Modelling the causation of accidents: human performance separated system and human performance included system

Jedes Jahr ereignen sich weltweit Millionen von Arbeitsunfällen, die zahlreiche Opfer fordern und enorme wirtschaftliche Verluste zur Folge haben. Vorangegangene Studien aus dem Feld der Risikoeinschätzung zeigten, dass es wichtig ist die Wahrscheinlichkeit von Faktoren, welche zum Auftreten von Unfällen beitragen, zu quantifizieren. Mehrere Methoden, wie z. B. die Technik zur Vorhersage der menschlichen Fehlerrate (Technique for Human Error Rate Prediction, THERP), wurden dafür vorgeschlagen, potenzielle Risikofaktoren zu bewerten und die Systemsicherheit zu verbessern. Diese Methoden haben jedoch einige Einschränkungen, wie z.B. ihre geringe Generalisierbarkeit, die Behandlung von Unfallursachen und menschlichem Einfluss als zwei voneinander getrennte Forschungsthemen, die Notwendigkeit ausgiebiger Datensätze, oder die ausschließliche Abhängigkeit von Expertenwissen. Um diese Einschränkungen zu überwinden, 1) klassifiziert diese Dissertation die Systeme in zwei Kategorien. Zum einen in von menschlichem Einfluss separierte Systeme (Human Performance Separated System, HPSS) und zum anderen in Systeme mit menschlichem Einfluss (Human Performance Included System, HPIS); 2) entwickelt ein auf Bayes‘schen Netzwerken (BN) basierendes Unfallkausalitätsmodell, das auf beide Arten von Systemen angewendet werden kann, um den Einfluss menschlicher Wahrnehmung in HPSS und den Einfluss menschlichen Versagens in HPIS zu untersuchen; 3) untersucht zwei Methoden zur Analyse menschlichen Versagens. Die erste Methode geht von einer kognitiven Wahrnehmung aus und die zweite behandelt das menschliche Versagen als essenziellen Teil des Systems. 4) schlägt eine innovative Taxonomie namens Contributors Taxonomy for construction Occupational Accidents (CTCOA) für HPIS vor, die nicht nur auf die Unfallkausalität abzielt, sondern auch zur Rückverfolgung menschlichen Versagens im Bauwesen verwendet werden kann. 5) erstellt BN-Beispielmodelle aus unterschiedlichen Industriesektoren. Dazu zählen Gasturbinenausfälle als typisches Beispiel für HPSS-Maschinenversagen, das Multi-Attribute Technological Accidents Dataset (MATA-D) für einfaches HPIS-Systemversagen und das Contributors to Construction Occupational Accidents Dataset (CCOAD) für komplexes HPIS-Systemversagen. Diese drei BN-Modelle zeigen, wie die von uns vorgeschlagene Methode in Bezug auf spezifische Probleme aus verschiedenen Industriesektoren angepasst und angewendet werden kann. Unsere Analyse zeigt die Effizienz der Kombination von Expertenwissen und mathematischer Unabhängigkeitsanalyse bei der Identifizierung der wichtigsten Abhängigkeitsbeziehungen innerhalb der BN-Struktur. Vor der Parameteridentifizierung auf Basis von Expertenwissen sollten die Auswirkungen der menschlichen Wahrnehmung auf die Modellparameter gemessen werden. Die vorgeschlagene Methodik basierend auf der Kombination der menschlichen Zuverlässigkeitsanalyse mit statistischen Analysen kann zur Untersuchung menschlichen Versagens eingesetzt werden.Millions of work-related accidents occur each year around the world, leading to a large number of deaths, injuries, and a huge economic cost. Previous studies on risk assessment have revealed that it is important to calculate the probabilities of factors that can contribute to the occurrence of accidents. Several methods, such as the Technique for Human Error Rate Prediction (THERP), have been proposed to evaluate potential risk factors and to improve system safety. However, these methods have some limitations, such as their low generalizability, treating accident causation and human factor as two separate research topics, requiring intensive data, or relying solely on expert judgement. To address these limitations, this dissertation 1) classifies systems into two types, Human Performance Separated System (HPSS) and Human Performance Included System (HPIS), depending on whether the system involves human performance; 2) develops accident causal models based on Bayesian Network (BN) that can be applied to both types of systems while examining the influence of human perception in HPSS and human errors in HPIS; 3) examines two methods for the analysis of human errors with the first method based on the cognitive view and the other method treating human errors as an essential part of the system; 4) proposes an innovative taxonomy as an example for HPIS, known as the Contributors Taxonomy for Construction Occupational Accidents (CTCOA), which not only targeting accident causation, but can also be used for tracking human error in construction; 5) builds example BN models in the different industrial sectors, including gas turbine failures as a typical example of HPSS machine failures, Multi-Attribute Technological Accidents Dataset (MATA-D) as simple HPIS failures, and Contributors to Construction Occupational Accidents Dataset (CCOAD) as complex HPIS failures. These three types of BN models demonstrate how our proposed methodology can be adapted to specific questions and how it can be applied in various industrial sectors. Our analysis demonstrates that it is efficient to combine expert judgement with mathematical independence analysis to identify the main dependency links for the BN structure in all models. The influence of human perception on model parameters should be measured before these parameters being identified based on expert judgement. Our proposed methodology can be used to study human errors by combining traditional human reliability analysis with statistical analysis

