Improve Safety Performance Research Articles

A new approach to assess safety performance of rail regions with an emphasis on the resources and equipment of each region

Providing a comprehensive overview of the safety status of similar large-scale rail spatial units (such as existing Rail Regions (RRs) in a country) is a first but very important step in identifying policies that can accelerate progress in improving rail safety. The aim of the current study is to present a new approach to assess RR's safety performance with an emphasis on the Resources and Equipment (R&E) of each RR. To reach the study goal, first, a conceptual framework is developed to make a relationship among exposure, safety, and R&E of RRs. In the next step, Data Envelopment Analysis (DEA) model with Fuzzy-Delphi method is used to evaluate the safety performance of different RRs. In this evaluation, the data regarding safety status of 20 RRs of Iran in 2020 are used. Results showed that Region #1 has the best safety efficiency and Northeast Region #20 has the lowest safety efficiency among other regions. It was also revealed how much of each RR's resources (e.g. track repairmen and maintenance crews, etc.) are using inefficiently. Moreover, some suggestions for improving safety performance of each region were also presented based on the amount of resources using inefficiently. By considering R&E of each region in the proposed approach, a more impartial comparison can be made on the RR's safety performance. Therefore, the decision maker could have a more realistic and fairer view of the safety status of different RRs. Finally, it is worth mentioning that previous studies generally aimed at assessing RRs' safety based on their safety level without involving R&E of each RR. However, it seems an incomplete assessment considering the fact that different RRs have different amount of R&E. Thus, the current study is trying to fill this gap by taking into account R&E of each RR.

Expected Safety Performance of Different Freeway Merging Strategies in an Environment of Mixed Vehicle Technologies

This study evaluates different proposed merging solutions that reduce the conflict between merging vehicles and mainline traffic within a mixed traffic environment using a safety measure to see which strategy might work better than others under specific traffic conditions. The mixed traffic includes various percentages of driver-operated vehicles (DVs) and connected autonomous vehicles (CAVs). The probability of noncompliance (PNC) is selected as a surrogate safety measure to assess the strategies. A MATLAB program is developed to simulate various traffic conditions at a merging area and to calculate the PNC merging for the different merging strategies. In addition, to examine the relationship between PNC and collision frequency at the merging area, the collision data at 15 merging ramps in Ottawa were collected to examine the relationship between PNC values obtained from the simulation for the case of a full-DV vehicle fleet and no management strategy (current conditions) and actual safety performance. The results confirmed the validity of PNC as a surrogate safety measure that is correlated to expected collision frequency at merge areas. By simulating all proposed merging management strategies, the results of this study showed a general trend of decreasing PNC and, hence, improved safety performance since the CAV penetration rate increases even when no management strategy is used or under the do-nothing option. However, most merging strategies had better expected safety performance than the do-nothing option, which indicates the value of implementing a merging management strategy, especially during the period of transition from a full-DV to a full-CAV fleet.

Influence of Prefabricated Construction on the Mental Health of Workers: Systematic Review

Despite the significant contribution of the construction industry to national and global economies, the risk it poses to the health and safety of its workers is concerning. With substantial improvement in physical health and safety performance, especially in industrialised and developed economies, attention has shifted to the mental health of construction workers. The construction industry has implemented several worker-focused and management-oriented intervention programs, but problems related to poor mental health persist, and the industry ranks high in suicide figures. Entering the Construction 4.0 era, the use of technologies and new construction methods have been touted to have the potential to improve mental wellbeing. Therefore, this research addresses this lingering problem by: (1) identifying and classifying stressors of mental health and (2) assessing the relevance of adopting prefabricated construction to improving mental health. A two-phased PRISMA-guided systematic review was conducted due to the nonavailability of past studies that combine the concepts of prefabrication and mental health. Mental health stressors were grouped into three categories, with industry-related identified as having an influence on management/organisational and personal stressors. Prefabricated construction, on the other hand, by virtue of its benefits over traditional construction, is found to be capable of eliminating, or at least reducing, the impact of industry-related stressors and, by extension, promoting good mental health.

