Education

Research Description

I have conducted research at the intersection of four interdisciplinary domains: structural engineering and mechanics, energy infrastructure, construction management, and computational / data science. Presently, my group works on using AI and deep learning approaches for developing the Digital Twin technology in the areas of structural health monitoring and construction management using reality capture. Application have focused on modeling degradation due to Alkali-Silica Reactor (ASR) and Chloride diffusion in concrete structures as well as flow assisted corrosion in nuclear piping systems. We also work on developing efficient Bayesian approaches for probabilistic risk assessment (PRA) and model updating.

Publications

Computationally efficient approach for risk-informed decision making

Vaishanav, P., Bodda, S. S., & Gupta, A. (2024), PROGRESS IN NUCLEAR ENERGY, 167. https://doi.org/10.1016/j.pnucene.2023.104983

A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities

Sandhu, H. K., Bodda, S. S., & Gupta, A. (2023). [Review of , ]. ENERGIES, 16(6). https://doi.org/10.3390/en16062628

Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks

Sandhu, H. K., Bodda, S. S., Sauers, S., & Gupta, A. (2023), JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME, 145(4). https://doi.org/10.1115/1.4062462

Performance-Based Characterization and Quantification of Uncertainty in Damage Plasticity Model for Seismic Fragility Assessment of Concrete Structures

Lee, S., Gupta, A., & Proestos, G. T. (2023), ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 9(1). https://doi.org/10.1061/AJRUA6.RUENG-913

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Sandhu, H. K., Bodda, S. S., & Gupta, A. (2023), INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING, 201. https://doi.org/10.1016/j.ijpvp.2022.104849

Rocking stiffness of electrical cabinets with tubular base in nuclear power plants

Patel, P., Gupta, A., & Kwon, Y. (2023), NUCLEAR ENGINEERING AND DESIGN, 414. https://doi.org/10.1016/j.nucengdes.2023.112603

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Nie, G.-Y., Bodda, S. S., Sandhu, H. K., Han, K., & Gupta, A. (2022), SENSORS, 22(18). https://doi.org/10.3390/s22186869

Digital Engineering for Integrated Modeling and Simulation for Building-Piping Systems Through Interoperability Solutions

Crowder, N., Lee, J., Gupta, A., Han, K., Bodda, S., & Ritter, C. (2022, May 13), NUCLEAR SCIENCE AND ENGINEERING. https://doi.org/10.1080/00295639.2022.2055705

Digital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system

Lin, L., Athe, P., Rouxelin, P., Avramova, M., Gupta, A., Youngblood, R., … Dinh, N. (2022), ANNALS OF NUCLEAR ENERGY, 166. https://doi.org/10.1016/j.anucene.2021.108715

Fission Battery transportation and siting aspects

Eidelpes, E., Bolisetti, C., Gupta, A., & Shafieezadeh, A. (2022), PROGRESS IN NUCLEAR ENERGY, 152. https://doi.org/10.1016/j.pnucene.2022.104362

View all publications via NC State Libraries

Grants

Statistical Approaches to Reduce Uncertainty in PSHA, CNEFS Enhancement Project

Electricite de France (EDF/DER)(1/01/23 - 12/31/23)

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Statistical Approaches to Reduce Uncertainty in PSHA, CNEFS Enhancement Project

Sponsor: Electricite de France (EDF/DER)

Start Date: 1/01/23

End Date: 12/31/23

Abstract

Probabilistic seismic hazard analysis (PSHA) is a key component in seismic design and performance assessment of engineering components. Accurate representations of rupture characteristics, wave propagation, and subsurface soil behavior are necessary to perform an accurate PSHA. However, in traditional PSHA, simplified empirical Ground Motion Models (GMMs) are used to estimate the ground motion levels. These GMMs neglect the inherent physical complexities in earthquake rupture and ground motion properties, such as slip heterogeneity, rupture directivity, and basin depth, among others. In addition, the paucity of ground motions recorded from large magnitude ruptures in the near-fault region, and from stable continentals regions (e.g. French Context) makes GMMs unsuitable for several contexts and applications. The objective of this research is to: 1) apply seismic ground motion methods to generate synthetic ground motions data set based on a real ground motion data set, 2) make use of synthetic ground motions to update GMMs using the Bayesian method already developed in a precedent collaboration (NCSU-EDF) and/or to develop a specific synthetic GMM (using only simulated motions) and/or to develop a hybrid GMM (using synthetic and real ground motions). The challenge of this research is to propose new alternatives to the PSHA based on simulated ground motions to complete the logic tree branches. The weight of logic tree branches of different simulated approaches and GMMs will be assigned to the validity of simulated and empirical models, with respect to the observed ground motions in regions under evaluation.

