Predicting the Duration of a General Contracting Industrial Project based on the Residual Modified Model

The general contracting projects are developed in China, in the past two decades. In the early stage of a project, because of the lack of specific design drawings and project plans, it is difficult for the general contractor to determine the construction period when signing the contract. However, little research has undertaken on quickly and efficiently estimating construction duration of a general contracting industrial project. Therefore, the purpose of this paper is to explore a suitable model for estimating construction duration of the general contracting industrial project in China. Data for 90 completed projects are collected in a company that undertakes nationwide industrial projects. Four single variable models and fourteen multivariate models are analyzed using statistical method. And the residual modified model integrating wavelet neural network (WNN) is also developed through using a predictive error to amend the statistical model. The results show that the residual modified models obtain more enhanced prediction accuracy than regression models, despite their good fitting performance. The modified model can be used for helping contractors forecast project duration in the early stage of a project.

[1]  Vadlamani Ravi,et al.  Software development cost estimation using wavelet neural networks , 2008, J. Syst. Softw..

[2]  Silvana Petruseva,et al.  Neural Network Prediction Model for Construction Project Duration , 2013 .

[3]  Jan Adamowski,et al.  Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network , 2017 .

[4]  D. L. Young,et al.  A novel dynamic progress forecasting approach for construction projects , 2012, Expert Syst. Appl..

[5]  Jun Wang,et al.  Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network , 2018 .

[6]  Jiwen Dong,et al.  Time-series prediction using a local linear wavelet neural network , 2006, Neurocomputing.

[7]  Andrew D.F. Price,et al.  Relationship between value and duration for construction projects , 1991 .

[8]  Reza Jafarzadeh,et al.  Seismic Retrofit Cost Modelling of Existing Structures , 2012 .

[9]  Trefor P. Williams,et al.  Predicting Changes in Construction Cost Indexes Using Neural Networks , 1994 .

[10]  Tarek Hegazy,et al.  Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression , 2003 .

[11]  Albert P.C. Chan.  Time–cost relationship of public sector projects in Malaysia , 2001 .

[12]  Min-Yuan Cheng,et al.  Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry , 2010, Expert Syst. Appl..

[13]  Ka Chi Lam,et al.  The predictive ability of Bromilow's timecost model , 2001 .

[14]  Marko Ćećez,et al.  Time and cost interdependence in water supply system construction projects , 2015 .

[15]  M. Zain,et al.  Filler effect of pozzolanic materials on the strength and microstructure development of mortar , 2017 .

[16]  David K. H. Chua,et al.  Model for Construction Budget Performance—Neural Network Approach , 1997 .

[17]  Sai On Cheung,et al.  An integrated regression analysis and time series model for construction tender price index forecasting , 2004 .

[18]  Mohan M. Kumaraswamy,et al.  Coping with structural change in construction: experiences gained from advanced economies , 2009 .

[19]  Wan-zhao Cui,et al.  Prediction of thin film thickness of field emission using wavelet neural networks , 2005 .

[20]  Jing Ma,et al.  Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting , 2017 .

[21]  Ying-Ming Wang,et al.  Risk Assessment for Bridge Maintenance Projects: Neural Networks versus Regression Techniques , 2007 .

[22]  David Lowe,et al.  Data modelling and the application of a neural network approach to the prediction of total construction costs , 2002 .

[23]  Savaş Bayram.  Duration prediction models for construction projects: In terms of cost or physical characteristics? , 2017 .

[24]  David J. Edwards,et al.  Time-cost relationships in Australian building construction projects , 2005 .

[25]  Wei Tong Chen,et al.  Approximately predicting the cost and duration of school reconstruction projects in Taiwan , 2006 .

[26]  Qing Yang,et al.  Wavelet Neural Network Approach for Fault Diagnosis to a Chemical Reactor , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[27]  Jun Zhang,et al.  Wavelet neural networks for function learning , 1995, IEEE Trans. Signal Process..

[28]  Mohamed Marzouk,et al.  Predicting Construction Materials Prices Using Fuzzy Logic and Neural Networks , 2013 .

[29]  Moses Tawiah.  Using Bromilow’s Model and Other Regression Model to Predict the Duration of Feeder Road Projects in Ghana , 2015 .

[30]  Diana Car-Pušić,et al.  Construction time-cost model in Croatia , 2009 .

[31]  T. Khayamian,et al.  Wavelet neural network-based QSPR for prediction of critical micelle concentration of Gemini surfactants , 2005 .

[32]  Mohan M. Kumaraswamy,et al.  A study of the factors affecting construction durations in Hong Kong , 1995 .

[33]  Ifte Choudhury,et al.  Time-cost relationship for residential construction in Texas , 2003 .

[34]  Tariq Shehab,et al.  Cost Estimating Models for Utility Rehabilitation Projects: Neural Networks versus Regression , 2010 .

[35]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[36]  Alfred E. Thal,et al.  Estimating Performance Time for Construction Projects , 2007 .

[37]  K. P. Sudheer,et al.  Potential application of wavelet neural network ensemble to forecast streamflow for flood management , 2016 .

[38]  Min-Yuan Cheng,et al.  Evolutionary Fuzzy Hybrid Neural Network for Conceptual Cost Estimates in Construction Projects , 2009 .

[39]  Abdulaziz M. Jarkas.  Predicting Contract Duration for Building Construction: Is Bromilow’s Time-Cost Model a Panacea? , 2016 .

[40]  Jiuping Xu,et al.  Estimation of Time for Wenchuan Earthquake Reconstruction in China , 2011 .

[41]  Mark J. King,et al.  Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China , 2014 .