Relevant bibliographies by topics / Human Error Probability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Human Error Probability'

Author: Grafiati
Published: 28 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Human Error Probability"

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Taylor-Adams, Sally E. "CORE-DATA, A Human Error Probability Database." Safety and Reliability 13, no. 4 (December 1993): 6–16. http://dx.doi.org/10.1080/09617353.1993.11690625.

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Basra, Gurpreet, and Barry Kirwan. "Collection of offshore human error probability data." Reliability Engineering & System Safety 61, no. 1-2 (July 1998): 77–93. http://dx.doi.org/10.1016/s0951-8320(97)00064-1.

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Liu, Zhao Xia, and Lian Jun Chen. "Post-Accident Human Reliability Analysis and Control of Mine Hoisting System." Advanced Materials Research 616-618 (December 2012): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.461.

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Using the method of THERPand HCR this paper studies post- accident human error events of mine hoisting system which reflects accident consequence seriousness and accident treatment urgency. It ascertains cognitive failure probability P1, non-response probability P2 and the failure probability P3, and quantities and appraises degree of human reliability. Finally this paper analyzes causes of hoisting accident human errors ,by which probability human error can be reduced to the lowest limit.
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Xi, Yong Tao, and Chong Guo. "A Method for Marine Human Error Probability Estimate: APJE-SLIM." Applied Mechanics and Materials 97-98 (September 2011): 825–30. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.825.

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Safety is the eternal theme in shipping industry. Research shows that human error is the main reason of maritime accidents. Therefore, it is very necessary to research marine human errors, to discuss the contexts which caused human errors and how the contexts effect human behavior. Based on the detailed investigation of human errors in collision avoidance behavior which is the most key mission in navigation and the Performance Shaping Factors (PSFs), human reliability of mariners in collision avoidance was analyzed by using the integration of APJE and SLIM. Result shows that this combined method is effective and can be used for the research of maritime human reliability.
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Yang, Wei Jun, and Peng Xiao Jiang. "The Probability Analysis of Human Error in Teaching." Applied Mechanics and Materials 239-240 (December 2012): 1611–14. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1611.

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This paper thoroughly analyzed the human error in teaching work of colleges and universities, discussed its causes, mode and consequences, proposed the probability analysis methods. The analysis can be treated as a reference to prevent, control and management human error in teaching work for colleges and universities.
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Visser, M., and P. A. Wieringa. "PREHEP: human error probability based process unit selection." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 31, no. 1 (2001): 1–15. http://dx.doi.org/10.1109/5326.923264.

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Sun, Zhiqiang, Zhengyi Li, Erling Gong, and Hongwei Xie. "Estimating Human Error Probability using a modified CREAM." Reliability Engineering & System Safety 100 (April 2012): 28–32. http://dx.doi.org/10.1016/j.ress.2011.12.017.

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Di Pasquale, Valentina, Salvatore Miranda, Raffaele Iannone, and Stefano Riemma. "A Simulator for Human Error Probability Analysis (SHERPA)." Reliability Engineering & System Safety 139 (July 2015): 17–32. http://dx.doi.org/10.1016/j.ress.2015.02.003.

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De Felice, F., A. Petrillo, and F. Zomparelli. "A Hybrid Model for Human Error Probability Analysis." IFAC-PapersOnLine 49, no. 12 (2016): 1673–78. http://dx.doi.org/10.1016/j.ifacol.2016.07.821.

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Sun, Zhiqiang, Erling Gong, Zhengyi Li, Yingjie Jiang, and Hongwei Xie. "Bayesian estimator of human error probability based on human performance data." Journal of Systems Engineering and Electronics 24, no. 2 (April 2013): 242–49. http://dx.doi.org/10.1109/jsee.2013.00031.

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Dissertations / Theses on the topic "Human Error Probability"

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Burford, Eva-Maria. "The analysis of the strain level and the predicted human error probability for critical hospital tasks." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1005182.

