The purpose of this study is to avoid delays and cost changes that occur in emergency reconstruction projects especially in post disaster circumstances. This study is aimed to identify the factors that affect the real construction period and the real cost of a project against the estimated period of construction and the estimated cost of the project. The case study is related to the construction projects in Iraq. Thirty projects in different areas of construction in Iraq were selected as a sample for this study. Project participants from the projects authorities provided data about the projects through a data collection distributed survey made by the authors. Mathematical data analysis was used to construct a model to predict change in time and cost of the projects before the start of the construction. The artificial neural networks analysis was selected as a mathematical approach. The most important factors identified leading to schedule delays and cost increase were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. The use of the ANN model for such a problem is expected to be an effective method for modeling this complicated phenomenon.

这项研究的目的是避免紧急重建项目中发生的延迟和成本变化，尤其是在灾后情况下。本研究旨在确定影响实际建设期和项目实际成本的因素，以估计的建设期和项目的估计成本为基础。案例研究与伊拉克的建设项目有关。本研究选择了伊拉克不同建筑领域的30个项目作为样本。来自项目主管部门的项目参与者通过作者进行的数据收集分布式调查提供了有关项目的数据。使用数学数据分析来构建模型，以预测在开始建造之前项目的时间和成本的变化。选择了人工神经网络分析作为数学方法。导致进度延误和成本增加的最重要因素是承包商故障，设计/计划和变更单的重新设计，安全问题，低价投标的选择，天气因素和业主故障。预期将ANN模型用于此类问题将是对这种复杂现象进行建模的有效方法。