Safety Risk Analysis Research Articles

Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method

China’s urban rail transit (URT) construction is coming into the stage of rapid development under the guidance of national policies. However, the URT construction projects belong to high-risk projects and construction safety accidents occur frequently. Presently, safety risk management is in continuous development. Unfortunately, due to risk data deficiencies and lack of relationship between participants and safety risk factors, most of the research results cannot be well applied to URT projects. To overcome the limits, this paper has applied the text mining method into safety risk analysis. Through word frequency analysis and cluster analysis, 15 safety risk factors and 3 participants are identified from 156 accident reports. In addition, the accident descriptive model has been established, which is composed of indirect safety risk factors (management defects), direct safety risk factors and participants. In this model, each accident is the standardized description of the corresponding accident information. This is useful for risk data accumulation and analysis. Then the network structure analysis and risk assessment methods are utilized to make clear 63 relationships among participants, management defects and direct safety risk factors. Subsequently, the risk value of each relationship is evaluated. These safety risk information is integrated into the accident descriptive model by using accident points. Finally, ABC analysis which is a popular and effective method used to classify items into specific categories that can be managed and controlled separately is used to analyze the safety risk management’s core process(A), important process(B) and general process(C) in the accident descriptive model. The research results show that the constructor should pay attention to construction coordination, safety specifications, safety measures and personnel education, the supervisor should attach importance to timely communication, the monitoring unit should pay attention to advanced forecast and dynamic control. The main research contributions are as follows: (1) A method of obtaining risk data from unstructured content has been provided; (2) The accident descriptive model could be utilized for risk data continuous accumulation; (3) The emphases of URT construction safety risk management are made clear.

Fuzzy-Bayesian-network-based Safety Risk Analysis in Railway Passenger Transport

This study presents a fuzzy Bayesian network (FBN) method to analyze the influence on the safety risk of railway passenger transport applying different risk control strategies. Based on the fuzzy probability of the basic event determined by the expert group decision method, the proposed FBN method can reasonably predict the probability of railway passenger safety risk. It is also proven that control the risk in the safety management of railway passenger transport will be the most effective way to reduce the risk probability of the railway passenger transport safety.

Food safety risk analysis from the producers' perspective: prioritisation of production process stages by HACCP and TOPSIS

From the manufacturers perspective, the hazard analysis and critical control point (HACCP) system nowadays represents the mainly way to implement the food safety risk management in food industries. Nevertheless, the identification and prioritisation of hazards as the outcome of the first principle of HACCP is not sufficient to identify production process stages that more significantly and critically contribute to the consumer's risks. With this recognition, the present paper proposes a quantitative risk assessment (QRA) approach based on HACCP and technique for order of preference by similarity to ideal solution (TOPSIS) to individuate production process phases on which implementing corrective actions to improve the consumers' safety. The designed methodological approach is implemented on the smoked salmon manufacturing process of a real Sicilian industry.

Food safety risk analysis from the producers

Food safety risk analysis from the producers

Industrial Safety Risk Analysis Using Spatial Analytics and Data Mining

Industrial Safety Risk Analysis Using Spatial Analytics and Data Mining

Slip safety risk analysis of surface properties using the coefficients of friction of rocks

This study was conducted to determine the most appropriate surface processing techniques (SPT), environmental conditions (EC) and surface roughness (SR) to minimize the risk of slipping when pedestrians walk on a floor covering of rocks barefoot and with shoes. Coefficients of friction (COFs) and values of SR were found using five different types of rocks, four SPT and two (ramp and pendulum) tests. Results indicate that the parameters which affect the COF values of rocks include SR, EC and SPT. Simple linear regression was performed to examine the relationship between the values of the COF and the SR. The value of the COF was identified as R2 ≥ 0.864. Statistical results, which are based on experimental measurements, show that rocks are classified according to their safe use areas depending on their COF and SR values.

Fuzzy comprehensive Bayesian network-based safety risk assessment for metro construction projects

This paper presents a systemic decision-support approach to safety risk analysis for metro construction projects under uncertainty using a fuzzy comprehensive Bayesian network (FCBN), which combines the fuzzy comprehensive evaluation method (FCEM) and a Bayesian network (BN). The results of the safety risk assessment based on the FCBN are composed of three aspects: risk probability, risk loss, and risk controllability. In this assessment, two of the aspects—risk loss and risk controllability—are calculated in terms of intervals or fuzzy numbers. Through the application of the FCEM, the levels of risk loss and risk controllability are then estimated. The risk probability is calculated from the BN, in which the relationships among the dependent variables are expressed in the form of a directed graph. A comprehensive safety risk assessment allows engineers to assess potential safety hazards and provides a basis for dynamic early risk warning and control ahead of metro construction, which is acquired by combining the FCEM and the BN. A case study relating to safety risk analysis in the construction of the Dalian Metro in China is used to verify the feasibility of the approach as well as its application potential. A comparison of the results with the actual construction state shows its effectiveness in estimating the risk level of a metro construction project under uncertainty. The proposed approach provides a powerful tool with which planners and engineers can systematically assess and mitigate the inherent risks associated with metro construction.

Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

Identification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis.

Identifying High-alert Medications in a University Hospital by Applying Data From the Medication Error Reporting System.

To facilitate safe use of high-alert medications, lists of medications posing higher risks for medication errors (MEs) and harmful effects have been compiled. These lists can be general or reflect clinical practices in specific settings. Less common has been to compile a hospital-specific list applying data from the organization's ME reporting system. Our objective was to demonstrate a method for compiling such a high-alert medication list in a university hospital. Of the eighteen 136 MEs reported during 2007 to 2013, ME reports with medications coded as a contributing factor to the incident were included (n = 249). The involved medications were identified and compared with the hospital's drug consumption and Institute for Safe Medication Practice's List of High-Alert Medications. The report narratives of MEs with most reported and high-alert medications (120 reports) were qualitatively content analyzed. The included 249 reports concerned 280 medications, of which 33% were classified as high-alert medications by the Institute for Safe Medication Practice. The most common therapeutic groups were antibacterials for systemic use (13%), psycholeptics (10%), analgesics (9%), and antithrombotic agents (9%). The most common high-alert medications were oxycodone (5%), enoxaparin (3%), and noradrenaline (3%). Serious patient harm (3%) was related to cefuroxime, enoxaparin, ibuprofen, midazolam, propofol, and warfarin. A half of the MEs were related to parenteral preparations. The qualitative content analysis revealed the key process safety risks of the most reported and high-alert medications. The method is applicable for compiling a hospital-specific high-alert medication list and related analysis of key process safety risks contributing to MEs.

Slip Safety Risk Analysis of Surface Properties by Determining Coefficients of Friction (COF) of Natural Stones

Calismada yalinayak ve ayakkabi ile yururken dogal tas zemin kaplamalarinda yayalarin kayma riskini azaltmak icin en uygun yuzey isleme tekniklerini ve cevre kosullarini belirlemeye calisilmistir. Bu calismada DIN EN 51097, DIN EN 51130 ve TS EN 14231 standartlari kullanilarak uc farkli yontemle yuzeyleri islenmis dogal taslarin surtunme katsayilari (COF) belirlenmistir. Surtunme katsayilari (COF), cilali, honlu, patinatolu ve eskitilmis yuzey isleme teknikleri ile uygulanan boyutlandirilmis siniflandirilmis 15 farkli dogal tas uzerinde egik duzlem testi ve pandul yontemi kullanilarak belirlenmistir. Dogal taslarin COF degerlerini etkileyen parametrelerin ortam kosullari (yas, kuru ve yagli) ve uygulanan yuzey isleme teknikleri oldugu bulunmustur. Dogal taslarin istatistiksel sonuclara gore COF degerlerine bagli olarak guvenli kullanim alanlarina gore siniflandirilmistir.

Dynamic safety risk modeling of process systems using bayesian network

Process complex systems in particular oil and gas plants due to dealing with hazardous materials at severe process conditions are much prone to catastrophic accidents. In this context, safety risk analysis is a crucial tool to develop effective strategies to prevent accident and provide mitigative measures. Dynamic risk analysis (DRA) is one of the most practical approaches for risk analysis that helps provide safer operations of complex process systems. The present work is aimed at demonstrating the application of an integrated DRA approach to comprehensive quantitative modeling and analysis of the both aspects of risk, that is, probability and consequence assessments. In this approach, first, the worst case scenario is identified and then a robust tool is developed for dynamic accident scenario modeling and risk assessment by means of Bayesian Network. This approach is applied to risk analysis of a flammable liquid storage system at a gas refinery. The work provides valuable information on the identification and comprehensive analysis of worst case accident scenarios, their main consequences, critical basic events, and minimal cut sets which lead to accident scenarios and also for dynamic updating of probabilities and risk. The obtained results are more appropriate and rigorous to developing preventive and mitigative strategies for potential accident scenarios and thus increase the safety level in the complex process systems. © 2017 American Institute of Chemical Engineers Process Saf Prog 36: 399–407, 2017

Construction Safety Risk Modeling and Simulation.

By building on a genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data-driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute-based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency-magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety-related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.

Energy-based safety risk assessment: does magnitude and intensity of energy predict injury severity?

Although the quantity and quality of safety risk data have improved in recent years, available data do not link directly to natural principles and are, therefore, limited in their application and scientific extension. The present study aims to test the hypothesis that the quantity and intensity of energy observable prior to an incident predicts the severity of the incident. The hypothesis is built upon the theory that energy is translated to an injury through uncontrolled release of the energy, transfer of the energy to the human body and the vulnerability of the body and associated protective equipment. To test the hypothesis, a multi-phase experiment was conducted. First, over 500 injury reports were gathered from national databases and private companies. For each report, the leading information describing the work operations and environment and the lagging information describing the injury were extracted, separated and isolated. Second, the magnitude of the energy was estimated using only leading information. Once energy magnitude was quantified, the distribution was compared across injury severity levels using analysis of variance tests. As hypothesized, energy magnitude is a strong predictor of injury severity. Although computationally intensive, energy intensity, defined as the magnitude of energy divided by the area of contact between an object and the human body, showed strong predictive validity. This research indicates that energy-based safety risk analysis has predictive validity and is a promising line of scientific inquiry with the potential to increase our understanding of the natural phenomena that contribute to injuries.

Enhancing Safety Risk Management with Quantitative Measures

At the airfield in hub airports, many activities occur that involve a range of participants, including various-size aircraft, ground vehicles, and workers. The safety management system is FAA's approach for systematically managing aviation safety. A major component of the safety management system is safety risk management (SRM), which entails analysis, assessment, and control of safety risks, including risks on the airfield. Current SRM has few specific safety models to estimate the likelihood or frequency of risks. This paper presents an example for development and incorporation of safety models into SRM. Specifically, it discusses safety models for runway incursion that use the following variables: total and general aviation operations, length of runway by type, number of taxiway intersections, snowfall, precipitation, number of hot spots, and construction activity. Categorization and processing of data were significant because each variable used could take on multiple forms, and some types of data involved review of airfield diagrams. The data used were from 137 U.S. hub airports for 2009 through 2014. For modeling, the negative multinomial distribution was used because it proved suitable for representing overdispersed data such as runway incursion frequency. Performance of the models was assessed through the goodness-of-fit measures of log likelihood, overdispersion, and cumulative residual plots. Models were developed for five severity categories of runway incursions and three types of surface events. The safety modeling approach presented here can serve as a foundation for development of other safety models that can be integrated into SRM to enable quantitative analysis of safety risks.

Mapping Risk Models/Methods onto a Complexity Spectrum

Abstract The rate at which accidents/incidents occur within the Air Transport System (ATS) has remained consistent over the past two decades (European Commission, 2016). If such a rate is maintained and the ATS grows as expected (AirBus, 2013; Boeing, 2013), then the ATS may no longer be able to be considered ultra-safe. The purpose of this work is to develop a framework by a set of defining system complexity parameters that allow mapping of conceptual safety models and risk analysis methods in relation to their complexity domain analysis range. The study asks: What is the complexity domain range for commonly used models/methods? To answer this question the following models and methods are described and analyzed: Domino model, Swiss Cheese model, Bow-Tie model, Functional Resonance Analysis Method (FRAM), Systems-Theoretic Accident Model and Processes (STAMP). The Cynefin framework uses four domains to represent the system complexity spectrum. In combination with the Cynefin domains, the following system complexity parameters are used for the analysis: Coupling - the ability to represent a loosely coupled system, Linearity- the ability to represent non-linear relationships, and Dynamics - the ability to represent a system through time. The obvious/simple domain of the Cynefin framework requires only that a model/method be able to represent tightly coupled, linear, static systems. While the complicated domain requires that a model/method be able to represent a moderately loose level of coupling, a degree of non-linearities among system actors/elements, and at least a moderate ability to dynamically represent the system. The complex domain requires a model/method to be able to represent a very loosely coupled system, with many non-linear relationships, in a highly dynamic manor. The results of this analysis showed that the FRAM method, Bridge model, and RMF are the most capable when assessing complicated highly interconnected systems (such as the ATS). However, it was also concluded that while when the ATS is operating within a normal range these models/methods may be sufficient, they are not able to effectively inform decision makers when on rare occasions the system shifts into the complicated domain. Therefore, since the ATS represents a common system type (having a large number of actors/elements that are very intertwined, and dependent upon the skills of their operators) a new term is coined – Highly Complicated and Occasionally Complex (HCOC) systems. An HCOC system is one that predominantly follows the same pattern of rules for the majority of its operations, however when occupational novel conditions are encountered which cause the system to shift to the complex domain and thus the underlying analysis methods are no longer sufficient.

