A Computational Model for Risk-Based Quality Governance Assessment in High-Risk Infrastructure Projects: A Weighted Composite Scoring Approach

Abstract

High-risk infrastructure projects, characterized by their complexity, substantial investment, and extended timelines, necessitate robust quality governance frameworks to mitigate potential failures and ensure successful delivery [1]. Traditional governance models often struggle to integrate the dynamic interplay of risk factors with quality assurance mechanisms across the project lifecycle. This paper introduces a computational model designed for risk-based quality governance assessment in such projects. The model employs a weighted composite scoring approach, systematically evaluating various dimensions of quality governance against identified project-specific risks. It synthesizes quantitative metrics and qualitative indicators, transforming complex risk landscapes into actionable governance scores. The methodology encompasses indicator selection, an algorithm for composite score calculation, and a multi-stage validation process. Application through simulated case studies demonstrates the model's capacity to provide a nuanced, real-time assessment of governance efficacy, identifying vulnerabilities and guiding strategic interventions. This approach supports project stakeholders in making informed decisions, thereby enhancing project resilience and reducing the likelihood of adverse outcomes. The model contributes a structured, quantifiable method for proactively managing quality governance in critical infrastructure developments. To operate this concept, the proposed model integrates governance indicators, risk criticality metrics, and decision-weighting mechanisms within a computational decision-support architecture. Specifically, the model combines expert-derived governance indicators with probabilistic risk assessment to compute a normalized Weighted Composite Score (WCS) representing governance effectiveness under risk exposure. Sensitivity analysis and Monte Carlo simulation are incorporated to evaluate the robustness of governance scoring under uncertain project conditions. This computational structure enables systematic benchmarking of governance performance across infrastructure projects with varying risk profiles.