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Alain Zarli, Eric Pascual, Daniel Cheung

Information and Communication Technology for Intelligent and Integrated Controls in Buildings: Current Developments and Future Research

Abstract: A common and acknowledged vision today is the one that, in the future, buildings, along with their components, equipments, and their environment will communicate and be able to provide information on their status ubiquitously. This real-time available information will be interoperable via common protocols for holistic automation & control. The whole building will be supervised by intelligent systems, able to combine information from all connected devices, from the Internet or from energy service providers in order to efficiently control HVAC (heating & cooling), lighting, and hot water systems along with energy production, storage and consumption devices inside the building, taking into account the users' needs and wishes. In such a context, ICT is recognised as key for empowering people in the (built) universe in which they live, with smart e-metering and new smart e-devices – as well as becoming fully pervasive in the future optimization of energy in the built environment - where “Energy-efficient smart buildings” are to be buildings which contain systems that manage information for an optimal operation of building energy flows over the whole building lifecycle.In such a context, CSTB has developed an open framework for data collection and processing, to be installed in any built environment. It supports networked heterogeneous sensors and actuators (with appropriate communication protocols technology), allows assembling various “business” functions (with easy evolution and extension capability thanks to a concept of service composition and event-driven management between modules), can accommodate any hardware platform constraint (memory, computing power), and can be executed in any environments supporting a Java SE implementation. The framework is itself based on an OSGi platform. The notion of “sensor” is to be considered in a comprehensive way: physical sensor (analogic or logic), complex sub-system or meta-sensor (e.g. Agilent data acquisition system or alike), or even external services (e.g. getting weather data via the Internet). Fields of applications are energy-efficiency in the built environment, but also Ambient-Assisted Living (AAL), internal air quality assessment, collection of data related to inhabitants behaviours, etc..The REEB coordination action (European strategic research roadmap to ICT enabled Energy-Efficiency in Buildings and construction), as a European R&D technology roadmap initiative (achieved in the context of an EC-funded Coordinated Action - http://www.ict-reeb.eu) has identified ICT contributions to the energy efficiency of buildings mainly via improvement (and corresponding RTD) in integrated design (and indeed ICT tools for Energy-Efficient design and production management), integrated and intelligent control, user awareness and decision support to various stakeholders throughout the whole life of buildings, energy management and trading, and integration technologies. As far as the integrated / intelligent control field is concerned, REEB has fundamentally identified the following areas for future investigation:• automation & control: system concepts, intelligent HVAC, smart lighting, ICT for micro-generation & storage systems, predictive control;• monitoring: instrumentation: smart metering;• quality of service: improved diagnostics, secure communications;• wireless sensor networks: hardware, operating systems, network design.The paper will first introduce to expectations, requirements and potential future scenarios for ICT to support integrated and optimised control in future so-called smart buildings. It will then introduce to the current trend of developments at CSTB in this area, and will present the CSTBox as a tool federating and/or complementing functions (potentially relying on already installed systems) in the built environment. After a short presentation of the REEB project, the paper will follow up with exhibiting the outcome of the REEB project in terms of roadmapping RTD activities in this technological field, also providing with a first insight of their potential impact in the future.Acknowledgement: the authors wish to thank the European Commission (DG INFSO) for its financial support to the REEB co-ordinated action. Moreover, the authors are also grateful to the REEB Consortium partners, namely ARUP, ACCIONA, CEA, LABEIN, TUD, UCC & VTT.

Keywords: Energy-efficient buildings, Intelligent and Integrated Control, REEB project, CSTBox framework, Data collection and storage

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Series: w78:2010 (browse)
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C Bogen, M Rashid, E W East

