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Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

The rapid expansion of artificial intelligence (AI) is progressively reshaping the dynamics of human interaction, communication, lifestyle, education and professional endeavors. The purpose of the study is to comprehend and address the barriers which are impeding the implementation of Generative AI Technologies, such as ChatGPT in the educational landscape.

The study used the Fuzzy analytic hierarchy process (AHP) model to analyze the responses gathered from 149 academicians belonging to the northern states of India.

The study established that the three most important criteria that influence the adoption of generative AI in the education sector are Risk of Academic Integrity, Risk of biased outcomes and Erosion of Critical Thinking.

The present study was confined to Fuzzy AHP to extract the critical criteria influencing the decision-making. Various other techniques such as PF-Delphi and PF-CoCoSo can be used further. The results provide significant inputs for future research to understand the effect of adoption of Generative AI in different contexts including both opportunities and the challenges faced by them.

The study will be beneficial to various stakeholders including students, educators, society and policymakers as the study will highlight the importance of AI tools, introduce the various challenges associated with and explain the use of these tools as productivity-enhancing tools.

To the best of the author’s knowledge, the present study is a novice as the use of AI in Academia is unexplored and the major criteria influencing the choices have yet been undiscovered.

This study aims to estimate farmers’ willingness to pay for crop insurance, utilizing a choice experiment case study in the South Gondar Zone, Ethiopia.

Primary cross-sectional data were collected in 2023 from 240 farm households. The choice experiment method was employed to elicit farmers’ willingness to pay for crop insurance. Five attributes, including monetary cost, were identified for the choice experiment, with two improved scenarios and a status quo presented to respondents. The mixed logit model and extended mixed logit model were used for analysis.

The econometric model indicated that, with the exception of one attribute, all were positive and statistically significant. Farmers showed a preference for improved scenarios over the status quo, demonstrating a willingness to pay for crop insurance. The extended mixed logit model revealed that factors such as livestock ownership, household head’s sex, family size, income, farming experience, crop risk exposure, and additional occupations significantly influenced farmers’ preferences for crop insurance.

The study’s sample size was limited to 240 farm households, which is relatively small. More reliable results could be obtained with a larger sample size. Another significant limitation is the study’s failure to account for institutional setups when assessing farmers’ willingness to pay for crop insurance.

Agricultural risk, particularly crop risk, is severe in the study area. The results suggest that farmers have a genuine need for risk mitigation mechanisms, such as crop insurance. The findings reflect farmers’ interest in crop insurance, indicating that responsible entities, whether governmental or private insurance companies, can readily implement crop insurance schemes.

The study has significant social implications, as the society in the study area is highly vulnerable to crop risk, which adversely affects their livelihood. Introducing a crop insurance scheme could enhance the welfare and livelihood of the local population.

To the best of our knowledge, this study is novel in both concept and methodology. Unlike previous studies, which focused on specific crop risks, this study considers multiple crop risks. The findings offer valuable insights for policymakers and other stakeholders involved in crop insurance. Understanding farmers’ preferences for crop insurance is crucial for designing effective crop insurance policies.

This study delves into the behavioural intentions of educators within medical colleges at Mosul Universities concerning the adoption of Knowledge Management-Driven Metaverse technology (KM-D-MT). Rooted in an adapted Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, the research aims to enrich the understanding of Metaverse adoption factors, exploring correlations among key constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value, hedonic motivation and interaction. Furthermore, the study investigates the mediating roles of knowledge generation and knowledge sharing in the relationship between interaction and behavioural intention.

The research employs a quantitative approach, gathering 278 responses from educators in medical colleges. Structural Equation Modelling-Partial Least Squares (SEM-PLS) is used to analyse the data, rigorously examining the reliability and validity of research instruments. The investigation involves an extensive evaluation of various factors influencing educators’ intentions to adopt KM-D-MT, using a cross-sectional design.

The study reveals significant positive impacts of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value and hedonic motivation on behavioural intention to adopt KM-D-MT. Interaction is identified as a key factor positively influencing knowledge sharing and knowledge generation. Furthermore, knowledge sharing and knowledge generation exhibit positive correlations with behavioural intention. Interaction indirectly impacts behavioural intention through the mediating roles of knowledge generation and knowledge sharing, highlighting the transformative potential of Metaverse technology in reshaping knowledge processes.

The findings of this study hold practical implications for educators, institutions and policymakers. The adoption of KM-D-MT can enhance educational experiences, facilitate global collaboration and contribute to the continuous professional development of educators in medical colleges. Institutions are encouraged to strengthen technological and organisational infrastructure to support effective Metaverse implementation. Furthermore, promoting positive social norms, providing technical support and offering training programs can contribute to overcoming barriers and fostering a conducive environment for Metaverse adoption in medical education.

This research significantly contributes to theoretical perspectives by advancing Metaverse research and addressing the call for extensive studies covering theoretical, conceptual and empirical elements. It extends current UTAUT2 frameworks, exploring correlations in the context of medical education and contributes to knowledge management paradigms. The study’s originality lies in its exploration of Metaverse acceptance in higher education institutions, specifically in medical colleges in Iraq, providing valuable insights for further research and practical applications globally.

