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Building Information Modeling (BIM) competitions are a beneficial approach to enhance BIM education, offering students practical experience in BIM application, including mastering workflows and technical tools. However, research exploring the individual perceptions influencing participation intentions and behaviors in BIM competitions is limited. Therefore, this study aims to investigate the factors affecting university students' behavioral intention and behavior in BIM competitions, providing theoretical support for BIM competitions and educational reform.

This study employs the Structural Equation Modeling (SEM) based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to analyze the factors influencing BIM competition participation among 970 Architecture, Engineering, and Construction (AEC) university students.

The results of the study show that social influence, attitude, and self-efficacy play critical roles in shaping students' intentions to participate in BIM competitions. Furthermore, self-efficacy, facilitating conditions, and behavioral intention significantly influence students' actual engagement in such competitions. Surprisingly, effort expectancy negatively influences intentions, as less challenging tasks can lead students to perceive their participation as less impactful on their skills and learning, reducing their behavioral intention to participate.

This research provides valuable insights into the effectiveness of BIM competitions in enhancing BIM education for AEC students. Extending the UTAUT model to include self-efficacy and attitude, provides a novel perspective for understanding students' intentions and behaviors regarding BIM competitions. The study’s theoretical support proposes incorporating BIM competitions to augment BIM teaching methods and offers suggestions for advancing the efficacy of students' involvement in BIM competitions within higher education, thus contributing to educational reform in the AEC sector.

The purpose of this paper is to propose a method that can calculate the transportation infrastructure network service capacity enhancement given by planned transportation infrastructure projects construction. Managers can sequence projects more rationally to maximize the construction effectiveness of infrastructure investments.

This paper designed a computational network simulation software to generate topological networks based on established rules. Based on the topological networks, the software simulated the movement path of users and calculated the average travel time. This software allows the adjustment of parameters to suit different research objectives. The average travel time is used as an evaluation index to determine the most appropriate construction sequence.

In this paper, the transportation infrastructure network of Sichuan Province in China was used to demonstrate this software. The average travel time of the existing transportation network in Sichuan Province was calculated as 211 min using this software. The high-speed railways from Leshan to Xichang and from Xichang to Yibin had the greatest influence on shortening the average travel time. This paper also measured the changes in the average travel time under two strategies: shortening the maximum and minimum priorities. All the transportation network optimisation plans for Sichuan Province will be somewhere between these two strategies.

The contribution of this research are three aspects: First, a complex network analysis method that can take into account the differences of node elements is proposed. Second, it provides an effective tool for decision makers to plan transportation infrastructure construction. Third, the construction sequence of transportation infrastructure development plan can effect the infrastructure investment effectiveness.

Tourism via virtual reality (VR) technology has become an interesting option for consumers to “travel.” The best approaches to optimizing the VR tourism environment, improving the interactive experience of tourists and encouraging tourists to adopt VR are not yet fully understood. This study explores the willingness of tourists to adopt VR tourism from the dual aspects, richness and dynamics, of virtual social cues.

To examine the effects of richness (multiple vs. few cues) and dynamic (changeable vs. static cues) on consumers' willingness to adopt VR tourism, three virtual tourism scenes were designed and presented by head-mounted displays. The data were collected for participants in the VR laboratory and tested by ANOVA and partial least squares–structural equation modeling.

Virtual social cues can generate mental imagery through interactivity, vividness and parasocial interactions, thus increasing the consumer's likelihood of adopting VR tourism. It was also found that imagination moderates mental imagery and adoption intention. When the consumer's imagination is stronger, their mental imagery stimulates a stronger willingness to adopt VR tourism.

The authors innovatively utilize concepts of parasocial interaction and mental imagery and discuss the various influences and mediation mechanisms of social cue characteristics on consumers' adoption of VR tourism. The conclusions may provide new insights for VR tourism managers and tourism scholars.

To further promote application of BIM technology in construction of prefabricated buildings, influencing factors and evolution laws of willingness to apply BIM technology are explored from the perspective of willingness of participants.

In this paper, a tripartite game model involving the design firm, component manufacturer and construction firm is constructed and a system dynamics method is used to explore the influencing factors and game evolution path of three parties' application of BIM technology, from three perspectives, cost, benefit and risk.

The government should formulate measures for promoting the application of BIM according to different BIM application willingness of the parties. When pursuing deeper BIM application, the design firm should pay attention to reducing the speculative benefits of the component manufacturer and the construction firm. The design firm and the component manufacturer should pay attention to balancing the cost and benefit of the design firm while enhancing collaborative efforts. When the component manufacturer and the construction firm cooperate closely, it is necessary to pay attention to balanced distribution of interests of both parties and lower the risk of BIM application.

This study fills a research gap by comprehensively investigating the influencing factors and game evolution paths of willingness of the three parties to apply BIM technology to prefabricated buildings. The research helps to effectively improve the building quality and construction efficiency, and is expected to contribute to the sustainability of built environment in the context of circular economy in China.

Promoting the adoption of digital payments by the elderly plays an important role in the development of the digital economy. The purpose of this study is to build an extended theory of planned behavior (TPB) model to predict the elderly's intention to pay for digital services under COVID-19 epidemic constraints.

Based on the extended TPB model, 320 qualified participants were recruited on the network. The structural equation model was tested using the SmartPLS3.3 tool, and the moderation effects were tested through SPSS26 and the Process macro.

The results showed that the three dimensions of TPB theory, the basic elements (perceived value and perceived risk), and the external environment (COVID-19 pandemic) were important factors that influence the elderly users' intention to adopt digital payments. Further research found that motivation factors (personal innovativeness, intergenerational support, and social support) can positively moderate these effects.

