TY - GEN
T1 - Dynamicizing SPAR-H: Generalized Form for Auto-Calculating the Performance Shaping Factor for Experience and Training
T2 - 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
AU - Boring, Ronald
AU - Mortenson, Torrey
AU - Ulrich, Thomas
AU - Park, Jooyoung
AU - Yang, Taewon
AU - Kim, Jonghyun
AU - Kim, Jisuk
N1 - Funding Information:
This work was supported for the first author by Nuclear Global Post-doc Fellowship Program through the Korea Nuclear International Cooperation Foundation (KONICOF) funded by the Korean Ministry of Science and ICT. Additionally, this work of authorship was prepared as an account of work sponsored by Idaho National Laboratory (under Contract DE-AC07-05ID14517), an agency of the U.S. Government. Neither the U.S. Government, nor any agency thereof, nor any of their employees makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
Publisher Copyright:
© 2023 American Nuclear Society, Incorporated.
PY - 2023
Y1 - 2023
N2 - One of the barriers to dynamic human reliability analysis (HRA) has been the complexity of working with performance shaping factors (PSFs). To create a reasonably complete model of human context requires considering dozens of PSFs, each of which must be calibrated to the overall characteristics of the dynamic HRA model. To downscale the problem suitable for a proof-of-concept demonstration, Boring et al. (2017) focused on the subset of eight PSFs used by the SPAR-H HRA method (Gertman et al., 2005). Initial work demonstrated the effect of calculating PSFs like complexity based on plant parameters, thereby removing the need for manual insertion of multipliers in an otherwise automated HRA process. Additionally, auto-calculating removes analyst subjectivity and ensures repeatability of analyses. The earlier efforts at auto-calculating the SPAR-H PSFs revealed that so-called external PSFs like complexity or human-machine interface could be readily derived from plant parameters, while so-called internal PSFs like stress or fitness for duty generally required an operator model and could not be deduced from the state of the plant alone. This paper expands on the earlier discussion by providing data to support auto-calculating the experience and training PSF in SPAR-H. It further explores the effects of the experience and training PSF beyond its influence solely on human error probabilities. For example, PSFs contribute not just to outrate error but also affect task duration.
AB - One of the barriers to dynamic human reliability analysis (HRA) has been the complexity of working with performance shaping factors (PSFs). To create a reasonably complete model of human context requires considering dozens of PSFs, each of which must be calibrated to the overall characteristics of the dynamic HRA model. To downscale the problem suitable for a proof-of-concept demonstration, Boring et al. (2017) focused on the subset of eight PSFs used by the SPAR-H HRA method (Gertman et al., 2005). Initial work demonstrated the effect of calculating PSFs like complexity based on plant parameters, thereby removing the need for manual insertion of multipliers in an otherwise automated HRA process. Additionally, auto-calculating removes analyst subjectivity and ensures repeatability of analyses. The earlier efforts at auto-calculating the SPAR-H PSFs revealed that so-called external PSFs like complexity or human-machine interface could be readily derived from plant parameters, while so-called internal PSFs like stress or fitness for duty generally required an operator model and could not be deduced from the state of the plant alone. This paper expands on the earlier discussion by providing data to support auto-calculating the experience and training PSF in SPAR-H. It further explores the effects of the experience and training PSF beyond its influence solely on human error probabilities. For example, PSFs contribute not just to outrate error but also affect task duration.
KW - Dynamic HRA
KW - PSF
KW - experience
KW - forgetting curve
KW - training
UR - https://www.scopus.com/pages/publications/85183325382
U2 - 10.13182/NPICHMIT23-41448
DO - 10.13182/NPICHMIT23-41448
M3 - Conference contribution
AN - SCOPUS:85183325382
T3 - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
SP - 855
EP - 864
BT - 13th Nuclear Plant Instrumentation, Control & Human- Machine Interface Technologies (NPIC&HMIT 2023)
PB - American Nuclear Society
Y2 - 15 July 2023 through 20 July 2023
ER -