Improving concrete placement simulation with a case-based reasoning input

Discrete-event simulation has been used extensively in the past to analyse construction operations and has been shown to be an effective tool for improving construction process planning. Unfortunately, the widespread application of simulation has been prevented, in part, by the requirement for the developer and user to understand the stochastic features of the process. Further, if the stochastic inputs are not representative of the real system, the simulation output will be misleading. This article proposes that case-based reasoning (CBR) could be used to: improve the input and output data of a simulation model and remove the requirement for a user to have specialist statistical knowledge of the process. Case-based reasoning works by solving new cases from knowledge stored in a case base. Two models are presented in this article: a discrete-event simulation model, MatSim, and a hybrid CBR-simulation model, CBRSim. Both models were developed using real construction data. The models were compared to measure any improvements in simulation output, by using a CBR suggested input. Data from an independent construction project were used to test both models, and the results indicate that CBRSim can achieve estimates of observed values that are more accurate and reliable than those from MatSim.