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Advances in Legal and Regulatory Risk Modeling for Complex Urban Construction Project Delivery
Subject area: Management and Commerce · Area of research: Construction Risk Management
DOI: https://doi.org/10.64388/IREV3I1-1719300
Abstract
This review paper explores recent advances in legal and regulatory risk modeling within the context of complex urban construction project delivery. As urban construction projects become increasingly intricate, with multi-faceted regulatory frameworks and legal requirements, the potential for risk and dispute escalation grows. This paper reviews state-of-the-art risk modeling techniques that have been applied to address legal and regulatory challenges, such as compliance with zoning laws, environmental regulations, and contract disputes. Emphasizing the importance of proactive risk management strategies, it highlights the role of predictive analytics, machine learning, and digital modeling tools in identifying, assessing, and mitigating risks throughout the lifecycle of construction projects. The paper discusses the evolution of legal frameworks, from traditional risk management models to more dynamic, real-time approaches, focusing on their ability to adapt to the ever-changing legal and regulatory landscapes. Additionally, the paper evaluates how emerging technologies, including artificial intelligence (AI) and blockchain, are reshaping the ways legal risks are managed in construction, offering new opportunities for transparency, accountability, and dispute resolution. This review offers a comprehensive examination of current methodologies and presents future directions for integrating legal and regulatory risk models into urban construction project management to improve project outcomes and ensure compliance with complex and evolving legal standards.
Keywords
Legal Risk Modeling, Regulatory Risk, Urban Construction, Project Delivery, Predictive Analytics, Emerging Technologies.
How to cite this paper
@article{1719300,
author = {Abeebat Ajirotutu, Ogochukwu T. Izuchukwu, Dominic Feboh},
title = {Advances in Legal and Regulatory Risk Modeling for Complex Urban Construction Project Delivery},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {1},
pages = {644-662},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719300.pdf},
abstract = {This review paper explores recent advances in legal and regulatory risk modeling within the context of complex urban construction project delivery. As urban construction projects become increasingly intricate, with multi-faceted regulatory frameworks and legal requirements, the potential for risk and dispute escalation grows. This paper reviews state-of-the-art risk modeling techniques that have been applied to address legal and regulatory challenges, such as compliance with zoning laws, environmental regulations, and contract disputes. Emphasizing the importance of proactive risk management strategies, it highlights the role of predictive analytics, machine learning, and digital modeling tools in identifying, assessing, and mitigating risks throughout the lifecycle of construction projects. The paper discusses the evolution of legal frameworks, from traditional risk management models to more dynamic, real-time approaches, focusing on their ability to adapt to the ever-changing legal and regulatory landscapes. Additionally, the paper evaluates how emerging technologies, including artificial intelligence (AI) and blockchain, are reshaping the ways legal risks are managed in construction, offering new opportunities for transparency, accountability, and dispute resolution. This review offers a comprehensive examination of current methodologies and presents future directions for integrating legal and regulatory risk models into urban construction project management to improve project outcomes and ensure compliance with complex and evolving legal standards.},
keywords = {Legal Risk Modeling, Regulatory Risk, Urban Construction, Project Delivery, Predictive Analytics, Emerging Technologies.},
month = {July},
doi = {https://doi.org/10.64388/IREV3I1-1719300}
}