Technical comparisons of simulation-based productivity prediction methodologies by means of estimation tools focusing on conventional earthmovings

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...

[1]  Giovanni C. Migliaccio,et al.  Construction Equipment Management , 2019 .

[2]  Simaan M. AbouRizk,et al.  Unified Modeling Methodology for Construction Simulation , 2002 .

[3]  Jonathan Jingsheng Shi,et al.  A neural network based system for predicting earthmoving production , 1999 .

[4]  Seung-Woo Han,et al.  Simulation analysis of productivity variation by global positioning system (GPS) implementation in earthmoving operations , 2006 .

[5]  Seungwoo Han.  Application modeling of the conventional and the GPS-based earthmoving systems , 2005 .

[6]  Photios G. Ioannou,et al.  Comparison of Construction Alternatives Using Matched Simulation Experiments , 1996 .

[7]  Simon Smith,et al.  Earthmoving productivity estimation using linear regression techniques , 1999 .

[8]  Krzysztof Schabowicz,et al.  Estimation of earthworks execution time cost by means of artificial neural networks , 2010 .

[9]  Jay L. Devore,et al.  Probability and statistics for engineering and the sciences , 1982 .

[10]  Simaan M. AbouRizk,et al.  Framework for Building Intelligent Simulation Models of Construction Operations , 2005 .

[11]  Khaled A El-Rayes,et al.  Parallel computing framework for optimizing construction planning in large-scale projects , 2005 .

[12]  S. Abourizk,et al.  STATISTICAL PROPERTIES OF CONSTRUCTION DURATION DATA , 1992 .

[13]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[14]  Simaan M. AbouRizk,et al.  A hybrid approach for developing special purpose simulation tools , 2006 .

[15]  Kannan Govindan,et al.  A framework for incorporating dynamic strategies in earth-moving simulations , 1997, WSC '97.

[16]  Daniel W. Halpin,et al.  The use of simulation for productivity estimation based on multiple regression analysis , 2005, Proceedings of the Winter Simulation Conference, 2005..

[17]  John G. Everett,et al.  TIME-LAPSE VIDEO APPLICATIONS FOR CONSTRUCTION PROJECT MANAGEMENT , 1998 .

[18]  Daniel W. Halpin,et al.  Simulation experiment for improving construction processes , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[19]  Govindan Kannan.  A Methodology for the Development of a Production Experience Database for Earthmoving Operations Using Automated Data Collection , 1999 .

[20]  Krzysztof Schabowicz,et al.  MATHEMATICAL-NEURAL MODEL FOR ASSESSING PRODUCTIVITY OF EARTHMOVING MACHINERY , 2007 .

[21]  Simaan M. AbouRizk,et al.  Simulation modeling decision support through belief networks , 2006, Simul. Model. Pract. Theory.

[22]  Daniel W. Halpin,et al.  Planning and analysis of construction operations , 1992 .

[23]  Janaka Y. Ruwanpura,et al.  Predicting construction productivity using situation-based simulation models , 2006 .