Exploring the metro construction accidents and causations for improving safety management based on data mining and network theory

PurposeThe 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.Design/methodology/approachTo 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).FindingsThe 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.Research limitations/implicationsIn 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.Practical implicationsThis 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.Originality/valueThis 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.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are proposed. Especially, the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition, and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve. By using low-order interpolation curve splicing, the planning computation is reduced, and the real-time performance of planning is improved, compared with one-segment curve fitting method. Furthermore, the comfort performance of the planned path is reflected intuitively by the curvature information of the path. Finally, the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim. The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Safety based dynamic uncertainty reduction to increase safety performance in aviation industry

There is a lot of evidence regarding air flight accidents caused by human error, especially air traffic controllers (ATC). On the other hand, the principle of work safety through safety performance can help organizations reduce the number of work accidents and create zero accidents in the aviation industry. This research aims to analyze the effect of leadership on safety culture and safety performance by investigating the mediating role of Safety Based Dynamic Uncertainty Reduction (SDUR) as an integral aspect in safety research. A total of 214 respondents were involved in this research. The analysis technique used in this study is Partial Least Square-Structural Equation Modeling (PLS-SEM). The results showed significant effects of leadership on safety culture and safety performance. The mediating analysis also reveals the significant effects of SDUR in strengthening the impact of leadership to safety performance. As implications, SDUR can be considered as an effective strategy in improving Safety Performance in the workplace.

Barriers to Incident Reporting in the Pakistani Construction Industry: An Exploratory Factor Analysis Approach

The construction industry is among the most hazardous industries, with workers routinely involved in critical accidents. Following proper safety procedures can help reduce these accidents. Incident reporting is widely regarded as an effective approach to improving worker safety on construction sites, as it allows relevant stakeholders to learn from past events. Despite the acknowledged significance of incident reporting in reducing accident rates, several researchers have reported that the construction industry still lacks effective reporting and investigation practices. This study aims to explore the barriers that inhibit the reporting of incidents in the Pakistani construction industry. Through a detailed literature review, 29 barriers that inhibit effective incident reporting were identified. A questionnaire survey was then carried out to include the perspective of construction industry professionals on the identified barriers. A total of 214 responses were collected. Further, for dimension reduction, exploratory factor analysis (EFA) was utilised and resultantly, four components were revealed and discussed. These components include organisational, individual, environmental and technical. The findings of this study will help the relevant stakeholders in the construction industry improve safety performance by mitigating these reporting challenges at the construction sites, resulting in a better working environment for workers with fewer risks and accidents.

Development of the road safety strategic plan in Greece, 2021-2030

Despite the most significant improvement in road safety performance among the EU countries during the period 2010-2020 and achieving the target of halving road fatalities, Greece still remains behind the EU average in terms of road fatalities per population. This paper presents the Greek Road Safety Strategic Plan for the decade 2021-2030. With this Plan, Greece adopts the Safe System Approach and the Vision Zero by 2050. The Strategic Plan is in alignment with the EU Road Safety Strategy aiming to halve road fatalities and serious injuries by 2030. Eight targets for road crash casualties’ reduction and eight targets for the improvement of road safety performance are proposed for 2030, with intermediate targets for 2025. A list of 200 Road Safety Measures within 44 Actions within the five UN Pillars is also set. Finally, an integrated system for monitoring road safety is proposed.

Interfacial charge mismatch enhanced energetic crystals for efficient energy-release and improved safety

The synergistic enhancement of energy-release and safety performance is significant for energetic crystals (ECs), such as 3-nitro-1,2,4-triazole-5-one (NTO) and ammonium perchlorate (AP). However, the sluggish energy-release kinetics, high decomposition temperature, and unsatisfied safety performance have plagued the high-efficiency applications of NTO and AP. Herein, graphene oxide (GO) was utilized to ameliorate the physicochemical features and performance characteristics of NTO and AP using a confined crystallization strategy in liquid nitrogen (LN-NTO@GO and LN-AP@GO). Lamellar GO could act as a lubricant, leading to an excellent improvement in mechanical safety performance. Besides, many functional groups in GO could act as building blocks and active sites for the confined crystallization of ECs. Theoretical investigations indicate an interfacial charge mismatch at the GO-EC interface. The as-obtained LN-NTO@GO and LN-AP@GO energetic nanocomposites demonstrate promoted exothermic decomposition temperatures of 18.2 and 70.1°C with substantially accelerated kinetics compared with pristine counterparts and samples crystallized naturally. Reasonable interfacial engineering by interfacial charge mismatch between the catalyst and ECs would break new grounds for designing fast energy-release and safe operation of energetic materials.

