Research – Socio-Technical Risk Analysis (SoTeRiA)

Evolution of Probabilistic Risk Assessment (PRA)

The SoTeRiA Research Laboratory is at the forefront of advancing Probabilistic Risk Assessment (PRA). SoTeRiA’s four key areas of scientific contributions to PRA include: Area (I) spatiotemporal coupling of physical failure mechanisms with human/social performance and generating Integrated PRA  (I-PRA); Area (II) incorporating artificial intelligence (AI), machine learning (ML), digital twins (DT), and virtual reality (VR) into PRA; Area (III) Probabilistic Validation; Area (IV) analyzing interdependencies among safety risk, financial risk and regulatory risk in support of risk-informed decision making.

Grounded in the underlying science, SoTeRiA employs theory-based causal modeling to explicitly model the underlying physical and social failure mechanisms contributing to risk. The use of artificial intelligence and machine learning in the Integrated-PRA approach is opening new opportunities for risk assessment of complex socio-technical systems. By integrating physical and social causes of failure into a cohesive modeling framework, the evolution of PRA is a critical step in moving toward a new era, void of catastrophic technological accidents, where industry-academia projects are leveraged to raise social responsibility for the protection of workers, the public, and the environment.

Area I. Spatiotemporal coupling of physical failure mechanisms with human/social performance and generating Integrated PRA (I-PRA)

The existing simulation of human performance in PRA (i.e., existing simulation-based Human Reliability Analysis [HRA]) is temporal but lacks spatial dimension. Although temporal HRA is adequate for modeling human error inside the Main Control Rooms of NPPs, it has limitations for modeling the human-physics interactions in External Control Room (Ex-CR) scenarios where spatial analysis is necessary. Our research was the first where the human performance model explicitly incorporates space (in addition to time), and the human-physics coupling transfers spatiotemporal information in a bidirectional way (Bui et al., 2019; Bui et al., 2020).

An Integrated PRA (I-PRA) methodology was developed (Sakurahara et al., 2018a; Sakurahara et al., 2018b; Bui et al., 2019) to incorporate the spatiotemporal human-physics coupling into the PRA model, creating a viable dynamic-static PRA platform. Several types of spatiotemporal human-physics couplings were created or enhanced for PRA:

Coupling of fire crew performance and fire physics (Sakurahara et al., 2020; Bui et al., 2020) has been enhanced by: (i) including multi-compartment fire scenario analysis and validating human-physics coupling using model uncertainty analysis and fire crew experimental evaluation, in collaboration with the Illinois Fire Service Institute (Albati et al., 2024a). This study is the first to validate a simulation-based Ex-CR human performance model utilizing a controlled experiment; and (ii) analyzing the performance of an Artificial Intelligence (AI)-based automated fire-watch system interacting with human operators and physics of fire propagation at NPPs (Khalid et al., 2023; Khalid et al., 2025a). This research is the first to incorporate the “AI-based automation-human-physics” coupling into the PRA of NPPs. In this study, we also pioneered the incorporation of Virtual Reality (VR) into PRA research, where VR is used for augmenting the simulation of fire scenarios and provides the high-quality data (fire images) essential for training and validating the AI-based automated fire-watch model. More information is available on the Fire Risk Analysis and Automation Trustworthiness and Transparency pages.
Coupling of maintenance team performance and component degradation has been pioneered by our team, integrating an HRA-based maintenance work process model with a physical degradation simulation (e.g., finite element analysis of thermal fatigue), utilizing a multi-phase Markov process model to account for time-based in-service inspections of piping systems in NPPs (Beal et al., 2025). Our approach was benchmarked against other data-driven and probabilistic fracture mechanics approaches in international studies reported in IAEA TECDOC-1988 (Heckmann et al., 2022). The benchmark results highlight the values of our approach, especially when historical data is limited, and show that, due to higher resolutions of physical parameters and maintenance factors, this approach helps generate a more realistic piping failure rate estimation reflecting design- and plant-specific conditions. In our DOE project, we have collaborated with Constellation to expand the maintenance-physics coupling methodology for advancing common cause failure analysis of existing plants and advanced reactors (Sakurahara et al., 2019; Albati et al., 2025b). In ongoing NRC research, we are investigating the integration of a digital twin model with the coupled physics-maintenance model to support the risk-informed regulation for digital twin applications in both existing and advanced NPPs (Fisher et al., 2023; Beal et al., 2026a). More information on SoTeRiA work on passive component reliability is available on the Risk Methodologies for Advanced Reactors and Spatiotemporal Coupling of Physics of Degradation pages (Cheng et al., 2022).
Coupling of natural hazards and emergency team performance has been created by developing an Integrated HRA (I-HRA) approach that can analyze Ex-CR human actions interacting with hardware system states, natural hazards, and surrounding environments (Albati et al., 2024b; Albati et al., 2025a). I-HRA takes advantage of the spatiotemporal resolution provided by agent-based modeling (ABM) and combines it with the cognitive and behavioral science bases of classical HRA techniques. I-HRA has been applied to risk-informed analysis of operator actions during beyond-design-basis external flooding. In a parallel project, our research laboratory has advanced the I-PRA methodology for multi-hazard scenarios involving external hazards to enhance the realism of external hazard PRAs and inform the design and operational strategies for NPPs facing multiple hazard conditions (e.g., seismically induced flooding scenarios) (Komaki, 2023). Ongoing research integrates I-HRA with the multi-hazard I-PRA.

