The construction industry has been suffering from delay and cost overrun for decades. Experienced schedulers programs and allocate contingencies (both cost and time) based on professional experiences and gained knowledge. Such tacit knowledge has not been captured, stored, and shared with inexperienced schedulers. This paper proposes a Graph-based Automated Scheduling (GAS) method to capture, store and reuse the tacit knowledge in the construction schedules. The proposed GAS method takes construction schedules as input, extracts schedule features, classifies schedules into different types of sequences, selects and assembles sequences into schedules, and eventually optimises time-and cost-efficiency of assembled schedules. The GAS method is validated on two case studies. The results indicate that the automatically generated construction schedule is, on average, 6.70% closer to the actual schedule than the planned schedule . Different from existing automatic scheduling methods, GAS relies little on the availability and data richness of BIM models. Hence, GAS helps schedulers initiate new schedules more efficiently at the early construction stages
The task of reading drawings on construction sites has significant efficiency and cost problems. Recent products utilising laser projectors attempt to address the issue of drawing comprehension by projecting full scale versions of the drawings onto 3D surfaces, giving an in-place representation of the steps required to complete a task. However, they only allow projection in red or green at a single brightness level due to the inherent constraints of using a laser-based system, which could cause problems depending on the surface to be projected on and the ambient conditions. Thus, there is a need for a solution that is able to adjust the visualisation parameters of the displayed information based on the surface being projected onto. This study presents a system that automatically changes the visualisation parameters based on the colour and texture of the current surface to make drawings visible under any planar-like surfaces. The proposed system consists of software and hardware, and the software algorithm contains of two parts 1) the optimisation run that computes and updates the visualisation parameters and 2) the detection loop which runs continually and checks if the optimisation run needs to be triggered or not. In order to verify the proposed system, tests on 8 subjects with 4 background surfaces commonly found on site were performed. The test subjects were timed to find 10 bolt holes projected onto the surface using the optimisation system, which was then compared to a control case of black lines projected onto a white background. The system allowed users to complete the task on the real-world backgrounds in the same time as the control case, with the system resulting in up to a 600% decrease in recognition time on some backgrounds.
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