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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 escalating integration of AI tools like ChatGPT within academia poses a critical challenge regarding their impact on faculty members’ and researchers’ academic performance levels. This paper aims to delve into academic performance within the context of the ChatGPT era by exploring the influence of several pivotal predictors, such as academic integrity, academic competence, personal best goals and perceived stress, as well as the moderating effect of ChatGPT adoption on academic performance.

This study uses a quantitative method to investigate the impact of essential variables on academic integrity, academic competence, perceived stress and personal best goals by analysing 402 responses gathered from ResearchGate and Academia.edu sites.

While affirming the established direct positive relationship between academic integrity and performance since adopting AI tools, this research revealed a significant moderating role of ChatGPT adoption on this relationship. Additionally, the authors shed light on the positive relationship between academic competence and performance in the ChatGPT era and the ChatGPT adoption-moderated interaction of competence and performance. Surprisingly, a negative association emerges between personal best goals and academic performance within ChatGPT-assisted environments. Notably, the study underscores a significant relationship between heightened performance through ChatGPT and increased perceived stress among academicians.

The research advocates formulating clear ethical guidelines, robust support mechanisms and stress-management interventions to maintain academic integrity, enhance competence and prioritise academic professionals’ well-being in navigating the integration of AI tools in modern academia.

This research stands out for its timeliness and the apparent gaps in current literature. There is notably little research on the use of ChatGPT in academic settings, making this investigation among the first to delve into how faculty and researchers in education use OpenAI.

The purpose of this study is to comprehensively evaluate the impact of the prefabrication rate on greenhouse gas (GHG) emissions and sustainability in prefabricated construction. In addition, it aims to identify the optimal prefabrication rate threshold that can promote the transformation of the construction industry toward more environmentally friendly practices.

This study uses an interdisciplinary methodology that combines emergy analysis with an extended input-output model to develop a GHG emission accounting model tailored for prefabricated buildings. The model assesses various construction schemes based on different rates of prefabrication and uses the emergy phase diagram from ecological economics to quantify the sustainability of these schemes.

This study indicates that within a prefabrication rate threshold of 61.27%–71.08%, a 5% increase in the prefabrication rate can significantly reduce emissions by approximately 36,800 kg CO₂(e). However, emissions begin to rise when the prefabrication rate exceeds this threshold. The case analysis identifies steel, concrete and electricity as the primary sources of GHG emissions, suggesting strategies for optimizing their usage and promoting the adoption of clean energy.

This study represents a novel tool for assessing the environmental impact and sustainability of prefabricated buildings. It offers scientific guidance for the construction industry’s environmental protection and sustainable development strategies, thereby contributing to a transition toward more environmentally friendly practices.

Construction renovation projects have been noted to suffer from uncertainties. While recent efforts have studied uncertainties affecting the duration of other types of projects, these efforts have overlooked construction renovation projects. Therefore, this study aims to evaluate the uncertainty factors affecting the duration of construction renovation projects.

In total, 226 responses from construction professionals were collected via a questionnaire survey on the impact of uncertainty factors on the duration of construction renovation projects. The subjective responses of experts from the industry were categorised using principal component analysis (PCA) before being exposed to objective analysis, assessment and modelling using a soft computing technique called fuzzy synthetic evaluation (FSE).

In total, 25 uncertainty factors were grouped as critical factors and were modelled. The PCA of the 25 critical uncertainty factors produced an 8-factor solution that grouped the uncertainty factors into 8 categories. The FSE modelling indicated that all eight groups are critical, but with varying levels of criticality on the duration of construction renovation projects.

The study provides a basis for a cost-effective uncertainty management guideline to avoid time overruns in construction projects. It also offers a platform for choosing among renovation projects to decide whether or not a project will overrun its time or not.

The study identified and established critical uncertainties affecting the duration of construction renovation projects, thus providing the first empirical multi-attribute objective uncertainty evaluation for the duration of construction renovation projects.

Mobile edge computing (MEC) services have long been used by private enterprises in Saudi Arabia with considerable success; however, there has been a stark lack of insight into how these services can be used to improve mobile government (M-Government) services for KSA citizens. This study aims to bridge this gap by integrating MEC with an enhanced version of the technology acceptance model (TAM) and examining its effects on user behavior and acceptance.

