Emamifar, Ali (2019) Modeling Productivity losses Due to Change Orders. Masters thesis, Concordia University.
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Abstract
Change orders are an integral part of construction projects regardless of project size or complexity. Changes may cause interruption to the unchanged scope of work and working conditions and, if poorly managed, may be detrimental to project success. Many studies have been carried out to quantify the impact of change orders on construction labour productivity, with varying degrees of accuracy and variables considered. These studies reveal that quantifying loss of productivity due to change orders is not an easy task and requires a comprehensive and holistic method.
There are several methods for quantifying loss of productivity, such as measured mile analysis (MMA) and the total cost method (TCM). Although measured mile analysis (MMA) is a well-known and widely accepted method for quantifying the cumulative impact of change orders on labour productivity, it is not readily applicable to many cases. In this research two models were developed to quantify losses arising from change orders. The first model does not account for the timing of change orders, but the second model considers the timing of change orders on labour productivity. Two models were developed and tested utilizing artificial neural networks and two sets of data collected by others in that field.
The two datasets were statistically analyzed and preprocessed in order to transfer the data to normal distribution form and eliminate insignificant variables considered in their development. Using best subset regression, a total of seventeen variables were reduced to nine variables accordingly. Also, the study datasets were categorized into three types of timing periods; early change, normal change and late change to create the timing model. This was implemented to enable a comparison with models developed by others.
Three types of artificial neural network techniques were experimented with and evaluated for possible use in the developed models. These three types are Feed Forward Neural Network, Cascade Neural Network, and Generalized Regression Neural Network. Candidate techniques were evaluated and analyzed by neural network parameters and analysis of variance (ANOVA) to select the most efficient type of neural networks, and subsequently using it to develop two models; one considers timing and the second does not. The analysis performed led to the selection of the cascade neural network for the development of the two models productivity losses due to change orders.
The developed models were tested and validated utilizing several actual cases reported by others. The models were applied to a number of cases and the results were compared to those generated by frequently cited models to demonstrate their accuracy. The comparison outcome showed that the developed models can generate more accurate and satisfactory results than those of reported in previous studies.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Emamifar, Ali |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Civil Engineering |
| Date: | 20 March 2019 |
| Thesis Supervisor(s): | Moselhi, Osama |
| ID Code: | 985096 |
| Deposited By: | Ali Emamifar |
| Deposited On: | 17 Jun 2019 19:03 |
| Last Modified: | 27 Mar 2021 01:00 |
References:
AACE. (2004). Estimating Lost Labor Productivity In Construction Claims. AACE Recomended Practice No. 25R-03.Ai, X., & Zsaki, A. M. (2017). Stability assessment of homogeneous slopes loaded with mobile tracked cranes—An artificial neural network approach. Cogent Engineering, 4(1), 1360236.
Ai, Xin. (2016). Stability Assessment of Homogeneous Slopes Loaded with Mobile Tracked Cranes – An Artificial Neural Network Approach,” Master of Applied Science Thesis, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada.
Al-Mahasneh, A. J., Anavatti, S. G., & Garratt, M. A. (2018). Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems. arXiv preprint arXiv:1805.11236.
American Association of Cost Engineers (AACE). (2013). AACE international recommended practice 10S-90, cost engineering terminology, AACE International, Morgantown, WV.
André, C. D., Narula, S. C., Elian, S. N., & Tavares, R. A. (2003). An overview of the variables selection methods for the minimum sum of absolute errors regression. Statistics in medicine, 22(13), 2101-2111.
Badde, D. S., Gupta, A., & Patki, V. K. (2009). Cascade and Feed Forward Back propagation Artificial Neural Network Models for Prediction of Compressive Strength of Ready Mix Concrete. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE).
Brams, R. S., & Lerner, C. (1996). Construction claims deskbook: Management, documentation, and presentation of claims (Construction Law Library). New York: Wiley.
BusinessDictionary. (2018). Scope Change.” Retrieved on Dec 5, 2018, from http://www.businessdictionary.com/definition/scope-change.html.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?. Geoscientific Model Development Discussions, 7, 1525-1534.
Cigizoglu, H. K. (2005). Generalized regression neural network in monthly flow forecasting. Civil Engineering and Environmental Systems, 22(2), 71-81.
