Modelling of input data uncertainty for the financial evaluation of complex infrastructure projects

The planning of complex infrastructure projects represents an interdisciplinary process, which is characterised and heavily influenced by uncertain information and imprecise input para-
meters. During early planning stages the majority of technical as well as economic parameters, which are of crucial importance for the detailed design and project implementation, cannot be determined with precision. It is therefore common practice that these figures are selected as deterministic values, which require extensive optimisation throughout subsequent planning stages. A major disadvantage inherent to commonly used deterministic analysis is the lack of objectivity for the selection of input parameters. Moreover, it cannot be ensured that the entire existing parameter range and
all possible parameter combinations are covered.
Probabilistic methods utilise discrete probability distributions or parameter input ranges to cover the entire range of uncertainties resulting from an information deficit during the planning phase and integrate them into the optimisation process by means of alterna-
tive calculation methods.
In the field of geotechnical engineering this approach has been employed successfully to objectively account for uncertainties related to geological conditions and material properties in the context of design analysis.