Area II. Incorporating artificial intelligence, machine learning, digital twins, and virtual reality into PRA

Conventional Probabilistic Risk Assessment (PRA) and Human Reliability Analysis (HRA) methods were developed for analog systems and rely mainly on structured data. Two developments motivate this research area: (i) nuclear organizations generate large volumes of unstructured operational data that contain evidence of human and organizational performance; and (ii) nuclear power plants (NPPs) are adopting Artificial Intelligence (AI)-driven technologies, such as Digital Twins (DTs), that change how plant personnel monitor equipment, make decisions, and perform maintenance. Research in Area II incorporates AI, Machine Learning (ML), DTs, and Virtual Reality (VR) into PRA to (a) improve the realism of risk models and (b) model how these technologies, and the personnel who use them, affect plant risk.

A Data-Theoretic methodology was created to quantify organizational factors in socio-technical risk analysis from the unstructured records that organizations already produce, such as corrective action programs, root cause analyses, and inspection data (Pence et al., 2019). The methodology has two modules: DT-BASE, which builds theory-based causal models of organizational factors grounded in the Socio-Technical Risk Analysis (SoTeRiA) framework (Mohaghegh and Mosleh, 2009), with a semi-automated baseline quantification; and DT-SITE, which extracts evidence from organizational event databases, guided by that causal structure, and updates the baseline through Bayesian updating. Supervised ML text mining was then applied to the U.S. Nuclear Regulatory Commission (NRC) Licensee Event Report database, with the selection of ML features guided by the theoretical causal structure rather than by the data alone, which improves the treatment of causality and avoids misleading results from purely data-driven ML (Pence et al., 2020). Our team was also the first to propose a formal uncertainty quantification process for ML-based data analytics used in PRA, quantifying the uncertainties associated with the dataset and with the ML model structure in a support vector machine application for measuring organizational factors (Yang et al., 2022). A categorical review of theories, predictive modeling techniques, and data analytics for incorporating organizational factors into PRA was also published (Pence and Mohaghegh, 2020).
Human-DT interactions and their impact on plant safety risk were first modeled by our team in an NRC-sponsored project, with a focus on DT-supported maintenance (Fisher et al., 2023; Morasca et al., 2023). Building on this, an Integrated PRA (I-PRA) methodological framework was developed for DT-enabled plants, coupling a high-fidelity simulated physical twin, a physical degradation-based DT, and a human model of operations and maintenance decision-making and HRA-based maintenance performance, while propagating aleatory and epistemic uncertainties (Beal et al., 2026a; Beal, 2025). Its novel features are (i) bidirectional, dynamic interactions between the human model and the physical twin, capturing the effect of repair and replacement on component state, and (ii) bidirectional integration of the coupled human-physical twin-DT system with PRA. The framework produces forward-looking plant risk estimates from DT predictions to inform both immediate maintenance actions and longer-term risk-informed, performance-based decisions, and was demonstrated for a piping component subject to stress corrosion cracking using the xLPR probabilistic fracture mechanics code.
AI-driven automation is the focus of an ongoing National Science Foundation (NSF) Mid-Career Advancement project. Using AI-driven predictive maintenance in NPPs as the context, the project theorizes and models the interactions among human operators, AI-driven automation, the physics of relevant phenomena, organizational and managerial factors, and regulatory considerations, and examines how risk-informed evidence can build the trust of operators, managers, and regulators in these technologies (Khalid et al., 2025b). In the DOE-sponsored automation project described under Area I, VR-generated, physics-informed fire images were used to train and validate an AI-based automated fire-watch system whose performance is then evaluated within the plant PRA (Khalid et al., 2023; Khalid et al., 2025a); the trustworthiness and transparency evaluation of such AI-driven automation is described under Area III.