Del Rosario Martinez-Blanco, M., Castañeda-Miranda, V. H., Ornelas-Vargas, G., Guerrero-Osuna, H. A., Solis-Sanchez, L. O., Castañeda-Miranda, R., ... & Martínez-Fierro, M. (2016). Generalized regression neural networks with application in neutron spectrometry. In Artificial Neural Networks-Models and Applications. InTech.
DTREG (2018).” About The Software.’ Retrieved on Nov 20, 2018, from https://www.dtreg.com/
Eden, Colin, Williams, Terry, and Franck A. (2003).Issues with the Measured Mile. European Academy of Management (EURAM).
Eldin, N. N. (1989). Measurement of work progress: Quantitative technique. Journal of Construction Engineering and Management, 115(3), 462-474.
Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. In Advances in neural information processing systems (pp. 524-532).
Fahlman, S.E.(1988).An Empirical Study of Learning Speed in Back-Propagation Networks. Technical Report.CMU-CSTR-88-162.Carnegie Mellon University.
Farbarik, J. J., III, G. O., Hanna, A. S., Moselhi, O., & Hassanein, A. (2004). Discussion of “Quantitative Definition of Projects Impacted by Change Orders” by Awad S. Hanna, Richard Camlic, Pehr A. Peterson, and Erik V. Nordheim. Journal of Construction Engineering & Management. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:4(612)
Ferreira, J. C., & Patino, C. M. (2015). What does the p value really mean?. Jornal Brasileiro de Pneumologia, 41(5), 485.
Gaikwad, D.P & Thool, Ravindra R. (2014). Intrusion Detection System using Cascade Forward Neural Network with Genetic Algorithm Based Feature Selection. International Journal of Advanced Computer Engineering and Communication Technology. Vol.4 (1).
Gershenson, C. (2003). Artificial Neural Networks for Beginners. ArXiv. 1-8. https://doi.org/10.1093/icesjms/fsp009
Gilmour, S. G. (1996). The interpretation of Mallows's C_p-statistic. The Statistician, 49-56.
Goh, A. T. C., & Kulhawy, F. H. (2005). Reliability assessment of serviceability performance of braced retaining walls using a neural network approach. International Journal for Numerical and Analytical Methods in Geomechanics, 29(6), 627-642.
Golnaraghi, S., Moselhi, O. E. S., Eng, P., Alkass, S., & Golkhoo, F. (2018). Productivity Loss Quantification Using a Novel Artificial Intelegince Approach. AACE International.
Golnaraghi, S., Zangenehmadar, Z., Moselhi, O., & Alkass, S. (2019). Application of Artificial Neural Network (s) in Predicting Formwork Labour Productivity. Advances in Civil Engineering.
Golub, G. H., & Van Loan, C. F. (1996). Matrix computations. Johns Hopkins University, Press, Baltimore, MD, USA. https://doi.org/10.1017/CBO9781107415324.004
Gorse, C., Johnston, D., & Pritchard, M. (2012). A dictionary of Construction, Surveying, and Civil Engineering. Oxford University Press.
Goyal, S., & Goyal, G. K. (2011). Cascade and feedforward backpropagation artificial neural networks models for prediction of sensory quality of instant coffee flavoured sterilized drink. Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, 2(6), 78-82.
Gulezian, R., & Samelian, F. (2003). Baseline determination in construction labor productivity-loss claims. Journal of management in engineering, 19(4), 160-165.
Hanna, A. S., & Iskandar, K. A. (2017). Quantifying and Modeling the Cumulative Impact of Change Orders. Journal of Construction Engineering and Management, 143(10), 04017076.
Hanna, A. S., & Sullivan, K. T. (2004). Factors affecting labor productivity for electrical contractors. The Electrical Contracting Foundation for Electrical Contractors, 31.
Hanna, A. S., Russell, J. S., & Vandenberg, P. J. (1999). The impact of change orders on mechanical construction labour efficiency. Construction Management & Economics, 17(6), 721-730.
Hanna, A., Camlic, R., Peterson, P. A., & Nordheim, E. V. (2002). Quantitative definition of projects impacted by change orders. Journal of Construction Engineering and Management, 36(10), 1109-1118.
