From the article: "Abstract Maintenance processes of Dutch housing associations are often still organized in a traditional manner. Contracts are based on lowest price instead of ‘best quality for lowest price’ considering users’ demands. Dutch housing associations acknowledge the need to improve their maintenance processes in order to lower maintenance cost, but are not sure how. In this research, this problem is addressed by investigating different supply chain partnering principles and the role of information management. The main question is “How can the organisation of maintenance processes of Dutch housing associations, in different supply chain partnering principles and the related information management, be improved?” The answer is sought through case study research."
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from the paper: "This paper presents a research endeavouring to model site work in a 4D BIM model. Next simulations are performed with this model in 5 scenarios including specific interventions in work organisation, notably changing positons of facilities for site workers. A case study has been done in a construction project in the Netherlands. The research has showed the possibility to model time use of site workers in 4D BIM. Next the research has showed potential to perform and calculate specific interventions in the model, and prospect realistic changes in productive time use as a result."
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Reduction of virgin materials use by the construction industry has high priority and can be achieved by reusing structural and non-structural building components from existing buildings. The high value reuse of building components has not been adopted yet on a large scale because of several reasons, one of them being poor building information management. Therefore, this paper investigates the role of building information modeling (BIM) for that purpose. Based on a review of literature, a preliminary decision making framework is proposed that will be elaborated in the nearby future. The literature review revealed that the use of BIM in combination with other digital technologies looks promising, but that additional research is needed into the governance related aspects of BIM. © 2017 Taylor & Francis Group, London.
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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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This research investigates the impact of early facility management involvement on the effective utilization of building information modelling during the operation and maintenance phase. It looks at understanding the factors that encourage building owners to prioritize early facility manager engagement. This research also examined the role of facility managers when involved early in the process, including the stage in which FM should be involved, the additional knowledge and competencies to add value, the main tasks to perform and what barriers should be overcome to involve FM early. Lastly, this research defines the potential added value that early engagement has on the use of BIM in the operational phase. Recognizing that facility managers bear the ultimate responsibility for building management, this study explores how their early engagement can ensure BIM model align with operational needs, maximizing the technology’s benefits throughout a building’s lifespan. By examining the impact of early FM input, this research aims to provide actionable insights for facility managers to contribute to the BIM development process.
MULTIFILE
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Mixed Reality for THUAS campus Advantages of Mixed Reality for sustainable built environments 1. Incorporate BIM models in real time 2. Multi operator / for quick and easy consultation 3. Onsite ease of use / you can always put on your headset 4. Overlay capabilities of the Mixed Reality Headsets 5. Hand tracking 6. Layering information in real-time 7. 3D view of the location/situation / information 8. Switch from Mixed Reality to Virtual Reality 9. Teleport capabilities
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In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.
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In: ‘Onderzoekend op weg’, een essaybundel over opdrachten voor de toekomst, geschreven door de lectoren van De Haagse Hogeschool t.g.v. het afscheid van Rob Brons als collegevoorzitter, januari 2014 In dit essay worden nieuwe ontwikkelingen in de sector van de gebouwde omgeving beschreven. Door de crisis zagen bedrijven zich genoodzaakt te innoveren teneinde te overleven. In deze bijdrage worden de strategieën die zij daartoe gevolgd hebben, in vogelvlucht beschreven. Vervolgens wordt gekeken naar de consequenties van de veranderingen in de sector voor het onderwijs en het onderzoek aan de hogeschool. Deze bijdrage is gebaseerd op onderzoek zoals beschreven in het boek Samen Sneller Slimmer en maakt ter illustratie gebruik van aldaar weergegeven interviews.
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The Utrecht SBE16 Conference. From the introduction: "The conference is part of the Sustainable Built conference series and is as such considered to be part of the pre-eminent international conference series on sustainable building and construction endorsed by iiSBE, UNEP-SBO and FIDIC. The Utrecht SBE16 conference is hosted by the Centre of Expertise Smart Sustainable Cities of HU University of Applied Sciences Utrecht, in partnership with six Dutch Universities of Applied Sciences (Avans, Saxion, Rotterdam, The Hague, Zuyd, InHolland) and the Utrecht Sustainability Institute (USI). The Transition Zero conference provides us with a unique opportunity to meet transition professionals in urban sustainability from all over Europe and beyond and to learn about the latest developments and best (inter)national practices in urban sustainability. The rich interest in the conference, made it possible to offer research as well as practitioner-driven tracks on topics related to the conference title. The conference brought together excellent future-minded practitioners, researchers and thought leaders from the R&I community, specialists and professionals on zero energy homes and transition of the built environment."
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