Search results

The serious wear problem of heavy-haul freight vehicle wheels affects the safety and economy of vehicle operation. This paper aims to study wheel wear evolution law and the influence of line parameters on wheel wear of heavy-haul freight, and provide the basis for operation and line maintenance.

The wheel wear test data of heavy-haul freight vehicles were analyzed. Then a heavy-haul freight vehicle dynamic model was established. The line parameters influencing wheel wear in heavy-haul freight vehicles were also analyzed by the Jendel wear model, and the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear were analyzed.

A rail cant of 1:40 results in less wheel wear; an increase in the rail gauge can reduce wheel wear; and when matched with the CHN60 rail, the wear depth is relatively small. A decrease of 9.21% in wheel wear depth when matched with the CHN60 rail profile. The ramp of the heavy-haul line is necessary to consider for calculating wheel wear. When the ramp is considered, the wear depth increases by 8.47%. The larger the ramp, the greater the braking force and therefore, the greater of the wheel wear.

This paper first summarizes the wear characteristics of wheels in heavy-haul freight vehicles and then systematically analyzes the effect of line parameters on wheel wear. In particular, this study researched the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0038/

This study introduces a novel approach to preventing construction quality problems by examining the complex interrelations among such issues. Recognizing the overlooked coupling between problems is essential, as it can exacerbate quality issues, triggering chain reactions that compromise project success. The research justifies its focus on these interrelations by highlighting the insufficiency of traditional quality management methods, which often fail to account for interconnected quality problems in the architecture, engineering and construction (AEC) industry.

At the core of this research is the establishment of a knowledge base for construction quality issues, marking a pioneering effort to systematically organize unstructured textual data on construction quality problems and their interconnections. This base serves as a platform for the subsequent application of advanced analytical techniques. Specifically, the study leverages preprocessing, text similarity algorithms and association rule mining to dissect and illuminate the nuanced coupling relationships among construction quality issues, a facet not thoroughly explored in prior research.

The innovative analytical methodology employed here reveals significant insights into the dynamics of construction quality issue coupling. These insights not only deepen the understanding of these complex interactions but also guide the development of targeted intervention strategies. The practical applicability and effectiveness of the proposed approach are demonstrated using selected textual materials as experimental evidence. The findings show that understanding and addressing these couplings can significantly mitigate potential chain reactions of defects, thus enhancing overall project quality.

The originality of this study lies in its threefold contribution: the creation of a dedicated knowledge base for construction quality issues, the application of novel analytical methodologies to decipher coupling relationships and the extension of text analysis techniques to the realm of construction quality problem prevention. Together, these innovations open new avenues for research and practice in construction management, offering a robust framework for the systematic identification and mitigation of quality issues in construction projects.

This paper aims to present a control method to realize the constant force grinding of automobile wheel hub.

A constant force control strategy combined by extended state observer (ESO) and backstepping control is proposed. ESO is used to estimate the total disturbance to improve the anti-interference and stability of the system and Backstepping control is used to improve the response speed of the system.

The simulation and grinding experimental results show that, compared with the proportional integral differential control and active disturbance rejection control, the designed controller can improve the dynamic response performance and anti-interference ability of the system and can quickly track the expected force and improve the grinding quality of the hub surface.

The main contribution of this paper lies in the proposed of a new constant force control strategy, which significantly improved the stability and precision of grinding force.

The purpose of this study is to develop a monolayer surface coating of stearic acid on Sn-Ag-Cu solder powder to limit oxidation.

Stearic acid was adsorbed onto Sn-Ag-Cu solder powder through liquid-phase adsorption. The isotherm of adsorption was measured and then the microstructure of coated powder was characterized by scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and Fourier-transform infrared spectroscopy.

The adsorption isotherm of stearic acid on the powder was “H” type, which revealed the layer-by-layer adsorption on non-porous surface. When the concentration of solution was in the range of 0.001-0.006 mol/L, with an adsorption amount of 0.12 ± 0.1 mg/g, monolayer stearic acid covered the solder powder completely. Uniform and integrated self-assembled monolayer coating was formed through hydrogen bonds between the oxygen ions in surface lattice of Sn3.0Ag0.5Cu solder powder and the —O—H hydroxyl group of stearic acid. The maximum angle of stability of coated powder also reduced by 2.87° compared with that of non-coated powder. The increase rate of oxygen content of coated powder was much slower than that of non-coated powder when they were exposed to humid air.

