InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph

Haitao Wang; Hongru Zhao; Jiejuan Tong; Jingang Liang; Jun Sun; Jun Zhao; Peng Chen; Xingyu Xiao; Zhe Sui

REVIEW 4 major objections 5 minor 19 references

InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read InSight-R maps operator clicks onto an interface knowledge graph to automate human-error risk identification

desk verdict A genuinely novel HFE-identification pipeline that maps behavioral logs onto an interface knowledge graph, held back by an untested duration-to-failure assumption and an 8-sample expert-labeled MLP. read the letter →

arxiv 2507.00066 v1 pith:YWURVC4S submitted 2025-06-28 cs.HC cs.AI

Xingyu Xiao , Jiejuan Tong , Peng Chen , Jun Sun , Zhe Sui , Jingang Liang , Hongru Zhao , Jun Zhao

show 1 more author

Haitao Wang

This is my paper · ORCID

classification cs.HCcs.AI

keywords HumanReliabilityAnalysisFailureEventIdentificationInterface-EmbeddedKnowledgeGraphPerformanceInfluencingFactorsInterfaceComplexityNuclearPowerControlRoomData-DrivenRiskAssessmentAuto

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that human failure events (HFEs) in a digital control room can be identified automatically rather than by expert judgment, by fusing recorded operator behavior with a knowledge graph of the interface. If this works, human reliability analysis becomes reproducible, data-driven, and updatable in real time, and interface-related performance influencing factors (PIFs) can be quantified instead of scored subjectively. The framework, InSight-R, builds an interface-embedded knowledge graph (IE-KG) from the screen layout, maps clicks, cursor paths, errors, and task durations onto its nodes, and flags both error-prone and time-deviated operational paths as candidate HFEs. It also defines three computable metrics, visual density (VD), semantic interference density (SID), and interaction span (IS), and uses them to predict interface-related PIF levels, including for interfaces that have not yet been tested.

What carries the argument

The load-bearing object is the interface-embedded knowledge graph (IE-KG), a graph whose nodes are clickable interface elements and whose edges encode hierarchical and logical relations, built automatically by the AutoGraph framework. The argument runs through three tools built on it: error-path identification, which maps recorded errors onto a Bayesian network of procedure paths to find high-error routes; time-path identification, which models each path's task duration as lognormal with a fixed shape parameter of $\sigma=0.28$ per IDHEAS-ECA guidance and flags the upper tail as tail-end risk; and three procedure-driven metrics, visual density ($VD$), semantic interference density ($SID$) from cosine similarity of element names, and interaction span ($IS$) from normalized cursor travel distance, whose values feed an MLP classifier that predicts interface-related PIF levels. The claim that these pieces interoperate is what makes automated HFE identification and quantitative PIF assessment possible.

What would settle it

A controlled study with licensed operators performing the same procedures, with outcome errors independently determined, would settle the claim: if the time-deviated paths flagged by the lognormal model do not show elevated error rates relative to unflagged paths, the tail-end-risk component of automatic HFE identification collapses.

Watch

Extended reading notes

Core claim

InSight-R's central claim is that an interface-embedded knowledge graph can carry a full human reliability analysis: it turns the interface into a structured graph, attaches empirical behavioral data to graph nodes, and derives HFEs from two signals, paths where operators actually made errors and paths where task duration is abnormally long. The paper argues that this procedure replaces expert elicitation with a mechanism-based, updatable analysis, and that the same graph yields quantitative interface metrics which can be mapped, through a trained classifier, onto the PIF scale used by the IDHEAS-ECA framework. In the reported case study, data-driven HFE identification produced a different list than expert judgment, catching low-complexity but high-variability actions that experts overlook, while expert judgment pointed to high-complexity paths. The paper also reports that designer-user conflict raises error probability statistically but does not determine it: two of three outcome errors occurred on HSI5-classified conflict-prone interfaces, yet some conflicting interfaces were completed without error.

Load-bearing premise

The time-based half of the automated HFE list stands on the assumption that longer task execution time means a higher chance of degraded performance or failure, an assumption borrowed from workload research and not validated by this experiment.

