Risk Assessment of Infrastructure System of Systems with Precursor Analysis

Physical infrastructure systems are commonly composed of interconnected and interdependent subsystems, which in their essence constitute system of systems (S-o-S). System owners and policy researchers need tools to foresee potential emergent forced changes and to understand their impact so that effective risk management strategies can be developed. We develop a systemic framework for precursor analysis to support the design of an effective and efficient precursor monitoring and decision support system with the ability to (i) identify and prioritize indicators of evolving risks of system failure; and (ii) evaluate uncertainties in precursor analysis to support informed and rational decision making. This integrated precursor analysis framework is comprised of three processes: precursor identification, prioritization, and evaluation. We use an example of a highway bridge S-o-S to demonstrate the theories and methodologies of the framework. Bridge maintenance processes involve many interconnected and interdependent functional subsystems and decision-making entities and bridge failure can have broad social and economic consequences. The precursor analysis framework, which constitutes an essential part of risk analysis, examines the impact of various bridge inspection and maintenance scenarios. It enables policy researchers and analysts who are seeking a risk perspective on bridge infrastructure in a policy setting to develop more risk informed policies and create guidelines to efficiently allocate limited risk management resources and mitigate severe consequences resulting from bridge failures.

[1]  R. Hastie,et al.  Hindsight: Biased judgments of past events after the outcomes are known. , 1990 .

[2]  Yacov Y. Haimes,et al.  Public policy implications of harmonizing engineering technology with socio-economic modeling: Application to transportation infrastructure management , 2013 .

[3]  George Apostolakis,et al.  Incorporating organizational factors into risk assessment through the analysis of work processes , 1994 .

[4]  Nancy G. Leveson,et al.  A new accident model for engineering safer systems , 2004 .

[5]  B. Fischhoff,et al.  Hindsight is not equal to foresight: The effect of outcome knowledge on judgment under uncertainty. , 1975 .

[6]  Keith W. Hipel,et al.  System of Systems Engineering and Risk Management of Extreme Events: Concepts and Case Study , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[7]  Yacov Y. Haimes Models for risk management of systems of systems , 2008, Int. J. Syst. Syst. Eng..

[8]  Y. Haimes Modeling complex systems of systems with Phantom System Models , 2012, Syst. Eng..

[9]  D M Murphy,et al.  The SAM framework: modeling the effects of management factors on human behavior in risk analysis. , 1996, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  J Reason,et al.  The contribution of latent human failures to the breakdown of complex systems. , 1990, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  George Apostolakis,et al.  The work process analysis model (WPAM) , 1994 .

[12]  萩原 伸次郎 B・ブルーストーン、B・ハリソン著『アメリカの脱産業化―工場閉鎖、地域放棄、基礎産業破壊』 , 1984 .

[13]  Benjamin A. Graybeal,et al.  Routine Highway Bridge Inspection Condition Documentation Accuracy and Reliability , 2004 .

[14]  Yacov Y. Haimes,et al.  Hierarchical Holographic Modeling , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Jens Rasmussen,et al.  Risk management in a dynamic society: a modelling problem , 1997 .