Predicting Performance Measurement of Residential Buildings Using an Artificial Neural Network
Application Earned Value Management (EVM) as a construction project control technique is not very common in the Republic of Iraq, in spite of the benefit from EVA to the schedule control and cost control of construction projects. One of the goals of the present study is the employment machine intelligence techniques in the estimation of earned value; also this study contributes to extend the cognitive content of study fields associated with the earned value, and the results of this study are considered a robust incentive to try and do complementary studies, or to simulate a similar study in alternative new technologies. This paper is aiming at introducing a novel and alternative method of applying Artificial Intelligence Techniques (AIT) for earned value management of the construction projects through using Artificial Neural Networks (ANN) to build mathematical models to be used to estimate the Schedule Performance Index (SPI), Cost Performance Index (CPI) and to Complete Cost Performance Indicator (TCPI) in Iraqi residential buildings before and at execution stage through using web-based software to perform the calculations in the estimation quickly, accurately and without effort. ANN technique was utilized to produce new prediction models by applying the Backpropagation algorithm through Neuframe software. Finally, the results showed that the ANN technique shows excellent results of estimation when it is compared with MLR techniques. The results were interpreted in terms of Average Accuracy (AA%) equal to 83.09, 90.83, and 82.88%, also, correlation coefficient (R) equal to 90.95, 93.00, and 92.30% for SPI, CPI and TCPI respectively.
Full Text: PDF
Koster, K., D. Wallace, J. Kinder, and C. Bell. "Earned Value Management for Dummies, Deltek Special Edition." (2011).
Lipke, Walt, Ofer Zwikael, Kym Henderson, and Frank Anbari. “Prediction of Project Outcome.” International Journal of Project Management 27, no. 4 (May 2009): 400–407. doi:10.1016/j.ijproman.2008.02.009.
Kadhum, Mohammed. "Prediction of Mechanical Properties of Reactive Powder Concrete by Using Artificial Neural Network and Regression Technique after the Exposure to Fire Flame." Jordan Journal of Civil Engineering 9, no. 3 (2015): 381-399.
Pajares, Javier, and Adolfo López-Paredes. “An Extension of the EVM Analysis for Project Monitoring: The Cost Control Index and the Schedule Control Index.” International Journal of Project Management 29, no. 5 (July 2011): 615–621. doi:10.1016/j.ijproman.2010.04.005.
Leśniak, Agnieszka, and Michał Juszczyk. “Prediction of Site Overhead Costs with the Use of Artificial Neural Network Based Model.” Archives of Civil and Mechanical Engineering 18, no. 3 (July 2018): 973–982. doi:10.1016/j.acme.2018.01.014.
Moslemi Naeni, Leila, Shahram Shadrokh, and Amir Salehipour. “A Fuzzy Approach for the Earned Value Management.” International Journal of Project Management 32, no. 4 (May 2014): 709–716. doi:10.1016/j.ijproman.2013.02.002.
Zhong, Shuheng, and Xin Wang. “Improvement and Application of Earned Value Analysis in Coal Project Management.” Procedia Engineering 26 (2011): 1983–1989. doi:10.1016/j.proeng.2011.11.2394.
Vanhoucke, M, and S Vandevoorde. “A Simulation and Evaluation of Earned Value Metrics to Forecast the Project Duration.” Journal of the Operational Research Society 58, no. 10 (October 2007): 1361–1374. doi:10.1057/palgrave.jors.2602296.
Chen, Hong Long, Wei Tong Chen, and Ying Lien Lin. “Earned Value Project Management: Improving the Predictive Power of Planned Value.” International Journal of Project Management 34, no. 1 (January 2016): 22–29. doi:10.1016/j.ijproman.2015.09.008.
Modranský, Róbert, Kateřina Bočková, and Michal Hanák. “Project Manager and Stress in Coping with Demanding Situations in Automotive Industry.” Emerging Science Journal 4, no. 5 (October 1, 2020): 418–426. doi:10.28991/esj-2020-01241.
Kadhum, Mohammed Mansour, and Zaid Ahmed Mohammed. "Predict the Ultimate Moment Capacity of Reactive Powder Concrete Beams Exposed to Fire Flame Using Artificial Neural Network and Multiple Linear Regression Models." International Journal of Engineering and Technology 9 (2017): 2637-2649.
Kim, Mansu, Sungwon Jung, and Joo-won Kang. “Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea.” Sustainability 12, no. 1 (December 22, 2019): 109. doi:10.3390/su12010109.
AL-Somaydaii, A. J., H. S. M. Aljumaily, and F. M. S. AL-Zwainy. "Utilization multifactor linear regression technique for prediction the earned value in bridges projects." Journal of Engineering and Applied Sciences 13, no. 7 (2018): 1676-13.
Khamees, Shahad S., Mohammed M. Kadhum, and Nameer A. Alwash. “Effects of Steel Fibers Geometry on the Mechanical Properties of SIFCON Concrete.” Civil Engineering Journal 6, no. 1 (January 1, 2020): 21–33. doi:10.28991/cej-2020-03091450.
Omotayo, Temitope, Awuzie Bankole, and Ayokunle Olubunmi Olanipekun. “An Artificial Neural Network Approach to Predicting Most Applicable Post-Contract Cost Controlling Techniques in Construction Projects.” Applied Sciences 10, no. 15 (July 28, 2020): 5171. doi:10.3390/app10155171.
Alawadi, Sadi, David Mera, Manuel Fernández-Delgado, Fahed Alkhabbas, Carl Magnus Olsson, and Paul Davidsson. “A Comparison of Machine Learning Algorithms for Forecasting Indoor Temperature in Smart Buildings.” Energy Systems (January 24, 2020). doi:10.1007/s12667-020-00376-x.
M. Kadhum, Mohammed, Salah M. Harbi, Shahad S. Khamees, Mustafa S. Abdulraheem, and Ehsan Noroozinejad Farsangi. “Punching Shear Behavior of Flat Slabs Utilizing Reactive Powder Concrete with and Without Flexural Reinforcement.” Practice Periodical on Structural Design and Construction 26, no. 1 (February 2021): 04020060. doi:10.1061/(asce)sc.1943-5576.0000551.
- There are currently no refbacks.
Copyright (c) 2021 Salah Jasim Mohammed
This work is licensed under a Creative Commons Attribution 4.0 International License.