@misc{sandhu_bodda_gupta_2023, title={A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities}, volume={16}, ISSN={["1996-1073"]}, DOI={10.3390/en16062628}, abstractNote={The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali–silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of existing techniques to nuclear systems is somewhat limited because its response requires characterization of high and low-frequency vibration modes, whereas previous studies focus on systems where a single vibration mode can define the degraded state. Data assimilation and storage is another challenging aspect of autonomous control. Advances in AI and data mining world can help to address these challenges.}, number={6}, journal={ENERGIES}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Gupta, Abhinav}, year={2023}, month={Mar} }
 @article{sandhu_bodda_sauers_gupta_2023, title={Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks}, volume={145}, ISSN={["1528-8978"]}, DOI={10.1115/1.4062462}, abstractNote={Abstract Various fields in engineering explore the applicability of deep learning within condition monitoring. With the resurgence of nuclear energy due to electricity and carbon-free power generation demand, ensuring safe operations at nuclear power plants is important. Nuclear safety systems can undergo vibrations due to operating loads such as pump operations, flow-induced, etc. Safety equipment-piping systems experience degradation over the course of time due to flow-accelerated erosion and corrosion. Undetected degradation at certain locations can be subjected to a buildup of cyclic fatigue due to operational vibrations and thermal cycles. A condition monitoring framework is required to avoid fatigue cracking and for early detection of degraded locations along with the severity of degradation. This study aims to propose a condition monitoring methodology for nuclear equipment-piping subject to pump-induced vibrations during normal operations by designing a novel feature extraction technique, exploring parameters and developing a deep neural network, incorporating uncertainty in degradation severity, conducting a thorough investigation of predicted results to analyze erroneous predictions, and proposing strategic recommendations for “safe” pump operating speeds, as per ASME design criteria. Even with nondestructive testing, the detection of fatigue in pipes continues to be a difficult problem. Thus, this novel strategic recommendation to the operator can be beneficial in avoiding fatigue in piping systems due to pump-induced vibrations. The effectiveness of the proposed framework is demonstrated on a Z-piping system connected to an auxiliary pump from the Experimental Breeder Reactor II nuclear reactor and a high prediction accuracy is achieved.}, number={4}, journal={JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Sauers, Serena and Gupta, Abhinav}, year={2023}, month={Aug} }
 @article{sandhu_bodda_gupta_2023, title={Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning}, volume={201}, ISSN={["1879-3541"]}, DOI={10.1016/j.ijpvp.2022.104849}, abstractNote={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. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. In this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.}, journal={INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Gupta, Abhinav}, year={2023}, month={Feb} }
 @article{nie_bodda_sandhu_han_gupta_2022, title={Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method}, volume={22}, ISSN={["1424-8220"]}, url={https://doi.org/10.3390/s22186869}, DOI={10.3390/s22186869}, abstractNote={Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.}, number={18}, journal={SENSORS}, author={Nie, Guang-Yu and Bodda, Saran Srikanth and Sandhu, Harleen Kaur and Han, Kevin and Gupta, Abhinav}, year={2022}, month={Sep} }
 @article{crowder_lee_gupta_han_bodda_ritter_2022, title={Digital Engineering for Integrated Modeling and Simulation for Building-Piping Systems Through Interoperability Solutions}, ISSN={["1943-748X"]}, DOI={10.1080/00295639.2022.2055705}, abstractNote={Designing piping systems for nuclear power plants involves engineers from multiple disciplines (i.e., thermal hydraulics, mechanical engineering, and structural/seismic) and close coordination with the contractors who build the plant. Any design changes during construction need to be carefully communicated and managed with all stakeholders in order to assess risks associated with the design changes. To allow the quick assessment of building and piping design changes through a streamlined building-piping coupled analysis, this paper presents a novel interoperability solution that converts bidirectionally between building information models (BIMs) and pipe stress models. Any design changes during construction that are shown in an as-built BIM are automatically converted into a pipe stress model. Any further design changes due to building-piping interaction analyses are converted back to the BIM for the contractor and other designers to access the latest model. Two case studies are presented to illustrate the bidirectional conversion that allows an integrated coupled analysis of the building-piping system to account for their interactions.}, journal={NUCLEAR SCIENCE AND ENGINEERING}, author={Crowder, Nicholas and Lee, Joomyung and Gupta, Abhinav and Han, Kevin and Bodda, Saran and Ritter, Christopher}, year={2022}, month={May} }
