Open Access
Issue |
MATEC Web Conf.
Volume 403, 2024
SUBLime Conference 2024 – Towards the Next Generation of Sustainable Masonry Systems: Mortars, Renders, Plasters and Other Challenges
|
|
---|---|---|
Article Number | 04002 | |
Number of page(s) | 13 | |
Section | Testing of Materials and Systems | |
DOI | https://doi.org/10.1051/matecconf/202440304002 | |
Published online | 16 September 2024 |
- A. Hendry, B. Sinha and S. Davies, Design of masonry structures, E & FN Spon, 1997. [Google Scholar]
- J. Ochsendorf, Collapse of Masonry Structures, University of Cambridge, 2002. [Google Scholar]
- A. D’altri, V. Sarhosis, G. Milani, J. Rots, S. Cattari, S. Lagomarsino, E. Sacco, A. Tralli, G. Castellazzi and S. d. Miranda, “Modeling Strategies for the Computational Analysis of Unreinforced Masonry Structures: Review and Classification,” Archives of Computational Methods in Engineering, 2020. [Google Scholar]
- A. Soleymani, H. Jahangir and M. Nehdi, “Damage detection and monitoring in heritage masonry structures: Systematic review,” Construction and Building Materials, 2023. [Google Scholar]
- F. Pallares, M. Betti, G. Bartoli and L. Pallares, “Structural health monitoring (SHM) and Nondestructive testing (NDT) of slender masonry structures: A practical review,” Construction and Building Materials, 2021. [Google Scholar]
- Z. Orban, “UIC Project on assessment, inspection and maintenance of masonry arch railway bridges,” in 5th International Conference on Arch Bridges, 2007. [Google Scholar]
- C. Gentile and A. Saisi, “ON-SITE INVESTIGATION AND DYNAMIC MONITORING FOR THE,” SAHC2014 - 9th International Conference on Structural Analysis of Historical Constructions F. Pena & M. Chavez (eds.), 2014. [Google Scholar]
- A. Ellengberg, A. Kontsos, i. bartoli and A. Prandham, “Masonry Crack Detection Application of an Unmanned Aerial Vehicle,” COMPUTING IN CIVIL AND BUILDING ENGINEERING ©ASCE, 2014. [Google Scholar]
- S. Hassani, U. Dackermann, M. Mousavi and J. Li, “A systematic review of data fusion techniques for optimized structural health monitoring,” Information Fusion, 2024. [Google Scholar]
- E. Valero, A. Forster, F. Bosche, E. Hyslop, L. Wilson and A. Turmel, “Automated defect detection and classification in ashlar masonry walls using machine learning,” Automation in Construction, 2019. [Google Scholar]
- Q. Qiu and D. Lau, “Real-time detection of cracks in tiled sidewalks using YOLO- based method applied to unmanned aerial vehicle (UAV) images,” Automation in Construction, 2023. [Google Scholar]
- D. Dais, I. E. Bal, E. Smyrou and V. Sarhosis, “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning,” Automation in Construction, 2021. [Google Scholar]
- M. Hallee, R. K. Napolitano, W. F. Reinhart and B. Glisic, “Crack Detection in Images of Masonry Using CNNs,” Sensors, 2021. [Google Scholar]
- S. Katsigiannis, S. Seyedzadeh, A. Agapiou and N. Ramzan, “Deep learning for crack detection on masonry facades using limited data and transfer learning,” Journal of Building Engineering, 2023. [Google Scholar]
- E. A. Shamsabadi, C. Xu and D. Dias-da-Costa, “Robust crack detection in masonry structures with Transformers,” Measurement, 2022. [Google Scholar]
- L. M. Dang, H. Wang, Y. Li, L. Q. Nguyen, T. N. Nguyen, H. K. Song and H. Moon, “Deep learning-based masonry crack segmentation and real-life crack length measurement,” Construction and Building Materials, 2022. [Google Scholar]
- X. Jin, M. Z. Haider, Y. Cui, J. G. Jang, Y. J. Kim, G. Fang and J. W. Hu, “Development of nanomodified self-healing mortar and a U-Net model based on semantic segmentation for crack detection and evaluation,” Construction and Building Materials, 2023. [Google Scholar]
- N. Giulietti, P. Chiariotti and G. M. Revel, “Automated Measurement of Geometric Features in Curvilinear Structures Exploiting Steger’s Algorithm,” Sensors, 2023. [Google Scholar]
- O. Ronneberger, P. Fischer and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Lecture Notes in Computer Science, 2015. [Google Scholar]
- S. Jadon, “A survey of loss functions for semantic,” 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2020. [Google Scholar]
- P. Yakubovskiy, Segmentation Models Documentation, Release 0.1.2, 2022. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.