Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM
The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming P...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Nuclear Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573324004133 |
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| author | Xin Liu Shengyuan Yan Xiaodan Zhang |
| author_facet | Xin Liu Shengyuan Yan Xiaodan Zhang |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming PSFs to be independent without considering any overlaps and dependencies. Therefore, this study introduces a new systematic method to analyze the relationships among the PSFs in SPAR-H qualitatively and quantitatively to obtain more reasonable HEP estimation results. The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2) the weighted influence non-linear gauge system-based adversarial interpretive structure modeling (WINGS-AISM) method to analyze the relationships among the PSFs and construct their causal hierarchy, and 3) a new relative weighting system to modify the PSF multipliers based on this hierarchy. The results of experiments comparing the proposed method with conventional methods highlight that our method effectively reduces the double counting of overlapping PSFs in SPAR-H, providing more reasonable and accurate HEP estimation results. |
| format | Article |
| id | doaj-art-bdba848d629e4c4b88f7970b2e062721 |
| institution | Kabale University |
| issn | 1738-5733 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj-art-bdba848d629e4c4b88f7970b2e0627212025-01-12T05:24:40ZengElsevierNuclear Engineering and Technology1738-57332025-01-01571103165Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISMXin Liu0Shengyuan Yan1Xiaodan Zhang2Corresponding author.; College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, PR ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, PR ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, PR ChinaThe standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming PSFs to be independent without considering any overlaps and dependencies. Therefore, this study introduces a new systematic method to analyze the relationships among the PSFs in SPAR-H qualitatively and quantitatively to obtain more reasonable HEP estimation results. The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2) the weighted influence non-linear gauge system-based adversarial interpretive structure modeling (WINGS-AISM) method to analyze the relationships among the PSFs and construct their causal hierarchy, and 3) a new relative weighting system to modify the PSF multipliers based on this hierarchy. The results of experiments comparing the proposed method with conventional methods highlight that our method effectively reduces the double counting of overlapping PSFs in SPAR-H, providing more reasonable and accurate HEP estimation results.http://www.sciencedirect.com/science/article/pii/S1738573324004133Nuclear power plantsHuman reliability analysisSPAR-HHuman error probabilityPerformance shaping factorRegularized autoencoder |
| spellingShingle | Xin Liu Shengyuan Yan Xiaodan Zhang Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM Nuclear Engineering and Technology Nuclear power plants Human reliability analysis SPAR-H Human error probability Performance shaping factor Regularized autoencoder |
| title | Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM |
| title_full | Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM |
| title_fullStr | Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM |
| title_full_unstemmed | Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM |
| title_short | Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM |
| title_sort | accounting for dependencies among performance shaping factors in spar h using a regularized autoencoder and wings aism |
| topic | Nuclear power plants Human reliability analysis SPAR-H Human error probability Performance shaping factor Regularized autoencoder |
| url | http://www.sciencedirect.com/science/article/pii/S1738573324004133 |
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