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South African hospitals, as a result of numerous factors, have the problem of an increasing workload for nursing staff, which in turn may affect patient treatment quality. This project aimed at addressing patient treatment quality specifically from the perspective of worker capabilities by investigating the strain level and predicted human error probability associated with specific patient-centered tasks in the South African health care sector. This was achieved through two independent yet interlinked studies which focused on seven patient-centred tasks. The tasks analysed were the tasks of setting up and changing intravenous medication, administering injection and pill medication, measuring blood glucose, temperature and heart rate and blood pressure. In the first study, work environment and task characteristics, task structure and execution were analysed. In addition to the task execution, the resulting strain levels, in the form of heart rate measures and subjective ratings of workload, were studied. The second study determined the error protocols and predictive error probability within the healthcare environment for the seven pre-defined tasks. The results for the first study established that different organizational and environment factors could affect task complexity and workload. The individual task components and information processing requirements for each task was also established. For the strain analysis, significant results for the tasks were determined for heart rate frequency and the heart rate variability measures, but some of these were contradictory. For the second study, specific error protocols and error reporting data were determined for the hospital where this research was conducted. Additionally the predictive error probability for the pre-defined tasks was determined. This combined approach and collective results indicate that strain and predictive error probability as a result of task workload can be determined in the field as well as being able to identify which factors have an effect on task strain and error probability. The value of this research lies in the foundation that the gathered information provides and the numerous potential applications of this data. These applications include providing recommendations aimed at improving nursing work environment with regards to workload, improving patient treatment as a result of a reduction in errors and the potential foundation these results provide for future research
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Jakl, Tomáš. "Posouzení lidského činitele při obsluze vybraného stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443165.

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The master thesis is focused on the reliability of the human factor in the operation of the production machine. In the first part the basic legislative requirements for safety and reliability of work are presented. In the second part, the reliability of the human factor is discussed along with a description of selected methods for assessing the reliability of the human factor. The research section then concludes with a proposed methodology for human factors assessment for the manufacturing process. In the practical part, the proposed methodology is applied to the selected process, which includes risk identification and outputs from the selected methods. The thesis then concludes by recommending preventive measures to eliminate the identified risks.
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NASCIMENTO, CLAUDIO S. do. "Aplicacao da metodologia fuzzy na quantificacao da probabilidade de erro humano em instalacoes nucleares." reponame:Repositório Institucional do IPEN, 2010. http://repositorio.ipen.br:8080/xmlui/handle/123456789/9496.

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IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
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Shen, Wei-min, and 沈暐閔. "Development of a quantitative human-error-probability method based on fuzzy set theory." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/90770927507729188482.

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碩士
國立臺灣海洋大學
商船學系所
97
Human errors occur so long as activities taken place involve human-beings regardless of domains and operations in which such performances are undertaken. Statistically, Human error is one of the crucial factors contributing to accidents. Accordingly, the human error study is a very important topic and a variety of human reliability assessment (HRA) methods has been developed to tackle such problems. The HRA approach can be divided into three categories, those using a database, using expert judgment and those using quasi-expert-judgment. The approaches falling into the first category apply a database containing generic Human Error Probability (HEP) to the specific circumstance being assessed. The HEPs obtained based on the approach in the second category are required by asking experts directly with regard to the scenario under consideration. Alternatively, some approaches in third category generate HEPs by manipulating and interrogating a quasi database incorporating with expert judgment. However, the risk analysis based on the techniques within the second and third categories may involve a high level of uncertainty due to the lack of data. This may jeopardize the reliability of the results. Some researches have been devised to resolve such a difficulty and the human error studies based on the fuzzy-number concept is one of them. This is due to its significance of transforming qualitative information into quantitative attributes under circumstances where the lack or incompleteness of data exists. However, a drawback occurs in situations in which some variables have sufficient data to evaluate risks while others do not since the discriminating ability of the studies based on the fuzzy-number concept is too low. In order to overcome such a difficulty, this research is planning to establish a framework equipped with a flexible data-acquirement method of which the objective is to provide a high level of discriminating ability. This will be achieved by first establishing membership function for linguistic variables, secondly combing such variables using the fuzzy rule base method, thirdly obtaining the crisp values through the defuzzification process and finally transferring such crisp values into Fuzzy Failure Rate(FFR). The methodology established will be verified and examined using the data from the traditional HRA studies.
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Islam, TMR. "Human reliability assessments for the maintenance operation of marine systems." Thesis, 2017. https://eprints.utas.edu.au/23790/7/Islam_whole_thesis_ex_pub_mat.pdf.

