Optimizing Modularity of Prefabricated Residential Plumbing Systems for Construction in Remote Communities

Prefabrication is often considered as a potential solution to address the challenges of building construction in remote communities. However, methods to find the best modularization for mechanical, electrical, and plumbing (MEP) systems are scarce. This prevents a proper assessment of cost reductions and local benefits, two key metrics for decision makers in remote communities. Therefore, in this study a computational framework is proposed for identifying the optimal modularity of piping to be assembled in residential buildings located in an isolated region, namely Nunavik (Quebec). The framework contributes to advancing existing modularity optimization approaches for MEP systems by integrating two objective functions, the impact of module characteristics on modularization, collision constraints during assembly, and the context of remote communities. The algorithm simultaneously minimizes system installation cost and maximizes local job creation, an important socioeconomical outcome of construction in remote areas. Fuzzy logic models and nondominated sorting genetic algorithm (NSGA-II) algorithm were used to evaluate configurations and identify nondominated solutions. Type of work, height of the assembly, direction changes, and stiffness of the modules were considered in estimating assembly time. The dimensions and weight of each module were used to estimate handling time. The optimization framework considers two possible prefabrication sites, outside and inside the remote region. By applying this approach to modularize an MEP system in typical Nunavik housing units, it was possible to demonstrate a reduction in installation cost and an increase in local job creation compared to the current situation (i.e., without MEP prefabrication). In particular, for a case study with 40 components, the proposed framework found a solution that can reduce installation cost by 81.9% and generate 23.4 h of local employment. (C) 2022 American Society of Civil Engineers