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Productivity is a very important element in the process of construction project management especially with regard to the estimation of the duration of the construction activities, this study aims at developing construction productivity estimating model for marble finishing works of floors using Multivariable Linear Regression technique (MLR). The model was developed based on 100 set of data collected in Iraq for different types of projects such as residential, commercial and educational projects. Which these are used in developing the model and evaluating its performance. Ten influencing factors are utilized for productivity forecasting by MLR model, and they include age, experience, number of the assist labor, height of the floor, size of the marbles tiles, security conditions, health status for the work team, weather conditions, site condition, and availability of construction materials. One model was built for the prediction of the productivity of marble finishing works for floors. It was found that MLR have the ability to predict the productivity for finishing works with excellent degree of accuracy of the coefficient of correlation (R) 90.6%, and average accuracy percentage of 96.3%. This indicates that the relationship between the independent and independent variables of the developed models is good and the predicted values from a forecast model fit with the real-life data.

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The aim of this study is identifying and diagnosing the causes of construction project failure by using different project management process groups. These groups were initiation process group, planning process group, design process group, contract process group, executing and monitoring process group, and close process group. Also, the relative importance of the causes of construction project failure was investigated. Three techniques were used in this study: Ishikawa diagrams, Pareto diagrams, and 5-why techniques. The results were generally identified and diagnosed thirty-five causes of the construction project failure; however, only twenty-three of the causes were the most important. The majority of causes (thirteen causes) were obtained by using executing and monitoring project management process group. Seven causes were obtained by using contract project management process group. In addition, fewer causes (only three causes) were obtained by using initiation project management process group.

Sandstorms (dust storms) are considered the most events which cause destructive and costly damages in lots of desert regions. These sandstorms may be a reason of huge disasters or damages on environmental as well as health aspects. The aim of this paper is to develop a mathematical model for predicting the Dust Storm in Republic of Iraq using Artificial Neural Network (ANN) technique. As a case study, four construction projects in Iraqi cities were selected (Baghdad, Basrah, Samawa, and Nasiriya) in order to identifying and prediction of the sandstorms, which significantly help to reduce the effects of damages. Only one ANN model was built to predict a dust storm. The datas of this model cited from Iraqi Meteorological Organization and Seismology. Four factors were adapted to develop the model (Max. Temperature, Min. Temperature, Rain and Wind), It was found that ANN has the ability to predict the dust storm with a high accuracys off the correlation coefficient (R) which is 90.00%, with a percentage of average accuracy is 89%.