Abstract

Over the past decades, the construction industry lags further and further behind the manufacturing sector when productivity is considered. This is due to inconsistency of internal factors that take place on site, which are mainly related to management and workforce issues. Almost all of them are directly related to the way that productivity is measured. The currently applied methods for measuring productivity are labour intensive, time – cost consuming and error prone. They are mainly reactive monitoring processes initiated after the detection of a negatively influencing factor. The data collection is manual, based on either work sampling or reviewing surveillance video/photo data. Although research studies have been performed towards leveraging these limitations, a gap still exists in extracting productivity rates for every construction entity and for all the tasks that take place at a jobsite simultaneously, without requiring prior knowledge regarding the type of task or work zone. In order to overcome the aforementioned limitations, the focus of this research is to propose a method that will proactively measure productivity for the entire range of operations, time and cost effectively. In general, the aim is to automatically identify cycle patterns in trajectory data taken from jobsite’s surveillance system. The goal of the first year of this project was to develop a method that is capable of detecting repetitive patterns at jobsites’ complex environments. For this purpose, semantic analysis and trajectory analysis were implemented. Tasks are divided into cycle events, which are consisting of three semantic components. Two “stops” parts depicting the execution of an activity and their in between connection with a “move” part. The initial results show that the former can be detected with density based clustering whereas the latter with curve simplification algorithms. Future work, will concentrate on 1) extracting “clean” trajectory data, registered on a single base in order to cover all types of activities and also detect possible causes of delays, 2) detection of cycle paths, representing summary tasks consisting of subtasks (cycle events), 3) computation of productivity rate (cycles per hour) and 4) online prediction of low productivity rates, given training data of operations fixed in time and space. Such an approach will contribute to the improvement of activities’ performance, since productivity will be measured proactively, covering the entire range of operations on an individual level. Therefore, the surveillance engineers will be provided with enough detailed information to be able to proceed with the appropriate corrective actions.