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Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.

A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.

The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.

The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

This paper aims to investigate the challenges faced by the transport infrastructure sector in its civil construction material supply processes, following the 2016 Kaikoura earthquake in New Zealand.

A case study approach was adopted, which included on-site observations, semi-structured interviews and literature-based desktop reviews.

The research findings show that there were supply problems for aggregates, concrete, stormwater pipes and some specialised products for the repair and rebuild of transport facilities. Those supply problems were largely caused by (1) difficulty in predicting material requirements, (2) constrained supply capability, (3) inadequate local freight capacity, (4) legal, cultural and/or environmental considerations on resource exploitation and utilisation, (5) impacts of COVID-19 and (6) ineffective communication and coordination.

For future research, it is recommended that a comparative analysis of multiple disaster cases be undertaken to further explore the generalisability of the research findings.

The research findings will inform the development of post-earthquake recovery policies and material supply chain operation strategies, in order to expedite the recovery of transport networks if a future earthquake strikes.

Turnover intention is a critical predictor of an employee’s turnover behaviour. A high level of turnover rate significantly affects the productivity and morale of an enterprise. Previous research has indicated that job satisfaction plays a critical role in influencing an employee's turnover intention, but the underlying factors related to job satisfaction remain under-explored, which impedes the development of effective strategies for reducing turnover intention. In addition, little research examined job satisfaction and turnover intention in the context of the COVID-19 pandemic, specifically in the Chinese construction industry. This study aims to investigate the impact of job satisfaction on turnover intention among professionals in the construction industry.

A questionnaire survey was employed to collect viewpoints from 449 professionals in the Chinese construction industry, followed by descriptive analysis, correlation analysis and structural equation modelling analysis to derive results.

The findings indicate that professionals in the industry generally have a slightly high level of job satisfaction while a slightly low level of turnover intention in the special period of the pandemic outbreak. Leadership and management, training and career development and interpersonal relationships are critical underlying factors leading to their turnover intention. Although demographic factors have no moderating effect between job satisfaction and turnover intention, among them, age, marital status and years of working experience have strongly positive relationships with job satisfaction while significantly negative relationships with turnover intention.

The findings provide valuable insights to fully understand the critical factors leading to turnover intention from the perspective of job satisfaction, which is helpful in developing effective measures to address the turnover problems for enterprises in the Chinese construction industry and those industries with similar characteristics in other regions.

This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.

The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.

The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.

The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.

This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

This paper aims to investigate the corrosion kinetics and corrosion behavior of NdFeB magnets with the addition of heavy rare earth dysprosium (Dy) for its inhibitory activity on poor corrosion resistance of NdFeB magnets.

To study the effect of dysprosium addition on corrosion behavior of NdFeB magnets and investigate its mechanism, potentiodynamic polarization, scanning electron microscopy (SEM), electrochemical impedance, energy dispersion spectrum (EDS) and scanning Kelvin probe force microscopy (SKPFM) were applied in the research. Besides, microstructures were observed by SEM equipped with EDS. Atomic force microscopy was introduced to analyze the morphology, potential image as well as the contact potential difference. The SKPFM mapping scan was applied to obtain the contact potential around Nd-rich phase at 0.1 Hz. The magnets were detected via X-ray diffraction.

Substitution of Nd with Dy led to improvement of corrosion resistance and reduced the potential difference between matrix and Nd-rich phase. Corrosion resistance is Nd-rich phase < the void < metal matrix; maximum potential difference between matrix and Nd-rich phase of Dy = 0, Dy = 3 and Dy = 6 Wt.% is 411.3, 279.4 and 255.8 mV, respectively. The corrosion rate of NdFeB magnet with 6 Wt.% Dy is about 67% of that without Dy at steady corrosion stage. The addition of Dy markedly enhanced the corrosion resistance of NdFeB magnets.

This research innovatively investigates the effect of adding heavy rare earth Dy to NdFeB permanent magnets on magnetic properties, as well as their effects on microstructure, phase structure and most importantly on corrosion resistance. Most scholars are studying the effect of element addition on magnetic properties but not on corrosion resistance. This paper creatively fills this research gap. NdFeB magnets are applied in smart cars, robotics, AI intelligence, etc. The in-depth research on corrosion resistance by adding heavy rare earths has made significant and outstanding contributions to promoting the rapid development of the rare earth industry.

We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.

This study aims to investigate the impact of ResearchGate (RG) scores on the scientific indicators of Qur’anic productions in Scopus.

This research is fundamental in method, created in a descriptive method and a scientometric approach. The statistical society includes 4,105 records produced by writers in the field of Qur’an during the time period of 2011–2020 indexed in the Scopus database. In this study, Pearson correlation coefficient test is used with aid of Excel and SPSS software.

Countries such as Malaysia, the USA and Indonesia, respectively, hold the highest number of scientific productions in the field of Qur’an. Iran holds the fourth place with a 544 record difference in comparison to Malaysia (866 documents). There is a positive and meaningful relation between measures of RG, readers, followers, citations and the H-index score regarding writers with received citations and the H-index score of scientific productions in the field of Qur’an in Scopus. However, no relation can be observed between the mentioned measures and indicators of the number of documents with cowriting.

Because of some authors not using the same name in their articles, it was not possible to access their profile in RG. For this reason, the information of these authors was not accessible in RG.

The activity of researchers in social networks will receive feedback from readers. This will result in more citations. Contrary to expectations, the research results showed that Iran does not have a good position in publishing Qur’anic works. Therefore, researchers should publish their findings in international journals.

Iranian writers should try to publish their scientific productions in international publications and also widely spread their content and research results across social networks to increase feedback and gain the attention of readers. This process leads to receiving reader feedback and publication of the Islamic teachings.

The relationship between the number of citations of Qur’anic works in Scopus and altmetric indicators in RG has not been investigated in previous researches. Therefore, this research is innovative in this field.