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Using AI and BIM Technologies In Complex Construction Projects
  • 2

  • Course Code:
  • University: Dublin Business School
  • Country: Ireland

Background And Problem Statement

Background

Construction projects face numerous risks and uncertainties (Flyvbjerg, 2017,p.230). Effective risk management is crucial for project success.

Previous studies have highlighted the importance of risk management strategies but have overlooked the integration of emerging technologies.

Problem statement 

Existing research lacks focus on incorporating technologies like Building Information Modelling (BIM and AI into risk management practices (Ghaffarianhoseini et al. 2017,p.1046).

This gap hinders the adoption of innovative approaches to address uncertainties in construction projects.

Research Aims And Objectives

To explore utilization of emerging technologies such as AI and BIM, for managing risk and uncertainty in complex construction projects, with the ultimate goal of providing actionable insights for project managers and stakeholders.

  • Assess the Integration of Building Information Modeling (BIM) in Construction Risk Management

  • Evaluate the Role of Artificial Intelligence (AI) in Enhancing Construction Risk Management

  • Develop Guidelines for Implementing BIM and AI in Construction Risk Management

Research Questions

How can emerging technologies such as AI and BIM be utilised to improve risk management processes in the construction industry?

•    Construction risk management must be tailored to project challenges.
•    These methods reduce uncertainty and improve project production by accurately determining, assessing, and reducing risks at different expense levels.
•    Stakeholder involvement, risk assessment, and mitigation planning help identify and address issues and adapt to hazards.
•    Mission accomplishments include improving project performance, adhering to financial restrictions and schedule goals, and addressing stakeholder concerns.
•    The  efficiency  of  risk  management  approaches  meant  to  improve construction project outcomes must be investigated.

Also Read - Project Management Assignment Help

Knowledge Gap

The existing literature lacks comprehensive exploration of how emerging technologies, such as BIM and AI, can be integrated into risk management strategies in complex construction projects.

This knowledge gap inhibits the advancement of innovative approaches to address uncertainties effectively (Kathirgamanathan et al., 2021, p.110120).

Data Collection

  • Data will be collected via an online survey conducted through Google Forms, facilitating accessibility and convenience for construction project managers.

  • Provide meaningful insights into risk management strategies using emerging technologies in complex construction projects.

  • With a targeted sample size of 150 responses, statistical validity will be ensured, enabling robust analysis to effectively address the research questions

Research Methodology

SAMPLING TECHNIQUE
•    Recruitment strategy

RESEARCH APPROACH
•    Quantitative Approach

DESIGN OF RESEARCH
•    cross- sectional design

RESEARCH THEORY
•    Theories of organizational behavior, project management, and risk management

Variables

Dependent Variables

Project Success in Risk Management

Independent  Variables

•    Integration of BIM in Risk Management
•    Utilisation of AI in Risk Management
•    Effectiveness of BIM and AI in Risk Mitigation
•    Perceived Barriers to BIM and AI Adoption
•    Knowledge and Training in BIM and AI


 

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References

•    Flyvbjerg,  B.  ed.,  2017.  The  Oxford  handbook  of  megaproject  management.  Oxford
University Press, p.230-311
•    Ghaffarianhoseini, A., Tookey, J., Ghaffarianhoseini, A., Naismith, N., Azhar, S., Efimova,
O. and Raahemifar, K., (2017). Building Information Modelling (BIM) uptake: Clear benefits, understanding its implementation, risks and challenges. Renewable and sustainable energy reviews, 75, pp.1046-1053.
•    Hopkin, P., (2018). Fundamentals of risk management: understanding, evaluating and
implementing effective risk management. Kogan Page Publishers, p.119-251
•    Kathirgamanathan, A., De Rosa, M., Mangina, E. and Finn, D.P., (2021). Data-driven predictive control for unlocking building energy flexibility: A review. Renewable and Sustainable Energy Reviews, 135, p.110120.
•    Kobrin, S.J., (2022). Managing political risk assessment: Strategic response to environmental change. Univ of California Press, p.45-176

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