Labeling data model


This paper presents a statistical modeling approach to develop a visually representative standardized rating scale based on the key performance indicators identified in a sustainable intelligent building. The identification and validation of key intelligent performance indicators and the development of a statistical data model to analyze the data, form the highlight of the research. The speculation of future advances in the fields of artificial intelligence, automation, architecture and its incorporation in the production of the rating scale strive to make the research future proof.


"The term "Intelligent building" originated in the early 1980's in the United States, where it was used to denote buildings with sophisticated telecommunications, building management and data networking services that provided shared tenant services to their occupants" (Harrison, Loe and Read, 1998: 1).

Bennett et al., 1987 (in Atkin, 1988: 1) identified three attributes that an intelligent building should possess:

  • "Buildings should 'know' what is happening inside and is immediately outside.
  • Buildings should 'decide' the most efficient way of providing a convenient, comfortable and productive environment for the occupants.
  • Buildings should 'respond' quickly to occupants' requests".

A 1995 CIB Working group report, on intelligent and responsive buildings (in Clement-Croome, 2004: 6) defined intelligent buildings as,

"An intelligent building is a dynamic and responsive architecture that provides every occupant with productive, cost-effective and environmentally approved conditions through a continuous interaction among its four basic elements: places (fabric; structure; facilities); processes (automation; control; systems); people (services; users) and management (maintenance; performance) and the interaction between them".

In 2003, Arup Consultants (in Clement-Croome, 2004: 6) informed that, "An intelligent building is one in which the building fabric, space, services and information systems can respond in an efficient manner to the initial and changing demands of the owner, the occupier and the environment".

The measure of intelligence is clearly dependent on how building intelligence is defined.

As evident, the early definitions of building intelligence were very technocentric. A rating method using these definitions would essentially consist of counting the number of computer systems. Definitions of building intelligence during the later part of the 1980's were expanded to include the idea of responsiveness to change. A building had to be adaptable to changing user requirements over time if it was to be considered 'intelligent', thus shifting the focus towards user comforts. A rating method using this definition would evaluate a combination of individual user needs, organization/owner needs and local and global environmental needs. (Harrison, Loe and Read, 1998: 131-132) Thus intelligent buildings were recognized as a subset of sustainable development.


The IBE model of building intelligence was developed in 1992 and the first steps were taken to understand the costs and benefits associated with intelligent buildings. It was then realized that a method of evaluating a building's level of intelligence needed to be developed. In 1992, there was an attempt to produce a rating methodology by David Boyd and Ljubomir Jankovic. The strengths of the approach were that it included a wide range of factors affecting both the building shell and the occupants of the building. The weaknesses, on the other hand, were a complete disregard for the contribution that basic building characteristics such as sectional height and floor depth make to the overall intelligence of the building (Harrison, Loe and Read, 1998: 3; 133).

Aims and Objectives

  • Review of current intelligent building appraisal research, and identification of existing research gaps;
  • Study of future technological advances and how the rating values can be made future proof to take account of these.
  • Development and validation of key intelligent performance indicators for the major building systems in the intelligent building;
  • Using statistical models to group and analyze data and develop relationships;
  • To get an index that corresponds to a visually representative rating scale.

Area of Research

"Intelligent buildings are not a fad, but simply progress" (Atkin, 1988: 6).

To embrace a holistic approach of the study of intelligent buildings and the development of a visually representative rating scale the following areas need to be explored:

  • Intelligent buildings
  • Building automated systems
  • Information management systems
  • Artificial intelligence
  • Rating methodologies and their development
  • Intelligence and its environmental impacts
  • Social psychology and behavioral patterns in intelligent environments
  • Statistical data modeling
  • Intelligent buildings Case studies

Research Questions

How to decide upon the key performance indicators in an intelligent building?

The supervisor can be consulted to get opinions from various departments in the University of Sheffield and other universities, as well as other experts in the field. A virtual conference is a desired way to decide upon the indicators.

How objective is the data collected for the purpose of analyzing?

