Building information modeling for planning and scheduling using multi-objective genetic algorithm in a renovation project

Abstract

Renovation is known to be a complicated type of construction project. More flexible and more efficiency planning models unique to the projects are necessary for improving the planning in terms of actual construction time, total cost and resource usage. The apparent benefit of Building information modeling (BIM) in managing the technical complexity of construction projects has seen the extensive adoption of BIM technology as a tool for improving construction management efficiency. In a real construction project, there are always multiple objectives that create a set of solutions to provide optimization information for project managers to decide. Multi-Objective Genetic Algorithms (MOGA) have been discussed in several construction optimization studies. Recently, an artificial intelligence optimization approach has been introduced to enhance BIM's capabilities. In this study, new approach Building Information Modeling based on Multi- Objective Genetic Algorithms (BIM-MOGA) for a renovation project are proposed and evaluated through a case study of two-year extra-large building renovation project. The project construction planning and scheduling data are optimized and building information modelling data are combined and optimized. The predominance of BIM in renovation project also pointed out. A detailed description of BIM-MOGA model and constructive optimization process is developed. The usage and evaluation of BIM implementation for a renovation project are investigated. The results of the study reveal that the development and creation of BIM to implement project management for extra- large governmental building renovation project have many benefits for the project. Two reliable methods, that is Weighting Sum Method (WSM) and Pareto front method, are adopted in order to develop this optimization process. The MOGA module starts by defining optimization objectives, constraints, and project parameters. The parameters which were retrieved from construction data were project activities, activity predecessors, successors, resource availability, cost data, and project calendars. Then, a genetic algorithm optimizer was employed. The WSM creates multi-objective functions made into a single solution. Analytical Hierarchy Process (AHP) was employed to define evolution weights for multi-objective optimization variables. The results show that scaling the variables in WSM MOGA yields improved solution. The performances of the proposed Mx-based optimization models which involve three activation functions, namely Mx, √Mx and Mx /1000, have been assessed. Model Mx adopted initial construction sequences and resource utilization improved by 5.61%. Model √Mx and model Mx /1000 adopted optional construction sequences with 63.24% and 64.51% improvements, respectively. By providing improved resource utilization, this work is proved to be useful to construction planners and schedulers working on extra-large building renovation. While the Pareto front method results were displayed as a Pareto front with the combinations among the multi-objective variables. The outputs provided many alternatives for different scenarios for further analysis. Decision maker needs to consider the tradeoff between each objective in order to choose the best plan for implementation. Lastly, the best schedule is sent back to BIM model for construction. The BIM-based schedule was integrated to the 4D and 5D model and used as a visualization tool.