ◽  
Whole Heart

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.

scholarly journals Material-removing machining wastes as a filler of a polymer concrete (industrial chips as a filler of a polymer concrete)

2021 ◽  
Vol 28 (1) ◽  
pp. 343-351
Author(s):  
Norbert Kępczak ◽  
Radosław Rosik ◽  
Mariusz Urbaniak
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Young’S Modulus ◽  
Industrial Waste ◽  
Splitting Tensile Strength ◽  
Polymer Concrete ◽  
Strength Parameters ◽  
Basic Parameters ◽  
Natural Fillers

Abstract The paper presents an impact of the addition of industrial machining chips on the mechanical properties of polymer concrete. As an additional filler, six types of industrial waste machining chips were used: steel fine chips, steel medium chips, steel thick chips, aluminium fine chips, aluminium medium chips, and titanium fine chips. During the research, the influence of the addition of chips on the basic parameters of mechanical properties, i.e., tensile strength, compressive strength, splitting tensile strength, and Young’s modulus, was analyzed. On the basis of the obtained results, conclusions were drawn that the addition of chips from machining causes a decrease in the value of the mechanical properties parameters of the polymer concrete even by 30%. The mechanism of cracking of samples, which were subjected to durability tests, was also explored. In addition, it was found that some chip waste can be used as a substitute for natural fillers during preparation of a mineral cast composition without losing much of the strength parameters.

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