Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

Abstract

Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets.

Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

“…Song et al [22] advocated for sustainable construction practices to minimize environmental impact and promote long-term economic viability. Khodabakhshian et al [23] highlighted the importance of lifecycle costing in assessing the total cost of ownership over a project's lifecycle, including construction, operation, and maintenance costs.…”

“…Previous studies involving the fuzzy integrated Delphi-ISM-MICMAC hybrid MCDM model have demonstrated its effectiveness in addressing complex decision-making problems across various domains. Fuzzy sets are a mathematical approach that the handles uncertainty and imprecision in decision-making processes by assigning degrees of membership to linguistic terms [23]. In the context of MCDM, fuzzy sets enable decision-makers to express subjective preferences and uncertainties in a quantitative manner, facilitating the integration of qualitative and quantitative criteria in decision-making models [24].…”

“…• Risk identification (F31): Risk identification allows for the early recognition of financial uncertainties, enabling proactive mitigation strategies to ensure budget adherence. It helps minimize cost overruns, optimize resource allocation, and improve the overall financial performance of civil engineering projects [20][21][22][23]43,48].…”

“…Natural language processing (NLP) can extract and interpret regulatory information, allowing organizations to proactively identify compliance gaps and take corrective action. AI-based compliance management systems enhance transparency, accountability, and regulatory oversight in civil engineering projects [3,5,9,23,24].…”

“…• Regulatory requirements (F61): Adhering to regulatory requirements avoids costly fines, legal disputes, and delays, ensuring that projects remain within budget. Understanding these requirements enables proper planning, risk mitigation, and efficient resource allocation, contributing to the success and financial viability of civil engineering projects [9,23,24].…”