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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Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it is vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital ‘self-instruction’ and ‘self-inspection’. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating the relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM (Building Information Modeling) and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, which are key for energy-efficient and nearly zero-energy buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.
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Er is een toename van het aanbod van e-health-toepassingen in Nederland, dat blijkt onder meer uit de e-health monitor 2016 (www.e-health-monitor.nl). Eén van de aanbevelingen uit deze monitor is dat meer onderzoek moet plaatsvinden naar veilige en effectieve e-health-toepassingen. In dit artikel bundelen onderzoekers van verschillende kenniscentra hun ervaringen en beschrijven de door hen geleerde lessen die zijn gebaseerd op diverse onderzoeksprojecten.
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This paper reports about preparatory work for future standardization that is carried out through an EU coordination and support action titled IM-SAFE. It focuses on applied digital technologies for monitoring and safety, including predictive maintenance of bridges and tunnels. Amidst the improved affordability of digitalization technologies and techniques, the biggest challenge in monitoring and maintenance of bridges and tunnels is no longer about collecting data as much as possible, but about obtaining and exploiting meaningful data throughout the lifecycle of the built assets. An effective and efficient data-driven approach is important to al-low both human experts and computers to make accurate diagnostics, predictions, and decisions. Further standardization is seen as an important part to reach that goal. The work in IM-SAFE related to ICT standardization focuses on the following topics: (1) the general requirements and preconditions for high quality and cost-effective acquisition, transmission, storage and processing of monitoring datasets to ensure the data is fully accessible and machine-interpretable; (2) the relations between the future standards in structural engineering with the open ICT standards for interoperability, especially on Internet of Things (IoT), Building Information Model (BIM), Geographical Information System (GIS), and Semantic Linked Data (LD); (3) a common design of IT platforms to manage monitoring and asset management data of transport infrastructures; (4) the ways to facilitate data analytics technologies, including AI, to be applied for monitoring and asset management of transport infrastructures, and to assess the added value of data-driven approach next to physics-based modelling. With regard to these topics, this paper reports the outcomes from the expert and stakeholder consultations that recently took place within the IM-SAFE pan-European Community of Practice.
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From the article: "To extend the lifetime of products, an agent is connected to the product. This agent has several roles. It depends on the phase of the lifecycle what these roles will be. One of the roles in the usage or recycling phase is to negotiate for buying spare parts in case a part of the product is broken. The same agent can also decide to offer spare parts to other agents to reuse working parts of a broken product. To accomplish this idea, a marketplace for agents has to be set up, where the auctions can take place. To support this concept, blockchain technology has been used. Blockchains are a new type of technology, known from bitcoins, but there are other cases where blockchains can be used. Blockchain is known for its decentralisation, transparency and for making trustful transactions. In this paper the working of different types of blockchains will be briefly explained and determined if they can be useful for online auctions by agents. A prototype of the marketplace using blockchains has been built."
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This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.
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Digitale architectuur wordt beoefend door digitale architecten. Deze digitale architecten spelen een cruciale rol in het tijdig en betrouwbaar realiseren van IT-oplossingen. De Nederlandse godfather van IT Edsger Dijkstra zei in 1962 bij zijn inaugurele rede (Dijkstra, 1962): “Wij hebben geen betere machines omdat wij geen betere machines verdienen.” De achterliggende oorzaak, betoogde hij, was dat fabrikanten precies bouwden wat de kopers vroegen zonder dat de kopers in enige mate geremd werden door de beperkingen van de technologie. Dit gebeurde onder het motto “In order to live we must sell. And we must sell to perfect idiots". Het onderliggende probleem is dat de vertaling van de wensen van de klant in een werkend compromis niet is geslaagd. Dit is exact het pijnpunt, waar de digitale architect een cruciale rol. speelt. Een kundig architect is in staat met zijn omgeving tot een compromis te komen dat voor alle belanghebbenden acceptabel is. Alternatief is dat niet tot bouw besloten wordt. De vastlegging van het ontwerp van dat compromis gebeurt in de digitale architectuur en ontwerpdocumentatie van de oplossing.
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In dit boek vindt u een beknopte weergave van de ideeën en plannen behorende bij de eerste twee lectoraten van het Kenniscentrum voor Procesinnovatie. In het eerste deel behandelt lector Extended Enterprise Studies Johan Versendaal het concept van de extended enterprise en belangrijke aandachtsgebieden daarbij zoals inkoopvolwassenheid, procesdenken, en e-business ontwikkelingen. Het succes van een extended enterprise is voor een groot deel afhankelijk van de kwaliteit van de architectuur en architecten die de bedrijfsvoering ondersteunen. Dit is dan ook het thema van het tweede deel van dit boek. Hierin neemt lector Architectuur voor Digitale Informatiesystemen Wiebe Wiersema u mee op een tocht die gaat van de opkomst van architectuur tot de knelpunten die zich voordoen binnen het hedendaagse informaticaonderwijs
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Learning teams in higher education executing a collaborative assignment are not always effective. To remedy this, there is a need to determine and understand the variables that influence team effectiveness. This study aimed at developing a conceptual framework, based on research in various contexts on team effectiveness and specifically team and task awareness. Core aspects of the framework were tested to establish its value for future experiments on influencing team effectiveness. Results confirmed the importance of shared mental models, and to some extent mutual performance monitoring for learning teams to become effective, but also of interpersonal trust as being conditional for building adequate shared mental models. Apart from the importance of team and task awareness for team effectiveness it showed that learning teams in higher education tend to be pragmatic by focusing primarily on task aspects of performance and not team aspects. Further steps have to be taken to validate this conceptual framework on team effectiveness.
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The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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