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The main objective of this research is to development a mathematical model for predicating the construction productivity of floor using artificial neural perceptron network ANPN, because the methods currently used to estimation the productivity such as the personal experience, is a traditional methods inaccurate. Therefore, it can be adopt new and advanced techniques to predicating the productivity construction with accurate, speed and ease of use.In this research have been identified ten independent variables affect on the construction productivity of floor, the data collection from construction project in Mosul (Iraq) through the design of the data collection form (Form measurement of work). In this research, One model was built for the prediction the total productivity of building project. A number of issues in relation to ANPN construction such as the effect of ANPN geometry and internal parameters on the performance of ANNs models were investigated. Information on the relative importance of the factors affecting the above productivity parameters predictions were presented and practical equations for the predictions of the above construction productivity were developed.. It was found that ANPNs have the ability to predict the Total productivity for finishing works for building project with a good degree of accuracy of the coefficient of correlation (R) was 96.2%, and average accuracy percentage of 96.4%.

The aim of this study is to investigate the fact of the process planning and time scheduling for the construction projects in Iraq. The research displays the main factors and causes that contribute in decay of preparing the time scheduling for construction projects.To achieve these objectives , a scientific methodology has been followed, represented in collecting the literature related to the subject of time scheduling and the field study represented by interviews and a questionnaire which takes into account the fact of the process planning and time scheduling in the construction companies and identifying the factors that affected on the preparing the time scheduling such as contract documents , natural of the project, management of the construction company and natural of the planning process. There are twenty three factors which have most importance affected on the preparing of the time scheduled, such as (not calculating some items of the bill of quantity) and (effective planners) which have the relatively importance (76.66%) and (75.17%) respectively.In the light of the conclusions obtained, the necessary recommendations have been established to develop time scheduling for the construction companies.

لقد تطورت أساليب التعليم كثيرا منذ ظهور الحاسوب، والتعلييم الهندسي كأحد العلوم المهمة.

The main objective of this research is to introduce a new and alternative approach of using a neural network for productivity estimation of the finishing works for building project.The application of Artificial Neural Networks, as a modern technique, in Iraqi construction industry is necessary to ensure successful management, and many of the construction companies feel the need of such system in project managementMulti-layer perceptron trainings using the back-propagation algorithm were used. In this work, the feasibility of ANNs technique for modeling these productivity parameters was investigated. A number of issues in relation to ANNs construction such as the effect of ANNs geometry and internal parameters on the performance of ANNs models were investigated. Information on the relative importance of the factors affecting the above productivity parameters predictions were presented and practical equations for the predictions of the above cost were developed.One model was built for the prediction the total productivity of building project. It was found that ANNs have the ability to predict the Total productivity for finishing works for building project with a good degree of accuracy of the coefficient of correlation (R) was 93.2%, and average accuracy percentage of 96.4%.The ANNs model developed to study the impact of the internal network parameters on model performance indicated that ANNs performance was relatively insensitive to the number of hidden layer nodes, momentum term, and learning rate.

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