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Abstract Managing wastewater systems effectively is vital, for planning as it impacts health and the environments sustainability. The financial aspect, especially estimating costs plays a role in project execution. Traditional cost estimation methods have often proven unreliable due to the nature of construction projects. This research introduces a perspective by using network (ANN) models to enhance the accuracy of cost predictions for sewer pipeline projects in Iraq. By analyzing a dataset that considers factors like project size, complexity, material types and regional aspects the study showcases how artificial neural networks can capture nonlinear relationships within the data. The main goals include pinpointing factors influencing cost estimation accuracy during the pre-design stage and crafting ANN-based tools tailored for various design phases. The approach involves constructing an ANN model validated against data from projects. It was found that ANN has the ability to predict the cost of implementing sewer pipe projects with a very good degree of accuracy, as the correlation coefficient (R) reached (97.1%), with an average accuracy rate of (98.5%). This research doesn’t just add value to construction management by offering a budgeting tool. It also helps allocate resources effectively ensuring the financial feasibility of important infrastructure projects.
Abbas et al. (Thu,) studied this question.