An expert system for the development of a health and safety policy/risk assessment in the plastics industry

Health and safety policies may be regarded as the cornerstone for positive prevention of occupational accidents and diseases. The Health and Safety at Work, etc Act 1974 makes it a legal duty for employers to prepare and revise a written statement of a general policy with respect to the health and safety at work of employees as well as the organisation and arrangements for carrying out that policy. Despite their importance and the legal equipment to prepare them, health and safety policies have been found, in a large number of plastics processing companies (particularly small companies), to be poorly prepared, inadequately implemented and monitored. An important cause of these inadequacies is the lack of necessary health and safety knowledge and expertise to prepare, implement and monitor policies. One possible way of remedying this problem is to investigate the feasibility of using computers to develop expert system programs to simulate the health and safety (HS) experts' task of preparing the policies and assisting companies implement and monitor them. Such programs use artificial intelligence (AI) techniques to solve this sort of problems which are heuristic in nature and require symbolic reasoning. Expert systems have been used successfully in a variety of fields such as medicine and engineering. An important phase in the feasibility of development of such systems is the engineering of knowledge which consists of identifying the knowledge required, eliciting, structuring and representing it in an appropriate computer programming language.

Safety culture during major organisational change

This research examines the effect of major changes, in the external context, on the safety culture of a UK generating company. It was focused on an organisation which was originally part of the state owned Central Electricity Generating Board and which, by the end of the research period, was a self-contained generating company, operating in a competitive market and a wholly owned subsidiary of a US utility. The research represents an attempt to identify the nature and culture of the original organisation and to identify, analyse and explain the effects of the forces of change in moulding the final organisation. The research framework employed a qualitative methodology to investigate the effects of change, supported by a safety culture questionnaire, based on factors identified in the third report of the ACSNI Human Factors Study Group; Organising for Safety, as being indicators of safety culture. An additional research objective was to assess the usefulness of the ACSNI factors as indicators of safety culture. Findings were that the original organisation was an engineering dominated technocracy with a technocentric safety culture. Values and beliefs were very strongly held and resistant to change and much of the original safety culture survived unchanged into the new organisation. The effects of very long periods of uncertainty about the future were damaging to management/worker relationships but several factors were identified which effectively insulated the organisation from any of the effects of change. The forces of change had introduced a beneficial appreciation of the crucial relationship between safety risk assessment and commercial risk assessment.Although the technical strength of the original safety culture survived, so did the essential weakness of a low level of appreciation of the human behavioural aspects of safety. This led to a limited, functionalist world view of safety culture, which assumed that cultural change was simpler to achieve than was the case and an inability to make progress in certain areas which were essentially behavioural problems.The factors identified by ACSNI provided a useful basis for the site research methodology and for identifying areas of relative strength and weakness in the site safety arrangements.

Experiences on dynamic simulation software in chemical engineering education

Commercial process simulators are increasing interest in the chemical engineer education. In this paper, the use of commercial dynamic simulation software, D-SPICE® and K-Spice®, for three different chemical engineering courses is described and discussed. The courses cover the following topics: basic chemical engineering, operability and safety analysis and process control. User experiences from both teachers and students are presented. The benefits of dynamic simulation as an additional teaching tool are discussed and summarized. The experiences confirm that commercial dynamic simulators provide realistic training and can be successfully integrated into undergraduate and graduate teaching, laboratory courses and research. © 2012 The Institution of Chemical Engineers.

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

Predicting employee compliance with safety regulations, factoring risk perception

The purpose of this research was to develop a methodology that would evaluate employees' personality traits, demographic characteristics, and workplace parameters to predict safety compliance along with the moderating effect of risk perception. ^ One hundred and twenty five employees of a manufacturing facility were given questionnaires to gather their demographic and perception information. Surveys were also used to measure their personality characteristics, and periodic observations were recorded to document employee's safety compliance. A significant correlation was found between compliance and the worker's perception of management's commitment to safety (r = 0.27, p < 0.01), as well as with gender (r = −0.19, p < 0.05). Females showed a significantly higher average compliance (78%), than males (69%). These findings demonstrated the value of developing a model to predict safety behavior that would assist companies in maintaining a safe work environment, preventing accidents, ensuring compliance, and reducing associated costs. ^