Area III. Probabilistic Validation

The SoTeRiA Research Laboratory created Probabilistic Validation (PV), a methodology for validating the modeling and simulation (M&S) used in support of Probabilistic Risk Assessment (PRA), particularly where the scarcity of empirical validation data makes common empirical validation approaches challenging (Bui et al., 2023). In PV, the validity of a simulation prediction used for PRA is determined by the magnitude of the epistemic uncertainty, that is, the degree of confidence, in that prediction. PV integrates five characteristics: multi-level, multi-model-form validation analysis; separation of aleatory and epistemic uncertainty, with systematic bias distinguished from statistical variability; risk-informed acceptability criteria applied through a predefined guideline; uncertainty analysis combined with a two-layer sensitivity analysis; and a theoretical causal framework supporting the completeness of uncertainty sources. A computational platform was developed and applied to evaluate the validity of a fire simulation model used in Fire PRA of nuclear power plants (Bui et al., 2024a).

PV has since been extended to the validation of coupled maintenance and physical degradation models for piping reliability (Beal et al., 2026b), to External Control Room human performance simulation supported by controlled experiments (Albati et al., 2024a), to probabilistic physics-of-failure models with finite element integration (Cheng et al., 2026), to the coupled physics-of-failure and maintenance models used in simulation-informed dependency analysis (Albati et al., 2025b), and, in a DOE-sponsored project, to the trustworthiness evaluation of AI-driven automation in existing plants and advanced reactors (Khalid et al., 2023; Khalid et al., 2025a), alongside new methods for evaluating automation transparency (Khalid et al., 2026). An overview of the methodology, its status, and future research directions was presented at PSAM 17 (Bui et al., 2024b).

Full details and publications are available on the Probabilistic Validation page.

Area IV. Analyzing interdependencies among safety risk, financial risk, and regulatory risk in support of risk-informed decision-making