This study examines how users' multidimensional representational fidelity factors affect sociability and cyberself engagement in the Metaverse platform; that is, how they interact with newly defined self-images as their personas in the environments. It investigates how representational fidelity serves platform users to perform social roles and increase their sociability by establishing a new cyberself, thus influencing continuous platform use.

This study surveyed 314 users of the Metaverse platform Horizon, where users can create a virtual agent avatar, meet people in the same online environment in real time, and interact with a sense of three-dimensional immersion. Data were analyzed using partial least squares regression models.

User socialization significantly influenced the intention to use the Metaverse platform. Representational fidelity was a crucial variable for sociability, and activity representational fidelity was the most influential aspect among the four other elements. Platforms should consider how to enable users to create and use activities that faithfully represent their personas.

The novelty of this study is that it introduces representational fidelity based on representation theory into the context of virtual persona in the Metaverse platform. This study extended representational fidelity to the socialization perspective by utilizing the integrated model of user satisfaction and the technology acceptance model. Through the results, this study emphasized that users' sociability significantly influences their intention to use the Metaverse platform. Finally, this study provides a feasible guideline on how practitioners could design and strengthen their platforms so that users can represent their cyberselves faithfully.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.

Bridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.

Three long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.

Results reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.

The study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

This study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

The issue of evaluating the dynamic characteristics of a bridge due to the presence of rapidly moving vehicles has considerable importance. This study aims to conduct a comprehensive study on the variables that influence the dynamic behavior of a thin-walled box-girder bridge exposed to high-speed train loads using regression analysis.

The high-speed train is mathematically represented by a system with 38 degrees of freedom (DOF), while the sub-track system uses China’s Railway Track System slab track. The numerical modeling of the bridge is accomplished using computationally efficient finite elements that represent thin-walled box-beams. The rail’s imperfections are also accounted for, and they are represented using a power spectral density function. The dynamic response of the bridge is calculated using the Newmark-beta technique, considering several DOFs and stress resultants.

A thorough parametric analysis of the factors affecting the dynamic response of the bridge is conducted and a regression model has been proposed. The regression equation yields an excellent fit for shear force, distortional moment and distortional bimoment, with an R² value near 1. It has also been observed that the range of the coefficient R² in case of bending moment, torsion, torsional bimoment and vertical deflection typically falls between 0.82 and 0.9. R² value near to 1 indicates that it is quite accurate in forecasting the dynamic influence of high-speed trains on the bridge’s response.

The originality of this research lies in pioneering the regression modeling of dynamic responses in thin-walled box-girder bridges and uniquely modeling high-speed trains with 38 DOF, which has not been previously explored in existing studies.

This systematic literature review aims to investigate the application and integration of Industry 4.0 (I4.0) technologies in transportation infrastructure construction projects focusing on sustainability pillars.

The study employs a systematic literature review approach, combining qualitative review and quantitative analysis of 142 academic articles published between 2011 and March 2023.

The findings reveal the dominance of Building Information Modelling (BIM) as a central tool for sustainability assessment, while other technologies such as blockchain and autonomous robotics have received limited attention. The adoption of I4.0 technologies, including Internet of Things (IoT) sensors, Augmented Reality (AR), and Big Data, has been prevalent for data-driven analyses, while Unmanned Aerial Vehicle (UAVs) and 3D printing are mainly being integrated either with BIM or in synergy with Artificial Intelligence (AI). We pinpoint critical challenges including high adoption costs, technical barriers, lack of interoperability, and the absence of standardized sustainability benchmarks.

This research distinguishes itself by not only mapping the current integration of I4.0 technologies but also by advocating for standardization and a synergistic human-technology collaborative approach. It offers tailored strategic pathways for diverse types of transportation infrastructure and different project phases, aiming to significantly enhance operational efficiency and sustainability. The study sets a new agenda for leveraging cutting-edge technologies to meet ambitious future sustainability and efficiency goals, making a compelling case for rethinking how these technologies are applied in the construction sector.

In this paper, taking a p-section girder cable-stayed bridge as an example, the construction monitoring and load test of the bridge are implemented.

In order to ensure the safety of cable-stayed bridge structure in construction and achieve the internal force state of the completed bridge, the construction process is monitored for liner and stress of the p-section girder, construction error and safety state during construction. At the same time, to verify whether the bridge can meet the design requirements, the static and dynamic load tests are done.

The results of construction monitoring show that the stress state of the structure during construction is basically consistent with the theoretical calculation and design requirements. The final measured stress state of the structure is within the allowable range of the cable-stayed bridge, and the structural stress state is normal and meets the specification requirements. The load tests results show that the measured deflection of the midspan section of the main girder is less than the theoretical calculation value. The maximum deflection of the main girder is 48.03 mm, which is less than 54.25 mm of the theoretical value, indicating that the main girder has sufficient structural stiffness. Under the dynamic load test, the natural frequency of the three spans of the bridge is less than the theoretical frequency.

This study can provide important reference value for the construction and maintenance of similar p-section girder cable-stayed bridges.