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Human intervention plays a critical role in the maintenance operations of marine systems. Consequently human factors are identified as one of the main causes of accidents in marine systems especially during maintenance operations. Characterisation and assessments of human factors in the form of Human Reliability Assessment (HRA) is an important step to better understand accident causation during maintenance operations. This would help minimize human errors and enhance overall safety and reliability of the marine systems. The International Maritime Organization (IMO) recommends implementing HRA to quantitatively assess the effect of human errors as a part of quantitative risk analysis of shipping operations. However, HRA for the maintenance operations of marine systems is not given due attention. This PhD research is focused on developing novel methodologies to accurately estimate the Human Error Probability (HEP) during the maintenance operations of marine systems. The developed methodologies will help in better understanding accident causation, estimation of HEPs, and to develop the required strategies to minimize the HEP. This thesis contains seven chapters. The first chapter provides the introduction and general structure of the thesis. Second chapter presents development of a novel methodology to assess the HEP for the maintenance operation of marine systems. The developed methodology is applied to the maintenance procedures of a marine engine as a case study. The results showed that among the 43 considered activities, ‘inspection and overhaul of piston/piston rings’ have the lowest HEP meaning it has a low consequence for accidents. On the other hand, ‘fuel and lubricating oil filters pressure difference checking’ and ‘renew filter element’s activity have the highest HEP indicating it has highest chances of accidents. The third chapter presents a novel monograph as an easy-to-use tool to estimate HEP for marine operations. The developed monograph is applied to the maintenance procedures of a High Pressure (HP) fuel pump for estimating HEP. The results showed that ‘inspection of fuel injectors’, ‘renewing nozzles’ and ‘testing’ has the highest HEP. While the fourth chapter proposes a novel technique by revising and modifying the Human Error Assessment and Reduction Technique (HEART) to assess the HEP during the maintenance activities in marine operations. The developed methodology is applied to the maintenance procedures of a marine engine exhaust turbocharger as a case study. Application of the developed methodology confirms that extreme weather condition, extreme workplace temperature, high ship motion, high level of noise and vibration, and work overload and stress all increase the likelihood of human error as well as likelihood of potential accidents. The fifth chapter presents development of an HEP assessment technique using an advanced probabilistic technique named Bayesian Network (BN). The developed methodology is tested on the maintenance of marine engine’s cooling water pump for engine department and anchor windlass for deck department. The case study results showed that category “A” chief engineer/captain (highest rank) with 10 years or more experience and voyage duration of 1 month has the lowest HEP, and category “D” fourth engineer/third officer with 5 years’ experience and voyage duration of 4 months has the highest HEPs. As part of the HRA, extensive data collection activity was conducted. The details of this activity and outcome are reported in this thesis. The collected data is analysed for normality and also pair-wise significance test and presented in chapter 6. It helps to study generalization of the data and also to identify the relative importance of the factors. Workload and stress, and ship motion (roll and pitch) are identified to be critical factors affecting human performance on on-board maintenance operations. The collected data played an important role in testing and verifying earlier developed techniques and models. Chapter 7 includes the conclusions of the thesis. This thesis aims to serve as a comprehensive source of knowledge and technique to form a better understanding of human factors associated with maintenance activities in marine operations. It will assist in ensuring implementation of IMO requirement for safe and reliable maintenance activities and marine operations.
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Books on the topic "Human Error Probability"

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Gibson, W. Huw. The implementation of CORE-DATA, a computerised human error probability database. Sudbury: HSE Books, 1999.

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Schneider, Jörg, and Ton Vrouwenvelder. Introduction to safety and reliability of structures. 3rd ed. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 1997. http://dx.doi.org/10.2749/sed005.

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<p>Society expects that buildings and other structures are safe for the people who use them or who are near them. The failure of a building or structure is expected to be an extremely rare event. Thus, society implicitly relies on the expertise of the professionals involved in the planning, design, construction, operation and maintenance of the structures it uses.<p>Structural engineers devote all their effort to meeting society’s expectations effi ciently. Engineers and scientists work together to develop solutions to structural problems. Given that nothing is absolutely and eternally safe, the goal is to attain an acceptably small probability of failure for a structure, a facility, or a situation. Reliability analysis is part of the science and practice of engineering today, not only with respect to the safety of structures, but also for questions of serviceability and other requirements of technical systems that might be impacted by some probability.<p>The present volume takes a rather broad approach to safety and reliability in Structural Engineering. It treats the underlying concepts of safety, reliability and risk and introduces the reader in a fi rst chapter to the main concepts and strategies for dealing with hazards. The next chapter is devoted to the processing of data into information that is relevant for applying reliability theory. Two following chapters deal with the modelling of structures and with methods of reliability analysis. Another chapter focuses on problems related to establishing target reliabilities, assessing existing structures, and on effective strategies against human error. The last chapter presents an outlook to more advanced applications. The Appendix supports the application of the methods proposed and refers readers to a number of related computer programs.<p>This book is aimed at both students and practicing engineers. It presents the concepts and procedures of reliability analysis in a straightforward, understandable way, making use of simple examples, rather than extended theoretical discussion. It is hoped that this approach serves to advance the application of safety and reliability analysis in engineering practice.<p>The book is amended with a free access to an educational version of a Variables Processor computer program. FreeVaP can be downloaded free of charge and supports the understanding of the subjects treated in this book.
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executive, Health and safety. The Implementation of Core Data, a Computerised Human Error Probability Database. Health and Safety Executive (HSE), 1999.