Our team was the first in integrating safety risk with financial risk, considering M&S of their underlying phenomena, to advance risk-informed decision-making. This research spanned three phases of the NPP life cycle: design, licensing, and operation and maintenance (O&M). In supporting the design phase, an Integrated Risk-Informed Design (I-RID) methodological framework (Schumock et al., 2020) was developed for generating and evaluating design options for NPP structures and for providing plant decision-makers with a quantitative comparison among design options based on safety risk and financial risk. In this study, M&S of underlying failure mechanisms are conducted using finite element analysis, connected to the plant PRA using structural reliability techniques, and applied for the design of FLEX equipment storage buildings of NPPs subjected to a potential tornado impact. For the O&M phase, we conducted a thorough review and analysis of literature on the interconnections of safety and financial performance (published in a review journal article, Farshadmanesh et al., 2024) and found that most of the existing approaches use correlation-based methods, having two limitations: (i) information on the underlying mechanisms is not provided and, hence, it offers limited support for mitigating risk; and (ii) the validity of correlation analysis can be questionable due to omitted variables and overlooked characteristics of the underlying mechanisms such as time dependency and invisible costs. Therefore, we have created an Integrated Enterprise Risk Management (I-ERM) methodological framework (Beal et al., 2024; Cheng et al., 2022; Mohaghegh et al., 2021), where underlying O&M processes such as physical degradation mechanisms and maintenance work processes that contribute to both safety and financial performance are simulated and incorporated into an integrated platform that helps estimate safety risk and financial performance considering their dependencies. I-ERM aids NPP decision-makers in identifying O&M strategies that can help reduce O&M costs, mitigate plant shut-downs and loss of power, and maintain (or improve) safety. Our team also developed a methodology to evaluate the monetary benefit of PRA by modeling the net value of risk-informed applications at NPPs (Pence et al., 2018). To support the licensing phase, our team has developed a decision-making methodology (Alkhatib et al., 2025a; Alkhatib et al., 2025b) to formalize the selection of the degree of realisms for M&S utilized in support of PRA. This offers a substitute method for the existing industry trial-and-error approach and helps balance the desired degree of realisms for the estimated risk and the cost of analysis required for conducting M&S in support of PRA.

Another research topic under Research Area (IV) is risk-informed regulation and licensing that considers both technical and legal analyses. Since the ADVANCE Act of 2024, the Supreme Court decisions in Loper Bright v. Raimondo and Trump v. Slaughter, and recent Presidential actions, United States nuclear regulation has entered a period of substantial legal and technological uncertainty. In an NRC-sponsored project with the University of Illinois College of Law, the team analyzes what “efficient” licensing means under the ADVANCE Act and how it can be reconciled with reasonable assurance of adequate protection (Joslin et al., 2025a; Joslin et al., 2025b; Joslin et al., 2026), and contributes these analyses to the rulemaking record through public comments to the NRC and the Department of Energy, including comments on the proposed Part 53 and Part 57 rules, the Sunset Rule, and the DOE categorical exclusion for advanced reactors. Full details are available on the Advanced Reactor Regulatory Challenges page.

The SoTeRiA Research Laboratory’s key research impacts include (1) scholarly contributions to risk analysis of complex socio-technical systems; (2) bridging foundational risk analysis research with industry and regulatory challenges; (3) strong leadership in risk analysis conferences; (4) advancing risk-informed solutions for new technologies; and (5) promoting international safety and security through risk analysis.

PRA Applications for Industry

There are many diverse types of risk assessments since they are applied in different industries and for different goals, often with the commonality of the incorporation of uncertainties. SoTeRiA Research Laboratory is focused on the systematic safety risk of high-consequence industries (e.g., nuclear, space, aviation, healthcare, chemical processing, transportation, oil and gas, etc.). One of the promising methodologies to estimate the safety risk of high-consequence industries is Probabilistic Risk Assessment (PRA). High-consequence industries are associated with complex socio-technical systems dealing with dependencies and uncertainties, where accidents are the result of potential weaknesses that dynamically interact with each other. Due to these interactions, infrequent and rare system-level accidents with high consequences (e.g., Nuclear Power Plant accidents) can occur, therefore, developing risk analysis, by relying solely on statistical data is not adequate for understanding, predicting, and preventing these rare events. To estimate systematic risk for high-consequence industries, in 1975, the PRA methodology was developed in academia (Nuclear Engineering Department at the Massachusetts Institute of Technology) in collaboration with industry.