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Digital Twin Demonstration for Piping System

Electric Power Research Institute, Inc.(4/05/23 - 5/31/24)

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Digital Twin Demonstration for Piping System

Sponsor: Electric Power Research Institute, Inc.

Start Date: 4/05/23

End Date: 5/31/24

Abstract

In recent years, there is an increasing interest in the nuclear industry to focus on identifying tools, methods and opportunities to optimize construction activities and reduce costs of operation and maintenace. One of the promising tools is the use of digital twins. A digital twin is a continuously updated representation of an actual structure as it degrades. It uses the observations from maintenance and sensor data as input to continuously update the simulation and data-driven models while considering the effect of uncertainties. There is a need for more demonstrations of digital twins use cases to open the door for more nuclear industry applications. Conduct an exploratory project to demonstrate the various steps needed in the development of a digital twin on a piping system and to develop a computational framework for assessing degradation mechanisms. To achieve the high level objective, the contractor will build a piping system consists of individual pieces of pipes, elbows and flanges. The details of the piping system will be discussed with the EPRI project manager (PM).

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Probabilistic and AI/ML Approaches in Structural Engineering, CNEFS Core Project

NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(1/01/22 - 12/31/23)

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Probabilistic and AI/ML Approaches in Structural Engineering, CNEFS Core Project

Sponsor: NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)

Start Date: 1/01/22

End Date: 12/31/23

Abstract

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. This project proposes development of an Artificial Intelligence (AI) framework for the data-driven condition monitoring of nuclear structural systems and equipment, where the vibration response is governed by multiple localized modes unlike that in buildings and bridges. Hence, techniques such as signal processing and pattern recognition will be employed to extract degradation-sensitive features. Degraded locations can potentially exhibit damage such as localized yielding, cyclic fatigue, or initiation of cracking. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, this research proposes a framework, which utilizes sensor response to generate an AI database for predicting degraded locations and severity in nuclear structural systems and equipment. Degradation severity will be classified as minor, moderate, and severe, along with incorporation of uncertainty.

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Digital Engineering Solutions in Construction Engineering and Integration with Structural Design and Risk Assessment - CNEFS Core -Project

NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(1/01/22 - 12/31/23)

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Digital Engineering Solutions in Construction Engineering and Integration with Structural Design and Risk Assessment - CNEFS Core -Project

Sponsor: NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)

Start Date: 1/01/22

End Date: 12/31/23

Abstract

There have been and will continue to be rapid advances in 3D scanning and augmented/virtual reality technologies to improve construction costs and schedules, especially in the nuclear energy industry that has suffered from construction cost escalation and delays. A key challenge faced in the implementation of these modern technologies relates to changes needed in the regulatory practice and approvals. It is vital for regulatory agencies like USNRC to understand the fundamental basis of these technologies and characterize the accuracy and consistency in them. This project is aimed at conducting the research needed to support this effort which would eventually be needed in the near future. More specifically, it is proposed to: (i) characterize technical specifications and the associated parameters that govern the accuracy of virtual inspections, and (ii) identify items at a construction or manufacturing site that are conducive to virtual inspections and the items that are not.

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An Open Source, Parallel, and Distributed Web-Based Probabilistic Risk Assessment Platform to Support Real Time Nuclear Power Plant Risk-Informed Operational Decisions

US Dept. of Energy (DOE)(10/01/21 - 9/30/24)

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An Open Source, Parallel, and Distributed Web-Based Probabilistic Risk Assessment Platform to Support Real Time Nuclear Power Plant Risk-Informed Operational Decisions

Sponsor: US Dept. of Energy (DOE)

Start Date: 10/01/21

End Date: 9/30/24

Abstract

The main objective of the proposed work is to develop, demonstrate, and evaluate a probabilistic risk assessment (PRA) software platform needed to address the major challenges of the current legacy PRA tools, such as better quantification speed, integration of multi-hazard models into traditional PRAs, and model modification simplification and documentation automation. To achieve the main objective, we will first perform benchmarking and profiling of current PRA tools, such as SCRAM and SAPHIRE, to investigate the current bottlenecks in the quantification speed and memory requirements. Secondly, we will design, implement, and benchmark a PRA software platform based on a web-based stack using the latest technologies available to overcome the mentioned challenges. Finally, we will evaluate the performance gains of this framework by modeling and quantifying large PRA models that would have been too expensive to run using the legacy PRA tools.

