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Artificial neural networks models for predicting performance measurement of oil projects

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Abstract

Currently, the oil and gas industry is one of the most significant world sectors, especially in the middle east region. However, sometimes the success of projects in the industry is at stake due to various factors. Poor ways of measuring performance in oil projects can lead to cost overruns, schedule delays, and changes in the scope of the project. As a result, this paper aims to create Artificial Neural Networks (ANN) Models for Predicting Performance Measurement of Iraqi oil projects to reduce estimation error for cost and time. ANN is utilized to develop three mathematical models for estimating Earned Value (EV) Indexes which are the Schedule Performance Index (SPI), Cost Performance Index (CPI), and To-Complete Cost Performance Indicator (TCPI). The data are based on (83) monthly reports starting on a date (26 Jun 2015) up to (25 August 2022) collected from the Karbala Refinery Project which is one of the huge and modern projects of the Oil Projects Company (SCOP), the Iraqi Ministry of Oil. The results show many important points such as average accuracy (AA%) for the CPI, SPI, and TCPI was 95.194%, 92.195%, and 83.706%, respectively, while the correlation coefficients (R) were 92.4%, 98.4%, and 93.7%. It has been shown that there are relatively few differences between the theoretical and actual results. Therefore, the ANN technique is used in this paper to emanate the prediction models for its more correct earned value indexes.

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Data availability

The corresponding author can provide any data, models, or code created or used during the study upon request.

Abbreviations

ANN:

Artificial neural networks

CPI:

Cost Performance Index

SPI:

Schedule Performance Index

TCPI:

To-Complete Cost Performance Indicator

MPE:

Mean percentage error

RMSE:

Root mean squared error

MAPE:

Mean absolute percentage error

AA %:

Average accuracy percentage

R 2 :

The coefficient of determination

R :

The coefficient of correlation

References

  • Alcineide, P., Gean, S., Luiz, M. F. M., Felipe, C. A., & de Débora, G. S. (2021). Cost forecasting of public construction projects using multilayer perceptron artificial neural networks: a case study. Ingenieria e Investigacion, 41(3), 1–11. https://doi.org/10.15446/ing.investig.v41n3.87737

    Article  Google Scholar 

  • AlNoaimi, F. A., & Mazzuchi, T. A. (2021). Risk management application in an oil and gas company for projects. International Journal of Business Ethics and Governance. https://doi.org/10.51325/ijbeg.v4i3.77

    Article  Google Scholar 

  • Fanaei, S.-S. (2019). Performance measurement, forecasting and optimization models for construction projects. Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (Building Engineering) at Concordia University Montreal, Quebec, Canada, April.

  • Hammoody, O., Al-Somaydaii, J., Al-Zwainy, F., & Hayder, G. (2022). Forecasting and determining of cost performance index of tunnels projects using artificial neural networks. International Journal for Computational Civil and Structural Engineering, 18(1), 51–60. https://doi.org/10.22337/2587-9618-2022-18-1-51-60

    Article  Google Scholar 

  • Jaber, F. K., Jasim, N. A., & Al-Zwainy, F. M. S. (2020). Forecasting techniques in construction industry: Earned value indicators and performance models. Scientific Review Engineering and Environmental Sciences, 29(2), 234–243. https://doi.org/10.22630/PNIKS.2020.29.2.20

    Article  Google Scholar 

  • Jasim, N. A., Maruf, S. M., Aljumaily, H. S. M. M. F., & Al-Zwain, S. (2020). Predicting index to complete schedule performance indicator in highway projects using artificial neural network model. Archives of Civil Engineering. https://doi.org/10.24425/ace.2020.134412

    Article  Google Scholar 

  • Kassem, M. A. (2022). Risk management assessment in oil and gas construction projects using structural equation modeling (PLS-SEM). Gases, 2(2), 33–60. https://doi.org/10.3390/gases2020003

    Article  Google Scholar 

  • Kaveh, A., & Iranmanesh, A. (1998). Comparative study of backpropagation and improved counterpropagation neural nets in structural analysis and optimization. International Journal of Space Structures, 13(4), 177–185. https://doi.org/10.1177/026635119801300401

    Article  Google Scholar 

  • Kaveh, A., & Khalegi, A. (1998). Prediction of strength for concrete specimens using Artificial neural networks (pp. 165–171). Civil Comp Press.

    Google Scholar 

  • Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52(256), 272. https://doi.org/10.1016/j.istruc.2023.03.178

    Article  Google Scholar 

  • Lee, L., & Panis, C. (2003). aML multilevel multiprocess modeling. statistical software, Version 2.0. Reference Book Statistical Software, p. 465.

  • Leśniak, A., & Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering, 18(3), 973–982. https://doi.org/10.1016/j.acme.2018.01.014

    Article  Google Scholar 

  • Maya, R., Hassan, B., & Hassan, A. (2021). Develop an artificial neural network (ANN) model to predict construction projects performance in Syria. Journal of King Saud University Engineering Sciences. https://doi.org/10.1016/j.jksues.2021.05.002

    Article  Google Scholar 

  • Reenu, M. S., Rajeev Kumar, P., BS. (2017). Construction project performance model using artificial neural network. International Journal of Recent Trends in Engineering and Research, 3(5), 77–86. https://doi.org/10.23883/ijrter.2017.3199.deyet

    Article  Google Scholar 

  • Salahaldain, Z., Naimi, S., & Alsultani, R. (2023). Estimation and analysis of building costs using artificial intelligence support vector machine. Mathematical Modelling of Engineering Problems, 10(2), 405–411.

    Article  Google Scholar 

  • Ujong, J. A., Mbadike, E. M., & Alaneme, G. U. (2022). Prediction of cost and duration of building construction using artificial neural network. Asian Journal of Civil Engineering, 23(7), 1117–1139. https://doi.org/10.1007/s42107-022-00474-4

    Article  Google Scholar 

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Acknowledgements

The corresponding author thanks the Karbala Refinery Project Authority, the General Company for Oil Projects (SCOPE), and the Iraqi Ministry of Oil, for providing us with all the information necessary for the research.

Funding

For this work, the authors did not receive funding.

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Authors and Affiliations

  1. College of Engineering, Department of Civil Engineering, University of Diyala, Baqubah, Iraq

    Nidal Adnan Jasim

  2. College of Engineering Department of Civil Engineering, University of Tikrit, Tikrit, Iraq

    Abdulrahman Adnan Ibrahim

  3. Middle Technical University, Baghdad, Iraq

    Wadhah Amer Hatem

Authors
  1. Nidal Adnan Jasim
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  2. Abdulrahman Adnan Ibrahim
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  3. Wadhah Amer Hatem
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This manuscript is part of a Ph.D. thesis Nidal Adnan Jasim wrote manuscript 2,3 reviewed the manuscript

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Correspondence to Nidal Adnan Jasim.

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Jasim, N.A., Ibrahim, A.A. & Hatem, W.A. Artificial neural networks models for predicting performance measurement of oil projects. Asian J Civ Eng 24, 3597–3612 (2023). https://doi.org/10.1007/s42107-023-00737-8

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