PRA is now one of the key pillars of the Risk-Informed regulatory framework for the Nuclear Regulatory Commission (NRC). In addition to the NRC, a growing number of government agencies in the U.S., including the Department of Energy (DOE), the Federal Aviation Administration (FAA), the National Aeronautics and Space Administration (NASA), the Department of Defense (DOD), the Environmental Protection Agency (EPA), and the Food and Drug Administration (FDA) have begun to utilize PRA for policy setting and decision-making.

Selected Publications

All publications are listed on the Journal and Conference Papers page.

Area I

H. Bui, T. Sakurahara, J. Pence, S. Reihani, E. Kee, Z. Mohaghegh, “An algorithm for enhancing spatiotemporal resolution of probabilistic risk assessment to address emergent safety concerns in nuclear power plants,” Reliability Engineering & System Safety, 185, 405-428, 2019. doi:10.1016/j.ress.2019.01.004

H. Bui, T. Sakurahara, S. Reihani, E. Kee, Z. Mohaghegh, “Spatiotemporal integration of an agent-based first responder performance model with a fire hazard propagation model for probabilistic risk assessment of nuclear power plants,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(1), 011011, 2020. doi:10.1115/1.4044793

T. Sakurahara, Z. Mohaghegh, S. Reihani, E. Kee, M. Brandyberry, S. Rodgers, “An integrated methodology for spatio-temporal incorporation of underlying failure mechanisms into fire probabilistic risk assessment of nuclear power plants,” Reliability Engineering & System Safety, 169, 242-257, 2018. doi:10.1016/j.ress.2017.09.001

T. Sakurahara, Z. Mohaghegh, S. Reihani, E. Kee, “Methodological and practical comparison of integrated probabilistic risk assessment (I-PRA) with the existing fire PRA of nuclear power plants,” Nuclear Technology, 204(3), 354-377, 2018. doi:10.1080/00295450.2018.1486159

T. Sakurahara, Z. Mohaghegh, E. Kee, “Human reliability analysis (HRA)-based method for manual fire suppression analysis in an integrated probabilistic risk assessment,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(1), 011010, 2020. doi:10.1115/1.4044792

M. Albati, T. Sakurahara, S. Reihani, E. Kee, Z. Mohaghegh, “Uncertainty-based validation methodology and experimental analysis for External Control Room human performance simulation: Application to fire probabilistic risk assessment of nuclear power plants,” Nuclear Science and Engineering, 2024. doi:10.1080/00295639.2024.2366735

M. H. Khalid, M. I. Ahmed, S. Roy, H. Bui, S. Reihani, A. Al Rashdan, Z. Mohaghegh, “Evaluation of automation trustworthiness in nuclear power plants: A risk-informed approach using probabilistic validation and integrated probabilistic risk assessment,” PSAM Topical Meeting on AI and Risk Analysis, Virtual, October 23-25, 2023. https://www.ideals.illinois.edu/items/129042

M. H. Khalid, H. Bui, A. Al Rashdan, S. Reihani, Z. Mohaghegh, “Automation trustworthiness and transparency in nuclear power plants: Definitions, methodologies, and case studies,” Proceedings of the 19th International Conference on Probabilistic Safety Assessment and Analysis (PSA 2025), American Nuclear Society, Chicago, IL, 2025.

J. Beal, S. Reihani, T. Sakurahara, E. Kee, Z. Mohaghegh, “Modeling nuclear power plant piping reliability by coupling human reliability analysis-based maintenance and physical degradation models,” Reliability Engineering & System Safety, 255, 110655, 2025. https://www.sciencedirect.com/science/article/pii/S0951832024007269

K. Heckmann, D.-H. Ahn, J. Beal, W.-C. Cheng, X. Duan, T. Jevremovic, E. Kee, Z. Mohaghegh, B. Lydell, S. Reihani, T. Sakurahara, M. Wang, “Estimation of pipe failure frequencies in the absence of operational experience data: A pilot study,” Nuclear Engineering and Design, 398, 2022. https://www.sciencedirect.com/science/article/pii/S0029549322003417

