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This chapter reviews the safety of railway operation in Europe particularly by examining the causes of fatalities over periods of up to three decades ending in 2017. Fatalities are examined to passengers, staff, level crossing users, and trespassers, together with a brief look at suicides. The accidents that attract most attention are fatal train collisions and derailments because they can result in multiple fatalities, and are in most cases wholly the responsibility of the railway operators. However, train accidents are infrequent, and account for only about 1% of all railway fatalities if suicides are included, or 3% if they are not. The fatal train accident rate per train-kilometre fell at a rate of 5.4% per year between 1990 and 2017 and was 77% lower in 2017 than it had been in 1990. This chapter goes on to discuss level crossings, which account for far more fatalities than train accidents, personal accidents, accidents to trespassers, and suicides. This chapter ends with a brief look at the evidence of the effect of rail restructuring on safety.

The manufacturing industry perceives government standards as an attempt to unnecessarily increase production cost. This may be due to lack of acceptable models for demonstrating the associated benefits to industry. It was the goal of this study to develop a simulation model for predicting the performance of a manufacturing safety programme (SP).

The principles of system‐dynamics were applied to identify the relevant safety‐related components and their relationships. A simulation model for evaluating periodic performance of a manufacturing SP was then developed. A set a dynamic equations for predicting factory accidents or preventions and the monetary saving were the performance measures. Two set of factory data: non‐SP (1979) and SP (1991‐2004) were collected from a bottling company. The parameters of the model were estimated using the first set while it was validated with the second and associated monetary saving computed.

Solutions to factory accidents or preventions yielded exponential functions. The means and standard deviations of the predicted and actual accidents were 32 and 5.66; and 30 and 7.46, respectively. The corresponding values for predicted and actual preventions were 55 and 10.47; and 59 and 7.45, respectively. There were no significant differences between the predicted and actual for the accidents and preventions, respectively, at 5 per cent level. The predicted SP saving per annum was 6.96 millions.

The model is a useful tool for setting profitable manufacturing safety standards and effective SP management.

The purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.

Based on the 2–4 Model, a framework of the ACM-HC was constructed, and the accident causal factors (CF) were extracted from four aspects of human, material, environment and management. In addition, association rule mining (ARM) was introduced to analyze accident investigation reports to obtain the interrelationships between the factors. Based on the framework, factors and ARM results, the ACM-HC was established. Finally, the ACM-HC was verified with a tunnel collapse accident.

Both the external and internal causes of contractor cause accidents. The flaws of safety management of other stakeholders are external causes. In terms of the internal causes, there are four stages: direct causes, indirect causes, radical causes and root causes. More specifically, the direct causes refer to the unsafe acts and the unsafe conditions; ineffective safety supervision and poor individual factors of frontline workers constitute the indirect causes; the radical causes lie in the flaws of construction procedures and technical schemes; the root causes are related to the poor individual factors of decision makers and managers.

The ACM-HC expresses the causes, sequence and mechanism of highway construction accidents in a visual way. In addition, this study describes a process of using a qualitative–quantitative hybrid approach to establish a modified ACM, which provides a different perspective for the establishment of an ACM.

Under-reporting of occupational accidents is a common problem in many countries. This is mainly because of the shortfalls in accident reporting and recording systems. Construction industry being a hazardous industry, the rate of accidents is higher compared with other industries and apparently a high rate of under-reporting. The purpose of this paper is to investigate the rate of under-reporting, significant reasons for under-reporting and identify the shortcomings in the existing accident reporting system in Sri Lanka in aiming to recommend efficient mechanisms for occupational accident recording and reporting to construction industry.

Both secondary and primary data were tapped to gather required data. The secondary data were extracted from the records available in year 2014-2015 at the office of the commissioner for workmen’s compensation and the industrial safety division of the Department of Labor (DoL) to analyze the rate of under-reporting. The primary data were obtained through expert interviews to explore the gaps in reporting system and to identify mechanisms to reduce under-reporting.

The findings revealed 80 per cent of construction accidents are under-reported. Eight gaps in the current accident recording and reporting system and key recommendations at organizational and national level for its improvements were identified.

The findings provide an insight of occupational safety and health (OSH) practices in construction industry and it can be used as an eye opening flash for safety law-makers and practitioners to revisit the existing regulations and practices.

The study aims to examine the safety attitudes of workers, supervisors and managers in a UK‐based car manufacturing plant, and their relationship with unsafe behaviour and accidents.

A questionnaire methodology is used to measure safety attitudes and perceptions. The data are analysed using factor analysis and hierarchical multiple regression.

The factor structure of the safety climate at the plant comprised three factors: managers' concern for safety; workers' response to safety; conflict between production and safety, which correspond to those found in previous studies in the UK manufacturing sector. Whilst safety climate did not predict accident involvement at the plant, workers' response to safety and conflict between production and safety significantly predicted unsafe behaviour. Perceptions of the work environment had important effects as a significant predictor of both accidents and unsafe behaviour. However, job communication failed to predict either safety outcome. There was little difference in the strength of the safety climate perceived across hierarchical levels.

It is recommended that future research should examine the direct effects of organisational factors beyond the strictures of the “safety culture” framework.

Safety interventions need to focus on how individuals perceive their immediate work environment, as well as improving safety policy and procedures, as these perceptions have most direct influence on safety outcomes.

This paper offers new direction for researchers and advice for those designing safety interventions aimed at reducing accidents.

The modern workplace contains many physical and interpersonal hazards to employee physical and psychological health/well-being. This chapter integrates the literatures on occupational safety (i.e., accidents and injuries) and mistreatment (physical violence and psychological abuse). A model is provided linking environmental (climate and leadership), individual differences (demographics and personality), motivation, behavior, and outcomes. It notes that some of the same variables have been linked to both safety and mistreatment, such as safety climate, mistreatment climate, conscientiousness, and emotional stability.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.