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.
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In order to guarantee structural integrity of marine structures in an effective way, operators of these structures seek an affordable, simple and robust system for monitoring detected cracks. Such systems are not yet available and the authors took a challenge to research a possibility of developing such a system. The paper describes the initial research steps made. In the first place, this includes reviewing conventional and recent methods for sensing and monitoring fatigue cracks and discussing their applicability for marine structures. A special attention is given to the promising but still developing new sensing techniques. In the second place, wireless network systems are reviewed because they form an attractive component of the desired system. The authors conclude that it is feasible to develop the monitoring system for detected cracks in marine structures and elaborate on implications of availability of such a system on risk based inspections and structural health monitoring systems
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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.
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Manual crack inspection is labor-intensive and impractical at scale, prompting a shift toward AI-based segmentation methods. We present a novel crack segmentation model that leverages the Segment Anything Model 2 (SAM 2) through transfer learning to detect cracks on masonry surfaces. Unlike prior approaches that rely on encoders pretrained for image classification, we fine-tune SAM 2, originally trained for segmentation tasks, by freezing its Hiera encoder and FPN neck, while adapting its prompt encoder, LoRA matrices, and mask decoder for the crack segmentation task. No prompt input is used during training to avoid detection overhead. Our aim is to increase robustness to noise and enhance generalizability across different surface types. This work demonstrates the potential of foundational segmentation models in enabling more reliable and field-ready AI-based crack detection tools.
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Drug consumption estimates are of relevance because of public health effects as well as associated criminal activities. Wastewater analysis of drug residues enables the estimation of drug consumption and drug markets. Short-term and long-term trends of cocaine, MDMA (ecstasy), amphetamine (speed) and methamphetamine (crystal meth), were studied for the city of Amsterdam. MDMA (+41%) and cocaine (+26%) showed significantly higher weekend vs. week consumption, while no differences were observed for the other drugs. The consumption of MDMA, cocaine, amphetamine and methamphetamine significantly increased between 2011 and 2019. Weekly trends emerging from wastewater analyses were supported by qualitative and quantitative data from a recreational drug use monitoring scheme. However, information collected in panel interviews within nightlife networks and surveys among visitors of pubs, clubs and festivals only partially reflected the long term increase in consumption as registered from wastewater analysis. Furthermore, methamphetamine use was not well presented in survey data, panel studies and test service samples, but could be monitored trough wastewater analysis. This illustrates that wastewater analysis can function as an early warning if use and user groups are small or difficult to reach trough other forms of research. All in all, this study illustrates that wastewater-based epidemiology is complementary to research among user groups, and vice versa. These different types of information enable to connect observed trends in total drug consumption to behaviour of users and the social context in which the use takes place as well as validate qualitative signals about (increased) consumption of psychoactive substances. Such a multi angular approach to map the illicit drug situation on local or regional scale can provide valuable information for public health.
MULTIFILE
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.
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This paper outlines an investigation into the updating of fatigue reliability through inspection data by means of structural correlation. The proposed methodology is based on the random nature of fatigue fracture growth and the probability of damage detection and introduces a direct link between predicted crack size and inspection results. A distinct focus is applied on opportunities for utilizing inspection information for the updating of both inspected and uninspected (or uninspectable) locations.
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Europe faces significant challenges in maintaining its aging infrastructure due to extreme weather events, fluctuating groundwater levels, and rising sustainability demands. Ensuring the safety and longevity of infrastructure is a critical priority, especially for public organizations responsible for asset management. Digital technologies have the potential to facilitate the scaling and automation of infrastructure maintenance while enabling the development of a data-driven standardized inspection methodology. This extended abstract is the first phase of a study that examines current structural inspection methods and lifecycle monitoring activities of the Dutch public and private entities. The preliminary findings presented here indicate a preference for data-driven approaches, though challenges in data collection, processing, personnel resources and analysis remain. The future work will experiment integrating advanced tools, such as artificial intelligence supported visual inspection, on the existing inspection datasets of these authorities for quantifying their readiness levels to the fully automated digital inspections.
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''Heritage buildings are often subjected to loading conditions that they were not exposed to in their earlier life span. Induced earthquakes in non-seismic regions caused by energy exploitation activities, or strains in the ground that are caused by the climate changes, are new phenomena that alter the usual loading situations for historical buildings.In this paper, monitoring results of a historical building in Groningen (Netherlands) in case of induced seismicity as well as climate change effects has been presented. Long-term monitoring results, detected cracks and relevance of the monitoring data are discussed. In the special case of Groningen, weak and agricultural soil properties dominate the structural response in the region. The gas extraction activities caused a soil subsidence in the giant Groningen Gas Field, resulting decameters of settlement in the entire area, thus an increase of the ground water level in respect to the ground surface. This is the reason why the heritage structures in the region are more vulnerable to soil-water-foundation interactions caused by climate change as compared to the time these heritage structures were constructed. The ground water monitoring as well as the interaction of soil movements with the structural response become important. The study presented here suggests ways on how to effectively monitor historical structures subjected to induced seismicity as well as harsh climate effects at the same time.It was shown here that the newly developed cracks on the structure were detected in a very narrow time window, coinciding with extreme drought and a small induced earthquake at the same time. One explanation provided here is that the soil parameters, such as shrinking of water-sensitive soil layers, in combination with small earthquakes, may cause settlements. The soil effects may superimpose with the earthquake effects eventually causing small cracks and damage. The effects of the climate change on historical buildings is rather serious, and structures on similar soil conditions around the world would need detailed monitoring of not only the structure itself but also the soil-foundation and ground water conditions.''
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The aim of this paper is to show the benefits of enhancing classic Risk Based Inspection (without fatigue monitoring data) with an Advisory Hull Monitoring System (AHMS) to monitor and justify lifetime consumption to provide more thorough grounds for operational, inspection, repair and maintenance decisions whilst demonstrating regulatory compliance.
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