Editorial extensions

If this is right

Automated HFE lists can be regenerated each time new operator data arrives, allowing human reliability analysis to track evolving error patterns rather than freezing expert judgment.
Interface-related PIF levels become computable from screen layout and task semantics, so untested procedures can be screened for design-induced risk before deployment.
Tail-end timing deviations give a measurable early-warning signal for cognitive overload, so slow paths can be treated as risk precursors rather than only counting mistakes.
The framework's HFE output can be reconciled with expert-based IDHEAS-ECA analysis, giving HRA analysts a quantitative cross-check instead of a single subjective channel.
Design guidance becomes concrete: reduce element count, differentiate semantically similar labels, and shorten cursor travel distances on interfaces predicted as HSI5.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Extension the paper leaves implicit: the same IE-KG pipeline is transferable to any screen-based safety-critical task, such as aircraft cockpit displays, anesthesiology workstations, or autonomous-vehicle supervision, because the graph is built from the interface alone and the behavioral data from logs.
The framework's real-time updating property implies that simulators can serve as continuous human-reliability data engines, with every training run refreshing error probabilities and PIF estimates automatically rather than through periodic expert re-scoring.
The paper's conflict analysis suggests a testable design rule: interfaces scoring HSI5 should be treated as necessary, not sufficient, causes of error, and interventions should target conflict-prone elements while preserving affordances that allow adaptive operators to compensate.
A larger operator sample with professional control-room crews would settle whether the lognormal time assumption and the 0.28 shape parameter hold outside students, and whether time-deviated paths actually predict future errors at the claimed rate.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The specific pipeline here is new and worth knowing: InSight-R maps behavioral logs onto an interface-embedded knowledge graph, then flags HFEs from both error-prone and time-deviated paths, and scores interface risk with three simple metrics mapped to IDHEAS-ECA PIF levels. I don't know of another paper that does exactly that. The framework is coherent, and the designer-user conflict discussion is honest—it explicitly says conflict doesn't deterministically cause error and shows examples both ways. Credit where due: this is a sensible step toward reducing expert-judgment dependence in HRA, and the authors acknowledge the student-participant limitation up front.

The problems are in the evidence, not the idea. The biggest one, as the stress-test note says, is the tail-end time assumption. Section 4.2.2 adopts "longer execution time implies higher failure probability" from a co-author's prior modeling study, plus the descriptive observation that 7 of 12 errors landed on longer paths. That is not a statistical test, and the final HFE list includes four paths (P123, P126, P322, P411) with no recorded errors at all. Calling slow operation a human failure event conflates inefficiency with failure, and the paper gives no validation that the relationship holds in this interface context. Second is the MLP: eight expert-assigned samples, 5-fold CV, fold accuracies swinging between 0.625 and 1.0. The VD/SID/IS features are objective, but the labels are expert judgment, so the model inherits the very subjectivity the paper criticizes. The HFE list is also presented as three bullet groups with duplicates and no explanation of how they combine; the VD equation doesn't match its prose definition; one sentence in 4.1 is cut off; and no data or code is released—a real gap for a framework whose selling point is reproducibility.

None of this kills the idea. It's a solid proof-of-concept that overclaims. The citation pattern is acceptable: the self-citations are the group's own prior HRA work, though note ref [16] is the source of the untested time assumption, so it isn't independent support.

Send it to review. A serious referee can push on the time-to-error relationship and the PIF validation. With a larger dataset, released artifacts, and a real statistical check, the paper could earn its claims.

Referee Report

4 major / 5 minor

Summary. The paper proposes InSight-R, a framework that combines an interface-embedded knowledge graph (IE-KG) built by AutoGraph with empirical operator behavior data to automate human failure event (HFE) identification and to estimate interface-related performance influencing factors (PIFs). HFE identification is based on two criteria: error-prone operational paths and time-deviated paths detected by lognormal modeling of task durations. PIF estimation uses three interface metrics (visual density, semantic interference density, and interaction span) as inputs to a multi-layer perceptron whose outputs are mapped to IDHEAS-ECA HSI levels. The framework is demonstrated in a human-in-the-loop study with six graduate students in the HTRPMSim simulator and is applied to predict PIF levels for three untested procedures. The paper also presents a designer-user conflict analysis intended to explain when interface problems translate into errors.

