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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.

In the construction sector, site excavation is one of the most dangerous and challenging activities. Proper training can be an effective way to mitigate excavation hazards. Virtual reality (VR) has been used as an effective training tool to enhance safety performance in various industries. However, little attention has been paid to the potential of this technology for construction excavation safety training.

This study proposes an immersive VR training system for excavation safety and hazard identification. The proposed VR training system was compared with a health and safety manual via a controlled experiment.

Results based on scores obtained immediately after training indicate that VR training significantly enhanced practical performance, knowledge acquisition and self-efficacy. Results also show that knowledge was retained four weeks after training. In addition, VR training outperformed health and safety manuals regarding knowledge retention.

This study measures the practical performance to evaluate the effectiveness of the proposed VR training system. Also, this study compares the VR training system with a traditional training method by measuring knowledge acquisition and retention. The results demonstrate the potential of VR as a training tool for excavation safety and hazards.

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.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

This paper aims to analyze the relationships between discourse leading indicators and citations from perspectives of integrating altmetrics indicators and tries to provide references for comprehending the quantitative indicators of scientific communication in the era of open science, constructing the evaluation indicator system of the discourse leading for academic journals and then improving the discourse leading of academic journals.

Based on the theory of communication and the new pattern of scientific communication, this paper explores the formation process of academic journals' discourse leading. This paper obtains 874,119 citations and 6,378,843 altmetrics indicators data from 65 international multidisciplinary academic journals. The relationships between indicators of discourse leading (altmetrics) and citations are studied by using descriptive statistical analysis, correlation analysis, principal component analysis, negative binomial regression analysis and marginal effects analysis. Meanwhile, the connotation and essential characteristics of the indicators, the strength and influence of the relationships are further analyzed and explored. It is proposed that academic journals' discourse leading is composed of news discourse leading, social media discourse leading, peer review discourse leading, encyclopedic discourse leading, video discourse leading and policy discourse leading.

It is discovered that the 15 altmetrics indicators data have a low degree of centralization to the center and a high degree of polarization dispersion overall; their distribution patterns do not follow the normal distributions, and their distributions have the characteristics of long-tailed right-peaked curves. Overall, 15 indicators show positive correlations and wide gaps exist in the number of mentions and coverage. The academic journals' discourse leading significantly affects total cites. When altmetrics indicators of international mainstream academic and social media platforms are used to explore the connotation and characteristics of academic journals' discourse leading, the influence or contribution of social media discourse, news discourse, video discourse, policy discourse, peer review discourse and encyclopedia discourse on the citations decreases in turn.

This study is innovative from the academic journal level to analyze the deep relationships between altmetrics indicators and citations from the perspective of correlation. First, this paper explores the formation process of academic journals' discourse leading. Second, this paper integrates altmetrics indicators to study the correlation between discourse leading indicators and citations. This study will help to enrich and improve basic theoretical issues and indicators’ composition, provide theoretical support for the construction of the discourse leading evaluation system for academic journals and provide ideas for the evaluation practice activities.

Climatic changes caused by greenhouse gases (GHGs) emissions are an urgent challenge for all regions around the globe while the livestock sector is an important source of GHGs emissions. The adoption of low-carbon manure treatment technology (LMTT) by farmers is emerging as an effective remedy to neutralize the carbon emissions of livestock. This paper aims to incorporate environmental literacy and social norms into the analysis framework, with the aim of exploring the impact of environmental literacy and social norms on farmers' adoption of LMTT and finally reduce GHGs emission and climate effects.

This research survey is conducted in Hebei, Henan and Hubei provinces of China. First, this research measures environmental literacy from environmental cognition, skill and responsibility and describes social norms from descriptive and imperative social norms. Second, this paper explores the influence of environmental literacy and social norms on the adoption of LMTT by farmers using the logit model. Third, Logit model's instrumental approach, i.e. IV-Logit, is applied to address the simultaneous biases between environmental skill and farmers’ LMTT adoption. Finally, the research used a moderating model to analyze feasible paths of environmental literacy and social norms that impact the adoption of LMTT by farmers.

The results showed that environmental literacy and social norms significantly and positively affect the adoption of LMTT by farmers. In particular, the effects of environmental literacy on the adoption of LMTT by farmers are mainly contributed by environmental skill and responsibility. The enhancement of social norms on the adoption of LMTT by farmers is mainly due to the leading role of imperative social norms. Meanwhile, if the endogeneity caused by the reverse effect between environmental skill and farmers’ LMTT adoption is dealt with, the role of environmental skill will be weakened. Additionally, LMTT technologies consist of energy and resource technologies. Compared to energy technology, social norms have a more substantial moderating effect on environmental literacy, affecting the adoption of farmer resource technology.

To the best of the authors’ knowledge, a novel attempt is made to examine the effects of environmental literacy and social norms on the adoption of LMTT by farmers, with the objective of identifying more effective factors to increase the intensity of LMTT adoption by farmers.

This paper forms an e-commerce supply chain that include a manufacturer providing products and an online platform providing service. The reselling platform mode and the agent platform mode are considered through an exploration of the manufacturer Stackelberg (MS), vertical Nash (VN), platform Stackelberg (PS) power structures. The purpose of this paper is to explore the pricing and platform service decisions under different platform selling modes and channel power structures.

Based on the game theory models, this paper investigates the interaction between the manufacturer and the online platform under four different scenarios. The optimal solutions of four models are provided. Through comparison analyses, this paper evaluates the impacts of platform selling mode and channel power structure on the pricing and platform service decisions and the members’ profits.

The manufacturer prefers the MS power structure in any platform mode. The online platform prefers the PS (MS) power structure under a low (high) service cost efficiency in the reselling platform mode, while prefers the PS and VN power structures in the agent platform mode. Moreover, the manufacturer prefers the agent (reselling) platform mode under a low (high) service cost efficiency in any power structure. The online platform prefers the reselling platform mode in the MS and PS power structures, while prefers the reselling (agent) platform mode under a low (high) service cost efficiency in the VN power structures.

The analysis result provides important managerial implications that help the supply chain members develop a better understanding of the selection of the platform selling mode and the effect of the channel power structure in the presence of platform service.

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.