Cash is one of the most critical resources of a construction company that determines survival. Cash-flow management is essential for contractors, as lack of cash resources is one of the leading causes of bankruptcy in the construction industry, compared to most other sectors. The purpose of this paper is to identify factors affecting time and cost trade-off in multiple construction projects in Iraq. After reviewing a wide range of literature to determine the most common elements, a questionnaire is distributed to owners, consultants, supervising engineers, and contractors engaged in construction projects. The results of the questionnaire were analyzed using the relative importance index, arithmetic mean and standard deviation. The respondents namely assured Seventeen most essential factors; payments delay from client, progress payment due period, payment conditions, advanced payment, project delay, inaccurate project scheduling, variation orders, project duration, inaccurate project duration, profit, risk margin, project cost, cash flow forecasts, retentions percentage, estimating errors, materials cost, equipment cost, and labour cost.

The cost overrun in road construction projects in Iraq is one of the major problems that face the construction of new roads. To enable the concerned government agencies to predict the final cost of roads, the objective this paper suggested is to develop an early cost estimating model for road projects using a support vector machine based on (43) sets of bills of quantity collected in Baghdad city in Iraq. As cost estimates are required at the early stages of a project, consideration was given to the fact that the input data for the support vector machine model could be easily extracted from sketches or the project’s scope definition. The data were collected from contracts awarded by the Mayoralty of Baghdad for completed projects between 2010–2013. Mathematical equations were constructed using the Support Vector Machine Algorithm (SMO) technique. An average of accuracy (AA) (99.65%) and coefficient of determination (R2) (97.63%) for the model was achieved by the created prediction equations.