Significance. If the framework's claims are supported, it would be a useful step toward reducing reliance on subjective expert judgment in HRA by providing a reproducible, data-driven pipeline from interface structure to risk indicators. The paper deserves credit for a concrete behavioral-data collection pipeline, a clear graph-based representation of interface elements, and an explicit attempt to quantify semantic interference and interaction span. However, the evidence base is currently small: six participants, no statistical validation of the time-to-failure assumption, and an MLP trained and evaluated on only eight expert-assigned samples. The significance is therefore conditional on substantial additional validation of the load-bearing assumptions.

major comments (4)

[Section 4.2.2] The central claim that time-deviated paths are human failure events rests on the unvalidated assumption that 'longer execution time implies a higher probability of performance degradation or failure.' This assumption is imported from Ref. [16] and IDHEAS-ECA guidance [17], not tested in this experiment. The only supporting evidence is the descriptive observation that 7 of 12 errors occurred on longer-duration paths, and the lognormal model fixes sigma=0.28 without any goodness-of-fit test or sensitivity analysis. Because the final HFE list includes paths that appear only in the time-deviated set (e.g., P126, P322, P411) and are not in the observed error set, the procedure conflates slow performance with failure. Please provide a statistical test of the duration-error relationship, report sensitivity to the threshold and to sigma, or explicitly reframe these as candidate risk indicators rather than confirmed HFEs.
[Section 5.2 and Figure 11] The MLP classifier is trained and evaluated on a dataset of only eight expert-assigned samples, as shown in the confusion matrix in Figure 11(a) (2 HSI0, 4 HSI1, 2 HSI5). Five-fold cross-validation on eight samples does not provide a reliable estimate of generalization, the reported mean accuracy of 0.875 is obtained without a held-out test set, and the class imbalance makes the result difficult to interpret. Moreover, because the labels are expert judgments, the PIF quantification inherits the very subjectivity the paper criticizes. The relationship between Table 1, which lists dozens of paths with PIF levels, and the eight samples in Figure 11(a) is also unclear. Please report the exact number of training samples, fold assignments, per-class metrics, and an external validation set; alternatively, present the MLP as an illustrative mapping rather than a validated model.
[Section 3.4, Eq. (1)] The definition of Visual Density is internally contradictory. The text defines VD as 'the ratio of the total area occupied by visible interface elements to the overall area of the interface,' but Equation (1) gives VD = N_target/N_elements, with N_target always equal to one. The latter is simply the reciprocal of the number of visible elements and is not an area-based density; no area computation appears anywhere in the case study. This metric is load-bearing because VD is one of the three MLP inputs used to predict PIF levels. Please correct the equation, compute an actual area-based density, or rename the metric so that it does not claim to measure visual density.
[Section 4.2, final HFE list] The final list of identified HFEs is presented as three separate bullet groups with duplicate entries (P216, P323, P211, P212, P321, P414 appear more than once), and no set operation such as union or intersection is specified. As a result, the reader cannot determine which paths are HFEs from the error-proneness criterion, from the time-deviation criterion, or from both. Please provide a single unambiguous set, with per-path notation of which criterion or criteria triggered identification.

minor comments (5)

[Throughout] The framework name is spelled inconsistently: the title uses 'InSight-R' while Section 3.1 and several other places use 'Insight-R'; please unify the spelling.
[Section 4.1] The sentence 'For full details on IE-KG construction.' is an incomplete fragment and does not point to an appendix, reference, or supplementary file; please complete the reference or remove the sentence.
[Section 5.3] The claim that '66% occurrence rate' indicates a strong correlation is based on only two outcome errors from three total cases; this should be reported with an explicit caveat about the tiny sample size.
[Reference [18]] The paper states that semantic similarity is computed with text-embedding-ada-002, but the cited reference [18] is the Nomic Embed paper; please correct the citation or use the appropriate model reference.
[Section 1 and Section 2.3] The paragraph about 'unmanned' plants and the hidden role of human factors appears nearly verbatim in both the Introduction and Section 2.3; please keep only one copy.

Circularity Check

2 steps flagged · score 6.0 of 10

Time-deviated HFE identification leans on a self-cited longer-time-implies-failure assumption, and the MLP-based PIF predictions are fitted to expert labels.

self citation load bearing [Section 4.2.2, paragraph 1 (time-deviated path detection)]
"Prior studies have demonstrated a strong correlation between extended task durations and increased cognitive workload [16], which in turn elevates the likelihood of human error. In this work, we adopt the assumption that longer execution time implies a higher probability of performance degradation or failure."

