Semantic web of building information: Cloud based ‘real world’ building data

Information required by practicing architects, engineers, construction managers, building operators, asset managers, owners, and users becomes more and more distributed, detailed, and richer. BIG DATA is on the rise and this trend will not stop. We rather expect that this trend will further accelerate in the upcoming years as;more and more sensor technologies will become widely available to access existing conditions in the built environment,more and more information streams will be combined for various purposes, e.g. mobile data access information to space use in order to evaluate wireless infrastructure performance but also to establish building use patterns in post-occupancy evaluations, advanced design tools will allow for more detailed data-driven simulation of an increasing number of design alternatives in shorter time spans, and participatory efforts will involve an ever larger number of specialists and non-specialists that all provide information that needs to be accounted for during design and construction planning.We expect that these will be key areas for research in the upcoming years. In this lighthouse project we made four important first steps to enable such research:3TU BIM Data RepositoryData format, standard, and dictionary mapInformation use and exchange processesAutomated indexing method

Enigma (skrivnost, uganka) BIM-a (Informacijskega modeliranja zgradb)

This position paper outlines a number of key questions concerning BIM (Building Information Modelling), as well as the arguments and the historical background behind them. These include the incomplete theory of BIM, the reasons for the emergence of understanding BIM as a panacea for all ills in AECO (architecture, engineering, construction and operation of buildings), the relation between BIM promise and BIM performance, some of the key misconceptions and misunderstandings concerning BIM, and fundamental concerns about what is assumed to be the future of BIM. The paper concludes by suggesting four themes for further discussion and research into the nature and future of BIM and of AECO computerization in general.Članek odpira številna ključna vprašanja v zvezi z BIM (Building Information Modelling), kot tudi argumente in zgodovinsko ozadje za njimi. Sem spadajo nepopolna teorija BIM-a, razlogi za nastanek razumevanja BIM-a kot rešitve za vse tegobe v AECO (arhitektura, inženirstvo, gradna in upravljanje zgradb), odnos med obljubo BIM-a in zmogljivostjo BIM-a, nekatere ključne napačne predstave in nesporazume v zvezi z BIM ter temeljne skrbi glede tega, kaj se domneva, da bo prihodnost BIM. Članek zaključuje s predlaganjem štirih tem za nadaljnjo razpravo in raziskovanje narave in prihodnosti BIM-a in informatizacije AECO nasploh

Advances in Construction and Demolition Waste Recycling: management, processing and environmental assessment

Advances in Construction and Demolition Waste Recycling: Management, Processing and Environmental Assessment is divided over three parts. Part One focuses on the management of construction and demolition waste, including estimation of quantities and the use of BIM and GIS tools. Part Two reviews the processing of recycled aggregates, along with the performance of concrete mixtures using different types of recycled aggregates. Part Three looks at the environmental assessment of non-hazardous waste. This book will be a standard reference for civil engineers, structural engineers, architects and academic researchers working in the field of construction and demolition waste

A novel metric to measure spatio-temporal proximity: a case study analyzing children’s social network in schoolyards

Abstract The present study aims to infer individuals’ social networks from their spatio-temporal behavior acquired via wearable sensors. Previously proposed static network metrics (e.g., centrality measures) cannot capture the complex temporal patterns in dynamic settings (e.g., children’s play in a schoolyard). Moreover, existing temporal metrics overlook the spatial context of interactions. This study aims first to introduce a novel metric on social networks in which both temporal and spatial aspects of the network are considered to unravel the spatio-temporal dynamics of human behavior. This metric can be used to understand how individuals utilize space to access their network, and how individuals are accessible by their network. We evaluate the proposed method on real data to show how the proposed metric impacts performance of a clustering task. Second, this metric is used to interpret interactions in a real-world dataset collected from children playing in a playground. Moreover, by considering spatial features, this metric provides unique knowledge of the spatio-temporal accessibility of individuals in a community, and more clearly captures pairwise accessibility compared with existing temporal metrics. Thus, it can facilitate domain scientists interested in understanding social behavior in the spatio-temporal context. Furthermore, We make our collected dataset publicly available for further research

A Novel Data-driven Approach to Examine Children’s Movements and Social Behaviour in Schoolyard Environments

Social participation at schoolyards is crucial for children’s development. Yet, schoolyard environments contain features that can hinder children’s social participation. In this paper, we empirically examine schoolyards to identify existing obstacles. Traditionally, this type of study requires huge amounts of detailed information about children in a given environment. Collecting such data is exceedingly difficult and expensive. In this study, we present a novel sensor data-driven approach for gathering this information and examining the effect of schoolyard environments on children's behaviours in light of schoolyard affordances and individual effectivities. Sensor data is collected from 150 children at two primary schools, using location trackers, proximity tags, and Multi-Motion receivers to measure locations, face-to-face contacts, and activities. Results show strong potential for this data-driven approach, as it allows collecting data from individuals and their interactions with schoolyard environments, examining the triad of physical, social, and cultural affordances in schoolyards, and identifying factors that significantly impact children's behaviours. Based on this approach, we further obtain better knowledge on the impact of these factors and identify limitations in schoolyard designs, which can inform schools, designers, and policymakers about current problems and practical solutions

A GNN-based Architecture for Group Detection from spatio-temporal Trajectory Data

Detecting and analyzing group behavior from spatio-temporal trajectories is an interesting topic in various domains, such as autonomous driving, urban computing, and social sciences. This paper revisits the group detection problem from spatio-temporal trajectories and proposes “WavenetNRI”, a graph neural network (GNN) based method. The proposed WavenetNRI extends the previously proposed neural relational inference (NRI) method (an unsupervised learning approach for inferring interactions from observational data) in two directions: (1) symmetric edge features and edge updating processes are applied to generate symmetric edge representations corresponding to the symmetric binary group relationships; (2) a gated dilated residual causal convolutional (GD-RCC) block is adopted to capture both short and long dependency of the edge feature sequences. We evaluated the performance of the proposed model on three simulation datasets and three real-world pedestrian datasets, using the Group Mitre metric to measure the quality of the predicted groups. We compared WavenetNRI with four baseline methods, including two clustering-based and two classification-based methods. In these experiments, NRI and WavenetNRI outperformed all other baselines on the group-interaction simulation datasets, while NRI performed slightly better than WavenetNRI. On the pedestrian datasets, the WavenetNRI outperformed other classification-based baselines. However, it did not compete against the clustering-based methods. Our ablation study showed that while both proposed changes cannot be effective at the same time, either of them can improve the performance of the original NRI on one dataset type.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Design & Construction Managemen