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.
Background: Falls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest. Methods: This study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model. Results: We found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults. Conclusion: The interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.
DBELA is a Displacement-Based Earthquake Loss Assessment methodology for urban areas which relates the displacement capacity of the building stock to the displacement demand from earthquake scenarios. The building stock is modeled as a random population of building classes with varying geometrical and material properties. The period of vibration of each building in the random population is calculated using a simplified equation based on the height of the building and building type, whilst the displacement capacity at different limit states is predicted using simple equations which are a function of the randomly simulated geometrical and material properties. The displacement capacity of each building is then compared to the displacement demand obtained, from an over-damped displacement spectrum, using its period of vibration; the proportion of buildings which exceed each damage state can thus be estimated. DBELA has been calibrated to the Turkish building stock following the collection of a large database of structural characteristics of buildings from the northern Marmara region. The probabilistic distributions for each of the structural characteristics (e.g. story height, steel properties etc.) have been defined using the aforementioned database. The methodology has then been applied to predict preliminary damage distributions and social losses for the Istanbul Metropolitan Municipality for a Mw 7.5 scenario earthquake.
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The postdoc candidate, Sondos Saad, will strengthen connections between research groups Asset Management(AM), Data Science(DS) and Civil Engineering bachelor programme(CE) of HZ. The proposed research aims at deepening the knowledge about the complex multidisciplinary performance deterioration prediction of turbomachinery to optimize cleaning costs, decrease failure risk and promote the efficient use of water &energy resources. It targets the key challenges faced by industries, oil &gas refineries, utility companies in the adoption of circular maintenance. The study of AM is already part of CE curriculum, but the ambition of this postdoc is that also AM principles are applied and visible. Therefore, from the first year of the programme, the postdoc will develop an AM material science line and will facilitate applied research experiences for students, in collaboration with engineering companies, operation &maintenance contractors and governmental bodies. Consequently, a new generation of efficient sustainability sensitive civil engineers could be trained, as the labour market requires. The subject is broad and relevant for the future of our built environment being more sustainable with less CO2 footprint, with possible connections with other fields of study, such as Engineering, Economics &Chemistry. The project is also strongly contributing to the goals of the National Science Agenda(NWA), in themes of “Circulaire economie en grondstoffenefficiëntie”,”Meten en detecteren: altijd, alles en overall” &”Smart Industry”. The final products will be a framework for data-driven AM to determine and quantify key parameters of degradation in performance for predictive AM strategies, for the application as a diagnostic decision-support toolbox for optimizing cleaning &maintenance; a portfolio of applications &examples; and a new continuous learning line about AM within CE curriculum. The postdoc will be mentored and supervised by the Lector of AM research group and by the study programme coordinator(SPC). The personnel policy and job function series of HZ facilitates the development opportunity.
