Abstract
The construction industry’s increasing complexity and dynamic project environments
engender advanced risk management strategies. AI-based risk management tools, reliant
on complex mathematical models, often impose specialised coding requirements, leading
to challenges in accessibility and implementation. In this vein, Generative Artificial
Intelligence (GenAI) emerges as a potentially transformative solution, leveraging adaptive
algorithms capable of real-time data analysis to enhance predictive accuracy and decisionmaking efficacy within Construction Risk Management (CRM). However, integrating
GenAI into CRM introduces significant challenges, including concerns around data
security, privacy, regulatory compliance, and a skills gap. Our research seeks to address
these issues by presenting a systematic bibliometric analysis that explores evolving trends,
key research contributions, and critical methodological approaches related to GenAI in
CRM. Thus far, our investigation has analysed 23 selected research articles from an initial
corpus of 212 papers, spanning the period from 2014 to 2024. Early insights delineate a
marked escalation in research activity from 2020 onwards, a surge likely engendered by
2
recent advancements in AI technologies and their applicability to construction
management. We categorise GenAI's potential benefits into technical, operational,
technological, and integration-related advantages, encompassing improvements in risk
identification, predictive capabilities, scheduling, and cybersecurity. Simultaneously, we
identify significant risks, particularly related to data governance, social acceptance, and
the operational impacts of AI-driven decisions. These preliminary findings underscore the
imperative for systematic governance frameworks and proactive stakeholder engagement
to optimise GenAI’s benefits whilst mitigating its latent risks.
engender advanced risk management strategies. AI-based risk management tools, reliant
on complex mathematical models, often impose specialised coding requirements, leading
to challenges in accessibility and implementation. In this vein, Generative Artificial
Intelligence (GenAI) emerges as a potentially transformative solution, leveraging adaptive
algorithms capable of real-time data analysis to enhance predictive accuracy and decisionmaking efficacy within Construction Risk Management (CRM). However, integrating
GenAI into CRM introduces significant challenges, including concerns around data
security, privacy, regulatory compliance, and a skills gap. Our research seeks to address
these issues by presenting a systematic bibliometric analysis that explores evolving trends,
key research contributions, and critical methodological approaches related to GenAI in
CRM. Thus far, our investigation has analysed 23 selected research articles from an initial
corpus of 212 papers, spanning the period from 2014 to 2024. Early insights delineate a
marked escalation in research activity from 2020 onwards, a surge likely engendered by
2
recent advancements in AI technologies and their applicability to construction
management. We categorise GenAI's potential benefits into technical, operational,
technological, and integration-related advantages, encompassing improvements in risk
identification, predictive capabilities, scheduling, and cybersecurity. Simultaneously, we
identify significant risks, particularly related to data governance, social acceptance, and
the operational impacts of AI-driven decisions. These preliminary findings underscore the
imperative for systematic governance frameworks and proactive stakeholder engagement
to optimise GenAI’s benefits whilst mitigating its latent risks.
| Original language | English |
|---|---|
| Publisher | Advance |
| Pages | 1-15 |
| Number of pages | 15 |
| DOIs | |
| Publication status | Published - 12 Nov 2024 |