The data collected by the means of the questionnaire is the variable data which can tend to be based on the personalized view of the person surveyed. The experts targeted may have a biased opinion/inclination towards certain technologies since they know them better. One of the methods to solve the dilemma is to re validate the variable data based on the mathematical model. A second method that can be used is to broaden the outlook by increasing the number of survey participants to include the general user. The views and comments of the general user are very essential to understand the comfort desires and using it to develop future technologies.

How valid is the methodology selected?

Study of existing research and attempts at developing rating systems can lead to an objective view on the drawbacks and advantages of certain methods. This can help in learning and developing a new methodology or building upon an old one.

Can a rating scale be made future proof and how?

An attempt can be made at studying the concepts of artificial intelligence and speculating upon the future trends in the field of architecture, intelligence, automation and information to detect patterns. These elements can also be included in the questionnaire so as to receive critical views from the experts as well as the general user.

How to develop a statistical model for validating and rating the data collected? How to develop a standardized visual rating scale? Will the same building evaluated by two different sets of people receive the same rating?

Reasons for Researching

"The phrase "intelligent building" conjures up images of futuristic, high-technology buildings, filled with computer systems and high technology devices, in which the work is undertaken by the building systems and the occupants are almost superfluous. By providing the appropriate synergy between people, place and IT, the most successful intelligent buildings are likely to be almost invisible" (Harrison, Loe and Read, 1998: 7).

Thus it is of utmost importance to standardize a visually representative rating scale based on key intelligent indicators which would interpret the overall performance of the building and its subsequent effects.

The significant benefits of developing a rating methodology as described by Harrison, Loe and Read (1998: 131):

  • "The rating process helps building users to ask sensible questions about their buildings and information technology and assess their building stock by determining which buildings best suit their needs, which may be refurbished to bring them up to a suitable level and which should be disposed of at the earliest opportunity.
  • It can identify the organizational requirements that will have an impact on the provision of an intelligent building.
  • It can be used to produce a database of building shells which will help in the comparison of buildings on a regional, national or international level.
  • The values and weighting systems which are part of the rating system can be varied through continuing experience and in the light of additional research data, to reflect changing expectations and requirements.
  • The rating process can form the basis of a computerized rating system, perhaps utilizing artificial intelligence. This would overcome many of the problems associated with a paper-based rating approach which is only able to consider one variable at a time".

Initial Literature Review

Atkin, B. (ed.) (1988) Intelligent Buildings-Applications of IT and Building Automation to High Technology Construction Projects, London: Unicom Seminars limited.

This book is a compilation of various papers, each of which focus on understanding the various elements of intelligent buildings. These papers present a multi-faceted view of the buildings and the projected future of the same.

Harrison, A., Loe, E. and Read, J. (ed.) (1998) Intelligent Buildings in South East Asia, London: E & FN Spon.

The book has a dedicated coverage on the topics related to evaluation of building intelligence. It constitutes of comprehensive case studies, advantages and disadvantages of certain earlier evaluation methods and inputs on the need for a rating system.

Clements-Croome, D. (ed.) (2004) Intelligent Buildings-Design, Management and Operation, London: Thomas Telford Publishing.

The book is a compilation of articles by various authors on topics relevant to intelligent buildings such as the design issues, management and operation and sustainable futures. The article discussing the intelligent building senses and its comparison to the human neural interface is of special interest in the study of artificial intelligence.

Chong, K.P., Liu, S.C. and Li, J.C. (2008) Intelligent Structures, Essex: Elsevier Science Publishers Ltd.

The book focuses on interpretation of Sensing and monitoring techniques, Structural control and intelligent systems.

Wigginton, M. and Harris, J. (2002) Intelligent Skins, Kent: Gray Publishing.

Along with discussing intelligent facade systems, a few chapters in the book emphasize on the concepts of artificial intelligence, artificial neural networks, evolving models, natural analogies with the human skin and brain.