A closed-ended survey was administered to 1,500 people, and the responses were analyzed using sophisticated advanced statistical techniques to test an expanded TAM, using a quantitative method that uses structural equation modeling to validate the proposed model and hypotheses.

This study reveals that MEC significantly influences users’ intentions about using M-Government services and their tolerance for new technology adoption. Specifically, service cost and social influence are positively linked with end users’ intention to adopt M-Government services.

The novelty and contribution of this paper to existing literature are in highlighting the pivotal role of MEC in transforming public sector service delivery through technology. This study not only supports the adoption of M-Government services to enhance social welfare but also demonstrates and concludes some practical and theoretical ramifications of MEC service adoption.

This paper aims to develop a methodology for the spatiotemporal analysis of urban household waste data and a geographic information system (GIS)-based dashboard for interactive outcomes that identifies emerging trends and spatial distribution.

The study visualized the emerging hotspot analysis of household waste data covering the waste in selected areas from 2014 to 2019 in New South Wales, Australia. Through analyses in ArcGIS Pro, multiple maps and diagrams can be created to display these results in ArcGIS Insights. To enable the spatial waste analysis outcomes accessible, a GIS-based dashboard including maps and charts, spatiotemporal visualization of household waste tonnage, and emerging hotspots was created.

Based on the development of the dashboard in the ArcGIS Suites, there is an accessible data pipeline from ArcGIS Pro to Insights. The cloud-mapping system in ArcGIS online serves as a foundation for temporary data storage. The results also show the emerging hotspots of recyclable, residual and organic (RRO) waste in the Greater Sydney Region, Wollongong, Newcastle and Tweed. This study found an emerging cold spot in Wagga Wagga.

A dashboard for monitoring waste streams can be developed to enable GIS specialists to use historical spatiotemporal datasets in ArcGIS suites easily. Policymakers, strategy developers, urban waste managers and organizations dealing with urban waste can utilize this analytical dashboard to identify the issues, patterns and trends concerning urban waste for better decision-making in allocating required resources to overcome the identified issues to make informed decisions and develop strategies to alleviate the trends and patterns of ongoing problems. Indeed, the GIS-based dashboard developed in this research provides deep analysis and insights from the spatial waste data, allowing them to understand the included insights at a glance quickly.

Deriving location information for urban household waste data is crucial for waste management since it offers a better understanding of urban household waste data patterns, issues and historical trends. Small-scale studies have examined spatial waste patterns, but the investigation of urban household waste focusing on RRO waste is limited. Moreover, there is a lack of GIS-based dashboard development to enable spatiotemporal waste analysis outcomes to be publicly accessible.

This study aims to investigate the practical utilisation of Artificial Intelligence (AI) techniques in combating credit card fraud (CCF) within the accounting and finance sectors. It will evaluate the efficacy of machine learning (ML), blockchain and fuzzy logic in detecting fraudulent transactions, aiming to provide valuable insights for professionals including fraud examiners, auditors, accountants, bankers and organisations. The research seeks to determine whether AI and ML methods yield beneficial outcomes in the realm of credit card fraud detection (CCFD). It will focus on applying AI and ML techniques in CCFD, incorporating interviews, cross-country questionnaires and a sample size of 403. The study endeavours to contribute to understanding the optimal mechanism for detecting CCF and its potential for widespread application.

This study, which utilizes the job demands-resources (JD-R) theory, aims to explicate the relationship between responsible leadership (RL), green behavior (GB) and the mediating role of voice behavior (VB).

This study used a quantitative research design. The research sample consists of 260 participants who work in five-star hotels in Izmir. The research data were collected through face-to-face and online survey methods. Confirmatory factor analysis (CFA) in AMOS was performed to assess the measurement model. The research hypotheses were tested with structural equation modeling (SEM).

The results determined that RL affects GB and VB positively and significantly. In addition, VB has a significant positive link with GB. Furthermore, this study discovered that VB acts as a mediator in the relationship between RL and GB, indicating that RL indirectly promotes GB through implementing VB.

This study has limitations, such as its dependence on self-reported data, cross-sectional design and exclusive emphasis on participants from a single nation. When RL encourages employees to take GB, they are more likely to engage in GB. This study contributes to the field by evaluating the structures discussed with the JD-R theory. In the management practice of organizations, RL should be strengthened, and training should be provided to enhance RL.