 @article{bodda_keller_gupta_senfaute_2021, title={A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference}, ISSN={["1420-9136"]}, DOI={10.1007/s00024-021-02915-8}, abstractNote={In recent decades, prediction of ground motion at a specific site or a region is of primary interest in probabilistic seismic hazard assessment (PSHA). Historically, several ground motion prediction equation (GMPE) models with different functional forms have been published using strong ground motion records available from NGA-West and European databases. However, low-to-moderate seismicity regions, such as Central & Eastern United States and western Europe, is characterized by limited strong-motion records in the magnitude–distance range of interest for PSHA. In these regions, the available data for the development of empirical GMPEs is very scarce and limited to small magnitude events. For these regions, the general practice in PSHA is to consider a set of GMPEs developed from data sets collected in other regions with high seismicity. This practice generates an overestimation of the seismic hazard for the low seismicity regions. There are two potential solutions to overcome this problem: (1) a new GMPE model can be developed; however, development of such a model can require significant amount of data which is not usually available, and (2) the existing GMPE models can be recalibrated based on the data sets collected in the new region rather than developing a new GMPE model. In this paper, we propose a methodological approach to recalibrate the coefficients in a GMPE model using different algorithms to perform Bayesian inference. The coefficients are recalibrated for a subset of European Strong-Motion (ESM) database that corresponds to low-to-moderate seismicity records. In this study, different statistical models are compared based on the functional form given by the chosen GMPE, and the best model and algorithm are recommended using the concept of information criteria.}, journal={PURE AND APPLIED GEOPHYSICS}, author={Bodda, Saran Srikanth and Keller, Merlin and Gupta, Abhinav and Senfaute, Gloria}, year={2021}, month={Nov} }
 @article{bodda_gupta_sewell_2021, title={Application of Risk-Informed Validation Framework to a Flooding Scenario}, volume={7}, ISSN={["2376-7642"]}, DOI={10.1061/AJRUA6.0001172}, abstractNote={In recent years, the use of advanced simulation tools for modeling the behavior of flooding at a nuclear power plant has gained significant importance. The credibility of advanced simulation codes is assessed using formal approaches for verification and validation. In this manuscript, a formal risk-informed validation approach is proposed that provides a basis to quantify credibility of system-level risk assessments. The credibility of system-level validation is represented using a probabilistic metric and maturity levels. The novelty of the proposed approach lies in the integration of risk-informed validation approach with the US Nuclear Regulatory Commission’s (USNRC) Evaluation Model Development and Assessment Process (EMDAP) framework. This allows transformation of EMDAP into a risk-informed EMDAP. The applicability of the proposed framework is evaluated by application to a flooding scenario of a sunny-day dam failure. Application to the flooding scenario illustrates that the credibility of flooding simulations can be assessed using formal quantification, which is otherwise not possible in the existing EMDAP framework.}, number={4}, journal={ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING}, author={Bodda, Saran Srikanth and Gupta, Abhinav and Sewell, Robert T.}, year={2021}, month={Dec} }
 @article{patel_bodda_gupta_2021, title={Modeling the behavior of reinforced concrete slabs subjected to impact}, volume={385}, ISSN={["1872-759X"]}, DOI={10.1016/j.nucengdes.2021.111512}, abstractNote={In recent years, safety of nuclear power plants against external missile impacts such as those due to tornadoes has gained significant attention. In many cases, advanced simulation tools based on finite element method (FEM) or smooth particle hydrodynamics (SPH) are being employed to simulate missile impact behavior and to evaluate vulnerability of nuclear facilities. Due to the complex nature of impact behavior, it requires appropriate calibration of parameters in the advanced simulation models that are used to represent them. In this manuscript, we propose a novel approach for modeling the behavior of reinforced concrete slabs subjected to missile impact. First, we use data from one experimental study to develop and calibrate various models needed to conduct the finite element analysis. Then, the calibrated models are used to conduct a predictive analysis for a different experimental setup. A comparison of the experiment and the analytical results for the new test provides confidence in the predictive capability of the simulation approach with calibrated parameters.}, journal={NUCLEAR ENGINEERING AND DESIGN}, author={Patel, Parth and Bodda, Saran Srikanth and Gupta, Abhinav}, year={2021}, month={Dec} }