Ehrlich, P. (2005) Intelligent Building Construction and Operation [Online] Available: [15 Nov 2009]

This article explains the concepts of the construction and operation of intelligent buildings along with a brief description of the building management tools that require evaluation.

Lohner, T. (2005) Building Automation Industry Cries for Valuation Tools [Online] Available: [30 Nov 2009]

The key issues discussed in this article are:

What constitutes an Intelligent Building? How do buildings compare to one another relative to technology?Can different combinations of technology be used to enhance the value of a building?Is there a difference in Life Cycle Costs for a building that integrates systems versus most of the current buildings that utilize proprietary - stand alone systems?

Continental Automated Buildings Association CABA (IS 2002-28) Best-Practices Guide for Evaluating Intelligent Building Technologies [Online] Available: [30 Nov 2009]

This guide consists of criteria by which intelligent building technologies can be evaluated.

Sinclair, K. (2005) Integrating Intelligence [Online] Available: [15 Nov 2009]

This article briefly discusses the future of automated systems as intelligent interactors and the market trends that support the same. The technologies discussed and the conclusions derived shall help in the speculation of the future of intelligent buildings.

Carey, A. and Parsons, S. (2009) Improving Sustainability through the 21st century Workplace, and IBM's Vision of the Office of the Future [Online] Available:,+and+IBM's+Vision+of+the+Office+of+the+Future&hl=en&gl=uk&sig=AHIEtbTqexfWO32kl1UxmwcDCcHMNS6qIg [6 Dec 2009]

The article focuses on three main attributes namely property, technology and people, and their integration to create optimized working conditions in a sustainable way. The future directions discussed shall be of great importance in the study and outstanding credibility of the IBM holds great value.

Research Methodology

  • A combination of desk and internet research, study of related research papers, intelligent building case studies, and face-to-face interviews shall be used to assemble data.
  • Development and validation of key intelligent performance indicators.
  • A detailed questionnaire shall be developed embracing general intelligent systems currently available and special emphasis shall be given to future technologies based on a sound study.
  • A quantitative survey conducted among academicians, industry professionals such as Architects, Engineers, Consultants, etc. as well as the common users of intelligent buildings and otherwise.
  • Analyzing, grouping, and re-validating the data collected using a statistical model.
  • To get an index that corresponds to a visually representative rating scale.

Data and/or information to be collected

The collection of data can be segregated in two phases,

  • data to be collected for the formation of a questionnaire (phase 1) and
  • using the questionnaire to conduct a quantitative survey (phase 2)

Phase 1:

  • Data related to intelligent systems shall have to be studied and analyzed so as to identify the key intelligent performance indicators.
  • Study of future technological advances that need to be included in the questionnaire and how the rating values can be made future proof to take account of these.
  • It shall also be necessary to study the user behavioral response patterns which shall be a part of the psychological implications of the intelligent systems. This study shall address social sustainability as a key issue which will also help in the development of future technologies based on the needs and requirements of the people.
  • Along with the technological data of the systems, a thorough study of its environmental impacts shall also be conducted so as to address environmental sustainability as a key issue in the development of intelligent buildings.

Based on the intricacy and comprehensiveness of this data a suitable questionnaire shall be developed which shall focus on the following areas:

  • Building Electrical Services
  • HVAC Systems/ Energy management (type of HVAC system, location of air plant, cooling capacity allowance, energy consumption, air distribution system, environmental considerations)
  • Lighting Systems (lighting sources, designed luminance, lighting control, energy conservation related to lighting, emergency stabilized power)
  • Security Systems (access control, asset security, information security, monitoring)
  • Lift Systems (type of lifts, lift strategy, control system, important lift criteria)
  • Responsive Acoustics
  • Fire Systems (fire detection, fire protection, type of alarm system)
  • Faade engineering (double skin systems, shading systems, environmental response, advanced glasses)
  • Building Automation Systems (type of BAS, systems integrated with BAS, network architecture, energy monitoring, operation and maintenance)
  • Communications Systems (digital PABX, computer integrated/cordless telephony, remote access, public address system, cabling systems, data functions, voice functions)
  • Business Systems (office automation systems, remote working, computer integrated telephony, electronic archives, document image processing, video conference)
  • Space Management Systems
  • Furniture Systems

Phase 2:

  • Academicians, Architects, Engineers, Computer programmers, Industry practitioners including design consultants, property developers, and facility managers, etc. who are involved in intelligent building design and development, shall participate in this survey.
  • The common user of intelligent buildings shall also participate in the survey so as to rate the desirable comfort needs and requirements of non-technical persons.