Harel, O. (2009). The estimation of R^2 and adjusted R^2 in incomplete data sets using multiple imputation. Journal of Applied Statistics. https://doi.org/10.1080/02664760802553000
Hasegawa, K. (1995). Pending Change/Modification Codes,” Documents of the Pacific Division of the Naval Facilities Engineering Command, Pearl Harbor Construction Office, Arizona.
Haykin, S. (1999). Support vector machines. Neural Networks: A Comprehensive Foundation, 318-350.
Helmy, M. (2002). Modeling pile group efficiency in cohesionless soil using artificial neural networks (Doctoral dissertation, Concordia University).
Hester, W. T., Chang, T. C., & Kuprenas, J. A. (1991). Construction changes and change orders: their magnitude and impact. Construction Industry Institute.
Ibbs, C. W., & Allen, W. E. (1995). Quantitative impacts of project change, Source document 118. Construction Industry Institute, Univ. of Texas, Austin, TX.
Ibbs, W. (2005). Impact of change’s timing on labor productivity. Journal of Construction Engineering and Management, 131(11), 1219-1223.
Ibbs, W. (2012). Construction change: Likelihood, severity, and impact on productivity. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 4(3), 67-73.
Ibbs, W., & Liu, M. (2005). Improved measured mile analysis technique. Journal of construction engineering and management, 131(12), 1249-1256.
Ibbs, W., & McEniry, G. (2008). Evaluating the cumulative impact of changes on labor productivity. Cost Engineering, 50(12), 23-29.
Jagdev, H. S., Browne, J., & Jordan, P. (1995). Verification and validation issues in manufacturing models. Computers in industry, 25(3), 331-353.
Jain, Y. K., & Bhandare, S. K. (2011). Min max normalization based data perturbation method for privacy protection. International Journal of Computer & Communication Technology, 2(8), 45-50.
Jason, W., Osborne, J. W. (2002). Normalizing Data Transformations. ERIC Digest.1-8.
Jones, R. M. (2001). Lost productivity: Claims for the cumulative impact of multiple change orders. Pub. Cont. LJ, 31, 1.
Kim, H. Y. (2014). Analysis of variance (ANOVA) comparing means of more than two groups. Restorative dentistry & endodontics, 39(1), 74-77.
Lee, M. J., Hanna, A. S., & Loh, W. Y. (2004). Decision tree approach to classify and quantify cumulative impact of change orders on productivity. Journal of Computing in Civil Engineering, 18(2), 132-144.
Lee, S. (2007). Understanding and quantifying the impact of changes on construction labor productivity: Integration of productivity factors and quantification methods. University of California, Berkeley.
Leonard, C. A. (1988). The effects of change orders on productivity (Doctoral dissertation, Concordia University), Montreal, Quebec, Canada.
Loulakis, M. C., & Santiago, S. J. (1999). Getting the most out of your'measured mile'approach. Civil Engineering, 69(11), 69.
Madigan, D., & Ridgeway, G. (2004). Discussion of “Least Angle Regression” By Efron Et Al. The Annals of Statistics. https://doi.org/10.1214/009053604000000067
MathWorks. (2018). Cascade-forward neural network. Retrieved on Nov 30, 2018, from https://www.mathworks.com/help/deeplearning/ref/cascadeforwardnet.html.
Moayeri, V., Moselhi, O., & Zhu, Z. (2017). BIM-based model for quantifying the design change time ripple effect. Canadian Journal of Civil Engineering, 44(8), 626-642.
Moayeri, V. (2017). Design Change Management in Construction Projects Using Building Information Modeling (BIM) (Doctoral dissertation, Concordia University Montreal, Quebec, Canada).
Moselhi, O. (2003). Estimating the cost of change orders. Cost Engineering, 45(8), 24-29.
Moselhi, O., Assem, I., & El-Rayes, K. (2005). Change orders impact on labor productivity. Journal of Construction Engineering and Management, 131(3), 354-359.
Moselhi, O., Leonard, C., & Fazio, P. (1991). Impact of change orders on construction productivity. Canadian journal of civil engineering, 18(3), 484-492.
National Electrical Contractors Association. (2000). Guide to Electrical Contractor’s Claims Management Vol. I. Bethesda, Maryland.