Mapping of Global Construction Safety Research using Scientometric Approach

The construction sector is one of the hazardous sectors across the world. In the Indian construction sector, on an average 38 fatal accidents happen every day. Research on construction safety and improvement of safety performance has been continuing for a few years. It explores multiple areas such as safety performance measurement, safety program, human factors, safety planning, site monitoring, etc. This study followed a three-stage literature review approach summarizing the research domain of construction safety. The science mapping approach consists of both bibliometric analysis and scientometric analysis which are carried out in this study. For the bibliometric analysis, Web of Science (WoS) database has been used while for the scientometric analysis, the Cite-Space software has been used in this study. This study identifies key areas of construction safety management by following by a qualitative discussion on research publications across the globe. It includes information and communication technology in safety management, workers' safety perception and behaviour, safety management system, hazard identification, accident causation, and risk management in safety, safety climate and safety culture. This study helped the authors to prepare the outline of construction safety manual. This paper is useful to academic community and industry practitioners to understand the trend of research in the field of construction safety. Also, the proposed directions for future work could benefit to the new researchers and industry practitioners in enhancing safety performance and improving site 'employees' health and wellbeing.

Making polycarbonate flame retardant: Flame retardant selection and calorimetric analyses

Polycarbonate (PC) has been widely applied in aerospace and railway industries, and it is imperative to improve its fire safety related properties, especially heat release properties. Particularly, the MARHE value (maximum average rate of heat emission) directly obtained from the cone calorimeter (CONE) test is a comprehensive property that is presented in the EN45545-2 standard developed by the EU. In order to study the effect of flame retardants on the MARHE of polycarbonate, four flame retardants with different flame retardant mechanisms were used to prepare low heat release PC. CONE was used to characterize the heat release performance of PC, focusing on exploring the factors affecting MARHE and the method of preparing low heat release materials. The flame-retardant mechanism of PC was studied by SEM and TG/DTG. Meanwhile, the flame retardant index (FRI) is calculated as a reference for the flame retardant performance of PC. Results show the MARHE of HPCTP/PC is reduced by 31.12%, and it is confirmed that MARHE is influenced by time to ignition (TTI), peak heat release rate (PHRR), total heat release rate (THR), and the shape of the peak. Mica-RDP/PC materials were prepared based on the conclusions of MARHE derivation. RDP and Mica play a typical synergistic flame-retardant mechanism, with MARHE reductions from 20.31% to 20.00% of the single-component systems to 31.39%. All FRI values of PC materials are in the range of Good flame retardancy. This study provides a feasible way for the deep understanding and wide application of MARHE and the improvement of the fire safety performance of PC.

Forecasting occupational safety performance and mining text-based association rules for incident occurrences

Occupational incidents are a major concern in steel industries due to the complex nature of job activities. Forecasting incidents caused by various activities and determining the root cause might aid in implementing appropriate interventions. Thus, the purpose of this study is to investigate the future trend and identify the pattern of contributing factors of incident occurrences. The study focuses on an integrated steel plant where different steel-making-related operations are carried out in separate units. The incident data of 45 months is used. Initially, a unit-wise trend of incidents (e.g., injury, near-miss and property damage) is forecasted using the autoregressive integrated moving average (ARIMA) model to determine the near-future incident trends and to identify the most incident-prone unit of the plant. The model is validated using six-month holdout data, and the predicted number of incidents is compared with the actual counts. The ARIMA model indicates that the safety performance of the iron making unit is found to be underperforming. In the second phase, meaningful association rules are extracted from text data using the apriori algorithm for the underperforming unit to discover the incident-causing factors. Results from text mining-based association mining suggest that bike and car-related incidents are the leading causes of injury. Similarly, gas leakage, slag spillage, and coke-oven door malfunctioning are causing near-miss incidents. The majority of property damage incidents are reported due to derailment, loading/ unloading and dashing of the dumper vehicle. Effective implementation of the study’s specified rules can aid plant administration in formulating policies to improve safety performance by designing focused interventions.

Bayesian Network Models for Evaluating the Impact of Safety Measures Compliance on Reducing Accidents in the Construction Industry

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance.

