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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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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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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Abstract: The key challenge of managing Floating Production Storage and Offloading assets (FPSOs) for offshore hydrocarbon production lies in maximizing the economic value and productivity, while minimizing the Total Cost of Ownership and operational risk. This is a comprehensive task, considering the increasing demands of performance contracting, (down)time reduction, safety and sustainability while coping with high levels of phenomenological complexity and relatively low product maturity due to the limited amount of units deployed in varying operating conditions. Presently, design, construction and operational practices are largely influenced by high-cycle fatigue as a primary degradation parameter. Empirical (inspection) practices are deployed as the key instrument to identify and mitigate system anomalies and unanticipated defects, inherently a reactive measure. This paper describes a paradigm-shift from predominant singular methods into a more holistic and pro-active system approach to safeguard structural longevity. This is done through a short review of several synergetic Joint Industry Projects (JIP’s) from different angles of incidence on enhanced design and operations through coherent a-priori fatigue prediction and posteriori anomaly detection and -monitoring.
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The majority of houses in the Groningen gas field region, the largest in Europe, consist of unreinforced masonry material. Because of their particular characteristics (cavity walls of different material, large openings, limited bearing walls in one direction, etc.) these houses are exceptionally vulnerable to shallow induced earthquakes, frequently occurring in the region during the last decade. Raised by the damage incurred in the Groningen buildings due to induced earthquakes, the question whether the small and sometimes invisible plastic deformations prior to a major earthquake affect the overall final response becomes of high importance as its answer is associated with legal liability and consequences due to the damage-claim procedures employed in the region. This paper presents, for the first time, evidence of cumulative damage from available experimental and numerical data reported in the literature. Furthermore, the available modelling tools are scrutinized in terms of their pros and cons in modelling cumulative damage in masonry. Results of full-scale shake-table tests, cyclic wall tests, complex 3D nonlinear time-history analyses, single degree of freedom (SDOF) analyses and finally wall element analyses under periodic dynamic loading have been used for better explaining the phenomenon. It was concluded that a user intervention is needed for most of the SDOF modelling tools if cumulative damage is to be modelled. Furthermore, the results of the cumulative damage in SDOF models are sensitive to the degradation parameters, which require calibration against experimental data. The overall results of numerical models, such as SDOF residual displacement or floor lateral displacements, may be misleading in understanding the damage accumulation. On the other hand, detailed discrete-element modelling is found to be computationally expensive but more consistent in terms of providing insights in real damage accumulation.
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This report summarises the findings of an international study of the ethical challenges faced by social workers during the Covid-19 pandemic, undertaken during 6th-18th May 2020. 607 responses from 54 countries were received via an online survey, additional interviews and local surveys. Six key themes relating to social workers’ ethical challenges and responses were identified: 1. Creating and maintaining trusting, honest and empathic relationships via phone or internet with due regard to privacy and confidentiality, or in person with protective equipment. 2. Prioritising service user needs and demands, which are greater and different due to the pandemic, when resources are stretched or unavailable and full assessments often impossible. 3. Balancing service user rights, needs and risks against personal risk to social workers and others, in order to provide services as well as possible. 4. Deciding whether to follow national and organisational policies, procedures or guidance (existing or new) or to use professional discretion in circumstances where the policies seem inappropriate, confused or lacking. 5. Acknowledging and handling emotions, fatigue and the need for selfcare, when working in unsafe and stressful circumstances. 6. Using the lessons learned from working during the pandemic to rethink social work in the future.
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The research explores what the SDG Framework, on the basis of the two major SDG challenges -the complexity challenge and the silo challenge-, are demanding of HEIs in terms of SDG Governance, the formulation of its Third Mission, its internal alignment to integrate the SDG and equip the Research and Educational departments with the right SDG competences. The research uses the conceptual approach of Intended SDG Policies, Actual SDG Practice and SDG Perceptions. Recent and relatively young SDG literature is explored and it draws conclusions that there are assumptions on the feasibility of achieving them. Are SDGs aspirational or inspirational? And should the Framework be considered a temporarily binding guidance rather than a global enforcement mechanism to prevent depletion of our social and natural capital? Much SDG research stops at providing analytical frameworks and tools to grapple with the complexity of SDG’s synergies, tradeoffs and spill-over effects. Some literature and tools are available on SDG pathways of urgency and priority ranking but this contradicts the Transformational claims of the SDGs of being Integrative, Indivisible and Universal. A theoretical and practical gap is observed how the SDGs must be viewed in the global community of national policies. But also as a derivative : How can organisations, private and public, view and address the challenges of the SDG Framework?
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The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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