Planners in constmction accordingly have been trying to predict productivity which is a significant criterion for construction performances prior to commencement of operations. Many various methods solely based on deterministic calculations, simulation techniques, statistic methods, or other decision making tools, have been iniroduced so far. In terms of application, however, these methods depending on one estimation tool have several limitations of each method. The present study presented new predictive models: 1) Model A, combining simulation and a multiple regression (MR) technique, a general estimation technique based on statistic concepts and 2) Model B combining simulation and an artificial neural network (ANN) technique, a powerful tool for prediction in engineering basis. Quantified reliability comparisons between actual and predicted productivity data by the presented models were conducted in this study. It found that a predictive result by Model B was closer to actual productivity data than that by Model A was. Model B based on the ANN analysis, however, showed the difficulty in technical implementation with a view of practical applications. These comparisons revealed the reliability of the predictive results and the implementation efficiency of each model. This study addresses basic characteristics and technical comparisons of each methodology simulation-based MR or ANN techniques. The findings allow researchers to create or develop a new predictive methodology for specific operations with shortage of actual datasets collected fromjobsites. Technical performance comparisons of results between MR and an ANN, representative estimation tools, enable users to select a more appropriate tool considering specific situations. The suggested methodology in this study can also be extended to apply to not only earthworks but also other constmction operations.
Bibliographical noteFunding Information:
This work was supported by the Research Grant.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Strategy and Management