The results of the study provide a further explanation for understanding the willingness of elderly people to adopt digital payments during the COVID-19 pandemic and bring inspiration to system developers and social managers to reduce the risk of COVID-19 pandemic and increase the share of digital payments for this category.

This paper used the extended TPB theory to construct a fundamental environmental motivation (FEM) framework for understanding the main influencing factors of elderly users' intention to adopt digital payments during the COVID-19 pandemic.

A humanoid intelligent robot (HIR) possessing a human-like appearance can undertake human jobs, interact, communicate and even transmit emotions to human beings. Such robots have gradually been integrated into people's daily life and production scenarios. However, it is unclear whether and by what mechanism HIRs can stimulate people’s risk perception and its impact on consumption attitudes. Based on the risk decision theory, this study aims to take the social value substitution attribute of a HIR as the incentive and analyzes the influence of social value substitution and risk perception on the customers’ consumption attitudes.

Three experiments were conducted to investigate the related questions about the social value substitution attribute of a HIR, its impact on risk perception and the customers’ consumption attitudes.

The results reveal that physical labor, intellectual labor, friendship, kinship and the ego constitute the hierarchical elements of social value substitution. Among them, physical labor and intellectual labor pertain to the dimension of social function value substitution, while friendship, kinship and ego pertain to the dimension of social presence value substitution; social function value substitution and social presence value substitution affect the subjects’ risk perception positively, but the latter arouses a stronger risk perception; the 2 (risk perception of social function value: security/danger) × 2 (risk perception of social presence value: security/danger) condition corresponds to diverse consumption attitudes.

The results enrich the theories of the “cha-xu pattern” and “uncanny valley” and provide reference for the healthy development of the HIR industry.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.

Oppositional brand loyalty poses a challenge to the management of virtual communities. This study aims to categorize these loyalty behaviors into positive (willingness to pay a price premium and brand evangelism) and negative (schadenfreude and anti-brand actions) dimensions. It then explores how customer engagement and moral identity influence these dimensions in the context of brand competition.

Structural equation modeling was conducted to analyze the main and moderating effects, using survey data obtained from 498 valid responses out of a total of 636 responses from Xiaomi's virtual communities.

The results indicate that customer engagement significantly influences all four dimensions of oppositional brand loyalty. The relationship between customer engagement and brand evangelism is notably stronger among customers with a strong moral identity. Conversely, the effects of customer engagement on schadenfreude and anti-brand actions are attenuated for these customers.

Anchored in theories of brand tribalism, social identity and brand polarization, this study bifurcates oppositional brand loyalty into directions of preference and antagonism, empirically showcasing moral identity's moderating effect. It contributes to the literature on antagonistic loyalty and moral identity, offering strategic insights for companies to navigate schadenfreude and anti-brand actions in online communities.

At present, teaching methods based on 2D drawings are still commonly used for educating students on the location of steel reinforcement bars in concrete. However, traditional teaching methods have limitations as students can find it difficult to understand 2D drawings. This study aims to develop an interactive and collaborative augmented reality environment (ICARE) using augmented reality (AR) technology to improve students' engagement in learning.

This study develops an ICARE prototype, which is organized into two stages: (1) The augmented teaching environment comprising of models and interactive components; (2) The AR collaborative application which uses Photon Unity Networking (PUN) plugin and Azure spatial anchors cloud service. The AR-based teaching environment runs with Universal Windows Platform (UWP) to enable development in the HoloLens 2 through Microsoft Visual Studio.

An experimental study was conducted, where 60 students were divided into three groups employing Drawings-based, building information modeling (BIM)-based and AR-based methods for teaching. After the test, the three groups of students were requested to complete a questionnaire. According to the analysis of the experimental results, the ICARE can improve students' comprehension, memory of learned materials and their ability to read and understand steel reinforcement drawings improving the quality of teaching, especially interactivity and engagement.

As illustrated in the experiments, the developed ICARE has outstanding performance over conventional approaches in civil engineering courses that can improve students' comprehension and memory of knowledge and their ability to read and understand steel bar drawings. This study provides empirical evidence that AR is a promising technology that can be integrated with traditional classroom instruction and can improve students' comprehension and memory of knowledge and their ability to read and understand steel bar drawings.

Effectively solving the large tonnage cable in the construction process due to the tensioning method of the inclined cable often appears in the overall cable force and the design value of the deviation is large, cable internal strand force is not uniform, the main girder stress exceeds the limit of the problem affecting the safety of the structure.

In this study, the finite element method and theoretical analysis method are utilized to propose a construction control method of tensioning the whole bunch of diagonal cables in two parts according to the deformation coordination relationship between the main girder and the diagonal cables. This methodology was implemented during the actual construction of the PAIRA Bridge in Bangladesh.

Tests conducted on cable-stayed bridges using this controlled tensioning method demonstrate that the measured cable strength of a single strand exhibits an error of less than 0.15% compared to the design target cable strength. The deviation between the measured and designed cable forces ranges from 0.16% to 0.27%. Furthermore, no tensile stress is observed in both the top plate and bottom plate of the root section of the main girder, indicating a state of full-section compression throughout the entire construction process.

Through the comparison with the test value, it can be proved that the whole bunch of diagonal cable tensioned in two parts of the construction control method proposed in this paper can make the internal strand force more uniform, to meet the precision requirements of the site construction, to protect the safety of the bridge construction process. The method proposed in this paper is highly accurate, easy to calculate, and has a high value of popularization and application.