Investigating human error in Iran’s copper mines using the CREAM based on human cognitive reliability analysis

Objectives Human error has been recognized as one of the top causes of most accidents in mines. This study aimed to identify and assess cognitive errors among copper miners using the cognitive reliability and error analysis method (CREAM). Methods. This cross-sectional study was carried out in one of the copper mines in Iran. First, all tasks were analyzed by hierarchical task analysis (HTA). Then cognitive errors were assessed using the CREAM. Results. With respect to the basic CREAM results in the operational units, including extraction, crushing and processing, human error probability (HEP) was obtained as 0.056, 0.0315 and 0.0177, respectively. Based on the results of the extended CREAM, the types of errors identified for all tasks in the three operational units were mainly associated with execution (53.4%), observation (40%), interpretation (5%) and planning (1.6%). Also, the cognitive errors were related to execution, monitoring, observation, communication, diagnosis, recording and planning, respectively. Conclusion. The results showed that human error is very high in the operational units of the mine. Therefore, immediate actions are needed to improve safety performance in the mine units, which determine the role of various factors in human errors and can provide the possibility of implementing more effective interventions.

Towards best practices for residential carpentry safety: Multiple case study analysis

IntroductionCarpentry firms face some of the most dangerous working conditions in residential construction. Since these companies are often small, they may not have developed safety policies commonly found in larger companies. The research presented in this paper examines the industry’s current practices and identifies ways to help improve their safety performance. MethodsA survey was conducted to benchmark practices used by carpenters in residential construction. Interviews were used to develop case studies documenting safety practices and policies. A cross-sectional analysis of the case studies allowed for identifying trends and differences based on company safety performance. ResultsThe results indicated that owner and upper-level management’s direct involvement in safety training and hiring practices affect the company’s overall safety performance. Additionally, when a company focuses on and checks worker competence, there are fewer issues related to safety and the benefit of improved quality. Further, the formality of policies and incorporating them into training indicates a better safety culture and improved safety performance. One area in the literature that affected worker safety performance is incentive programs; however, no company involved in the current study utilized incentives. Most expressed a negative view of incentive use. ConclusionSome differences between companies with a better safety performance record by Experience Modifier Rate (EMR) and those with a poorer performance record are how the owner or senior-level management is involved in safety and hiring. Additionally, these companies manage risk differently by ensuring that workers are skilled correctly, or they will find someone else to do it, even if it means utilizing an independent contractor. Practical ApplicationsThis study identified practices that owners could incorporate into company-level management to help improve safety performance. Ultimately, an improved safety culture will reduce the potential for accidents and injuries.

Nanocomposite polymer electrolytes for solid‐state lithium‐ion batteries with enhanced electrochemical properties

AbstractSolid‐state polymer electrolytes (SPEs) have attracted significant attention owing to their improvement in high energy density and high safety performance. However, the low lithium‐ion conductivity of SPEs at room temperature restricts their further application in lithium‐ion batteries (LIBs). Herein, we propose a novel poly (ethylene oxide) (PEO)‐based nanocomposite polymer electrolytes by blending boron‐containing nanoparticles (BNs) in the PEO matrix (abbreviated as: PEO/BNs NPEs). The boron atom of BNs is sp2‐hybridized and contains an empty p‐orbital that can interact with the anion of lithium salt, promoting the dissociation of the lithium salts. In addition, the introduction of the BNs could reduce the crystallinity of PEO. And thus, the ionic conductivity of PEO/BNs NPEs could reach as high as 1.19 × 10−3 S cm−1 at 60°C. Compared to the pure PEO solid polymer electrolyte (PEO SPEs), the PEO/BNs NPEs showed a wider electrochemical window (5.5 V) and larger lithium‐ion migration number (0.43). In addition, the cells assembled with PEO/BNs NPEs exhibited good cycle performance with an initial discharge capacity of 142.5 mA h g−1 and capacity retention of 87.7% after 200 cycles at 2 C (60°C).