 @article{vaishanav_gupta_bodda_2020, title={Limitations of traditional tools for beyond design basis external hazard PRA}, volume={370}, ISSN={["1872-759X"]}, DOI={10.1016/j.nucengdes.2020.110899}, abstractNote={Abstract Probabilistic risk assessment (PRA) is being used increasingly by the nuclear industry for safety during normal operations as well as for the protection against external hazards. Computation of total risk in an external hazard PRA is dependent on hazard assessment, fragility assessment, and systems analysis. A systems analysis for propagation of component fragilities is conducted using event and fault trees. The event and fault trees for an actual power plant can be fairly large in size, which imposes computational challenges. Hence, certain assumptions are employed for computational efficiency. These assumptions typically represent the conditions imposed during the design basis (DB) scenario. The traditional PRA tools based on these assumptions are also widely applied to perform risk assessment in the context of beyond design basis (BDB) scenarios. However, some of these assumptions may not be valid for certain BDB scenarios. In addition, the probability of dependent failures also increases in BDB scenarios due to common cause failures (CCF) which usually results from design modifications, human errors, etc. In this manuscript, a simple and a relatively more complex illustrative examples are used to show the limitation of these assumptions in the numerical quantification of risk for the case of BDB conditions. Case studies with CCF events across multiple fault trees are also presented to illustrate the effect of these assumptions when traditional approach is used in BDB risk assessment. It is shown that the assumptions are valid for the case of DB conditions but may lead to excessively conservative risk estimates in the case of BDB conditions. A Bayesian network based top-down algorithm is proposed as an alternative tool for accurate numerical quantification of total risk in systems analysis.}, journal={NUCLEAR ENGINEERING AND DESIGN}, author={Vaishanav, Pragya and Gupta, Abhinav and Bodda, Saran Srikanth}, year={2020}, month={Dec} }
 @article{bodda_gupta_dinh_2020, title={Risk informed validation framework for external flooding scenario}, volume={356}, ISSN={["1872-759X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85074190032&partnerID=MN8TOARS}, DOI={10.1016/j.nucengdes.2019.110377}, abstractNote={Safety of nuclear plants against external flooding has gained significant attention following the accident at Fukushima Daiichi nuclear power station. In United States, Oyster Creek nuclear plant was safely shutdown when high storm surge during hurricane Sandy caused a potential flooding threat. Subsequently, the nuclear energy industry experienced a significant activity in Probabilistic Risk Assessment (PRA) for external flooding. Increasingly, methods of computational fluid dynamics including advanced simulation codes are being considered to evaluate the sequence of events during different scenarios of flooding at a plant. One of the key limitations in the use of advanced codes for external flooding is related to a lack of credibility of such simulations. The motivation of this study is to develop a formal validation approach that provides a basis to quantify credibility of risk assessments that are based on advanced simulation codes. In this study, we illustrate the application of existing performance based risk-informed validation framework to an external flooding event. However, it is determined that a direct application of this approach to flooding is restricted due to a lack of relevant data to evaluate experimental fragilities for flooding failures. Therefore, we take a simple synthetic example to evaluate the applicability of the proposed framework to validation of flooding PRA scenario and update the proposed framework as needed.}, journal={NUCLEAR ENGINEERING AND DESIGN}, author={Bodda, Saran Srikanth and Gupta, Abhinav and Dinh, Nam}, year={2020}, month={Jan} }
 @inproceedings{bodda_sandhu_gupta_2016, title={Fragility of a flood defense structure subjected to multi-hazard scenario}, DOI={10.1115/icone24-60508}, abstractNote={The March 2011 Fukushima Daiichi nuclear power plant disaster has highlighted the significance of maintaining the integrity of flood protection systems in the vicinity of a nuclear power plant. In the US, Oyster Creek nuclear plant was shut down when high storm surge during hurricane Sandy threatened its water intake and circulation systems. A gravity dam located upstream of a power plant can undergo seismic failure or flooding failure leading to flooding at the nuclear plant. In this paper, we present the results from a study on evaluating the fragilities for failure of a concrete gravity dam under both the flooding and the seismic events. Finite element analysis is used for modeling the seismic behavior as well as the seepage through foundation. A time-dependent analysis is considered to account for appropriate nonlinearities. Failure of dam foundation is characterized by rupture, and the failure of dam body is characterized by excessive deformation for the flooding and seismic loads respectively. The study presented in this paper has focused on a concrete gravity dam because of the need of validation of models which exist in prior studies only for concrete gravity dams. However, the concepts are directly applicable to any concrete flood defense structure.}, booktitle={Proceedings of the 24th International Conference on Nuclear Engineering, 2016, vol 4}, author={Bodda, S. S. and Sandhu, H. K. and Gupta, A.}, year={2016} }