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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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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An illustrative non-technical review was published on Towards Data Science regarding our recent Journal paper “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning”.While new technologies have changed almost every aspect of our lives, the construction field seems to be struggling to catch up. Currently, the structural condition of a building is still predominantly manually inspected. In simple terms, even nowadays when a structure needs to be inspected for any damage, an engineer will manually check all the surfaces and take a bunch of photos while keeping notes of the position of any cracks. Then a few more hours need to be spent at the office to sort all the photos and notes trying to make a meaningful report out of it. Apparently this a laborious, costly, and subjective process. On top of that, safety concerns arise since there are parts of structures with access restrictions and difficult to reach. To give you an example, the Golden Gate Bridge needs to be periodically inspected. In other words, up to very recently there would be specially trained people who would climb across this picturesque structure and check every inch of it.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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BackgroundWhile the indoor environmental quality of classrooms is a potential issue because it may affect the wellbeing of school children, the relations are still poorly studied. This study aimed to investigate the relations between classroom characteristics and health and comfort of school children.Material and methodsA questionnaire was distributed among 1311 school children (8–12 years old, average 10) of 54 classrooms at 21 schools in The Netherlands. Additionally, the survey included an inspection of the school and its installations and an inspection of the classrooms surveyed using checklists, and monitoring of some environmental parameters (temperature, relative humidity and CO2 concentration) in the classrooms.ResultsAmong the children studied, 87% was bothered by noise, 63% by smells, 42% by sunlight when shining, 35% didn't like the temperature in the classroom (too cold or too warm) and 34% experienced temperature changes. Main diseases reported comprised of allergies (26%), rhinitis (17%), hay fever (16%) and eczema (16%). Health and comfort in non-traditional schools was better than in the traditional schools studied (A non-traditional school is a school in which the way of educating children is different from the traditional way of education, according to a different educational theory). Physical building characteristics of the classrooms studied in the traditional schools were associated with the Classroom Symptom Index (location of school building, heating system, solar devices hampering opening windows or ventilation) and the Classroom Comfort Index (ventilation type, window frame colour, floor material and vacuum cleaning frequency).ConclusionsMeasures to improve acoustical, air, and thermal conditions of children in classrooms are needed. More research is required on the use of different lighting systems and use of different colours in classrooms.
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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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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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De PDCA- of Demingcyclus is het hart van de kwaliteitskunde en heeft haar nut bewezen. Het is echter geen panacee, datin alle culturen en op alle tijden even bruikbaar is. Met nanme in andere dan westerse culturen in tijden van snelle veranderingen voldoet Plan-Do-Check-Act niet.
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The relationship between project management and sustainable development is rapidly gaining interest from both practitioners and academics. Studies on the integration of the concepts of sustainability into project management, approach this topic mostly from a conceptual, logical or moral point of view. Given the fact that the relationship between sustainability and project management is still an emerging field of study, these approaches make sense. However, they do not diminish the need for more empirical studies to understand how the concepts of sustainable development are implemented in practice. This paper reports an analysis of 56 case studies on the integration of the concepts of sustainability in the way organizations initiate, develop and manage projects. The research question of the study was: To what extent, do organizations consider the concepts of sustainability in the initiation, development and management of projects? The study uses the maturity model for sustainability integration that was presented at the 2010 IPMA World Congress for the assessment of the level of sustainability consideration. The study found an overall average level of sustainability consideration in the actual situation of 25.9%. For the desired situation, this score is almost 10 percent higher, showing an ambition to take sustainability more into consideration. The study also showed that the way sustainability currently is considered, shows the traditional ‘less bad’ approach to sustainability integration and not a more modern social responsibility approach.
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