Combining Block-based and Pixel-based Approaches to Improve Crack Detection and Localisation

A variety of civil engineering applications require the identification of cracks in roads and buildings. In such cases, it is frequently helpful for the precise location of cracks to be identified as labelled parts within an image to facilitate precision repair for example. CrackIT is known as a crack detection algorithm that allows a user to choose between a block-based or a pixel-based approach. The block-based approach is noise-tolerant but is not accurate in edge localization while the pixel-based approach gives accurate edge localisation but is not noise-tolerant. We propose a new approach that combines both techniques and retains the advantages of each. The new method is evaluated on three standard crack image datasets. The method was compared with the CrackIT method and three deep learning methods namely, HED, RCF and the FPHB. The new approach outperformed the existing arts and reduced the discretisation errors significantly while still being noise-tolerant

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

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

Monitoring, modelling and quantification of accumulation of damage on masonry structures due to recursive loads

The use of induced seismicity is gaining in popularity, particularly in Northern Europe, as people strive to increase local energy supplies. Τhe local building stock, comprising mainly of low-rise domestic masonry structures without any aseismic design, has been found susceptible to these induced tremors. Induced seismicity is generally characterized by frequent small-to-medium magnitude earthquakes in which structural and non-structural damage have been reported. Since the induced earthquakes are caused by third parties liability issues arise and a damage claim mechanism is activated. Typically, any damage are evaluated by visual inspections. This damage assessment process has been found rather cumbersome since visual inspections are laborious, slow and expensive while the identification of the cause of any light damage is a challenging task rendering essential the development of a more reliable approach. The aim of this PhD study is to gain a better understanding of the monitoring, modelling and quantification of accumulation of damage in masonry structures due to recursive loads. Fraeylemaborg, the most emblematic monument in the Groningen region dating back to the 14 th century, has experienced damage due to the induced seismic activity in the region in recent years. A novel monitoring approach is proposed to detect damage accumulation due to induced seismicity on the monument. Results of the monitoring, in particular the monitoring of the effects of induced seismic activity,, as well as the usefulness and need of various monitoring data for similar cases are discussed. A numerical model is developed and calibrated based on experimental findings and different loading scenarios are compared with the actual damage patterns observed on the structure. Vision-based techniques are developed for the detection of damage accumulation in masonry structures in an attempt to enhance effectiveness of the inspection process. In particular, an artificial intelligence solution is proposed for the automatic detection of cracks on masonry structures. A dataset with photographs from masonry structures is produced containing complex backgrounds and various crack types and sizes. Moreover, different convolutional neural networks are evaluated on their efficacy to automatically detect cracks. Furthermore, computer vision and photogrammetry methods are considered along with novel invisible markers for monitoring cracks. The proposed method shifts the marker reflection and its contrast with the background into the invisible wavelength of light (i.e. to the near-infrared) so that the markers are not easily distinguishable. The method is thus particularly vi suitable for monitoring historical buildings where it is important to avoid any interventions or disruption to the authenticity of the basic fabric of construction.. Further on, the quantification and modelling of damage in masonry structures are attempted by taking into consideration the initiation and propagation of damage due to earthquake excitations. The evaluation of damage in masonry structures due to (induced) earthquakes represents a challenging task. Cumulative damage due to subsequent ground motions is expected to have an effect on the seismic capacity of a structure. Crack patterns obtained from experimental campaigns from the literature are investigated and their correlation with damage propagation is examined. Discontinuous modelling techniques are able to reliably reproduce damage initiation and propagation by accounting for residual cracks even for low intensity loading. Detailed models based on the Distinct Element Method and Finite Element Model analysis are considered to capture and quantify the cumulative damage in micro level in masonry subjected to seismic loads. Finally, an experimental campaign is undertaken to investigate the accumulation of damage in masonry structure under repetitive load. Six wall specimens resembling the configuration of a spandrel element are tested under three-point in-plane bending considering different loading protocols. The walls were prepared adopting materials and practices followed in the Groningen region. Different numerical approaches are researched for their efficacy to reproduce the experimental response and any limitations are highlighted