T. Sakurahara, G. Schumock, S. Reihani, E. Kee, Z. Mohaghegh, “Simulation-informed probabilistic methodology for common cause failure analysis,” Reliability Engineering & System Safety, 185, 84-99, 2019. doi:10.1016/j.ress.2018.12.007

M. Albati, M. I. Ahmed, H. Bui, S. Reihani, R. Johnson, S. Loyd, H. Liao, J. Anderson, Z. Mohaghegh, “Simulation-informed dependency treatment to enhance operational flexibility, avoid production loss, and maintain safety of operating plants and advanced reactors,” Proceedings of the 19th International Conference on Probabilistic Safety Assessment and Analysis (PSA 2025), American Nuclear Society, Chicago, IL, 1002-1011, 2025.

M. Albati, H. Bui, S. Reihani, Z. Mohaghegh, “Development of a spatiotemporal Integrated Human Reliability Analysis (I-HRA) method,” PSAM 17 and ASRAM 2024, Sendai, Japan, October 7-11, 2024.

M. Albati, H. Bui, S. Reihani, V. Yadav, D. Mandelli, Z. Mohaghegh, “Integrated human reliability analysis methodology for External Control Room emergency response scenarios: Application to modeling FLEX human actions in nuclear power plants,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 11(4), 041208, 2025. doi:10.1115/1.4068539

J. Komaki, “Multi-hazard integrated probabilistic risk assessment framework for seismically induced internal flooding in nuclear power plants,” M.S. Thesis, Nuclear, Plasma, and Radiological Engineering, University of Illinois Urbana-Champaign, 2023. https://hdl.handle.net/2142/122272

Area II

R. Fisher, S. Fargusson, J. Mitstifer, J. Beal, H. Bui, P. Farshadmanesh, T. Sakurahara, S. Reihani, E. Kee, Z. Mohaghegh, “On modeling human-digital twin interactions and their safety risk impact in nuclear power plants,” PSAM Topical Meeting on AI and Risk Analysis, Virtual, October 23-25, 2023. https://www.ideals.illinois.edu/items/129045

J. Beal, S. Fargusson, H. Bui, S. Reihani, M. H. Khalid, Z. Mohaghegh, “Developing temporal coupling of human performance, physical twin, and digital twin models for probabilistic risk assessment in nuclear power plants,” Nuclear Engineering and Design, 447, 114620, 2026. doi:10.1016/j.nucengdes.2025.114620

J. Pence, T. Sakurahara, X. Zhu, Z. Mohaghegh, M. Ertem, C. Ostroff, E. Kee, “Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis,” Reliability Engineering & System Safety, 185, 240-260, 2019. doi:10.1016/j.ress.2018.12.020

Z. Mohaghegh, A. Mosleh, “Incorporating organizational factors into probabilistic risk assessment of complex socio-technical systems: Principles and theoretical foundations,” Safety Science, 47(8), 1139-1158, 2009. doi:10.1016/j.ssci.2008.12.008

J. Pence, P. Farshadmanesh, J. Kim, C. Blake, Z. Mohaghegh, “Data-theoretic approach for socio-technical risk analysis: Text mining licensee event reports of U.S. nuclear power plants,” Safety Science, 124, 104574, 2020. doi:10.1016/j.ssci.2019.104574

J. Yang, J. Kim, P. Farshadmanesh, T. Sakurahara, S. Reihani, C. Blake, Z. Mohaghegh, “Uncertainty analysis on support vector machine for measuring organizational factors in probabilistic risk assessment of nuclear power plants,” Progress in Nuclear Energy, 153, 104411, 2022. doi:10.1016/j.pnucene.2022.104411

J. Pence, Z. Mohaghegh, “A discourse on the incorporation of organizational factors into probabilistic risk assessment: Key questions and categorical review,” Risk Analysis, 40(6), 1183-1211, 2020. doi:10.1111/risa.13468