A Framework for Building Information Fusion

Abstract: Data reported by supervisory control and data acquisition (SCADA) systems is critical for evaluating the as-operated performance of a facility. Typically these systems are designed to support specific control domains, but facility performance analysis requires the fusion of data across these domains. Since a facility may have several disparate, closed-loop SCADA systems, resolution of data interoperability issues (heterogeneities) is a prerequisite to cross-domain data fusion. There are no general methods for resolving these heterogeneities in the context of a nonproprietary core building information model (BIM) format. This article describes how these standard data models are applied to a general framework for the integration of building information models and building sensor telemetry. Given the number of very large corporations, each with its own research agendas and proprietary products, and the large number of installed buildings, each with its own control systems, yet another control scheme or technology will not make an impact on improving this market. The authors propose solutions to these underlying data heterogeneities by adopting existing data standards and introducing new data schemas (only when necessary) based on consensus between industry, government, and academic stakeholders. The Industry Foundation Class (IFC) 2X4 controls domain is the foundation of the authors decomposition of SCADA systems as components, assemblies, and connections that relate to other objects in the facility. The Open Building Information eXchange (oBIX) provides the basis for the authors representation of raw telemetry streams that map to the underlying IFC model. The system concept described in this article is part of an effort that is expected to produce an Industry Foundation Class Model View Definition (MVD) for building SCADA systems, product type templates for building SCADA products, the architectural design of an integration platform, and the specification of common predictive and analytical functions for deriving usable intelligence from the integration framework.

Keywords: Smart Buildings, Data Fusion, Building Controls and Automation, Building Information Modeling (BIM), Industry Foundation Classes IFC

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Series: w78:2011 (browse)
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Christopher Raghubar, Nima Shahbazi, Brandon Bortoluzzi, Aijun An and J.J. McArthur

Integrating Visual Analytics and Machine Learning Into BIM-Enabled Facilities Management.

Abstract: Building Information Modelling is becoming increasingly used for Asset Information Management in Facility Operations, where semantic and relational information are of primary importance. "Big Data" analytics tools provide new opportunities within this domain to classify and synthesize data, integrate it with the Computer-Aided Facilities Management system, and develop predictive models to assign priority and resources to address issues arising. The resulting information integrated into building information models provides a powerful tool for facilities management teams to prioritize and streamline operations and maintenance tasks.This paper presents the development, comparison, and application of two supervised machine learning models to classify and evaluate maintenance requests generated both from within the maintenance team and occupant complaints. Three algorithms: Term Frequency (TF), Term Frequency-Inverse Category Frequency (TF-ICF), and Random Forest are used to analyse the text of the maintenance request description and assign problem types to each. Approximately 150,000 historical maintenance requests were used for model development and the models have overall prediction accuracies of 69%, 70%, and 90% for problem type prediction, respectively.

Keywords: Machine Learning, Building Information Modelling, Visual Analytics, Facility Management, Predictive Models, Big Data

DOI: https://doi.org/10.24928/JC3-2017/0143

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E Sabbatini, G M Revela, A Sicilia, M Bhms

Integration of an Infrared-based monitoring system with an EIIP (Energy Information Integration Platform) for innovative efficient indoor environment control

Abstract: An innovative thermography based measurement system for real-time estimation of thermal behaviour of a room is already developed as part of the FP7 project IntUBE. The applied approach is based on indoor measurements by an infrared (IR) camera and image post-processing to derive mean surface temperatures, thermal comfort indices, air temperature, number of occupants with the relative heat gains generated and presence of other heat sources (e.g. computers). The purpose is to provide spatially distributed room energy information in order to obtain instantaneous feedback displayed for the users or eventually for automatic HVAC control. Lumped parameter model of the room receives data from IR camera to compute exchanged heat rate and air temperature. A low-cost IR sensor, commercially available as surveillance system with automatic movement control that can provide qualitative data output, has been upgraded with a new interface to achieve quantitative data. The paper describes the integration of energy information related to the developed monitoring device (e.g. PMV - Predictive Mean Vote, PPD - Predicted Percentage Dissatisfied, room air temperature as output, humidity value from external sensor as input) within the IntUBE Energy Information Integration Platform (EIIP). The key aspect of the platform is smartness or semantics: ICT applications will communicate via this integration platform on the basis of semantic building objects. Performance Information Model (PIM) server stores data regarding the actual monitored performances of a building (energy, temperature, humidity, PMV etc.). These operational data together with the actual weather data can be used e.g. to compare actual performances with simulated performances and can lead to corrective actions. The paper demonstrates that an advanced monitoring/control system (as the IR-based one) can benefit from retrieving data from the EIIP through SPARQL queries, thus activating new functionalities with interoperability guaranteed by the Platform semantics.