Computer vision-based human-machine collision warning system on construction site

With the increase of mechanization in the construction industry, more and more construction machines appear on construction sites. This improves construction efficiency but leads to the high risk of human-machine collision. At present, human-machine collision risks are usually monitored and warned manually. It is difficult to identify relevant risks in time due to the lack of sufficient safety supervisors. This paper proposes an efficient human-machine collision warning system based on computer vision and deep learning. The designed system and relevant devices are installed on construction machinery. It can quickly identify workers and measure the distance between workers and machinery in the complex and dynamic environment so as to quickly warn the risk of human-machine collision based on a threshold. Power supply restriction and harsh environment such as dust and vibration are also considered to ensure the stable and effective operation of the system. The system was tested on a construction site for 6 weeks. The result shows that the system has a significant positive effect on the early warning of human-machine collision risk, thus benefiting the improvement of safety performance of workers in human-machine collaboration scenes.

An investigation on the effects of psychological contract (PC) towards site safety in the south Indian construction industry

The construction sector is India’s second-largest industry, contributing to the country’s economy and providing many job opportunities. However, construction has been described as a hazardous industry with a high rate of injuries, accidents, and fatalities compared to other sectors worldwide. The “psychological contract of safety (PCS)” is one of the safety climate (SC) variables that influence worker safety behavior (WSB) actions on construction sites to improve safety. This research investigates the influence of SC factors on PCS and PCS on WSB in construction site safety. A quantitative research method has been adopted in this study, and the data is collected from the construction professionals working in South India. Statistical analyses were performed for the collected data; consisted of stepwise regression analysis and structural equation modelling (SEM) analysis to determine the significance of PCS. The findings disclose that in direct effect, four out of six SC factors contribute to enhancing PCS, namely supervisor safety behavior, co-worker safety behavior, worker involvement, and safety system, and it shows that PCS influences WSB. In indirect effect of SC factor on WSB two factors positively influence namely: supervisor safety behavior, co-worker safety behavior and remaining two negatively influences. The outcome of this study helps the construction professionals to improve safety performance by fulfilling their obligations and improving WSB in construction sites.

Understanding and Exploring the Concept of Fear, in the Work Context and Its Role in Improving Safety Performance and Reducing Well-Being in a Steady Job Insecurity Period

Job insecurity is attributable to the fear of being exploited by the organization and may have negative effects on employees. For example, it may lead employees to adopt non-compliant or deviant behaviors that harm individuals and their organization. However, knowledge about specific fears that have positive or negative effects on employees and organizations is limited. Therefore, this study aimed to detect the main fears related to job insecurity in an organization and the hidden meanings included in each of these fears that may cause negative and positive effects on important work-related outcomes. Further, we identified safety behaviors related to the previously identified fears. Notably, we investigated whether fears reduce well-being, the number of accidents and near misses, and behaviors related to safety and whether they increase the perceived probability of making mistakes. We conducted 65 focus groups with 37 managers/employees and 180 workers in separate groups. Furthermore, they completed a self-report questionnaire. We identified eleven fears and related meanings and ten safety behaviors. Multivariate regression analyses revealed that fears were significantly negatively associated with well-being and safety behaviors but not with the probability of making mistakes and causing accidents. Thus, fears play a key role in promoting behaviors, organizational strategies, and employees’ well-being. The analysis results show that fears are included in a main general factor and suggest a new way to consider fears at work. Given our findings, we propose a new definition of fear in the organization.

A Conceptual Framework for BIM Process Flow to Mitigate the Causes of Fall-Related Accidents at the Design Stage

Safety training is essential in enhancing safe environments, attitudes, and employee safety behaviour. It has been recognized that the construction industry must tackle the inefficiencies of conventional safety training methods. Using innovative training practices for construction workers is one of the most effective ways to improve safety performance. There is a lack of a standard framework and of necessary information for building information modelling (BIM) process flow needed by designers and safety and health officers to aid construction workers’ safety and job hazard identification (JHI) at the design stage. This study aims to create a framework for BIM process flow to minimize the causes of fall-related accidents in the architecture, engineering, and construction (AEC) industry. This framework was developed based on an integrative review approach and other empirical findings. The connection of the different components makes up the framework. This research recommends practical enhancements, innovation, and adjustments to construction employees’ safety training and JHI. One of the suggested framework’s research advantages is that it evolved through expert validation and contains the necessary components to facilitate practical construction safety training and JHI. These components could serve as a starting point for developing guidelines for practical safety training and JHI.