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The purpose of this paper is to investigate the application of reliability engineering to oil and gas (O&G) pipeline systems with the aim of identifying means through which reliability engineering can be used to improve pipeline integrity, specifically with regard to man-made incidents (e.g. material/weld/equipment failure, corrosion, incorrect operation and excavation damages).

A literature review was carried out on the application of reliability tools to O&G pipeline systems and four case studies are presented as examples of how reliability engineering can help to improve pipeline integrity. The scope of the paper is narrowed to four stages of the pipeline life cycle; the decommissioning stage is not part of this research. A survey was also carried out using a questionnaire to check the level of application of reliability tools in the O&G industry.

Data from survey and literature show that a reliability-centred approach can be applied and will improve pipeline reliability where applied; however, there are several hindrances to the effective application of reliability tools, the current methods are time based and focus mainly on design against failure rather than design for reliability.

The tools identified do not cover the decommissioning of the pipeline system. Research validation sample size can be broadened to include more pipeline stakeholders/professionals. Pipeline integrity management systems are proprietary information and permission is required from stakeholders to do a detailed practical study.

This paper proposes the minimum applied reliability tools for application during the design, operation and maintenance phases targeted at the O&G industry. Critically, this paper provides a case for an integrated approach to applying reliability and maintenance tools that are required to reduce pipeline failure incidents in the O&G industry.

This paper aims to propose a train timetable rescheduling (TTR) approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.

Firstly, a single-train trajectory optimization (STTO) model is constructed based on train dynamics and operating conditions. The train kinematics parameters, including acceleration, speed and time at each position, are calculated to predict the arrival times in the train timetable. A STTO algorithm is developed to optimize a single-train time-efficient driving strategy. Then, a TTR approach based on multi-train tracking optimization (TTR-MTTO) is proposed with mutual information. The constraints of temporary speed restriction (TSR) and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train. The multi-train trajectories at each position are optimized to generate a time-efficient train timetable.

The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF. The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay (TTD). As for the TSR scenario, the proposed TTR-MTTO can reduce TTD by 60.60% compared with the traditional TTR approach with dispatchers’ experience. Moreover, TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.

With the cooperative relationship and mutual information between train rescheduling and control, the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

The purpose of present paper is to enlarge the knowledge about the performance of gypsum powder to realize complex molds or cores for aluminum casting.

The research was divided into two activities: simple; and complex-part production capability. In the simple-part step, the performance of gypsum powder and the minimum mold thickness that would withstand the casting process. In the complex-part step, the authors first investigated the powder removability as a function of geometry complexity and then binder jetting performance was evaluated for the case of lattice-structure fabrication.

All the geometries tested withstand the casting process demonstrating the benefits in terms of complexity part design; however, the process suffers of all the typical defect of casting as misrun, porosity and cold shut.

The results found in this research improve the benefits related to additive manufacturing application in industrial environment and in particular to the binder jetting technology and the rapid casting approach.

Time headway (THW) is an essential parameter in traffic safety and is used as a typical control variable by many vehicle control algorithms, especially in safety-critical ADAS and automated driving systems. However, due to the randomness of human drivers, THW cannot be accurately represented, affecting scholars’ more profound research.

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

The results show that the proposed model has a 62.7% performance improvement over the distribution model with fixed parameters. Moreover, the parameter function of the distribution model can be regarded as a quantitative analysis of the degree of influence of the traffic flow state on THW.

In this paper, a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks, respectively, for a quantitative evaluation of the condition of railway ballast bed.

Based on original radar signals, the time–frequency characteristics of radar signals were analyzed, five ballast bed condition characteristic indexes were proposed, including the frequency domain integral area, scanning area, number of intersections with the time axis, number of time-domain inflection points and amplitude envelope obtained by Hilbert transform, and the effectiveness and sensitivity of the indexes were analyzed.

The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm, which meets the requirements of ballast bed detection. Compared with clean ballast bed, the values of the five indexes of fouled ballast bed are larger, and the five indexes could effectively show the condition of the ballast bed. The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s·km⁻¹, and the computational efficiency of other indexes is 5 s·km⁻¹. The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive, followed by scanning area. The number of intersections with the time axis in the bridge sections was the most sensitive, followed by the scanning area. The scanning area can adapt to different substructures such as subgrade, bridges and tunnels, with high comprehensive sensitivity.

The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.

This study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures.

The Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures.

The findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures.

The results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures.

Drawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.

This paper attempts to theorise about China’s strategy in combating the coronavirus pandemic with an embryonic framework - 3H (Heart-Head-Hand) framework. By adopting a descriptive approach, the paper introduces the case of coronavirus outbreak in China and how the public health administration coped with it. The 3H framework has been applied to analyse China’s strategy, and the framework’s assumptions are initially tested.

The pandemic case is created based on credible reports, press releases from different respected sources, World Health Organization (WHO) statistics, interview transcripts and broadcasting stations’ video clippings. Interpretive analysis with pragmatism approach has been conducted in analysing the data and information collected. Triangulation, wherever possible, has been done to validate the data and information.

As an exploratory study, its findings show that 3H framework distinguishes the effectiveness of a country’s strategy and practice for combating the pandemic. Countries, which failed to observe the assumed principles of 3H domains tend to have much more infected cases and deaths.

The 3H framework conceptualised a holistic management approach and its assumptions have been initially tested with this pandemic case. The framework shows its predictability value for a country’s pandemic management effectiveness.