International construction projects inherently involve risks that fundamentally differ from those encountered in domestic projects due to variations in legal, political, and economic environments. Given the confidentiality and data limitations of such projects, this study introduces an innovative approach by conceptualizing expert judgment as sensor-like data to compensate for the absence of direct measurements. Specifically, Expert-Driven Experience and Intuition-Based Sensing Data are treated as qualitative proxies for sensor input, enabling the extraction of structured knowledge from tacit insights. These data are integrated with Partial Least Squares Structural Equation Modeling to quantitatively analyze the organic interactions and causal relationships among country-specific risk factors and their impact on cost overruns. The proposed framework demonstrates enhanced reliability and robustness, even in data-constrained contexts. The empirical analysis reveals that ‘Environmental’ risk exerts the most significant direct effect on cost overruns (standardized path coefficient = 0.360), whereas ‘Legal’ risk has the smallest indirect impact (0.067). The results emphasize the importance of economic and environmental risks, offering a foundation for developing targeted risk management strategies to ensure the financial stability of construction projects.