124,630 research outputs found
Physical Knowledge in Patterns: Bayesian Network Models for Preliminary Design
No full textComputer applications in design have pursued two main development directions: analytical modelling and information technology. The former line has produced a large number of tools for reality simulation (i.e. finite element models), the latter is producing an equally large amount of advances in conceptual design support (i.e. artificial intelligence tools). Nevertheless we can trace rare interactions between computation models related to those different approaches. This lack of integration is the main reason of the difficulty of CAAD application to the preliminary stage of design, where logical and quantitative reasoning are closely related in a process that we often call'qualitative evaluation'. This paper briefly surveys the current development of qualitative physical models applied in design and propose a general approach for modelling physical behaviour by means of Bayesian network we are employing to develop a tutoring and coaching system for natural ventilation preliminary design of halls, called VENTPad. This tool explores the possibility of modelling the causal mechanism that operate in real systems in order to allow a number of integrated logical and quantitative inference about the fluid-dynamic behaviour of an hall. This application could be an interesting connection tool between logical and analytical procedures in preliminary design aiding, able to help students or unskilled architects, both to guide them through the analysis process of numerical data (i.e. obtained with sophisticate Computational Fluid Dynamics software) or experimental data (i.e. obtained with laboratory test models) and to suggest improvements to the design
An application of Bayesian Belief Networks (BBNs) for an integrated landscape management
No full textAugmented Reality and Deep Learning towards the Management of Secondary Building Assets
No full textThe retrieval of as-is information for existing buildings is a prerequisite for effectively operating facilities, through the creation or updating of Building/Asset Information Models (BIM/AIM), or Digital Twins. At present, many studies focus on the capture of geometry for the modelling of primary components, overlooking the fact that many recurring actions need to be conducted on assets inside buildings. Furthermore, highly accurate survey techniques like laser scanning need long offsite processing for object recognition. Performing such process on site would dramatically impact efficiency and also prevent the need to revisit the site in the case of insufficient/incomplete data. In this paper, an Augmented Reality (AR) system is proposed enabling inventory, information retrieval and information update directly on-site. It would reduce post-processing work and avoid loss of information and unreliability of data. The system has a Head-Mounted Display (HMD) AR interface that lets the technician interact handsfree with the real world and digital information contained in the BIM/AIM. A trained Deep Learning Neural Network operates the automatic recognition of objects in the field of view of the user and their placement into the digital BIM. In this paper, two uses cases are described: one is the inventory of small assets inside buildings to populate a BIM/AIM, and the second is the retrieval of relevant information from the AIM to support maintenance operations. Partial development and feasibility tests of the first use case applied to fire extinguishers, have been carried out to assess the feasibility and value of this system
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Development of digital twin models supporting ambient assisted living
No full textWorld population aging requires finding solutions to
improve independent living options. Ambient Assisted
Living (AAL) is making step forward developing services
supporting the elderly, but the implementation of
predictive environments is still far away.
Besides, the emerging Digital Twin (DT) concept has
begun to shape the first cognitive environments that
integrate users into assessments, improving efficiency,
prevention, and prediction of likely events through realtime
AI computing.
This paper aims to provide a prototype of a Cognitive
Building framework based on DT models that develop
high-level knowledge to achieve real-time Scenario
Awareness and offer appropriate AAL services once
anomalies are detected
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Blockchain based choreographies: The construction industry case study
No full textBPMN choreography is a modeling language capable to describe scenarios where several independent participants have to collaborate in a climate of opposing interests and therefore are forced to trust each other. For this reason, in many contexts, a strong need for transparency, responsibility, and choreography compliance arise by the various participants. Blockchains and smart contracts, thanks to their characteristic of providing a decentralized and consensus-based validation mechanism, seem to be able to meet these needs in an untrusted scenario. Nevertheless, most of the related work focused either on transparency, accountability, or compliance, but none on all three of them. Furthermore, such works do not take into account the nondeterministc nature of choreographies. This work aims at using blockchains and smart contracts in this scenario providing a formally well-defined set of tools to match all three the aforementioned requirements. This work applies the proposed techniques to a case study from the construction industry, an economical relevant application domain where the demand for transparency, accountability, and compliance with procurement contracts (that can be modeled as choreographies) is very strong
Augmented Reality Application Supporting On-Site Secondary Building Assets Management
No full textSecondary building assets management requires a large amount of information related to them. Nevertheless, building assets surveysare cost and time demanding, especially because they need long post-processing efforts in order to systematize collected data.Furthermore, with the recent transition towards the BIM methodology for building management also modeling building objectsboth in their geometric features and in their related information is a long process and error-prone task. Under these circumstancesthe possibility of performing the majority of operation on-site would definitely make the process more efficient and it would reduceerrors. Augmented Reality (AR) with its capability of overlapping digital data to the real scene is the right tool to support operatorson-site.The proposed system has the aim of reducing the time of secondary building assets survey and provide a tool for the automaticenrichment of BIM models. An AR device (Hololens) with an embedded computer and a neural compute stick constitute the portableon-site system for the automatic recognition of assets objects, removing the necessity of reworking data off site. A trained DeepLearning Neural Network inside the neural compute stick performs the recognition providing the operator with objects features andposition. The AR application inside the Hololens operates as an interface between the user and the digital information just created.Finally, data is stored in a NoSQL database linked to the BIM model so as to be available for further operations. The visuallysupporting information provided by the AR tool, the possibility of working on data directly on site and the portability of the systemrepresent means for increasing efficiency in survey operations. First tests have been conducted so as to prove the feasibility of thesystem and its use on site without further equipment
Leveraging Extended Reality technologies with RFID to enhance on field maintenance of buildings
No full textThe combined use of BIM and the advanced visualization provided by Extended Reality technologies can improve productivity in the project management processes in construction. This paper concerns an application of MR for seamless data retrieval from a BIM platform towards field workers in charge of maintenance. For instance, in case a failure of any systems has been claimed, workers must retrieve the information about the localization of components prior to repairing. This step can be facilitated by on field data visualization through MR. As the number of on field repair actions is huge in complex buildings, minimizing the time required for the alignment of virtual models is beneficial. Hence, an approach for model alignment that is based on the use of RFID tags has been developed. The first advantage is that these embedded devices are suitable for reuse at any survey with no need for re-deployment. Secondly, this approach does not require that the virtual model is displayed during the alignment, which makes it suitable for large models
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