Estimating the productivity of cyclic construction operations using case-based reasoning

The estimation of productivity rates in cyclic construction processes is a difficult, but essential task in the planning of construction projects. The conventional method—a calculation by intuition—for devising such estimates is often laborious and inaccurate. To overcome these difficulties this paper proposes that Case-Based Reasoning could be employed to provide estimates from historical cases of construction. To this end a model, Case-Based Estimator (CBE) is presented. CBE is a small model, consisting of five input features, one output and a small case base, and it is suggested that the findings within this paper could be applied to any small model of similar characteristics—not only within the construction field. The development and implementation of the model is discussed and an experiment was performed to assess the ability of two retrieval mechanisms (one a simple mathematical formula, the other an adaptation of the ID3 decision tree generating algorithm) to measure similarity. The simple formula was found to be more preferable, both in terms of consistency and development effort. s validated, not only against the performance of past operations (which were not used in the model development), but also against estimates provided by a professional construction planner. The model was found to provide more precise and consistent estimates than the planner, with 90% of the estimates being within a 10% relative error of the observed value. However, CBE did not perform as accurately when estimating operations which were thought to occur only rarely (outliers).