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Osterlind, Steven J. The Error of Truth. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198831600.001.0001.

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The Error of Truth recounts the astonishing and unexpected tale of how quantitative thinking was invented and rose to primacy in our lives in the nineteenth and early twentieth centuries, bringing us to an entirely new perspective on what we know about the world and how we know it—even on what we each think about ourselves. Quantitative thinking is our inclination to view natural and everyday phenomena through a lens of measurable events, with forecasts, odds, predictions, and likelihood playing a dominant part. This worldview, or Weltanschauung, is unlike anything humankind had before, and it came about because of a momentous human achievement: namely, we had learned how to measure uncertainty. Probability as a science had been invented. Through probability theory, we now had correlations, reliable predictions, regressions, the bell-shaped curve for studying social phenomena, and the psychometrics of educational testing. Significantly, these developments in mathematics happened during a relatively short period in world history: roughly, the 130-year period from 1790 to 1920, from about the close of the Napoleonic era, through the Enlightenment and the Industrial Revolutions, to the end of World War I. Quantification is now everywhere in our daily lives, such as in the ubiquitous microchip in smartphones, cars, and appliances, in the Bayesian logic of artificial intelligence, and in applications in business, engineering, medicine, economics, and elsewhere. Probability is the foundation of our quantitative thinking. Here we see its story: when, why, and how it came to be and changed us forever.
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Ryle, Cym Anthony. Risk and Reason in Clinical Diagnosis. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190944001.001.0001.

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This book provides, without the use of specialist language, a description of diagnostic reasoning and error and a discussion of steps that could improve diagnostic accuracy. Drawing on work in cognitive psychology, it presents the key characteristics of human reasoning. It notes that complex cognitive tasks such as medical diagnosis require a synergy of intuition and analytical thinking and introduces the concept of bias. The book considers the value of current classifications of disease, the meaning of diagnostic thresholds, and the potential for overdiagnosis. It examines the role of the patient-centred approach in this context. It develops a description of the diagnostic process, provides illustrative examples and metaphors, and refers to the dual-process model. It suggests that medical training does not consistently provide a coherent account of diagnostic thinking and the associated risks of error. It considers the role of probability in diagnostic reasoning, noting the contribution and the limitations of both informal and mathematical estimates. It refers to clear evidence that error in medical diagnosis is a prevalent and potent cause of harm and may result from systems factors or cognitive glitches such as bias and logical fallacy. It presents cases with commentaries, highlighting the cognitive processes in diagnostic successes, near misses, and disasters. It concludes with proposals for change, notably in institutional culture; in professional culture, education, and training; and in the structure of medical records. The book advocates the development and deployment of computerized diagnostic decision support. It argues that these changes could significantly enhance patient safety.
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Book chapters on the topic "Human Error Probability"

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Gyoji, Hikaru, Tania Giovannetti, Rachel Mis, Caitlyn Vega, Lorena Silva, Atsuya Shirotori, Yuki Nagasawa, et al. "Statistical Analysis of Micro-error Occurrence Probability for the Fitts’ Law-Based Pointing Task." In Human Interface and the Management of Information. Visual Information and Knowledge Management, 317–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22660-2_22.

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Ayele, Y., and A. Barabadi. "Human error probability estimation of maintenance activities in cold operating environment based on Bayesian network." In Risk, Reliability and Safety: Innovating Theory and Practice, 938–44. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-141.

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Jin, Jianqiang, Kaicheng Li, and Lei Yuan. "A Fuzzy and Bayesian Network CREAM Model for Human Error Probability Quantification of the ATO System." In Lecture Notes in Electrical Engineering, 567–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2914-6_53.