APPLYING LIFE CYCLE ASSESSMENT TO ANALYSE RESOURCE CONSUMPTIONS AND WASTE GENERATION IN A LABORATORY ANIMALS PRODUCTION

The objective of this study was to quantify and analyze resource consumptions of a laboratory rabbits production, in a Brazilian animal research facility, in order to produce a harmonized Health-Care Waste (HCW) classification, according to national regulations and international guidelines, as it is a basis to perform health and safety risk analysis, of animals, humans and environment impacts in HCW management. Methodologically, the principles and framework from Life Cycle Assessment were used to produce a door‑to‑door Life Cycle Inventory to produce the HCW classification and a short environmental impact assessment. A high electricity consumption (about 96%), for conditioning facilities maintenance, and a high water consumption transformed into effluents and emissions (about 97.5%) were observed. Specifically, from HCW classification a first analysis of mass pointed: effluents (45.92 %), emissions (52.54 %), biological waste (1.45%) and common waste (0.001 %). These data are primary estimates that lead to deeper analysis of toxic materials and contamination. Finally, the entire study produced a cross fertilization of information giving more transparency about the rationality implied in the modeling of processes and presentation of reports, enabling to relate the local scope of impact assessment through broader reflections on global social responsibility and eco-efficiency.

Reform and Innovation on the Organization Pattern of Food Safety in Sports Training and Sports Events

This study presents implement approaches for innovating the organization pattern of food safety in sports events from the perspective of establishing risk analysis and management of food safety in sports events, perfecting and improving the standard system of food safety in sports events and so on. Innovation means establishing a new production factor or provision function and brings a new and unprecedented combination of production factor and production condition into production system. Combining innovation theory, the paper discusses ways of innovating sports training by depending on the construction of the innovation system of sports training.

Identification and Analysis of Metro Foundation Construction Safety Risk Based on Fault Tree Analysis

Fuzzy theory and Bayesian network theory were based on the fault tree. The fault tree analysis method improved the fault analysis to obtain number of event's exact problem. Considering the characteristics of the fuzzy risk analysis at the same time, it also solved the problem of scale fault analysis modeling. The theory is applied to the cost of construction shield tunnel risk analysis, floating shield tunnel segments, dislocation of failure risk analysis and deep station foundation construction risk analysis. By security risk identification and evaluation process, it can be improved to subway construction process of deep foundation pit hazard anticipation. Before construction accidents to avoid and resolve security risks, thereby improving overall system safety performance. Based on this, the paper depth study the theory and methods of security risk identification and security risk assessment, further enrich the security risk management theory, and put forward to deal with the security risk response countermeasures.

Determining and verifying the safety integrity level of the safety instrumented systems with the uncertainty and security aspects

Safety and security aspects consist of two different group of functional requirements for the control and protection systems. In the paper it is proposed that the security analysis results can be used as a factor increasing or decreasing the risk level. It concerns a process of determining required safety integrity level of given safety functions. The authors propose a new approach for functional safety risk analysis. In this case the security factor influences the value of required safety integrity level SIL by changing the frequency of accident scenario. It can be done by using the methodology of modified risk graph. On the other hand there is a verification of required SIL fulfillment for designed safety-related system which implements safety function. In this case the result of security analysis is affecting uncertainty of probabilistic model parameters. The proposed method takes into consideration the sensitivity analysis of probabilistic models of E/E/PE or safety instrumented systems SIS as well as the uncertainty of probabilistic results. It uses differential factors, which are helpful for effective verification of required SIL of the E/E/PE or SIS systems taking into account results of sensitivity analysis and/or assessment of uncertainty ranges obtained from probabilistic models developed.

Review of Research Trends and Methods in Nano Environmental, Health, and Safety Risk Analysis.

Despite the many touted benefits of nanomaterials, concerns remain about their possible environmental, health, and safety (EHS) risks in terms of their toxicity, long-term accumulation effects, or dose-response relationships. The published studies on EHS risks of nanomaterials have increased significantly over the past decade and half, with most focused on nanotoxicology. Researchers are still learning about health consequences of nanomaterials and how to make environmentally responsible decisions regarding their production. This article characterizes the scientific literature on nano-EHS risk analysis to map the state-of-the-art developments in this field and chart guidance for the future directions. First, an analysis of keyword co-occurrence networks is investigated for nano-EHS literature published in the past decade to identify the intellectual turning points and research trends in nanorisk analysis studies. The exposure groups targeted in emerging nano-EHS studies are also assessed. System engineering methods for risk, safety, uncertainty, and system reliability analysis are reviewed, followed by detailed descriptions where applications of these methods are utilized to analyze nanomaterial EHS risks. Finally, the trends, methods, future directions, and opportunities of system engineering methods in nano-EHS research are discussed. The analysis of nano-EHS literature presented in this article provides important insights on risk assessment and risk management tools associated with nanotechnology, nanomanufacturing, and nano-enabled products.