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.
GOLIZADEH, H., Nobahar Sadeghifam, A., Aadal, H., & Zaimi Abd Majid, M. (2016). Automated tool for predicting duration of construction activities in tropical countries. KSCE Journal of Civil Engineering, 20(1), 12–22. https://doi.org/10.1007/s12205-015-0263-x