A MULTI-FUNCTIONAL PROVENANCE ARCHITECTURE: CHALLENGES AND SOLUTIONS

In service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements)

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks

In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety

Extreme Design Events due to Combined, Non-Gaussian Loading

A major design driver for marine systems is lifetime performance. How a vessel responds in harsh environments has stark consequences for safety and operability, necessitating the consideration of lifetime performance analysis during the design stage. However, extreme events associated with marine dynamic systems may not be caused by the most extreme ocean environments, like the largest wave. Some severe vessel responses may be due to simultaneous combinations of potentially correlated, non-Gaussian loading, which may be excited by any number of wave profiles. Different analytical methods based on extrapolation or solving for threshold exceedances can examine certain aspects of this problem: extreme system responses, combined loading, and long exposures to harsh excitation. But these methods, in general, do not retain the wave profiles which lead to extreme responses. These waves profiles can drive high-fidelity codes, like Computational Fluid Dynamics or Finite Element Analysis time-domain simulations, to give pressure and loading distributions. Such analyses can give an overall account of a system during lifetime events and refine estimates of system performance due to lifetime loading. The Design Loads Generator (DLG) was developed to construct wave profiles that lead to a distribution of linear extreme responses, given an operational profile and exposure period. However, there are some limitations when applying the DLG method to non-linear problems. Some marine systems may experience extreme responses due to varying combinations of non-linear loading. If those loads are strongly correlated, or have an unknown correlation, it is unclear how the capability of the DLG, which considers a single linear load, can be utilized. It may also be desirable to consider lifetime system performance, and not performance conditioned on a specific excitation input, as is estimated by the DLG framework. This dissertation addresses those concerns by expanding the DLG method to what is called the non-linear Design Loads Generator (NL-DLG) process. Given a complex system, operational profile, and exposure, the NL-DLG process uses the DLG capability to determine an ensemble of excitation inputs which lead to lifetime extreme events. Unlike the DLG, which is developed for a single response, the NL-DLG process considers the interaction of multiple stochastic processes which excite the system. These processes, which may be non-Gaussian, are examined so that the resulting ensemble of excitation inputs are demonstrably exhaustive in generating possible defined responses. Short excitation inputs are constructed that estimate the same distribution of responses as would full Monte Carlo Simulations (MCS). Instead of conducting the necessary large number of full-exposure MCS for converged statistics of joint responses, the ensemble of short excitation inputs assembled by the NL-DLG process approximates that same distribution. Various examples are given in this dissertation where comparisons between MCS and NL-DLG extreme value probabilities validate the method. For a complex system with a threshold of allowable responses, the ensemble of NL-DLG generated inputs can estimate an exceedance probability, given the exposure and operational profile. This threshold may be multi-dimensional and a non-linear function of multiple loads. The NL-DLG process examines complex system responses due to combined loading, and maintains links back to the excitation environment, without the computational cost associated with brute-force MCS. These capabilities give deeper insights into system responses, and aid in the design of safer, better operating systems.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144024/1/harleigh_1.pd

Quantitative Research Methods for Political Science, Public Policy and Public Administration for Undergraduates: 1st Edition With Applications in Excel

Quantitative Research Methods for Political Science, Public Policy and Public Administration for Undergraduates: 1st Edition With Applications in Excel is an adaption of Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R). The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. This new version is designed specifically for undergraduate courses. It omits large portions of the original text that focused on calculus and linear algebra, expands and reorganizes the content on the software system by shifting to Excel and includes guided study questions at the end of each chapter.https://dc.etsu.edu/etsu-oer/1003/thumbnail.jp

PSA 2016

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems