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This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

Purpose – The purpose of this study is to develop a numerical framework to predict the time-dependent probability of failure of a bridge subjected to multiple vehicle impacts. Specially, this study focuses on investigating the inter-relationship between changes in life-cycle parameters (e.g., damage size caused by vehicle impact, loss of initial structural capacity, and threshold intervention) and bridges probability of failure.

Design/Methodology/Approach – The numerical procedure using MATLAB program is developed to compute the probability failure of a bridge. First, the importance and characteristics of life-cycle analysis is described. Then, model for damage accumulation and life cycle as a result of heavy vehicle impacts is discussed. Finally, the probability of failure of a bridge subjected to vehicle impacts as a result of change in life-cycle parameters is presented.

Findings – The results of study show that damage size caused by both vehicle impacts and loss of initial structural capacity have a great impact on the long-term safety of bridges. In addition, the probability of failure of a bridge under different threshold limits indicates that the structural intervention (e.g., repair or maintenance) should be undertaken to extend the service life of a bridge.

Research Limitations/Implications – The damage sizes caused by heavy vehicle impacts are based on simple assumptions. It is suggested that there would be a further study to estimate the magnitude of bridge damage as a result of vehicle impact using the full-scale impact test or computational simulation.

Practical Implications – This will allow much better predictions for residual life of bridges which could potentially be used to support decisions on health and maintenance of bridges.

Originality/Value – The life-cycle performance for assessing the time-dependent probability of failure of bridges subjected to multiple vehicle impact has not been fully discussed so far.

Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

This study aims to analyze the factors, evaluation techniques of the durability of existing railway engineering.

China has built a railway network of over 150,000 km. Ensuring the safety of the existing railway engineering is of great significance for maintaining normal railway operation order. However, railway engineering is a strip structure that crosses multiple complex environments. And railway engineering will withstand high-frequency impact loads from trains. The above factors have led to differences in the deterioration characteristics and maintenance strategies of railway engineering compared to conventional concrete structures. Therefore, it is very important to analyze the key factors that affect the durability of railway structures and propose technologies for durability evaluation.

The factors that affect the durability and reliability of railway engineering are mainly divided into three categories: material factors, environmental factors and load factors. Among them, material factors also include influencing factors, such as raw materials, mix proportions and so on. Environmental factors vary depending on the service environment of railway engineering, and the durability and deterioration of concrete have different failure mechanisms. Load factors include static load and train dynamic load. The on-site rapid detection methods for five common diseases in railway engineering are also proposed in this paper. These methods can quickly evaluate the durability of existing railway engineering concrete.

The research can provide some new evaluation techniques and methods for the durability of existing railway engineering.

The purpose of this paper is to investigate the effect of round pits arrangement patterns on tribological properties of journal bearing. In this paper, the tribological behaviors of journal bearing with different arrangement patterns under lubrication condition were studied based on M-2000 friction and wear tester.

The friction and wear of journal bearing contact surface were simulated by ANSYS. The wear mechanism of bearing contact surfaces was investigated by the means of energy dispersive spectrum analysis on the surface morphology and friction and wear status of the journal bearing specimens by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometer (EDS). Besides, the wearing capacity of the textured bearing was predicted by using the GM (1,1) and Grey–Markov model.

As the loads increase, the friction coefficient of journal bearing specimens decrease first and then increase slowly. The higher rotation speed, the lower friction coefficient and the faster temperature build-up. The main friction method of the bearing sample is three-body friction. The existence of texture can effectively reduce friction and wear. In many arrangement patterns, the best is 4# bearing with round pits cross-arrangement pattern. Its texturing diameters are 60 µm and 125 µm, and the spacing and depth are 200 µm and 25 µm, respectively. In addition, the Grey–Markov model prediction result is more accurate and fit the experimental value better.

The friction and wear mechanism is helpful for scientific research and engineers to understand the tribological behaviors and engineering applications of textured bearing. The wear capacity of textured bearing is predicted by using the Grey–Markov model, which provides technical help and theoretical guidance for the service life and reliability of textured bearing.

Apart from the basic material properties of liquid lubricants, such as, e.g., the viscosity and density of the hydraulic fluid, it is also important to have information regarding the electrical properties of the fluid used. The latter is closely related to the purpose, type, structure, and conditions of use of a hydraulic system, especially the powertrain design and fluid condition monitoring. The insulating capacity of the hydraulic fluid is important in cases where the electric motor of the pump is immersed in the fluid. In other cases, on the basis of changing the electrical conductive properties of the hydraulic fluid, we can refer its condition, and, on this basis, the degree of degradation.

The paper first highlights the importance of knowing the electrical properties of hydraulic fluids and then aims to compare these properties, such as the breakdown voltage of commonly used hydraulic mineral oils and newer ionic fluids suitable for use as hydraulic fluids.

