The Synergy and Accumulation Model for Analysis (SAMA): A Novel Approach to Transforming Risk Analysis in Construction with a Focus on the Deepwater Horizon Disaster Case

Risk analysis is critical for preventing catastrophic failures in complex systems, as exemplified by the Deepwater Horizon disaster, a stark reminder of systemic vulnerabilities in offshore drilling operations, where inadequate appraisal of overlapping failures led to severe environmental and human losses. This study addresses the absence of a predictive framework capable of capturing cumulative risk interactions across both time stages and defensive layers. To fill this gap, and by drawing on prior frameworks such as the Swiss Cheese Model (SCM) and the Risk Matrix (RM), as well as critiques of their limitations, we introduce the Synergy and Accumulation Model for Analysis (SAMA). This model defines project life-cycle stages and risk recipients, characterizes each risk by four parameters (the focus of impact, suddenness, frequency, and effectiveness), and calculates horizontal (RF_h) and vertical (RF_v) risk factors. We applied SAMA to fifteen identified failure modes of the Macondo well, categorizing them across two time stages (operational and construction) and four defensive layers. Horizontal analysis revealed that the regulatory-laws layer accumulated the highest risk factors, RFh_1laws = 129.25 during the operational stage and RFh_2laws = 95.98 during the construction stage. Vertical analysis showed that the safety objective experienced the greatest systemic vulnerability, with RF_vsafety = 135.8 across ten overlapping risks, followed by the quality objective at RF_vquality = 128.39. These findings demonstrate SAMA’s enhanced capability to identify critical collapse paths often overlooked by conventional models. For researchers, SAMA offers a transparent, parameter-driven methodology applicable across engineering and construction domains. For industry stakeholders, regulators, project managers, and safety engineers, this model provides actionable insights to prioritize resource allocation and strengthen specific defensive layers, thereby enhancing both preventive planning and resilience against future disasters.