Keywords: Thermography, Thermal comfort, Integration Platform, Interoperability, Semantics

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Haksever A M

A model to predict the occurrence of information overload of project managers

Abstract: "This paper investigates information overload of construction project managers. The aim is to identify its occurrence pattern and predict the occurrence probabilities in a given circumstance, as a project managers information load is inconstant in nature, fluctuating over time, changing character and source. First, a conceptual definition of information overload is developed, using time as the criterion to describe information load. Information overload for a project manager is taken as occurring when the demands on information processing time exceed the supply of time. Second, the variation of information load throughout the project is structured using the interaction of a project manager with project members through the stages of a project. These two elements are combined in a matrix format where values for information overload are ascribed to cells representing the interaction with each member during each stage of the project. Six key project members, and four project stages are defined. To allow the subjective quantification of information overload, five practical situations of real life information overload are described, of which one must be chosen for each of the twenty four stage-member cells. To test the model and calculate the probabilities of information overload, data were collected using a questionnaire survey of 140 project managers in the UK. Respondents were asked to select the relevant situation for each cell in the matrix. The resulting matrices had a weighting system applied and a mean calculated for each circumstance to create an Information Load Point (ILP), presented in an Information Load Matrix (ILM). The application of Ordinal Logistic Regression into the ILP scores provides a predictive outcome, which gives the probabilities of a project manager being in any of the predetermined five information overload situations at any stage with any member. The detailed account of the calculations and the outcome of the analysis are presented. The results revealed that the extent and sources of information overload of construction project managers vary throughout the stages of a project. The construction stage has the highest probability of information overload, followed by the design stage. The main sources of information overload are the project participants contributing the key expertise in each stage. In the design stage, the key contributors are architects and consultants, and in the construction stage, contractors and sub-contractors. Architects and consultants contributions to information overload show a similar pattern through the project duration, as do those of contractors and sub-contractors. This is the first of its kind in construction project management and provides an invaluable source of reference and guidance on the probabilities of the occurrence of information overload in a construction project. The model predicts the situations where information overload is high, moderate, low or non-existent. It is then possible to concentrate on those overloaded areas by using the appropriate means or strategies."

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Series: w78:2000 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.strategies (0.016354) class.man-software (0.013484) class.impact (0.012353)
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Permission to reproduce these documents have been graciously provided by Icelandic Building Research Institute. The assistance of the editor, Mr. Gudni Gudnason, is gratefully appreciated


Jiwen Zhang, Tim Taylor, Roy Sturgill, Gabe Dadi and Nikiforos Stamatiadis

Predictive Risk Modeling of Differential Bridge Settlement

Abstract: Differential settlement between the roadway pavement resting on embankment fill and the bridge abutment built on more rigid foundation often creates a bump when driving from roadway to bridge, and vice versa. This paper studies the problem at a macroscopic level by determining a method to predict the levels of approach settlement to assist designers in developing remediation plans during project development to minimize the lifecycle costs of bridge bump repairs. A macro method considering a combination of maintenance times, maintenance measures, and observed settlement was used to classify the differential settlement scale as minimal, moderate, and severe. A set of project characteristics including approach, abutment type, embankment, foundation, and traffic volume that may influence the formation of differential settlement were identified and used as parameters to develop a model to predict the settlement severity for a given approach. Logistic regression analyses were implemented to identify the relationships between the levels of differential settlement and the input variables for a sample of 600 randomly selected bridges in Kentucky. Geographic region, approach age, average daily traffic, and the use of approach slabs are identified as the four most predominant factors that can significantly affect the formation of differential settlement. Based on the performance of bridge approaches in Kentucky, how those parameters interacted in the prediction model is illustrated in the logistic regressions.

