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Partnering is the master key element for project quality. It opens the doors to commitment and communication between the parties and provides the mastic which turns disparate groups with varying aims into a coherent team with common objectives (Baden-Hellard).There has been considerable business emphasis placed upon the development of collaborative relationships across companies in recent years. Construction is no exception. The presence of trust in the relationships has been cited as central to successful business and project outcomes (Partnership Sourcing Ltd. 1994, Latham1994). Trust is central to the development of non- adversarial business relationships, and hence provides fertile ground for the development of practices such as partnering, strategic alliances and supply chain management.The paper focuses upon the partnering concept in construction and discusses the role of trust as a key dimension for reducing adversarial relations in project working environments.

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Cash flow forecasting is an indispensable tool for construction companies, and is essential for the survival

of any contractor at all stages of the work. The time available for a detailed pre-tender cash flow forecast is often

limited. Therefore, contractors require simpler and quicker techniques which would enable them to forecast cash flow

with reasonable accuracy. Forecasting S-curves in construction in developing countries like Iran in compare with

developed countries has many difficulties. It is because of uncertainty and unknown situation in nature of construction

industry of these countries. Based on knowledge of authors there is a little attempt for cash flow forecasting in

construction industry of Iran. As a result authors produced An S-curve equation for construction project from historical

data which has reasonable accuracy. A sample of 20 completed projects was collected and classified in to the three

different groups. In order to model S-curves for each group, a simple and reliable method of S curve fitting has been

used. S-curves were fitted into each group by using different techniques. Errors incurred when fitting these curves were

measured and compared with those associates in fitting individual projects. At the end, accuracy of each model has

been calculated and an equation has been proposed to forecast S-curves.

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Java is an object-oriented program that has abundant open-source libraries for application development and 3D model rendering. Spatial database is the database that can efficiently store and manage geographic information data though various spatial data management techniques. This paper explores the rationale of coupling java with spatial database to develop an effective platform for future Building Information Modeling (BIM) application. The paper methodically presents the prototype system integration design to demonstrate how the system can be developed. The paper also meticulously presents the logical and physical data models in designing optimum BIM database for a reinforced concrete building. An 8-storey reinforced concrete building was used as an implementation case study to validate the proposed prototype system design and investigates the implementation issues. The outcome shows that not only the proposed prototype system offers technological advantages over the traditional BIM applications, its open-source solution can also overcome the financial constraint that currently inhibits the implementation of BIM especially for medium and small enterprises.

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The decisions made in the planning phase of a building project greatly affect its future operation and maintenance (O&M) cost. Recognizing the O&M cost of condominiums’ common facilities as a critical issue for home owners, this research aims to develop an artificial neural network (ANN) O&M cost prediction model to assist developers and architects in effectively assessing the impacts of their decisions made in the planning phase of condominium projects on future O&M costs. A regression cost prediction model was also developed as a benchmark model for testing the predictive accuracy of the ANN model. Six critical building design attributes (building age, number of apartment units, number of floors, average sale price, total floor area, and common facility floor area) which are usually available in the project planning phase, were identified as the input factors to both models and average monthly O&M cost as the output factor. 55 of the 65 existing condominium properties randomly selected were treated as the training samples whose data were used to develop the ANN and regression models the other ten as the test samples to compare and verify the predictive performance of both models. The study results revealed that the ANN model delivers more accurate and reliable cost prediction results, with lower average absolute error around 7.2% and maximum absolute error around 16.7%, as compared with the regression model. This study shows that ANN is an effective method in predicting building O&M costs in the project planning phase. Keywords: Project management, Facility management, Common facilities, Cost modeling.

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Project Management knowledge has been used in many project oriented organizations in last two decades across the world. Despite, rate of project success did not change during these years. We believe there is a basic challenge in projects environment for managing them based on its inherent characteristics. In fact, project management knowledge use theories and concepts that are belong to process management world, as a different world. There is no enough attention to project characteristics as a fundamental differentiation for coping projects. Identification of construction projects nature in order to discern variables that create the project behaviors is main concern of the paper.Considering project characteristics in this research revealed construction project nature creates from combination two aspects. First, detecting environmental changes to develop a need and second prepare resources structure to respond the need. Important management challenge in this model is environmental continuous changes that alter the need and exchange resources structure. So, the paper considers how these aspects can be operationalized for developing a dynamic project management model. It gives some ideas about why project complexity might be considered to be increasing, and how construction projects move towards shorter timescales. The effectiveness of the model is verified by applying it for predicting some construction projects behavior. The results of the paper may capable future project managers to test any decision before its applying and lead to a new project management tool for construction projects management.