The literature analysis revealed that, while studies have been undertaken using RL, the idea has not been tested using VB or has it been investigated in the hotel business, which has grown vital to the global economy. With these aspects, the work stands apart and serves as a source of motivation for researchers.

The aim of the current study is to recommend and compare the estimates of finite element model (FEM), analytical model, and artificial neural networks (ANN) model for capturing the LCC of FCSC members. A database comprising 325 FCSC columns was constructed from previous studies to propose FEM and ANN models while the analytical model was proposed based on a database of 712 samples and encasing mechanics of steel tube and FRP wraps. The concrete damage plastic model was used for concrete along with bilinear and linear elastic models for steel tube and FRP wraps, respectively. Analytical and ANN models effectively considered the lateral encasing mechanism of FCSC columns for accurate predictions.

The study aimed to compare the prediction accuracy of finite element (FEM), analytical, and artificial neural network (ANN) models for the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC). A database of 325 FCSC columns was developed for FEM and ANN models, while the analytical model was based on 712 samples, utilizing encasing mechanics of steel tube and FRP wraps. FEM used a concrete damage plastic model, bilinear steel tube, and linear elastic FRP models. Statistical accuracy was evaluated using MAE, MAPE, R², RMSE, and a 20-index across all models.

Based on the experimental database, the FEM presented the accuracies in the form of statistical parameters MAE = 223.76, MAPE = 285.32, R² = 0.94, RMSE = 210.43 and a20-index = 0.83. The analytical model showed the statistics of MAE = 427.229, MAPE = 283.649, R2 = 0.8149, RMSE = 275.428 and a20-index = 0.73 while ANN models portrayed the predictions with MAE = 195, MAPE = 229.67, R² = 0.981, RMSE = 174 and a20-index = 0.89 for the LCC of FCSC columns.

Although various investigations have already been performed on the prediction of the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC) using small and noisy data, none of them compared the accuracy of prediction of different modeling techniques based on a refined large database.

This study aims to investigate the factors influencing the adoption of online pharmacies in Qatar using the unified theory of acceptance and use of technology-2 (UTAUT-2) framework. Specifically, this study examines the impact of performance expectancy, effort expectancy, social influence, hedonic motivation, habit, technology trust, perceived risk and users’ level of awareness of behavioral intention, which in turn affects the adoption of online pharmacies. Furthermore, this study explores the moderating role of word-of-mouth (WOM) recommendations on the relationship between behavioral intention and online pharmacy adaptation.

This study adopted a descriptive, quantitative approach to investigate the UTAUT-2 model in the context of consumers’ adoption of e-pharmacy in Qatar. Through convenience sampling, 455 responses were collected from regular customers accessing online pharmacy services. The data were analyzed using Smart-PLS 3.2 software to examine the hypothesized relationships.

The results showed that WOM recommendations significantly enhanced the relationship between behavioral intention and adopting online pharmacies in Qatar. This study identified the factors that may hinder or enable the adoption of online pharmacies, including performance expectancy, effort expectancy, social influence, hedonic motivation, habit, technology trust, perceived risk and users’ level of awareness.

This study contributes to the existing literature on technology acceptance by extending the UTAUT-2 model and recognizing three additional variables (perceived risk, technology trust and technology awareness). These need to be investigated against UTAUT-2 variables to detect the significance of their impact on adapting the e-health concept in Qatar. The potential for cultural change to accelerate the adoption of online pharmacies is highlighted. Future research should explore the role of moral and cultural factors in technology adoption.

The results underscore the economic and social significance of e-pharmacy adoption, particularly within the context of a developing country. Considering the positive intentions expressed by individuals toward e-pharmacy, it becomes crucial for managers and decision-makers to make strategic choices to address any challenges that may arise. Policymakers are encouraged to enhance their services and implement various development initiatives to expand e-pharmacy accessibility and availability.

This study builds upon previous research on e-commerce in the pharmaceutical industry and provides a comprehensive understanding of customers in developing countries. Extending the UTAUT-2 model and identifying additional variables contributes to the knowledge of e-health concepts in Qatar. The findings have practical implications for developing strategies to promote online pharmacy adoption in Qatar and other countries.