Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects

Authors

  • Atheer M. Al-Saady College of Engineering - University of Baghdad
  • Sedqi E. Rezouki College of Engineering - University of Baghdad

DOI:

https://doi.org/10.31026/j.eng.2023.01.06

Keywords:

Artificial neural network, wastewater projects, coefficient of correlation, mean absolute percentage error

Abstract

Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was (26.24%), and (5.5%), and AA was (74%), and (94.5%), for cost and time model, respectively. The researcher concluded that the ANN model has a strong correlation and high accuracy, indicating that these models are characterized by high efficiency and good performance in predicting cost and time.

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References

Abd, A.,M., Jasim, N.,A., Naseef, F.,S., 2019. Predicting the Final Cost of Iraqi Construction Project Using Artificial Neural Network (ANN), Indian Journal of Science and Technology, 12(28).

Al-Musawi, N. O. A., 2016. Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City, Journal of Engineering, 22(9), pp. 72–82.

Almusawi, H., T., and Burhan, A., M., 2020. Developing a Model to Estimate the Productivity of Ready Mixed Concrete Batch Plant, Journal of Engineering, 26(10), pp. 80-93.

Al-Saadi, A., M., Zamiem,S., KH., Al-Jumaili, L., A., Jubair, M., and Al- Hashemi, H., A. 2017. Estimating the Optimum Duration of Road Projects Using Neural Network Model, International Journal of Engineering andTechnology (IJET), 9 (5).

Altaie, M., and Borhan, A., M., 2018. Using Neural Network Model to Estimate the Optimum Time for Repetitive Construction Projects in Iraq, Association of Arab Universities Journal of Engineering Sciences, 5 (25), pp. 100-114.

Attal, A., 2010. Development of Neural Network Models for Prediction of Highway Construction Cost and Project Duration, M.Sc. thesis presented to the Faculty of the Russ College of Engineering and Technology of Ohio University.

Al-Zwainy, F. M., and Amer, R., 2016. Investigation Cost Deviation of Highway Project, Research Journal of Applied Sciences, Engineering and Technology, 13(11), pp. 843-855.

AL-Zwainy, F., M., and Aidan, I., A., 2017. Forecasting the Cost of Structure of Infrastructure Projects Utilizing Artificial Neural Network Model (Highway Projects as Case Study, Indian Journal of Science and Technology, 10 (20), pp. 1-12.

Mohammed, I., A., Mohsen, D., S., and Al-Zwainy, F., M., 2015. Earned Value Management in Construction Project, LAP LAMBERT Academic Publishing, Germany.

Peško, I., Trivunić,M., Cirović,G., and Mučenski, V., 2013. A Preliminary Estimate of Time and Cost in Urban Road Construction Using Neural Networks, Preliminarna procjena vremena i troškova izgradnje gradskih prometnica primjenom neuronskih mreža ,20(3), pp., 563- 570.

Shaban, K., El-Hag, A., and Matveev, A., 2009. A Cascade of Artificial Neural Networks to Predict Transformers Oil Parameters, IEEE Transactions on Dielectrics and Electrical Insulation, 16(2).

Sharma, V., Rai, S., and Dev, A., 2012. A Comprehensive Study of Artificial Neural Networks, International Journal of Advanced Research in Computer Science and Software Engineering, 2(10), pp. 278-284.

Singh, D. K., Thakur E. S, Kesharwani, S, Zadgaonkar, A., S., 2014. Analysis of Generated Harmonics Due to Single Phase PWM AC Drives Load on Power System Using Artificial Neural Network, International Journal of Advanced Research in Engineering and Technology (IJARET), 5(2), pp. 173-185.

Vahdani, B., Mousavi, S., M., Mousakhani, M., Hashemi, H., 2016. Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry, Journal of Optimization in Industrial Engineering, 19, pp., 97-103.

Waheeb, R., A., Andersen, B., and Suhili, R., 2020. Using ANN in Emergency Reconstruction Projects Post Disaster, International Journal of Engineering Business Management, 20(1), pp. 1-21.

Published

2023-01-01

How to Cite

Al-Saady , A. M. . and Rezouki , S. E. . (2023) “Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects”, Journal of Engineering, 29(1), pp. 93–109. doi: 10.31026/j.eng.2023.01.06.