Abstract

Offsite construction has gained momentum in recent years due to its improved performance in projects‘ schedule, quality, safety, and environmental impact without increasing cost. Several research studies have introduced planning and scheduling techniques for modular and offsite construction using Building Information Modeling (BIM) and simulation tools. In this research, a questionnaire survey was carried out in collaboration with the modular building institute (MBI), Niagara Relocatable Buildings Inc. (NRB Inc.), and the School of Building Science and Engineering at the University of Alberta. The questionnaire focused on two issues: (1) modular and offsite construction industry characteristics, and (2) barriers to increased market share in this industry. For the latter issue, effort was made to address five factors that emanated from the workshop on ―challenges and opportunities for modular construction in Canada,‖ held in October 2015, Montreal to analyze barriers to modular construction growth in Canada. Key findings of this questionnaire include requests for the use of a separate modular construction design code, innovative financing and insurance solutions, standards that consider procurement regulations, and lending institutions that partner with financial houses to create special lending programs for modular construction. Findings of this questionnaire were published on the official MBI website.
This research presents an alternative BIM-based integrated framework for modeling, planning, and scheduling of modular and offsite construction projects. BIM Vertex BD software was used in the proposed framework for automating data exchange between projects‘ BIM model and the proposed scheduling method. The proposed method integrated linear scheduling method (LSM), critical chain project management (CCPM), and the last planner system (LPS) into a comprehensive BIM-based framework for scheduling, monitoring, tracking, and controlling of projects while considering uncertainty associated with activity durations. A procedure for integrating offsite and onsite construction was introduced based on the proposed scheduling methodology. Then, a new multi-objective optimization model was developed using genetic algorithm (GA) to optimize the integration between the LSM and CCPM. This optimization model minimizes time, cost, and work interruptions simultaneously while considering uncertainty in productivity rates, quantities, and availability of resources. The developed model was based on the integration of six modules: 1) uncertainty and defuzzification module, 2) schedule calculations module, 3) cost calculations module, 4) optimization module, 5) module for identifying multiple critical sequences and schedule buffers, and 6) reporting module. Schedule buffers were assigned whether or not the optimized schedule allows for interruptions. This method considers delay and work interruption penalties and bonus payments. The developed integrated scheduling model for offsite and onsite construction was automated in newly developed software named ―Mod-Scheduler‖ using the ASP.NET system coded in C# programming language. A number of case studies were presented and analyzed to demonstrate the developed methodologies‘ features and capabilities.
This research also introduces a novel modular suitability index (MSI), which utilizes five indices; 1) connections index (CI), 2) transportation dimensions index (TDI), 3) transportation shipping distance index (TSDI), 4) crane cost penalty index (CCPI), and 5) concrete volume index (CVI). Calculating the MSI provided a unified indicator to assist in selecting near optimum module configurations for efficient planning of modular residential construction.
This research identifies the main factors affecting the configuration of modules in hybrid construction projects to introduce a new configuration model that is expected to assist hybrid construction stakeholders in identifying the most suitable configuration for each type of modules (i.e. panels) in their projects. A hybrid construction case study was selected to demonstrate the applicability of proposed model and to highlight its capabilities in selecting the most suitable configuration of panelized projects.