This sentence is the only inferred bridge from 'long duration' to 'human failure event.' The HFE list built in Section 4.2.2 includes time-deviated paths with no recorded errors (P123, P126, P322, P411), so those entries are not summaries of observed errors. The cited support [16] is a modeling study co-authored by two of the present authors (Chen and Tong), and the paper's own 'preliminary empirical support' is descriptive only ('7 out of 12' errors on relatively longer paths). No statistical test of the monotonicity assumption is given. Without this imported assumption the time-deviated paths are merely slow paths; calling them HFEs therefore reduces to an unverified premise supplied by a self-citation.
fitted input called prediction [Section 5.2 and Section 5.4 (MLP PIF prediction; Table 3)]
"The PIFs confirmed through expert judgment for validation purposes are presented in Table 1. ... Using the deep learning-based approach proposed in Section 5.2, the performance influencing factor (PIF) levels for the untested procedures were computed, as shown in Table 3."

The MLP classifier is fitted to the expert-assigned HSI labels in Table 1 (8 samples) and then Section 5.4 presents its outputs as 'predicted PIF levels' for TP1-TP3, using those outputs to infer HEP risk. The predicted category is a learned function of the very expert judgment the paper claims to replace. The reported 0.875 cross-validation accuracy measures how well the model reproduces expert labels, not any independently measured relationship to human error. Thus the 'quantitative, objective' interface PIF values are the subjective expert ratings re-expressed through a fitted mapping; the prediction is largely forced by the training labels rather than by an independent interface-risk theory.

full rationale

The error-path half of HFE identification is empirical and not circular: it maps recorded errors onto Bayesian-network paths and derives candidates directly from data. The graph construction and the VD/SID/IS metrics are defined objectively. The circularity is concentrated in two load-bearing places. First, the time-deviated HFE list depends on the assumption that longer execution time implies higher failure probability, which is adopted from reference [16], a prior work by the same research group, and is not validated by the present experiment; the final HFE list contains time-deviated paths with no observed errors, so those identifications reduce to the imported assumption. Second, the quantitative PIF predictions are produced by an MLP trained on expert-assigned HSI labels, so the claimed objective PIF output is effectively the expert input re-encoded by a fitted model; cross-validation accuracy measures agreement with those labels, not independent predictive validity. These two steps make the strongest claims of objectivity and automation partially circular, giving a score of 6.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The framework's central claims rest on several imported assumptions: the error-time relationship, the lognormal duration model, the semantic similarity threshold, expert-derived labels for PIF training, and a manually constructed Bayesian network. No independent evidence is provided for these within the paper, and one (the expert labels) directly undermines the claimed objectivity.

free parameters (3)

Semantic similarity threshold = 0.8
Cosine similarity cutoff for SID in Section 3.4, chosen ad hoc; no sensitivity analysis is reported.
Lognormal shape parameter sigma = 0.28
Fixed task-duration distribution parameter taken from IDHEAS-ECA (Eq. 5); it is an input from prior literature but is a modeling parameter the time-risk analysis depends on.
MLP weights and hyperparameters = Not reported
The classifier in Section 5.2 is fitted to 8 expert-labeled samples; optimizer, learning rate, split details, and final weights are not disclosed.

assumptions (5)

domain assumption Longer task duration implies higher probability of human error.
Adopted in Section 4.2.2 on the basis of ref [16]; underpins the identification of time-deviated paths as HFEs.
domain assumption Operator task durations follow a lognormal distribution with sigma fixed at 0.28.
Taken from IDHEAS-ECA guidance (ref [17]) in Eq. (5); used to model tail-end time risk.
ad hoc to paper Cosine similarity of parameter names above 0.8 reliably indicates semantic interference.
Threshold set without calibration in Section 3.4; drives the SID metric.
domain assumption Expert-assigned HSI categories in Table 1 are the correct ground truth for training the PIF classifier.
The MLP in Section 5.2 is trained and evaluated against these labels, so the paper relies on expert judgment for its 'objective' objective.
domain assumption The Bayesian network structure in Figure 4 correctly represents the procedure hierarchy and error paths.
The network is presented as given in Section 4.2.1, with no learning or validation described.

