Meta-Analysis of Risk-Based Inspection Strategies for Improving Reliability and Safety Across Critical Energy Infrastructure Systems

Abstract

This study investigates how integrated risk-based inspection strategies contribute to reliability and safety performance across critical energy infrastructure systems by combining risk prioritization, smart inspection technologies, sustainable resource allocation, and maintenance decision integration within a unified quantitative framework. The research addresses the problem that conventional periodic inspection can allocate resources inefficiently, overlook differences in equipment criticality and degradation, and leave valuable condition information disconnected from maintenance decisions, while smart monitoring and sustainable inspection practices are often implemented separately. The purpose of the study is to determine the individual and combined effects of Risk Prioritization and Failure-Criticality Assessment, Smart Sensor and Predictive Inspection Integration, Sustainable Inspection Resource Optimization, and Inspection-to-Maintenance Decision Integration on Critical Energy Infrastructure Reliability and Safety Performance. A quantitative, cross-sectional, case-study-based design was employed across selected enterprise-level critical energy infrastructure cases, including oil, gas, pipelines, power generation, refineries, petrochemical facilities, offshore and LNG operations, renewable energy, and energy-storage environments. The purposive sample comprised inspection and asset-integrity engineers, reliability and maintenance engineers, operations and plant engineers, safety and risk professionals, condition-monitoring and digital specialists, and managers or supervisors. Of 300 questionnaires distributed, 279 were returned and 268 valid responses were retained, yielding an effective response rate of 89.3%. CEIRSP recorded M = 4.21, SD = 0.50, followed by RPFCA, M = 4.18, SD = 0.54; IMDI, M = 4.17, SD = 0.55; SSPII, M = 4.15, SD = 0.57; and SIRO, M = 4.10, SD = 0.59. Correlations with CEIRSP were strongest for IMDI, r = .76, followed by RPFCA, r = .74, SSPII, r = .71, and SIRO, r = .66, all p < .001. The regression model explained 70.3% of CEIRSP variance, R² = .703, adjusted R² = .698, F (4,263) = 155.63, p < .001. IMDI emerged as the strongest predictor, β = .292, followed by RPFCA, β = .264, SSPII, β = .236, and SIRO, β = .179. These findings support H1 through H4 and imply that infrastructure reliability and safety improve when inspection information is systematically connected with risk criticality, predictive monitoring, efficient resource deployment, and timely maintenance action.