Exposing the myth of the 1:5:200 ratio relating initial cost, maintenance and staffing costs of office buildings

In an attempt to focus clients' minds on the importance of considering the construction and maintenance costs of a commercial office building (both as a factor in staff productivity and as a fraction of lifetime staff costs) there is an often-quoted ratio of costs of 1:5:200, where for every one pound spent on construction cost, five are spent on maintenance and building operating costs and 200 on staffing and business operating costs. This seems to stem from a paper published by the Royal Academy of Engineering, in which no data is given and no derivation or defence of the ratio appears. The accompanying belief that higher quality design and construction increases staff productivity, and simultaneously reduces maintenance costs, how ever laudable, appears unsupported by research, and carries all the hallmarks of an "urban myth". In tracking down data about real buildings, a more realistic ratio appears to depend on a huge variety of variables, as well as the definition of the number of "lifetime" years. The ill-defined origins of the original ratio (1:5:200) describing these variables have made replication impossible. However, by using published sources of data, we have found that for three office buildings, a more realistic ratio is 1:0.4:12. As there is nothing in the public domain about what comprised the original research that gave rise to 1:5:200, it is not possible to make a true comparison between these new calculations and the originals. Clients and construction professionals stand to be misled because the popularity and widespread use of the wrong ratio appears to be mis-informing important investment and policy decisions.

Carbon reduction in buildings (CaRB) - Understanding the social and technical factors that influence energy use in UK buildings

Attempts to reduce the energy consumed in UK homes have met with limited success. One reason for this is a lack of understanding of how people interact with domestic technology – heating systems, lights, electrical equipment and so forth. Attaining such an understanding is hampered by a chronic shortage of detailed energy use data matched to descriptions of the house, the occupants, the internal conditions and the installed services and appliances. Without such information it is impossible to produce transparent and valid models for understanding and predicting energy use. The Carbon Reduction in Buildings (CaRB) consortium of five UK universities plans to develop socio-technical models of energy use, underpinned by a flow of data from a longitudinal monitoring campaign involving several hundred UK homes. This paper outlines the models proposed, the preliminary monitoring work and the structure of the proposed longitudinal study.

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems.

Past, present and future mathematical models for buildings (ii)

This article is the second part of a review of the historical evolution of mathematical models applied in the development of building technology. The first part described the current state of the art and contrasted various models with regard to the applications to conventional buildings and intelligent buildings. It concluded that mathematical techniques adopted in neural networks, expert systems, fuzzy logic and genetic models, that can be used to address model uncertainty, are well suited for modelling intelligent buildings. Despite the progress, the possible future development of intelligent buildings based on the current trends implies some potential limitations of these models. This paper attempts to uncover the fundamental limitations inherent in these models and provides some insights into future modelling directions, with special focus on the techniques of semiotics and chaos. Finally, by demonstrating an example of an intelligent building system with the mathematical models that have been developed for such a system, this review addresses the influences of mathematical models as a potential aid in developing intelligent buildings and perhaps even more advanced buildings for the future.