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Development and Assessment of Digital Twin for Advanced Reactors

US Dept. of Energy (DOE)(8/16/21 - 6/30/24)

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Development and Assessment of Digital Twin for Advanced Reactors

Sponsor: US Dept. of Energy (DOE)

Start Date: 8/16/21

End Date: 6/30/24

Abstract

A DT-DAP (Digital Twin Development and Assessment Process) methodology has been formulated at NCSU in the ARPA-E sponsored project. DT-DAP can be very effective in guiding the design, training, testing, and application of DTs to improve effectiveness, accuracy and acceptance of system design and safety analysis.

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Probabilistic Risk-Informed Approaches in Structural and Earthquake Engineering, CNEFS Enhancement Project

Electricite de France (EDF/DER)(1/01/20 - 12/31/21)

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Probabilistic Risk-Informed Approaches in Structural and Earthquake Engineering, CNEFS Enhancement Project

Sponsor: Electricite de France (EDF/DER)

Start Date: 1/01/20

End Date: 12/31/21

Abstract

In recent years, flooding at nuclear power plants (NPP) has increased emphasis on using high fidelity simulations to evaluate the vulnerability of nuclear plants. One of the key limitations in the use of high fidelity simulations is related to a lack of verification and validation (V&V) of such simulations. One outcome of incomplete and insufficient V&V relates to a large degree of uncertainty in the simulation results which in turn leads to conservative assumptions by the decision makers. Past experience has shown that such conservative assumptions in the context of safety assessment for other external hazards such as seismic have resulted in highly overdesigned NPP systems and excessively high costs. Therefore, it is quite important that various uncertainties in this process are appropriately identified and included through formal uncertainty quantification (UQ). A robust framework for verification and validation is needed to not only include uncertainties but also to formalize the confidence in predictions of system level validation that are based on component level data using Bayesian Network. In addition, the framework has to identify the basic events that are critical in the perspective of overall validation. This process helps in allocating the resources efficiently thereby reducing the effort to conduct high fidelity simulations and large-scale experiments.

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Probabilistic Risk-Informed Approaches in Structural and Earthquake Engineering, CNEFS Core Project

NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(3/01/20 - 12/31/22)

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Probabilistic Risk-Informed Approaches in Structural and Earthquake Engineering, CNEFS Core Project

Sponsor: NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)

Start Date: 3/01/20

End Date: 12/31/22

Abstract

In recent years, flooding at nuclear power plants (NPP) has increased emphasis on using high fidelity simulations to evaluate the vulnerability of nuclear plants. One of the key limitations in the use of high fidelity simulations is related to a lack of verification and validation (V&V) of such simulations. One outcome of incomplete and insufficient V&V relates to a large degree of uncertainty in the simulation results which in turn leads to conservative assumptions by the decision makers. Past experience has shown that such conservative assumptions in the context of safety assessment for other external hazards such as seismic have resulted in highly overdesigned NPP systems and excessively high costs. Therefore, it is quite important that various uncertainties in this process are appropriately identified and included through formal uncertainty quantification (UQ). A robust framework for verification and validation is needed to not only include uncertainties but also to formalize the confidence in predictions of system level validation that are based on component level data using Bayesian Network. In addition, the framework has to identify the basic events that are critical in the perspective of overall validation. This process helps in allocating the resources efficiently thereby reducing the effort to conduct high fidelity simulations and large-scale experiments.

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Membership in the Center for Nuclear Energy Facilities and Structures (CNEFS), Full Member

Korea Atomic Energy Research Institute (KAERI)(1/01/20 - 12/31/24)

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Membership in the Center for Nuclear Energy Facilities and Structures (CNEFS), Full Member

Sponsor: Korea Atomic Energy Research Institute (KAERI)

Start Date: 1/01/20

End Date: 12/31/24

Abstract

The Center for Nuclear Energy Facilities & Structures, has been established and is administered by North Carolina State University to conduct research in the areas of structural engineering, mechanics, risk assessment, hazard mitigation, and construction engineering and to promote research, education, and training in the Research Area. The CENTER has developed core research, non-core research, and technology transfer activities.

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Probabilistic Risk Assessment Modeling Using MASTADON

US Dept. of Energy (DOE)(11/15/19 - 12/31/23)

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Probabilistic Risk Assessment Modeling Using MASTADON

Sponsor: US Dept. of Energy (DOE)

Start Date: 11/15/19

End Date: 12/31/23

Abstract

The proposed project builds upon the previous work of CNEFS in which CNEFS helped develop capabilities for Fault Tree Analysis in the Idaho National Lab (INL)’s MASTADON toolkit. The proposed project focuses on the following specific tasks • Extend the fault-tree analysis and quantification to include event-trees • Convert C++ code to MOOSE objects and implement in MASTODON. • Create examples for PRA in MASTODON using the new fault-tree and event-tree quantification implementations • Benchmark examples with Saphire • Document these examples on the MASTODON website

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