As a result, oxidation of fine solder powder was effectively limited. Essentially, this method can also be applied to the coating of other types of solder powder and has reference significance to other coating by liquid-phase method.

The purpose of this paper is to review systematically the research of grey relation analysis (GRA) models.

Three different approaches, the springboard to build a GRA model, the angle of view in modelling, and the dimension of objects, are analysed, respectively.

The GRA models developed from the models based on relation coefficients of each point in the sequences in early days to the generalized GRA models based on integral or overall perspective. It evolved from the GRA models which measure similarity based on nearness, into the models which consider similarity and nearness, respectively. The objects of the research advanced from the analysis of relationship among curves to that among curved surfaces, and further to the analysis of relationship in three‐dimensional space and even the relationship among super surfaces in n‐dimensional space.

The further research on GRA models is proposed. One is about the property of GRA model. An in‐depth knowledge about the properties of GRA model will help people to understand its function, applicable area and requirements for modelling. The other one is about the extension of research object system. The object to be analysed should be extended from the common sequence of real numbers to grey numbers, vectors, matrices, and even multi‐dimensional matrices, etc.

Strategic trends towards service operations have been widely reported in the recent literature, but organisational capabilities to support such service-centred strategies are less well understood. The purpose of this paper is to identify key organisational issues in managing complex engineering service operations throughout the lifecycle.

Using instruments developed from the product lifecycle management technologies and the network configuration concept, key organisational issues for engineering service operations were identified through case studies focusing on complex engineering products and services systems across a variety of industrial sectors.

The case studies demonstrated different organisational features and strategic priorities of engineering service operations along the whole lifecycle. A generic trend has been observed for engineering systems to move from being design, development and manufacturing focused to embracing support and end-of-life recycling matters.

This paper provides an overall framework for integrating key organisational issues in engineering service operations. It contributes to the service literature by highlighting the need of developing appropriate organisational capabilities to support service-centred strategies with engineering cases. It also provides guidance for companies to manage their engineering network operations throughout the whole lifecycle of complex products and services systems.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

Purpose – To review the place of bicycle transportation within the Chinese national objective of sustainable development.

Methodology – The chapter provides an analysis of the evolution of bicycle transportation policies in China, and a discussion of the latest developments in the function and operation of public bicycle hire schemes.

Findings – Due to high population density, the prevailing mix of land use and a lack of affordability of cars and motor scooters, bicycle transportation has historically been very common in the urban areas of China. However, since the 1990s, many Chinese cities implemented restrictive policies on the development of bicycle transportation and the modal share of bicycles has reduced sharply.

Practical implications – The chapter suggests that China would need to create favourable conditions for bicycle transportation in urban areas through means such as policy support, land use planning, use of economic levers and through creating an acceptable social and cultural atmosphere for cycling. Finally, the maintenance of a relatively high proportion of bicycle traffic would need to be regarded as an index for sustainable urban development.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

The multidimensional effects of farmland transfer in China have been profoundly unstudied. The purpose of this paper is to provide insights on the effects of the intermediary role of agricultural total factor productivity (TFP) of farmland transfer on agricultural economic growth in China.

Based on the agricultural data of 30 provinces in China over the period 2005–2018, this paper uses the intermediary effect model to test the relationship between farmland transfer, agricultural TFP and agricultural economic growth. This paper employed an intermediary effect test model to investigate the intermediary role of agricultural TFP in the influence of farmland transfer on agricultural economic growth.

The findings indicated that farmland transfer has a significant effect on promoting agricultural economic growth. There is a significant “inverted U-shaped” relationship between farmland transfer and agricultural TFP. The sample value of 84.3% of farmland transfers in China is still within the TFP promoting effect range. In addition, farmland transfer has an indirect impact on agricultural economic growth through the channel of agricultural TFP. Agricultural TFP plays a significant intermediary role, but the effect is relatively low

This paper is the first to provide fundamental evidence on the impact of farmland transfers on agricultural economic growth in China, driven by agricultural TFP as an intermediary factor. Agricultural TFP can reduce the involution effect of farmland transfer and promote an indirect effect on agricultural economic growth.