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The Nibelungen bridge as a pilot project for digitally assisted structural maintenance The construction industry is one of the main consumers of natural resources and contributes significantly to CO2 emissions in Germany with an annual waste volume of 54 %. Therefore, the preservation of existing buildings can make a significant contribution to environmental protection and resource conservation. However, effective monitoring and maintenance concepts are needed to extend the service life of structures while maintaining their stability and functionality. This paper deals with a pilot project in which the Nibelungen Bridge Worms serves as a validation object to develop a new, digitally supported maintenance strategy. First, the bridge and its maintenance history will be presented. Subsequently, the pilot project is put into the context of the DFG priority programme SPP 2388 "Hundert plus". Afterwards, the specific key aspects including geometric modelling, structural health monitoring (SHM), digital twinning, level 4 recalculation and the assessment of detected structural damages are described. Finally, conclusions and outlooks are given. [ABSTRACT FROM AUTHOR]

The study aims to develop the failure analysis predictive models, which prognosticate first failure event (FFE), failure-to-year ratio (FYR), and failure rectification group (FRG). The construction of predictive models involved nineteen categories of 13,350 units of medical equipment. We proposed thirteen novel features in assessing medical equipment failures. The failure analysis predictive models were categorised into several classes for training and testing the developed models. There was seven supervised machine learning classifiers and performance metrics applied in the experiment. The experiment demonstrates that Support Vector Machine is the best classifier for the FFE predictive model, which achieves an accuracy of 96.9% after hyperparameter optimisation. Furthermore, Decision Tree is the best classifier for FYR, with an accuracy of 83.9%. Meanwhile, the comparative analysis for FRG discovered that Artificial Neural Network achieved the highest accuracy among others with 76.7% accuracy after the hyperparameter optimisation process. Findings from this study indicate that this failure analysis predictive model functions as a main instrument for conducting predictive maintenance in the direction of smart maintenance practices. Through the developed predictive systems, timely maintenance of medical equipment can be performed. This will also assist healthcare service providers in initiating the remanufacturing and refurbishment programme, ensuring efficient medical care delivery. The suggested framework of machine learning-assisted failure analysis for medical equipment maintenance management may provide clinical engineers with guidance for managing the strategic maintenance management for medical equipment. • The predictive models may help clinical engineers in identifying the actual problem of failures and provide indication on appropriate course of action for better equipment reliability. • The predictive models assist clinical engineers in making a right decision of the implementation of strategic maintenance management for myriad medical equipment. • The failure analysis predictive model functions as a supplementary instrument for conducting predictive maintenance in the direction of sustainable maintenance practices. • The developed failure analysis predictive model may reveal actual deficiency of equipment component and initiate a remanufacturing and refurbishment programme for the manufacturers. • The predictive models provide quick and automatic analysis without complex calculating procedure. [ABSTRACT FROM AUTHOR]