Artificial Neural Network Model for Wastewater Projects Maintenance Management Plan
محتوى المقالة الرئيسي
الملخص
Wastewater projects are one of the most important infrastructure projects, which require developing strategic plans to manage these projects. Most of the wastewater projects in Iraq don’t have a maintenance plan. This research aims to prepare the maintenance management plan (MMP) for wastewater projects. The objective of the research is to predict the cost and time of maintenance projects by building a model using ANN. The research sample included (15) completed projects in Wasit Governorate, where the researcher was able to obtain the data of these projects through the historical information of the Wasit Sewage Directorate. In this research artificial neural networks (ANN) technique was used to build two models (cost and time) for the maintenance of wastewater projects. The output shows there is a high correlation (R) between real and expected cost with 95.4%, minimized testing error (8.5%), and training error (19%). The mean absolute present error (MAPE) and Average Accuracy Percentage (AA) are (13.9% and 86.1%) respectively. Also, the results showed a strong correlation (R) between actual and predicted time (99.1%), minimized testing error (8%), and an additional MAPE% and AA% with (11.7% and 88.3%) respectively. These models are in agreement with the real values, as well as gives good prediction for future maintenance projects.
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Al- Saadi, A., M., Zamiem, S., Kh., Al-Jumaili, L., A., Jubair, M., J., and Al- Hashemi, H., A., 2017. Estimating the Optimum Duration of Road Projects Using Neural Network Model. International Journal of Engineering and Technology, 9(5), pp.3458-3469.
Ahmad R., Kamaruddin S., 2012. An Overview of Time-Based And Condition-Based Maintenance In Industrial Application., Comput Ind. Eng., Vol. 63, pp. 135–49.
Al-Janabi, K. R., 2006. Laboratory Leaching Process Modeling in Gypseous Soils using Artificial Neural Networks (ANN). Ph.D. Thesis, Building and Construction Engineering Department, University of Technology.
Ali J.M., Hussain M.A., Tade M.O., and Zhang J., 2015. Artificial Intelligence Techniques Applied as an Estimator in Chemical Process Systems–A Literature Survey, Expert Syst Appl, 42: 5915–31.
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-Zubaidy, D., S., Aljanabi, Kh., R., and Khaled, Z., S., 2022. Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks, Journal of Engineering,4(28), pp.39-50.
Düzakın E., and Demircioğlu M., 2005. MAINTENANCE STRATEGIES AND WAITING LINE MODEL APPLICATION, Çukurova University Journal of Social Sciences Institute, 14(1), pp. 211-230.
Eren T., Gencer M.A., 2016. Scheduling of Ankara Metro M1 (Kızılay-Batikent) Line Departure Hours. Journal of Engineering and Science, 4(2), pp. 50-59
El Fahham, Y., 2019. Estimation And Prediction of Construction Cost Index Using Neural Networks, Time Series, And Regression, Alexandria Engineering Journal, 58, pp.499-506.
Lal, B., and Tripathy, S., 2012. Prediction of Dust Concentration in Open Cast Coal Mine using Artificial Neural Network, Atmospheric Pollution Research, 3(2), pp. 211-218.
Mahmood, K. R., and Aziz, J., 2011. Using Artificial Neural Networks for Evaluation of Collapse Potential of Some Iraqi Gypseous Soils, Iraqi Journal of Civil Engineering, 7(1), pp. 21-28.
Pessoa, A., Sousa, G., Maués, L., M., Alvarenga, F., C., and Santos, D., G., 2021.Cost Forecasting of Public Construction Projects Using Multilayer Perceptron Artificial Neural Networks: A Case Study, INGENIERI´A E INVESTIGACIO´ N, 41(3), pp.1-11
Sahar, 2011. Forecasting of Factors Affecting Brickwork Productivity Estimation by using Artificial Neural Network, MSc Thesis, Civil Engineering Department, College of Engineering, University of Baghdad.
Zidan, Kh. A., 2008. Implementation of an Efficient Access Control System for Secure Building, Journal of Engineering and Development, 12(3).
Zhang, Q.J., 2002. Artificial Neural Network—Advanced Theories and Industrial Applications. Civil and Environmental Engineering Department, University of Alberta: Edmonton, Canada. p. 183.