Keywords: Differential Settlement, Logistic Regression, Prediction Model

DOI: https://doi.org/10.24928/JC3-2017/0060

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Kirsten A. Davis

ASSESSING INDIVIDUALS RESISTANCE PRIOR TO IT IMPLEMENTATION IN THE AEC INDUSTRY

Abstract: Ever increasing technological capabilities exist in the architecture/engineering/construction (AEC) industry. Email, project specific websites, Computer Aided Drafting (CAD), animations, and Building Information Modeling (BIM) are but a few information technologies adopted in recent years within the industry. The change methods used in the adoptions suggest a focus on technology, yet the technology itself is seen as a primary barrier to successful implementation. In general, the AEC industry is extremely slow to embrace available information technology. Companies often have difficulty with technology implementations because technology is the driver of change, rather than an enabler of change. Resistance of people is the primary reason for failure of any organizational change, including an information technology change. Technological changes will be more successful when researchers develop a fundamental understanding of how people change. Studying individuals and their change processes is essential to improving implementation of technology change, yet change management theories present processes and guidelines for changing organizations and tasks with limited emphasis on individuals involved in change. This research uses a people centered paradigm for developing technology implementation models, placing technology in a change enabling position rather than being a driver of change. This research investigates individuals resistance to change brought about by new information technology implementation in the AEC industry. Resistance to change is a combination of three factors: cause of resistance, level of resistance, and manifestation of resistance. Previous work investigated the importance of specific behavioral characteristics indicative of resistance to change and correlated these characteristics to the level of resistance in individuals. This paper discusses methodology continuing this work, which aims to confirm the previous work, as well as to develop and validate new predictive tools to identify potential resisters prior to an information technology change implementation. The results from analysis of preliminary data are also discussed.

Keywords: Resistance, Change Management, Information Technology, Technology

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Series: w78:2008 (browse)
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L Bobadilla, A Mostafavi, T Carmenate, S Bista

Predictive Assessment and Proactive Monitoring of Struck-By Safety Hazards in Construction Sites: An Information Space Approach

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Rafaela Bortolini and Nria Forcada

Discussion About the Use of Bayesian Networks Models for Making Predictive Maintenance Decisions

Abstract: The performance of a building decreases with time and this process is accelerated if proper maintenance is not carried out. This paper presents a discussion about the importance of predictive maintenance actions to enhance the condition of existing buildings. An approach based on Bayesian networks (BN) is proposed to predict the condition of a building. The proposed approach consists of a conceptual and generic model, including the factors with more influence in the condition of a building, which were identified by a literature review. The relationships between these factors and a discussion about the application of this model in maintenance decision-making are provided.

Keywords: Building Performance, Decision-Making, Predictive Maintenance

DOI: https://doi.org/10.24928/JC3-2017/0145

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X Vasques, T Possompes, H Rey, M Le Touz, N Auboin, E Passot, B Lange

Analysis and knowledge discovery from sensors data to improve energy efficiency

Abstract: Increases in energy prices and the global goal of mitigating CO2 emissions necessitate the development of intelligent Building Management Systems (BMS) that operate on an energy-efficient basis. Data Centers, buildings and/or group of buildings are often responsible for huge energy consumption. One way to monitor and optimize energy consumption is to instrument buildings using sensors (temperature, pressure, humidity ) in order to track and solve wrong usage of energy management systems. The majority of the BMS are processing the data dynamically without taking into account the data history due to their constraint problems (time, bandwidth and calculation capability) and data resources. The RIDER project brings together a consortium of research laboratories and enterprises including IBM, to share their expertise in research and development of smart Information Technology (IT) energy platforms. In this context, we aim to improve energy efficiency of buildings or group of building (including data centers) using IT. One of the objectives is to identify valid, potentially useful, and ultimately understandable patterns in data for improving energy efficiency. We propose in this paper an approach of using an integrated platform able to interconnect instrumented buildings and sites, and to provide a high-level point of view for increasing our knowledge from sensors. The expected results are to estimate physical parameters that influence energy consumption based on data set history. Different correlation could be found between different variables, for example, indoor air quality and energy consumption. These results could be applied at a location where no sensor is placed and predict energy consumption from different variables.

Keywords: Energy-efficient buildings, Data Centers, Sensors, Predictive Analysis, Data set history.

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Series: w78:2011 (browse)
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