Knowledge of this property is crucial for the design approach of modern hydraulic compact power packs. In the following, the emphasis is on the more advanced use of known electrical quantities, such as electrical conductivity and the dielectric constant of a liquid.

Based on the changes in these quantities, we have the possibility of real-time monitoring the hydraulic fluid condition, on the basis of which we judge the degree of fluid degradation and its suitability for further use.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.

This paper aims to assess whether the current eResearch Knowledge Centre’s (eRKC) research support practices align with researchers’ requirements for achieving their research objectives. The study’s objectives were to assess the current eRKC research support services and to determine which are adequate and which are not in supporting the Human Sciences Research Council (HSRC) researchers.

This study uses interviews as part of the qualitative approach. The researcher chose to use interviews, as some aspects warranted further explanation during the interview. The interviews were scheduled using Zoom’s scheduling assistant. The interviews were semi-structured, guided by a flexible interview procedure and supplemented by follow-up questions, probes and comments. The research life cycle questions guided the interviews. The data obtained were coded and transcribed using MS Excel. The interview data were analysed, using NVivo, according to the themes identified in the research questions and aligned with the theory behind the study. Pre-determined codes were created in line with the six stages of the research life cycle and applied to group the data and extract meaning from each category. Interviewee responses were assigned to groups in line with the stages of the research life cycle.

The current eRKC research support services are aligned with the needs of HSRC researchers and highlight services that could be expanded or promoted more effectively to HSRC researchers. It proposes a new service, data analysis, and suggests that the eRKC could play a more prominent role in research impact, research data management and fostering collaboration with HSRC research divisions.

This study is limited to assessing the eRKC’s support practices at the HSRC in Pretoria, South Africa. A more comprehensive study is needed for HSRC research services, capabilities and capacity.

Assessment of eRKC followed a comprehensive interviewee schedule that followed Raju and Schoombee’s research life cycle model.

Zoom’s scheduling assistant may have generated Zoom fatigue and reduced productivity. Technical issues, losing time, communication gaps and distant time zones may have affected face-to-face interaction.

eRKC research support practices are rare in South Africa and most parts of the world. This study bridges the gap between theory and practice in assessing eRKC research support practices.

This study aims to explore the experiences of non-offending partners (NOPs) of men who perpetrated contact and non-contact sexual offences.

In-depth semi-structured interviews were carried out with eight women and analysed using interpretative phenomenological analysis.

Findings yielded two superordinate themes, eight subordinate themes and an overarching theme. The first superordinate theme “Paying for their Husband’s Transgressions” captured many ways in which the women’s lives were impacted by their husbands offending. The second superordinate theme “Navigating the Darkness” encompassed the women’s experiences of trying to adapt to their new lives. The overarching theme “A Contaminated Life” pertained to the shared experiences of the women who all described encountering instant and profound consequences. This research highlighted the need for immediate signposting to support services for NOPs. The value of a humanistic counselling approach paired with forensic expertise was also identified. Future research with cross-cultural samples and same sex-couples would enrich the current understanding of this experience.

This research highlighted the need for immediate signposting to support services for NOPs. The value of a humanistic counselling approach paired with forensic expertise was also identified.

Qualitative research on the experiences of NOPs of men who perpetrated sexual offences is sparse. Furthermore, existing research focuses on the experiences of women who’s own children were abused, with the partners of men who have perpetrated extra-familial or non-contact offenses remaining largely neglected.

The purpose of this article is to describe and analyze the occupational life cycle of Swedish real estate brokers.

Voluntary turnover among real estate brokers could lead to occupational turnover and/or employee turnover and has been described as problematic by both practitioners and researchers alike. Most previous studies focusing on this issue have explored connections between real estate brokers' personality, economic and market conditions and turnover. Employee turnover involves shifting jobs within the profession (real estate brokerage), whereas occupational turnover concerns movement to a job not related to the real estate brokerage profession. Both perspectives on turnover are however lacking data about the average time spent as a broker. This study fills this gap by exploring real estate brokers' life cycle through data analysis using a cohort study consisting of a sample of 5,304 real estate brokers registered and/or deregistered over a ten-year period from 2010 to 2019.

The analysis show that the decline is almost linear, resulting in 50% of the newly registered real estate brokers remain in the occupation eight years after registration. These findings are not in line with previous assumptions as the real estate brokers' life cycle is substantially longer. The results also reveal that there are differences in life cycles due to gender and year of registration.

The analysis of longitudinal, aggregated data on the life cycle of real estate brokers is highly relevant as it serves as a point of reference for future longitudinal studies analyzing the motives for leaving the occupation.