Analysis of Data

  • After collecting data through books, research papers, interviews, internet searches key intelligent performance indicators shall be identified.
  • The data collected from the surveys shall be re-validated and analyzed using a statistical data model to develop relationships.
  • An index shall be obtained from the mathematical model which shall correspond to a visually representative rating scale.

Work Plan

Nov, Dec 2009, Jan 2010:

  • To come up with a research topic after detailed study and discussion with professors and others.
  • To write a research proposal so as to develop an idea, prepare an initial literature review, convince faculty and justify intentions.
  • Start with background study related to the topic.

Feb, Mar, Apr 2010:

  • Data to be collected for the formation of a questionnaire (phase 1)
  • Using the questionnaire to conduct a quantitative survey (phase 2)

May, June, July 2010:

  • Continuation of survey and simultaneous data collection and grouping.
  • Start with the studies to develop a statistical data model.
  • Re-validating the data.

July, mid Aug 2010:

  • Developing the data model and analyzing the data to develop relationships.
  • Using the index to develop a visually representative rating scale.

Remaining Aug 2010:

  • Compiling the data and results in the form of a report.

Future prospects:

  • To continue research on the topic with an emphasis on development of innovative intelligent technologies as a PHD research area.
  • To work on developing software related to the interpretation of the scale.
  • To apply for a patent in the light of success.

Possible Outcomes and Contribution of Knowledge

The aim of this project is to develop a standardized scale which would inform how intelligent an intelligent technology is and as a result grade the building based on the incorporation of such technologies.

Minimizing operating costs, enhancing the productivity and effectiveness of organizations, addressing key environmental issues shall be the outcomes after assessing and analyzing an Intelligent Building on a standardized scale.


  • Atkin, B. (1988) 'Progress towards Intelligent Building', in Atkin, B. (ed.) Intelligent Buildings-Applications of IT and Building Automation to High Technology Construction Projects, London: Unicom Seminars limited.
  • Chong, K.P., Liu, S.C. and Li, J.C. (2008) Intelligent Structures, Essex: Elsevier Science Publishers Ltd.
  • Clements-Croome, D. (2004) 'Intelligent Buildings', in Clements-Croome, D. (ed.) Intelligent Buildings-Design, Management and Operation, London: Thomas Telford Publishing.
  • Harrison, A., Loe, E. and Read, J. (ed.) (1998) Intelligent Buildings in South East Asia, London: E & FN Spon.
  • Himanen, M. (2004) 'The Intelligence of Intelligent Buildings', in Clements-Croome, D. (ed.) Intelligent Buildings-Design, Management and Operation, London: Thomas Telford Publishing.
  • Hyde, R., Watson, S., Cheshire, W. and Thomson, M. (2007) The Environmental Brief - Pathways for Green Design, Oxon: Taylor and Francis Group.
  • Wigginton, M. and Harris, J. (2002) Intelligent Skins, Kent: Gray Publishing.
  • Gray, A. (2006) How Smart Are Intelligent Buildings? [Online] Available: [12 Dec 2009]
  • IBM website, Green buildings are smart buildings [Online] Available: [6 Dec 2009]
  • Sinclair, K. (2005) Growing Greener Buildings with Automation[Online] Available: [30 Dec 2009]

Please be aware that the free essay that you were just reading was not written by us. This essay, and all of the others available to view on the website, were provided to us by students in exchange for services that we offer. This relationship helps our students to get an even better deal while also contributing to the biggest free essay resource in the UK!