Two-stage BIM-enabled decision support system for the selection of a suitable industrialized building system in off-site construction

Abstract

The construction industry is undergoing transformative changes driven by the integration of innovative technologies and strategic decision-making frameworks. The aim of the research described herein is to enhance construction efficiency by advancing Off-site Construction (OSC) techniques and their integration with Building Information Modelling (BIM) through informed decision-making. This thesis presents a holistic approach to enhancing productivity and efficiency in OSC through a Two-Stage BIM-Enabled Decision Support System (BeDSS) for selecting a suitable Industrialized Building System (IBS). The first stage of the BeDSS involves assessing the pertinence of using OSC for a building project, considering dimensions such as project characteristics, supply chain, time, cost, quality, procurement, and sociocultural. This assessment, augmented by digitalization and BIM, enables stakeholders to make informed decisions regarding the adoption of OSC for a given project, taking into account benefits such as accelerated construction schedules and improved quality control. Building upon the OSC pertinence assessment, the second stage of the BeDSS focuses on the selection of a suitable IBS tailored to the project's requirements. This stage leverages a digitalized workflow specifically designed for off-site construction building projects, where the aim is to successfully implement IBS in a particular project through a systematic and digitalized approach. By adopting a Design Science Research approach, the proposed BeDSS, as the artifact of this project, integrates fuzzy logic with the Analytic Hierarchy Process (AHP) in a multi-criterion decision method incorporating BIM. This integration streamlines the decisionmaking process and ensures reliable and informed decisions that take into consideration all aspects of the project, thereby contributing to the success of the project. In addition to expert validation, a case study and empirical data analysis validate the identified Key Decision Support Factors (KDSFs) and the effectiveness of the BeDSS in optimizing decision-making. For instance, the implementation of automation in the manufacturing process in IBS, and its impact on the time and cost of manufacturing and assembly as KDMFs applied in the proposed solution, are investigated and discussed in depth. The findings contribute valuable insights to the field of OSC, IBS, and decision support systems, offering practical guidance for industry professionals, researchers, and policymakers navigating the evolving landscape of construction productivity and efficiency.

Date	25 Sept 2024
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Ivanka Iordanova (Supervisor) & Mohamed Al-Hussein (Co-supervisor)