how reviews work

0 comments

Cite this review

Pith. "Pith review of InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph." pith.science (2026). https://pith.science/paper/YWURVC4S

@misc{pith2026250700066,
  author       = {Pith},
  title        = {Pith review of: InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YWURVC4S}},
  note         = {Machine review of arXiv:2507.00066}
}

read the original abstract

Human reliability remains a critical concern in safety-critical domains such as nuclear power, where operational failures are often linked to human error. While conventional human reliability analysis (HRA) methods have been widely adopted, they rely heavily on expert judgment for identifying human failure events (HFEs) and assigning performance influencing factors (PIFs). This reliance introduces challenges related to reproducibility, subjectivity, and limited integration of interface-level data. In particular, current approaches lack the capacity to rigorously assess how human-machine interface design contributes to operator performance variability and error susceptibility. To address these limitations, this study proposes a framework for risk-informed human failure event identification and interface-induced risk assessment driven by AutoGraph (InSight-R). By linking empirical behavioral data to the interface-embedded knowledge graph (IE-KG) constructed by the automated graph-based execution framework (AutoGraph), the InSight-R framework enables automated HFE identification based on both error-prone and time-deviated operational paths. Furthermore, we discuss the relationship between designer-user conflicts and human error. The results demonstrate that InSight-R not only enhances the objectivity and interpretability of HFE identification but also provides a scalable pathway toward dynamic, real-time human reliability assessment in digitalized control environments. This framework offers actionable insights for interface design optimization and contributes to the advancement of mechanism-driven HRA methodologies.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 17 canonical work pages

[16]

In: International Conference on Nuclear Engineering, vol

Chen, P., Tong, J.: Influence of nuclear power plant interface complexity on oper- ator performance: A modeling study. In: International Conference on Nuclear Engineering, vol. 88230, pp. 003–03011 (2024). American Society of Mechanical Engineers

work page 2024
[17]

In: Proceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021), pp

Chang, Y.J., Xing, J., DeJesus Segarra, J.: Idheas suite for human reliability anal- ysis. In: Proceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021), pp. 1231–1238 (2021)

work page 2021
[1]

Reason, J.: Human error (1990)

work page 1990
[2]

Hollnagel, E.: Cognitive reliability and error analysis method (cream) (1998)

work page 1998
[3]

arXiv preprint arXiv:2412.18627 (2024)

Xiao, X., Chen, P., Qi, B., Zhao, H., Liang, J., Tong, J., Wang, H.: Krail: A knowledge-driven framework for base human reliability analysis integrating idheas and large language models. arXiv preprint arXiv:2412.18627 (2024)

arXiv 2024
[4]

Reliability Engineering & System Safety, 111080 (2025)

Xiao, X., Qi, B., Liu, S., Chen, P., Liang, J., Tong, J., Wang, H.: A dynamic risk- informed framework for emergency human error prevention in high-risk industries: A nuclear power plant case study. Reliability Engineering & System Safety, 111080 (2025)

work page 2025
[5]

Energies 17(10), 2439 (2024)

Xiao, X., Liang, J., Tong, J., Wang, H.: Emergency decision support techniques for nuclear power plants: Current state, challenges, and future trends. Energies 17(10), 2439 (2024)

work page 2024
[6]

A Cognitive-Mechanistic Human Reliability Analysis Framework: A Nuclear Power Plant Case Study

Xiao, X., Chen, P., Tong, J., Liu, S., Zhao, H., Zhao, J., Jia, Q., Liang, J., Wang, H.: A cognitive-mechanistic human reliability analysis framework: A nuclear power plant case study. arXiv preprint arXiv:2504.18604 (2025)

work page Pith review arXiv 2025

Show all 19 references

[7]

Nuclear Engineering and Technology 52(1), 87–100 (2020)

Park, J., Jung, W., Kim, J.: Inter-relationships between performance shaping factors for human reliability analysis of nuclear power plants. Nuclear Engineering and Technology 52(1), 87–100 (2020)

2020
[8]

US Nuclear Regulatory Commission, Washington, DC (2020)

Xing, J., Chang, Y., DeJesus, J.: Integrated human event analysis system for event and condition assessment (idheas-eca). US Nuclear Regulatory Commission, Washington, DC (2020)

2020
[9]

