Technical Comparisons of Simulation-Based Productivity Prediction Methodologies by Means of Estimation Tools Focusing on Conventional Earthmovings / Imitacinių Produktyvumo Prognozavimo Metodikųtechninis Palyginimas, Pasitelkus Vertinimo Priemones, Akcentuojantųprastus Žemės Darbus

Abstract

Abstract. Planners in construction 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 introduced 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 from jobsites. 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 construction operations.

Application of Artificial Neural Networks in Construction Management: Current Status and Future Directions

“…Cheng et al [77] developed and applied EFHNN to evaluate project success by fusing hybrid neural network, FL and GA. For evaluation of project productivity, ANN is mainly used for construction operations and the prediction of construction worker productivity. Han et al [78] combined simulation and an ANN technique to produce more reliable prediction results for earthworks productivity. To improve the predictive performance of labor productivity, Heravi and Eslamdoost [79] proposed using Bayesian regularization to optimize BPNN.…”

Framework for the validation of simulation-based productivity analysis: focused on curtain wall construction process

“…According to these changes in construction projects, there has increased the demand for reliable operational plans before the start of construction project. In accordance with a desire for a scientific approach to operational plans, several methods have been used to establish the construction operational plans effectively in advance (Thomas, Yiakoumis 1987;Herbsam, Ellis 1990;Chao, Skibniewski 1994;Portas, AbouRizk 1997;Sonmez, Rowings 1998;Shi 1999;Smith 1999;Goodrum, Haas 2002;Tam et al 2002;Lu et al 2006;Han et al , 2011. Some studies used multiple linear regression methods for predicting construction productivity (Thomas, Yiakoumis 1987;Herbsam, Ellis 1990;Smith 1999;Goodrum, Haas 2002).…”

“…Recently, WebCYCLONE has been mostly used to conduct operation analysis and to predict construction productivity. The WebCYCLONE has the advantages of an easy application in a web-based environment and the strong adoption of the CYCLONE method, which has been ranked as one of the best discrete operation simulators in manufacturing industries (Han et al 2006(Han et al , 2011Cho et al 2011Cho et al , 2013Hong et al 2011). This simulation technique has been validated with actual construction operations (Zayed, Halpin 2004, 2005Zayed 2009).…”