Simple cost-significant models for total life-cycle costing in buildings

In this thesis a number of simple cost models are presented for calculating the total life-cycle cost in buildings. The models embody the principle of cost significance, thereby focusing management effort on the high cost items, and reducing the effort involved in data collection analysis. The models are based on the findings that for defined building categories identical cost-significant items can be derived which contribute a constant fraction of the total life cycle expenditure. The techniques used to develop the models are described. Cost models have been developed for running costs (defined as operation plus maintenance costs), and maintenance costs using historical buildings data records, and tested on virgin data from similar types of buildings. Three building categories have been analyzed: teaching, residential, and laboratory buildings. The cost models calculated the total cost of running expenditure to an average accuracy of ±1.5%, ±3.5%, and ±2.6% for teaching, residential and laboratory buildings respectively. The cost models for maintenance costs calculated the costs to an average accuracy of ±4%, ±7%, and ±6.5% for the same three categories. When combining all categories the total cost was calculated to an accuracy of ±5% using the running cost model, and ±7% using the maintenance cost model. Typically, in running cost models, only 11 cost items have to be priced; in maintenance cost models only 18 items are needed. This represents a significant simplification over traditional techniques. An associated cost model factor was derived for each cost model to calculate the total cost of any building's running and maintenance expenditure. Since the models highlight a small but important number of items, the models will facilitate the prediction of life-cycle costs, and the collection of information on running costs in buildings. Thus, better feedback of information can be provided to the design stage. Furthermore, designers' attention will be directed to those areas of cost-significance, and provide a means to choose between alternatives when deciding on building investment. Further improvements to the models as well as recommendations for future work are suggested.