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Achieving zero accidents—A strategic framework for continuous safety improvement in the construction industry

In the U.S., construction accidents remain a significant economic and social problem. Despite recent improvement, the Construction industry, generally, has lagged behind other industries in implementing safety as a total management process for achieving zero accidents and developing a high-performance safety culture. One aspect of this total approach to safety that has frustrated the construction industry the most has been “measurement”, which involves identifying and quantifying the factors that critically influence safe work behaviors. The basic problem attributed is the difficulty in assessing what to measure and how to measure it—particularly the intangible aspects of safety. Without measurement, the notion of continuous improvement is hard to follow. This research was undertaken to develop a strategic framework for the measurement and continuous improvement of total safety in order to achieve and sustain the goal of zero accidents, while improving the quality, productivity and the competitiveness of the construction industry as it moves forward. The research based itself on an integral model of total safety that allowed decomposition of safety into interior and exterior characteristics using a multiattribute analysis technique. Statistical relationships between total safety dimensions and safety performance (measured by safe work behavior) were revealed through a series of latent variables (factors) that describe the total safety environment of a construction organization. A structural equation model (SEM) was estimated for the latent variables to quantify relationships among them and between these total safety determinants and safety performance of a construction organization. The developed SEM constituted a strategic framework for identifying, measuring, and continuously improving safety as a total concern for achieving and sustaining the goal of zero accidents.

Development of safety performance functions for SafetyAnalyst applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Research-to-Practice (R2P) Tools for Improving Safety in Nighttime Highway Construction Work Zones

The safety of workers in nighttime roadway work zones has become a major concern for state transportation agencies due to the increase in the number of work zone fatalities. During the last decade, several studies have focused on the improvement of safety in nighttime roadway work zones; but the element that is still missing is a set of tools for translating the research results into practice. This paper discusses: 1) the importance of translating the research results related to the safety of workers and safety planning of nighttime work zones into practice, and 2) examples of tools that can be used for translating the results of such studies into practice. A tool that can propose safety recommendations in nighttime work zones and a web-based safety training tool for workers are presented in this paper. The tools were created as a component of a five-year research study on the assessment of the safety of nighttime roadway construction. The objectives of both tools are explained as well as their functionalities (i.e., what the tools can do for the users); their components (e.g., knowledge base, database, and interfaces); and their structures (i.e., how the components of the tools are organized to meet the objectives). Evaluations by the proposed users of each tool are also presented.

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

Modelling Provenance of Sensor Data for Food Safety Compliance Checking

Postprint

Safety Assessment Tool for Construction Zone Work Phasing Plans, 2016

The Highway Safety Manual (HSM) is the compilation of national safety research that provides quantitative methods for analyzing highway safety. The HSM presents crash modification functions related to freeway work zone characteristics such as work zone duration and length. These crash modification functions were based on freeway work zones with high traffic volumes in California. When the HSM-referenced model was calibrated for Missouri, the value was 3.78, which is not ideal since it is significantly larger than 1. Therefore, new models were developed in this study using Missouri data to capture geographical, driver behavior, and other factors in the Midwest. Also, new models for expressway and rural two-lane work zones that barely were studied in the literature were developed. A large sample of 20,837 freeway, 8,993 expressway, and 64,476 rural two-lane work zones in Missouri was analyzed to derive 15 work zone crash prediction models. The most appropriate samples of 1,546 freeway, 1,189 expressway, and 6,095 rural two-lane work zones longer than 0.1 mile and with a duration of greater than 10 days were used to make eight, four, and three models, respectively. A challenging question for practitioners is always how to use crash prediction models to make the best estimation of work zone crash count. To solve this problem, a user-friendly software tool was developed in a spreadsheet format to predict work zone crashes based on work zone characteristics. This software selects the best model, estimates the work zone crashes by severity, and converts them to monetary values using standard crash estimates. This study also included a survey of departments of transportation (DOTs), Federal Highway Administration (FHWA) representatives, and contractors to assess the current state of the practice regarding work zone safety. The survey results indicate that many agencies look at work zone safety informally using engineering judgment. Respondents indicated that they would like a tool that could help them to balance work zone safety across projects by looking at crashes and user costs.

Assessing Potential Laser Strike Protection Engineering Control for United States Coast Guard Aircraft

Thesis (Master's)--University of Washington, 2016-06