Journal of The Royal Society Interface 12(110), 20150685 (2015)

Thimbleby, H., Oladimeji, P., Cairns, P.: Unreliable numbers: error and harm induced by bad design can be reduced by better design. Journal of The Royal Society Interface 12(110), 20150685 (2015)

2015
[10]

International journal of environmental research and public health 16(13), 2427 (2019)

Roll, L.C., Siu, O.-l., Li, S.Y., De Witte, H.: Human error: The impact of job insecurity on attention-related cognitive errors and error detection. International journal of environmental research and public health 16(13), 2427 (2019)

2019
[11]

Technical report, Brookhaven National Lab.(BNL), Upton, NY (United States) (2020)

O’Hara, J.M., Fleger, S.: Human-system interface design review guidelines. Technical report, Brookhaven National Lab.(BNL), Upton, NY (United States) (2020)

2020
[12]

Stone, D., Jarrett, C., Woodroffe, M., Minocha, S.: User interface design and evaluation (2005)

2005
[13]

Thimbleby, H.: User interface design (1990) 23

1990
[14]

Commu- nications of the ACM 26(7), 484–494 (1983)

Morland, D.V.: Human factors guidelines for terminal interface design. Commu- nications of the ACM 26(7), 484–494 (1983)

1983
[15]

The Design Journal 17(3), 403– 423 (2014)

Carlgren, L., Elmquist, M., Rauth, I.: Design thinking: Exploring values and effects from an innovation capability perspective. The Design Journal 17(3), 403– 423 (2014)

2014
[18]

arXiv preprint arXiv:2402.01613 (2024)

Nussbaum, Z., Morris, J.X., Duderstadt, B., Mulyar, A.: Nomic embed: Training a reproducible long context text embedder. arXiv preprint arXiv:2402.01613 (2024)

2024 arXiv
[19]

Chang, Y.J., Xing, J., Peters, S.: Human reliability analysis method develop- ment in the us nuclear regulatory commission. In: 13th International Conference on Probabilistic Safety Assessment and Management (PSAM 13), Seoul, Korea (2016) 24 Path VD SID IS PIF P100 1/4 0/3 126...

2016

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [16]

In: International Conference on Nuclear Engineering, vol

Chen, P., Tong, J.: Influence of nuclear power plant interface complexity on oper- ator performance: A modeling study. In: International Conference on Nuclear Engineering, vol. 88230, pp. 003–03011 (2024). American Society of Mechanical Engineers

work page 2024

[2] [17]

In: Proceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021), pp

Chang, Y.J., Xing, J., DeJesus Segarra, J.: Idheas suite for human reliability anal- ysis. In: Proceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021), pp. 1231–1238 (2021)

work page 2021

[3] [1]

Reason, J.: Human error (1990)

work page 1990

[4] [2]

Hollnagel, E.: Cognitive reliability and error analysis method (cream) (1998)

work page 1998

[5] [3]

arXiv preprint arXiv:2412.18627 (2024)

Xiao, X., Chen, P., Qi, B., Zhao, H., Liang, J., Tong, J., Wang, H.: Krail: A knowledge-driven framework for base human reliability analysis integrating idheas and large language models. arXiv preprint arXiv:2412.18627 (2024)

arXiv 2024

[6] [4]

Reliability Engineering & System Safety, 111080 (2025)

Xiao, X., Qi, B., Liu, S., Chen, P., Liang, J., Tong, J., Wang, H.: A dynamic risk- informed framework for emergency human error prevention in high-risk industries: A nuclear power plant case study. Reliability Engineering & System Safety, 111080 (2025)

work page 2025

[7] [5]

Energies 17(10), 2439 (2024)

Xiao, X., Liang, J., Tong, J., Wang, H.: Emergency decision support techniques for nuclear power plants: Current state, challenges, and future trends. Energies 17(10), 2439 (2024)

work page 2024

[8] [6]

A Cognitive-Mechanistic Human Reliability Analysis Framework: A Nuclear Power Plant Case Study

Xiao, X., Chen, P., Tong, J., Liu, S., Zhao, H., Zhao, J., Jia, Q., Liang, J., Wang, H.: A cognitive-mechanistic human reliability analysis framework: A nuclear power plant case study. arXiv preprint arXiv:2504.18604 (2025)

work page Pith review arXiv 2025