M. Morasca, S. Fargusson, B. Scott, H. Bui, P. Farshadmanesh, Z. Mohaghegh, “The incorporation of digital twin models into maintenance operations in nuclear power plants,” American Nuclear Society Student Conference, 2023. https://experts.illinois.edu/en/publications/the-incorporation-of-digital-twin-models-into-maintenance-operati

J. Beal, “Temporal coupling of maintenance human performance, physical degradation, and digital twin models for probabilistic risk assessment in nuclear power plants,” Ph.D. Dissertation, Nuclear, Plasma, and Radiological Engineering, University of Illinois Urbana-Champaign, 2025. https://www.ideals.illinois.edu/items/137168

M. H. Khalid, J. Beal, H. Bui, S. Reihani, Z. Mohaghegh, “Interactions of human performance, physical failure mechanisms, and organizational factors in socio-technical risk analysis of complex technological systems,” Proceedings of the 19th International Conference on Probabilistic Safety Assessment and Analysis (PSA 2025), American Nuclear Society, Chicago, IL, 2025.

Area III

H. Bui, T. Sakurahara, S. Reihani, E. Kee, Z. Mohaghegh, “Probabilistic validation: Theoretical foundation and methodological platform,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 9(2), 2023. doi:10.1115/1.4056883

H. Bui, T. Sakurahara, S. Reihani, E. Kee, Z. Mohaghegh, “Probabilistic validation: Computational platform and application to fire probabilistic risk assessment of nuclear power plants,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 10(2), 021202, 2024. doi:10.1115/1.4063071

J. Beal, T. Sakurahara, S. Reihani, H. Bui, Z. Mohaghegh, “Uncertainty-based validation of maintenance-physical degradation coupled models in the absence of maintenance performance data: Application for nuclear power plant piping reliability analysis,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 12(1), 011204, 2026. doi:10.1115/1.4069525

W.-C. Cheng, T. Sakurahara, S. Reihani, Z. Mohaghegh, “Evaluating the validity of a probabilistic physics-of-failure model with finite element integration for nuclear power plant piping reliability,” Nuclear Technology, 1-27, 2026. doi:10.1080/00295450.2026.2683769

M. H. Khalid, H. Bui, A. Al Rashdan, R. L. Boring, Z. Mohaghegh, “Automation transparency: A literature review, methodology development, and application to an AI-driven anomaly detection system in nuclear power plants,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2026. doi:10.1177/1748006X261440735

H. Bui, M. H. Khalid, T. Sakurahara, S. Reihani, Z. Mohaghegh, “Probabilistic validation methodology for probabilistic risk assessment: Overview, status, and future research directions,” PSAM 17 and ASRAM 2024, Sendai, Japan, October 7-11, 2024.

Area IV

W.-C. Cheng, J. Beal, T. Sakurahara, S. Reihani, E. Kee, Z. Mohaghegh, “Modeling interconnections of safety and financial performance of nuclear power plants, Part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance,” Progress in Nuclear Energy, 153, 104382, 2022. doi:10.1016/j.pnucene.2022.104382

G. Schumock, S. Zhang, P. Farshadmanesh, J. G. Owens, N. Kasza, J. Stearns, T. Sakurahara, Z. Mohaghegh, “Integrated Risk-Informed Design (I-RID) methodological framework and computational application for FLEX equipment storage buildings of nuclear power plants,” Progress in Nuclear Energy, 120, 103186, 2020. doi:10.1016/j.pnucene.2019.103186

P. Farshadmanesh, J. Beal, T. Sakurahara, S. Reihani, E. Kee, A. Rowell, F. Yilmaz, Z. Mohaghegh, “Modeling interconnections of safety and financial performance of nuclear power plants, Part 1: Categorical review and theoretical bases,” Progress in Nuclear Energy, 171, 105123, 2024. https://www.sciencedirect.com/science/article/pii/S0149197024000738

