Cost overruns – helping to define what they really mean

Civil engineers are often in the firing line for alleged cost overruns, particularly on major publicly funded infrastructure projects. This usually occurs when the final cost of a project is simply compared with the original estimate, even though this was published a long time ago, in different circumstances and for a quite different project to the one carried out. This paper proposes a systematic approach to ensure that cost overruns, should they occur, are more accurately defined in terms of when the initial and end costs are assessed, from which point of view, at which project stage, and including scope changes and financial assumptions. The paper refers to the UK’s £163 billion nuclear decommissioning programme

Moving radiation protection on from the limitations of empirical concentration ratios

Radionuclide activity concentrations in food crops and wildlife are most often predicted using empirical concentration ratios (CRs). The CR approach is simple to apply and some data exist with which to parameterise models. However, the parameter is highly variable leading to considerable uncertainty in predictions. Furthermore, for both crops and wildlife we have no, or few, data for many radionuclides and realistically, we are never going to have specific data for every radionuclide - wildlife/crop combination. In this paper, we present an alternative approach using residual maximum likelihood (REML) fitting of a linear mixed effects model; the model output is an estimate of the rank-order of relative values. This methodology gives a less uncertain approach than the CR approach, as it takes into account the effect of site; it also gives a scientifically based extrapolation approach. We demonstrate the approach using the examples of Cs for plants and Pb for terrestrial wildlife. This is the first published application of the REML approach to terrestrial wildlife (previous applications being limited to the consideration of plants). The model presented gives reasonable predictions for a blind test dataset