Karim Jbbar, R., Hussein Khudair, B., Soaded Alsaqqar, A. (2017). Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq. , 23(8), 70-83.
Rehab Karim Jbbar; Basim Hussein Khudair; Awatif Soaded Alsaqqar. "Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq". , 23, 8, 2017, 70-83.
Karim Jbbar, R., Hussein Khudair, B., Soaded Alsaqqar, A. (2017). 'Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq', , 23(8), pp. 70-83.
Karim Jbbar, R., Hussein Khudair, B., Soaded Alsaqqar, A. Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq. , 2017; 23(8): 70-83.

Rigid Trunk Sewer Deterioration Prediction Models using Multiple Discriminant and Neural Network Models in Baghdad City, Iraq

Journal of Engineering
Article 1, Volume 23, Issue 8, 2017, Pages 70-83
Authors
Rehab Karim Jbbar; Basim Hussein Khudair; Awatif Soaded Alsaqqar
Abstract
The deterioration of buried sewers during their lifetime can be affected by several factors leading to bad performance and can damage the infrastructure similar to other engineering structures. The Hydraulic deterioration of the buried sewers caused by sewer blockages while the structural deterioration caused by sewer collapses due to sewer specifications and the surrounding soil characteristics and the groundwater level. The main objective of this research is to develop deterioration models, which are used to predict changes in sewer condition that can provide assessment tools for determining the serviceability of sewer networks in Baghdad city. Two deterioration models were developed and tested using statistical software SPSS, the multiple discriminant model (MDM) and neural network model (NNM). Zublin trunk sewer in Baghdad city was selected as a case study. The deterioration model based on the NNDM provide the highest overall prediction efficiency which could be attributed to its inherent ability to model complex processes. The MDDM provided relatively low overall prediction efficiency, this may be due to the restrictive assumptions by this model. For the NNDM the confusion matrix gave overall prediction efficiency about 87.3% for model training and 70% for model validation, and the overall conclusion from these models may predict that Zublin trunk sewer is of a poor condition.
Keywords
deterioration model; trunk sewer pipe; sewer conditions; multiple discriminant analysis; artificial neural network
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