J. Beal, T. Sakurahara, P. Farshadmanesh, S. Reihani, E. Kee, A. Rowell, F. Yilmaz, Z. Mohaghegh, “Modeling interconnections of safety and financial performance of nuclear power plants, Part 2: Methodological developments and case study,” Progress in Nuclear Energy, 171, 105100, 2024. doi:10.1016/j.pnucene.2024.105100

Z. Mohaghegh, T. Sakurahara, J. Beal, W.-C. Cheng, P. Farshadmanesh, S. Reihani, E. Kee, A. Rowell, F. Yilmaz, “Systematic Enterprise Risk Management by Integrating the RISMC Toolkit and Cost-Benefit Analysis (Final Report),” U.S. Department of Energy, 2021. doi:10.2172/1817917

J. Pence, M. Abolhelm, Z. Mohaghegh, S. Reihani, M. Ertem, E. Kee, “Methodology to evaluate the monetary benefit of probabilistic risk assessment by modeling the net value of risk-informed applications at nuclear power plants,” Reliability Engineering & System Safety, 175, 171-182, 2018. https://www.sciencedirect.com/science/article/pii/S0951832018302540

S. Alkhatib, T. Sakurahara, S. Reihani, Z. Mohaghegh, “A systematic decision-making methodology to formalize the selection of degree of realism in screening analysis of probabilistic risk assessment,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2025. doi:10.1177/1748006X251334481

S. Alkhatib, T. Sakurahara, S. Reihani, Z. Mohaghegh, “A novel methodology to scientifically justify the formulation of modeling assumptions in screening analysis of probabilistic risk assessment (PRA),” Annals of Nuclear Energy, 214, 111237, 2025. https://www.sciencedirect.com/science/article/pii/S0306454925000544

A. Rowell, Z. Mohaghegh, H. Bui, G. Joslin, “Part 53: As proposed, Part 53 fails to address important legal changes to the NRC’s authority,” public comment on NRC Docket NRC-2019-0062, 2025. https://www.regulations.gov/comment/NRC-2019-0062-0462

G. Joslin, A. Rowell, Z. Mohaghegh, comments on the NRC proposed rule “Licensing Requirements for Microreactors and Other Reactors With Comparable Risk Profiles,” NRC Docket NRC-2025-0379, 2026. https://www.regulations.gov/comment/NRC-2025-0379-0126

A. Rowell, G. Joslin, S. Reihani, Z. Mohaghegh, adverse comments on the NRC “Sunset Rule,” 90 Fed. Reg. 55621 and 90 Fed. Reg. 55699, 2026. https://www.regulations.gov/comment/NRC-2025-0479-0015

G. Joslin, A. Rowell, S. Reihani, Z. Mohaghegh, public comments on the Department of Energy “Categorical Exclusion for Advanced Nuclear Reactors,” DOE Docket DOE-HQ-2025-0405, 2026. https://www.regulations.gov/comment/DOE-HQ-2025-0405-0187

G. Joslin, A. Rowell, H. Bui, S. Reihani, Z. Mohaghegh, “Understanding uncertainties in the regulation of advanced nuclear power reactors: Legal and technological revolutions,” Proceedings of the 19th International Conference on Probabilistic Safety Assessment and Analysis (PSA 2025), American Nuclear Society, Chicago, IL, 2025.

G. Joslin, A. Rowell, H. Bui, J. Valentino, Z. Che, S. Reihani, Z. Mohaghegh, “Nuclear power’s new rule book: Managing uncertainty in efficiency, safety, and independence,” ANS Nuclear Newswire, November 21, 2025. https://www.ans.org/news/2025-11-21/article-7572/nuclear-powers-new-rule-book-managing-uncertainty-in-efficiency-safety-and-independence/

G. Joslin, A. Rowell, S. Reihani, Z. Mohaghegh, “Don’t scrap ALARA: Modernize it,” ANS Nuclear Newswire, July 7, 2026. https://www.ans.org/news/2026-07-07/article-8164/dont-scrap-alaramodernize-it/