Proper and accurate estimating of construction activities’ duration is a key factor, as it can cause the success or failure of a project. The common methods of duration estimating have shown some inaccuracies according to previous studies. This study aims to develop a tool for estimating the duration of construction’s major activities regarding to the structural elements of concrete frame buildings. This tool is appropriate for tropical regions. In order to reach this purpose, Artificial Neural Network (ANN) is employed as the core calculating engine of the tool. Through literature survey and experts interviewing, the factors which can critically influence the activity duration have been identified. By means of the collected data from actual cases, four ANN models have been trained and tested for estimating the duration of installing column reinforcements, installing beam reinforcements, column concreting and beam concreting activities. Finally, a web-based program was designed and tested as an automated tool for suiting engineers to estimate the duration of scoped activities based on ANN method. Engineers and decision makers in the tropical regions can utilize the developed tool in the planning phase of their projects to produce more accurate estimations of activity durations.

During the previous two decades, the energy saving potential using systematic building management is considered to be important which should be considered through the building lifecycle. Among the wide range types of different buildings, Public buildings are considered as one of the biggest energy-consuming sector in the world and major part of this amount is used by the air conditioning system especially in tropical climates. The most effective decisions related to sustainable design of a building facility are made in the feasibility and early design stages. Building Information Modeling (BIM) can expedite this process and provide the opportunity of testing and assessing different design alternatives and materials selection that may impact on energy performance of buildings. This paper aims at evaluating the efficiency of various types of wall materials with regard to theirs properties on energy saving. The case study in this paper is modeled by means of BIM application and then simulated by software, which is appropriate for energy analysis. The current energy consumption patterns of this case identified and shifted to the optimized level of energy usages by changing the walls materials to find most optimized of walls materials. Modification most optimized wall materials and energy analysis indicated 9347 Wh in Per meter square of electrical energy saving.

An accurate estimation of construction duration for the successful completion of infrastructure projects is as important a task as keeping a project within the stipulated budget and quality. This research aims at developing an automated approach for estimating the duration of construction for dam projects by virtue of artificial neural network (ANN). Through literature searches and expert interviews, variables that critically influence the construction of dam projects were identified. A wide range of data on Iranian dam projects was used to develop seven ANN models. Different datasets were used to achieve the best performance by assessing the RMSE and R² values as the reliability and validity indicators. Finally, a web-based automated prototype was developed and validated in order for stakeholders to estimate the duration of dam projects based on the ANN method and benefit from better infrastructure project management practices.