Modular construction has the advantage of producing structures quickly and efficiently, while not requiring the resources to build a structure to be co-located with the construction site. Large modules can be produced in quality controlled environments, and then shipped to the construction site and assembled with minimal labor requirements. An additional advantage is that once the modules are on-site, construction can proceed extremely quickly. This is ideal for situations where compressed schedules are required in order to meet client's time constraints. This paper examines using software simulation, specifically Simphony.NET, in the design and analysis of the construction process. This is done both before and after project execution to predict productivity and duration and also to allow for exploration of alternate construction scenarios.

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Waste auditing is one of the tools used to quantify waste generation in construction processes, especially in industrialized building construction facilities that aim to reduce waste. These audits are organized following a regular schedule to monitor manufacturing activities with respect to the waste generated. However, the identification and quantification of waste through occasional audits of activities at any particular workstation remains a biased, manual, error-prone, and monotonous task. This paper proposes the automation of waste auditing in industrialized construction facilities, using as a case study a cutting station on a window manufacturing line. The waste generated during the cutting process is quantified using contour-based image processing algorithms, and the identification of the material is determined by optimized deep learning classification models. This approach allows the continuous acquisition of waste generation data at the workstation level and enables data-driven waste management decision-making that has the potential to support the reduction of waste in industrialized building construction facilities.