This paper aims to quantify the evolution of damage in masonry walls under induced seismicity. A damage index equation, which is a function of the evolution of shear slippage and opening of the mortar joints, as well as of the drift ratio of masonry walls, was proposed herein. Initially, a dataset of experimental tests from in-plane quasi-static and cyclic tests on masonry walls was considered. The experimentally obtained crack patterns were investigated and their correlation with damage propagation was studied. Using a software based on the Distinct Element Method, a numerical model was developed and validated against full-scale experimental tests obtained from the literature. Wall panels representing common typologies of house façades of unreinforced masonry buildings in Northern Europe i.e. near the Groningen gas field in the Netherlands, were numerically investigated. The accumulated damage within the seismic response of the masonry walls was investigated by means of representative harmonic load excitations and an incremental dynamic analysis based on induced seismicity records from Groningen region. The ability of this index to capture different damage situations is demonstrated. The proposed methodology could also be applied to quantify damage and accumulation in masonry during strong earthquakes and aftershocks too.
This paper presents a proof of concept for monitoring masonry structures using two different types of markers which are not easily noticeable by human eye but exhibit high reflection when subjected to NIR (near-infrared) wavelength of light. The first type is a retroreflective marker covered by a special tape that is opaque in visible light but translucent in NIR, while the second marker is a paint produced from infrared reflective pigments. The reflection of these markers is captured by a special camera-flash combination and processed using image processing algorithms. A series of experiments were conducted to verify their potential to monitor crack development. It is shown that the difference between the actual crack width and the measured was satisfactorily small. Besides that, the painted markers perform better than the tape markers both in terms of accuracy and precision, while their accuracy could be in the range of 0.05 mm which verifies its potential to be used for measuring cracks in masonry walls or plastered and painted masonry surfaces. The proposed method can be particularly useful for heritage structures, and especially for acute problems like foundation settlement. Another advantage of the method is that it has been designed to be used by non-technical people, so that citizen involvement is also possible in collecting data from the field.
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.
A series of tests performed on as-built and strengthened timber joist-masonry-wall specimens. The test aims at providing a complete characterization of the behaviour of the timber-joist connections under axial cyclic loading. The obtained results will be used as inputs to calibrate numerical models to simulate the connection between the cavity wall and timber joist.
Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection. The validated product of the experiment will be tested on the monument of Fraeylemaborg.
Governments, fishermen, dredgers, nature organizations and researchers see that sand stocks are dwindling worldwide, while more and more sand from the North Sea will be needed to protect our coast against rising sea levels. We also extract a lot of sand in the Netherlands, especially from the North Sea. Every year we extract about 12 to 15 million cubic meters to protect our coast and about 15 million cubic meters as filling sand for roads and residential areas and for concrete and masonry sand. Every year we excavate a piece of seabed with the surface of the Schiermonnikoog island at a depth of about eighty centimeters. But our sand requirement continues to rise. Not only because we want to build more roads, homes and residential areas, but also because rising sea levels mean we need more and more sand for coastal protection. In this project a consortium of 21 partners and stakeholders will develop new knowledge and tools about the effects of sand extraction, with the goal to understand how it may be done sustainably despite the rising need for it. The project is led by Wageningen Marine Research and has been awarded funding under the ‘Onderzoek op Routes door Consortia’ (NWA ORC-call 2020/2021) scheme of the Dutch Research Council (NWO). Breda University of Applied Sciences will contribute with its MSP Challenge Simulation Platform, thereby developing and applying a bespoke sand extraction oriented North Sea edition, in close collaboration with data and simulation providing partners in the project.
