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While efficient design in engineering projects is crucial, this paper aims to examine the integration of Building Information Modeling (BIM) into railway Intelligent Transportation Systems (ITS). The paper provides some key understanding of integrating BIM and ITS to improve the efficiency of railway infrastructure projects.

An in-depth qualitative analysis of three ITS case studies was conducted to understand BIM’s global impact and benefits in railway infrastructure projects. While case study one investigated the Crossrail (UK), the other two case studies were TUC Rail (Belgium) and the Intercity railway network (Norway).

The findings include the specific benefits of BIM, regarding the railway infrastructure. The result indicated that BIM benefits were consistently the same across all case studies. Although Case study 1 was the only one that boasts a high reduction in waste and reworks, all of the case studies showed less rework and delays due to BIM. The results indicated that the advantages of BIM for such projects are cost optimization, reduction in waste, rework and lessening delays. Subsequently, this leads to the ease and efficiency with which structures and railways can be built. The outcomes can ultimately assist transportation planners in better planning and managing railway projects.

This study proposes the integration of BIM into railway projects as a part of their ITS. The BIM integration into railway projects as a part of their ITS fits within the overall planning to handover phases. Specifically, the BIM integration improves the design process of typical railway projects. Thus, the most significant advantage of BIM for railway projects is to further improve their design process leading to a higher degree of constructability.

Railway infrastructure performs a major role in economic and regional development. The complexity of railway projects continues to increase as the need for more railway infrastructure is on the rise globally. BIM is proving to be an effective tool for improving the efficiency of railway infrastructure projects. As the utilization of BIM is intensifying, the railway industry can further exploit BIM to improve project delivery adeptness by offering greater collaboration leading to efficient design processes. As a result, the understanding of BIM for horizontal projects such as railway infrastructure on a global scale is a substantial exercise that this research aims to respond to.

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.

The purpose of this study is to investigate the value of cloud computing adoption (CCA) as a proxy for information technology (IT) flexibility (ITF) and IT effectiveness (ITE) among Small- and medium-sized enterprises (SMEs) in Jordan.

A research framework with five hypotheses has been developed based on the results of previous studies. Partial least squares-structural equation modeling has been used for data analysis.

The results revealed that elements of ITF in two domains, IT technical resource (connectivity, modularityand compatibility) and IT human resource (IT personnel skills) specific to CCA, were significantly correlated with ITE.

The findings have crucial implications: they contribute to the research community, administrators and cloud computing providers (CSPs) concerning the framework-improved procedure for CCA. The proposed model can enhance the awareness of service providers about why some SMEs accept cloud computing services, whereas actually the same ones having the same type of business do not. In addition, the above providers should enhance their interaction with the SMEs that contributed to the cloud computing knowledge to make a well-organized setting for the CCA, specifically, SMEs need that adopt an on-premise private cloud architecture. Moreover, necessary to determine the challenges in deploying solutions from the perspective of CSPs. The sample has been limited to Jordan respondents.

The research studies about the usage of cloud computing have shown its effects on SMEs today. Also, the different impacts of cloud computing on other sectors are at the center of attention. SMEs could get significant advantages by carefully considering and managing CCA from the ITF and ITE perspective.

To the best of the author’s knowledge, this is the first study to develop a framework for CCA based on the standpoint of ITF in two domains, IT technical resources (connectivity, modularity and compatibility) and IT human resources (IT personnel skills) and effectiveness.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

The study aims to understand the way metaverse might revolutionize the governance format – precisely the e-government concept – besides underlining the challenges leaving suggestive contours for further research in this realm.

An inductive research approach included the content analysis of relevant literature to identify patterns and generalize them into areas and approaches. Developing a conceptual framework seeks to systematically organize knowledge on metaverse government and provide an understanding of its contributions to enhancing the e-government maturity models.

The authors identified three general areas and approaches – content and data lifecycle management; platforms, tools and services; and infrastructure and computing resources – that shape how challenges of enterprise architecture layers should be resolved in a future research agenda. Integration of these government metaverse areas and approaches is linked with four enterprise architecture layers (technology, applications, data and business) that advance a new e-government meta stage of e-government maturity and the related challenges.

Hitherto, metaverse literature has hinged itself on the benefits and challenges of metaverse in the private sector. However, the exact dimensions have not been probed in the public sector and its e-government domain – the present study seeks to plug this gap.

The Kingdom of Saudi Arabia’s dedicated pursuit of technological modernization positions it as a forefront leader in integrating advanced systems, aligning smoothly with the ambitious goals outlined in Vision 2030. The purpose of this study is to investigate the influence of integrating enterprise resource planning (ERP) and business intelligence (BI) systems on decision-making processes within the industrial sector of Saudi Arabia.

Using a quantitative research design, this study uses a bootstrapping approach and partial least squares structural equation modeling to meticulously analyze data collected from Saudi industrial firms.

The research reveals favorable relationships among infrastructure readiness, data quality, security and access control, user capabilities, user training and the integration of ERP and BI. These positive associations collectively affirm the overarching positive impact of ERP and BI integration on decision-making processes within the industrial sector.

The study underscores the strategic imperative of aligning organizational practices with the identified characteristics to fully unlock the potential benefits of ERP and BI integration in the Saudi Arabian industrial sector.

This study contributes significantly to the existing literature by delving into the integration of ERP and BI in the industrial sector and its nuanced impact on decision-making processes, specifically in the context of the Kingdom of Saudi Arabia – an area that has not been extensively studied.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

Recent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain. While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms.

This research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York.

This research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions.

This study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.