Nechyba, M. C., & Xu, Y. (1997). Cascade neural networks with node-decoupled extended Kalman filtering. In Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97.'Towards New Computational Principles for Robotics and Automation' (pp. 214-219).
O'Keefe, R. M., & O'Leary, D. E. (1993). Expert system verification and validation: a survey and tutorial. Artificial Intelligence Review, 7(1), 3-42.
O’Leary, T. J., Goul, M., Moffitt, K. E., & Radwan, A. E. (1990). Validating Expert Systems. IEEE Expert-Intelligent Systems and Their Applications. (3), 51-58.
O'Brien, J. J. (1998). Construction Change Orders: Impact, Avoidance, and Documentation, McGraw-Hill Professional Publishing.
Osama, K., Somvanshi, P., Pandey, A. K., & Mishra, B. N. (2013). Modelling of nutrient mist reactor for hairy root growth using artificial neural network. European Journal of Scientific Research, 97(4), 516-526.
Patro, S., & Sahu, K. K. (2015). Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462.
Pinnell, S. S. (1998). How to get paid for construction changes: Preparation resolution tools and techniques. McGraw Hill.
Presnell, T. W. (2003). " Measured mile" process. Cost Engineering, 45(11), 14.
Revay, S. O. (2003). Coping with changes. AACE International Transactions, CD251.
Ruengvirayudh, P., & Brooks, G. P. (2016). Comparing Stepwise Regression Models to the Best-Subsets Models, or, the Art of Stepwise. General Linear Model Journal. Vol42(1).
Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23(4), 899-916.
Sawyer, S. F. (2009). Analysis of variance: the fundamental concepts. Journal of Manual & Manipulative Therapy, 17(2), 27E-38E.
Sazli, M. H. (2006). A brief review of feed-forward neural networks. Communications, Faculty of Science, University of Ankara, 50(1), 11-17.
Schetinin, V. (2005). An evolving cascade neural network technique for cleaning sleep electroencephalograms. arXiv preprint cs/0504067.
Schwartzkoph, W. (1995). Calculating Lost Labor Productivity in Construction Claims (Construction Law Library). John Wiley and Sons, Inc., NY.
Schwartzkoph, W., & McNamara, J. J. (2000). Calculating construction damages. Aspen Publishers Online.
Serag, E. (2006). Change orders and productivity loss quantification using verifiable site data. Doctor of Philosophy Thesis, Department of Civil and Environmental Engineering, University of Central Florida, Orlando, Florida.
Shwartzkoph, W., J. McNamara, and J. Hoffar. (1992).Calculating Construction Damages. John Wiley &Sons, USA.
Specht, D. F. (1991). A General Regression Neural Network. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.97934
Statista .(2016). “U.S. Construction Industry - Statistics & Facts.” Retrieved on Oct 2018 from https://www.statista.com/topics/974/construction/.
Statista .(2018). “Gross Domestic Product (GDP) of Canada in July 2018, by industry.” Retrieved on Oct 12, 2018, from https://www.statista.com/statistics/594293/gross-domestic-product-of-canada-by-industry-monthly/.
Statitics Canada .(2018).Labour force characteristics by industry, annual (x 1,000). Retrieved on Oct 2018 from https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1410002301.
Schwartzkopf, W. (1995). Calculating Lost Labor Productivity in Construction Claims (Construction Law Library). John Wiley and Sons, Inc., NY.
Thomas, H. R., & Napolitan, C. L. (1995). Quantitative effects of construction changes on labor productivity. Journal of construction engineering and management, 121(3), 290-296.
Thomas, H. R., & Sanvido, V. E. (2000). Quantification of losses caused by labor inefficiencies: Where is the elusive measured mile?. Constr. Law Bus, 1(3), 1-14.
Thomas, H. R., & Završki, I. (1999). Construction baseline productivity: Theory and practice. Journal of construction engineering and management, 125(5), 295-303.
Yi, W., & Chan, A. P. (2013). Critical review of labor productivity research in construction journals. Journal of management in engineering, 30(2), 214-225.
Zhang, W. (2017). The effect of piles and their loading on nearby retaining walls–an artificial neural network approach (Doctoral dissertation, Concordia University).
Zhao, T., & Dungan, J. M. (2013). Improved baseline method to calculate lost construction productivity. Journal of Construction Engineering and Management, 140(2), 06013006.
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