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- Gaur, Ashish, et al.
(författare)
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A novel approach for industrial concrete defect identification based on image processing and deep convolutional neural networks
- 2023
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The preservation of structural integrity and durability is essential for the long-term viability of civil infrastructure projects. Addressing concrete defects promptly is crucial to achieving this objective. In this research, the research proposes a novel method for concrete defect analysis, harnessing the potential of image processing and deep learning techniques. It employs a fusion-based deep convolutional neural network (CNN), amalgamating the features of Inception V3, VGG16, and AlexNet architectures to identify and classify six distinct concrete defect characteristics, namely Cracks, Crazing, Efflorescence, Pop-out, Scaling, and Surface Cracks. Through rigorous training and validation, we thoroughly assess the performance of the proposed fusion-based CNN model. The testing phase reveals precision rates, with Crazing showing the lowest precision at 95%, and Cracks/Pop-outs achieving 98%, while other defect classifications exhibit exceptional 100% precision rates. The overall efficacy of our proposed model is evaluated using accuracy and F1-score metrics. Our findings demonstrate an impressive overall accuracy of 98.31% and an F1-score of 0.98, affirming the robustness and reliability of our approach. The outcomes of this study signify a significant advancement toward accurate and automated detection and classification of concrete defects.
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| 2. |
- Gurwitz, Kim T., et al.
(författare)
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A framework to assess the quality and impact of bioinformatics training across ELIXIR
- 2020
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 16:7
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Tidskriftsartikel (refereegranskat)abstract
- ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR’s framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.
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