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 historical datasets at operating mine sites are usually large. Directly applying large datasets to build prediction models may lead to inaccurate results. To overcome the real-world challenges, this study aimed to handle these large datasets using Gaussian mixture modelling (GMM) for developing a novel and accurate prediction model of truck productivity. A large dataset of truck haulage collected at operating mine sites was clustered by GMM into three latent classes before the prediction model was built. The labels of these latent classes generated a latent variable. Two multiple linear regression (MLR) models were then constructed, including the ordinary-MLR (O-MLR) and the hybrid GMM-MLR models. The GMM-MLR model incorporated the observed input variables and a latent variable in the form of interaction terms. The O-MLR model was the baseline model and did not involve the latent variable. The GMM-MLR model performed considerably better than the O-MLR model in predicting truck productivity. The interaction terms quantitatively measured the differences in how the observed input variables affected truck productivity in three classes (high, medium, and low truck productivity). The haul distance was the most crucial input variable in the GMM-MLR model. This study provides new insights into handling massive amounts of data in truck haulage datasets and a more accurate prediction model for truck productivity.