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Walrand, Jean. "Speech Recognition: A." In Probability in Electrical Engineering and Computer Science, 205–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_11.

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AbstractSpeech recognition can be formulated as the problem of guessing a sequence of words that produces a sequence of sounds. The human brain is remarkably good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that the listeners hear a corrupted version of the speech.Computers are getting much better at speech recognition and voice command systems are now common for smartphones (Siri), automobiles (GPS, music, and climate control), call centers, and dictation systems. In this chapter, we explain the main ideas behind the algorithms for speech recognition and for related applications.The starting point is a model of the random sequence (e.g., words) to be recognized and of how this sequence is related to the observation (e.g., voice). The main model is called a hidden Markov chain. The idea is that the successive parts of speech form a Markov chain and that each word maps randomly to some sounds. The same model is used to decode strings of symbols in communication systems.Section 11.1 is a general discussion of learning. The hidden Markov chain model used in speech recognition and in error decoding is introduced in Sect. 11.2. That section explains the Viterbi algorithm. Section 11.3 discusses expectation maximization and clustering algorithms. Section 11.4 covers learning for hidden Markov chains.
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Embrey, David. "Approaches to human error probability data collection." In Safety, Reliability and Risk Analysis, 367–73. CRC Press, 2013. http://dx.doi.org/10.1201/b15938-59.

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Duffey, R. "The Probability and Statistics of Human Error." In International Encyclopedia of Ergonomics and Human Factors, Second Edition - 3 Volume Set. CRC Press, 2006. http://dx.doi.org/10.1201/9780849375477.ch152.

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"The Probability and Statistics of Human Error." In International Encyclopedia of Ergonomics and Human Factors - 3 Volume Set, 790–800. CRC Press, 2006. http://dx.doi.org/10.1201/9780849375477-162.

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Porter, Theodore M. "The Errors of Art and Nature." In The Rise of Statistical Thinking, 1820-1900, 97–115. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691208428.003.0005.

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This chapter analyzes the law of facility of errors. All the early applications of the error law could be understood in terms of a binomial converging to an exponential, as in Abrahan De Moivre's original derivation. All but Joseph Fourier's law of heat, which was never explicitly tied to mathematical probability except by analogy, were compatible with the classical interpretation of probability. Just as probability was a measure of uncertainty, this exponential function governed the chances of error. It was not really an attribute of nature, but only a measure of human ignorance—of the imperfection of measurement techniques or the inaccuracy of inference from phenomena that occur in finite numbers to their underlying causes. Moreover, the mathematical operations used in conjunction with it had a single purpose: to reduce the error to the narrowest bounds possible. With Adolphe Quetelet, all that began to change, and a wider conception of statistical mathematics became possible. When Quetelet announced in 1844 that the astronomer's error law applied also to the distribution of human features such as height and girth, he did more than add one more set of objects to the domain of this probability function; he also began to break down its exclusive association with error.
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"Prediction Model of Human Error Probability in Autonomous Cargo Ships." In Proceedings of the International Seminar on Safety and Security of Autonomous Vessels (ISSAV) and European STAMP Workshop and Conference (ESWC) 2019, 110–24. Sciendo, 2020. http://dx.doi.org/10.2478/9788395669606-010.

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Tentori, Katya. "What Can the Conjunction Fallacy Tell Us about Human Reasoning?" In Human-Like Machine Intelligence, 449–64. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.003.0022.

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This chapter briefly summarizes some the main results obtained from more than three decades of studies on the conjunction fallacy. It shows that this striking and widely discussed reasoning error is a robust phenomenon that can systematically affect the probabilistic inferences of both laypeople and experts, and it introduces an explanation based on the notion of evidential impact in terms of contemporary Bayesian confirmation theory. Finally, the chapter tackles the open issue of the greater accuracy and reliability of impact assessments over posterior probability judgments and outlines how further research on the role of evidential reasoning in the acceptability of explanations might contribute to the development of effective human-like computing.
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Conference papers on the topic "Human Error Probability"

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Duffey, Romney D., and John W. Saull. "The Probability and Management of Human Error." In 12th International Conference on Nuclear Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/icone12-49287.

