Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
Adaptive governance describes the purposeful collective actions to resist, adapt, or transform when faced with shocks. As governments are reluctant to intervene in informal settlements, community based organisations (CBOs) self-organize and take he lead. This study explores under what conditions CBOs in Mathare informal settlement, Nairobi initiate and sustain resilience activities during Covid-19. Study findings show that CBOs engage in multiple resilience activities, varying from maladaptive and unsustainable to adaptive, and transformative. Two conditions enable CBOs to initiate resilience activities: bonding within the community and coordination with other actors. To sustain these activities over 2.5 years of Covid-19, CBOs also require leadership, resources, organisational capacity, and network capacity. The same conditions appear to enable CBOs to engage in transformative activities. How-ever, CBOs cannot transform urban systems on their own. An additional condition, not met in Mathare, is that governments, NGOs, and donor agencies facilitate, support, and build community capacities. This is the peer reviewed version of the following article: Adaptive governance by community-based organisations: Community resilience initiatives during Covid‐19 in Mathare, Nairobi. which has been published in final form at doi/10.1002/sd.2682. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