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Embedded within modern technological systems, human error is the largest, and indeed dominant contributor to accident cause. The consequences dominate the risk profiles for nuclear power and for many other technologies. We need to quantify the probability of human error for the system as an integral contribution within the overall system failure, as it is generally not separable or predictable for actual events. We also need to provide a means to manage and effectively reduce the failure (error) rate. The fact that humans learn from their mistakes allows a new determination of the dynamic probability and human failure (error) rate in technological systems. The result is consistent with and derived from the available world data for modern technological systems. Comparisons are made to actual data from large technological systems and recent catastrophes. Best estimate values and relationships can be derived for both the human error rate, and for the probability. We describe the potential for new approaches to the management of human error and safety indicators, based on the principles of error state exclusion and of the systematic effect of learning.
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Angelopoulou, A., K. Mykoniatis, and N. R. Boyapati. "A simulation model for estimating human error probability." In THE EUROPEAN MODELING AND SIMULATION SYMPOSIUM. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.emss.038.

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Duffey, Romney B., and Tae Sung Ha. "Human Reliability, Experience and Error Probability: A New Benchmark." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75861.

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In this paper, we apply a totally new approach to benchmark human reliability analysis, which is derived directly from the Universal Learning Curve (ULC) for homo-technological system (HTS) outcomes. We compare the latest second-generation predictions, based on the well-known and proven learning hypothesis, against some of the common Human Reliability Analysis (HRA) methods used to date for analyzing human reliability in transient and accident analysis. Therefore, we provide a straightforward, general and simple methodology for evaluating and predicting the Human Error Probability (HEP) in transients for accident risk prediction and reduction, as validated against all the available data, producing a completely independent assessment of the uncertainty.
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Brehm, Eric, Robert Hertle, and Markus Wetzel. "Influence of Human Error on Structural Reliability." In IABSE Workshop, Helsinki 2017: Ignorance, Uncertainty, and Human Errors in Structural Engineering. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2017. http://dx.doi.org/10.2749/helsinki.2017.024.

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In common structural design, random variables, such as material strength or loads, are represented by fixed numbers defined in design codes. This is also referred to as deterministic design. Addressing the random character of these variables directly, the probabilistic design procedure allows the determination of the probability of exceeding a defined limit state. This probability is referred to as failure probability. From there, the structural reliability, representing the survival probability, can be determined. Structural reliability thus is a property of a structure or structural member, depending on the relevant limit states, failure modes and basic variables. This is the basis for the determination of partial safety factors which are, for sake of a simpler design, applied within deterministic design procedures. In addition to the basic variables in terms of material and loads, further basic variables representing the structural model have to be considered. These depend strongly on the experience of the design engineer and the level of detailing of the model. However, in the clear majority of cases [1] failure does not occur due to unexpectedly high or low values of loads or material strength. The most common reasons for failure are human errors in design and execution. This paper will provide practical examples of original designs affected by human error and will assess the impact on structural reliability.
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DeKett, N. "Potential Improvements in Human Error Probability Seed Optimizer Algorithms." In Transactions - 2020 Virtual Conference. AMNS, 2020. http://dx.doi.org/10.13182/t122-32609.

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DeKett, N. "Potential Improvements in Human Error Probability Seed Optimizer Algorithms." In Transactions - 2020 Virtual Conference. AMNS, 2020. http://dx.doi.org/10.13182/t32609.

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Yang, Zaili, Jin Wang, Merzouki Rochdi, and Ouldbouamama Belkacem. "Bayesian modelling for human error probability analysis in CREAM." In 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE). IEEE, 2011. http://dx.doi.org/10.1109/icqr2mse.2011.5976584.

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Duffey, Romney B., and John W. Saull. "The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89476.

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Abstract:
Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the “learning hypothesis” that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new “best” equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world’s commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum failure rate, and can be utilized for probabilistic risk analysis purposes.
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Jiang, Yingjie, Zhiqiang Sun, Hongwei Xie, and Erling Gong. "A Human Error Probability Quantification Method Based on SRK Framewok." In 2010 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2010. http://dx.doi.org/10.1109/iciii.2010.186.

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Jiang, Ying-jie, Zhi-qiang Sun, Hong-wei Xie, and Er-ling Gong. "A human error probability quantification method based on CREAM+Bayes." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5578966.

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Reports on the topic "Human Error Probability"

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Reece, W. J., B. G. Gilbert, and R. E. Richards. Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR): Data manual. Part 2: Human error probability (HEP) data; Volume 5, Revision 4. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10188390.

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