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This research paper investigates the factors influencing wind energy adoption and sustainability in the United Arab Emirates (UAE). The investigation is motivated by a research gap, as there is limited existing research on the specific factors influencing the adoption of wind energy in the UAE. Understanding these factors is identified as a crucial problem for the successful transition to sustainable energy sources in the nation. Employing a systematic review methodology, data spanning from 1999 to 2023 across six databases were collected using specified keywords. A total of 203 articles were screened, resulting in 64 selected articles. The study categorizes derived factors into technological innovation, environmental impact, grid integration, and socioeconomic aspects. Utilizing a structured methodology involving questionnaire surveys, structural equation modeling (SEM), and multiple linear regression (MLR) analyses. 75 % of hypotheses were accepted, affirming significant positive relationships between technological innovation, environmental impact, grid integration, wind energy adoption, and sustainability were confirmed. While socioeconomic factors exhibit a nuanced influence, the findings underscore the UAE's remarkable progress in harnessing wind energy for a sustainable future. These findings underscore the importance of further investigating these factors and sub-factors for successfully transitioning to sustainable energy sources in the UAE.

Abstract This paper provides a comprehensive review on the Artificial Intelligence (AI) based models for predictive maintenance (PdM) of water injection pumps (WIPs) in the oil and gas industry (OGI). The review encompasses the selection of algorithms, data requirements, and optimization strategies, offering insights into advancements, challenges, and theoretical foundations including data pre-processing and feature selection. This review highlights AI-based PdM developments for WIPs, focusing on techniques and algorithms that enhance water injection pump performance and accurately predict maintenance needs. It emphasizes the effectiveness of algorithms in capturing pump data patterns and anomalies for proactive maintenance. Additionally, the review offers valuable insights for future research directions and practical implementations of AI in pump maintenance. This comprehensive assessment serves as a beacon for OGI experts in the selection of AI methods for pump maintenance, enabling them to refine their procedures, enhance efficiency, and reduce operational interruptions. A prominent highlight is the importance of data quality and interpretability, which play a pivotal role in facilitating well-informed decision-making during the integration of AI technologies into maintenance processes. This article focuses on the theoretical foundations of AI in the context of pump maintenance, providing a contribution to OGI industry. By integrating theoretical perspectives with real-world evidence, it offers insights for guiding future research and enhancing maintenance techniques. As a resource, it holds relevance for researchers, practitioners, and decision-makers within the OGI sector, contributing to the ongoing advancement of this field. Article Highlights Artificial Intelligence (AI) optimizes Oil & Gas pump maintenance for efficiency and reliability. Real-world case studies validate cost and time reductions, improving pump performance. Challenges in data management and ethical AI implementation require careful consideration.

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure.

This article contributes to the existing literature by modeling and automating the learning process from previous aviation construction projects (ACPs) using artificial intelligence tools, where it will be easier to characterize aviation construction projects and identify the specifications of different aspects of the projects throughout their entire life cycle. An artificial intelligence (AI) framework is proposed for the categorization of aviation construction projects using different machine-learning (ML) methods with a focus on the UAE as a source of data. Airport construction projects have been seen to share a good deal of similar attributes, which should simplify the decision-making process regarding layouts, design, equipment, labor, budget, complexity, etc. However, the gap in reality is that the huge and scattered sources of data, project specifications, characteristics, and the knowledge from past projects are not utilized in an automated way that could simplify the navigation through projects for better future decision-making. The utilization of AI/ML tools is expected to be useful here in order to reduce the revisions of design and construction rework by classifying the projects and the elements that managers need to consider. The planning, design, and construction of new projects can be improved by identifying the attributes of past projects and categorizing them according to similarities, differences, and complexities. Specifically speaking, a framework of hierarchical clustering and neural networks is integrated together to form the classification model. Upon implementing hierarchical classification and neural networks, it was found that neural networks could demonstrate remarkable classification results; the error in classification was minimal in most of the cases. The advantage of such classification is to help decision-makers utilize best practice from the groups of previous projects, which were classified using both hierarchical and neural networks models. With this classification, rework can be minimized, overhead costs may be reduced, and past best practices can be utilized.

Surface mining is one of the hazardous industries that have several risky operations, including transportation, treatment, and mineral extraction. To avoid the risk of disaster, it is important to evaluate safety procedures and determine expected hazards. The aim of this study is to develop a thorough safety evaluation model for the surface mining industry based on the analytic hierarchy process (AHP), one important multi-criteria decision-making approach. A total of 11 criteria and 36 sub-criteria that are both independent and homogeneous were involved in the decision problem. Further, a deep sensitivity analysis was conducted to assess the stability of the ranking preference. The findings indicate that four out of the eleven criteria are particularly significant. To test the model’s applicability and effectiveness, a case study was conducted involving three surface mining companies located in the north of Jordan. The results demonstrate that the model is reliable, applicable, and effective in addressing real-world problems.

This research aims to develop and validate a smart PMS. The PMS will create a foundation for PMSs that will be used by organizations in the digital era. A three-step methodology was used in the current research. First, the archival literature analysis was used to identify the features and elements of the robust PMSs. Second, a generic PMS was constructed based on the results of the first step. Third, the generic PMS was amended, implemented in the workshop, and validated by discussing the results with a focus group of experts. The academic and technical contribution can be seen in proposing a generic Virtual Organizational Deoxyribonucleic Acid (V-DNA) concept and smart PMS (Performance Management Dashboard (PMD) and Decision-Making Tool (DMT)) based on the features and elements of the robust PMSs. The generic V-DNA and PMS were amended and implemented in the stated workshop. Then, the validation process was done by presenting the implementation results to a focus group of academic experts and taking their feedback. Applying the PMD and the DMT to monitor, analyze, and manage workshop performance was successful. The PMD proved a useful tool that can provide a holistic view of the workshop performance areas instead of focusing on isolated business aspects such as workshop productivity or efficiency. The decision-makers directly identified the low-performing and highly performing KPIs/processes/sub-processes and identified the root causes of low and high performance. The DMT proved a useful tool. The decision-makers could evaluate all sub-processes and rank them based on the values and weights of the decision-making criteria, highlighting the areas that need improvement. The originality and novelty of the proposed PMS and the V-DNA were proved through a systematic literature review process. The implications of the research can be seen in the possibility of testing the generic V-DNA and the PMS templates in organizations of different sizes and sectors to check their applicability. Moreover, other layers of the organizational V-DNA can be proposed. The current research assists the practitioners and managers in constructing the PMSs they need for their workshops/factories/companies.

Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper’s main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.

The global COVID-19 pandemic forced the construction industry to a standstill. In the wake of the pandemic, this sector must be prepared to make bold, innovative moves to prepare for the future. Over the past few years, the use of drones and robotics has expanded with many commercial uses, including in the construction industry. Drone-driven automation has an enormous impact in improving productivity and reducing cost and schedule overruns. The use of drones, along with the application of Internet of Things (IoT) and robotics, can make a significant impact on the supply chain and improve inventory accuracy, leading to faster and more cost-effective building projects. This paper will propose and statistically substantiate an optimization model for supply chain management through the accelerated use of drones and Artificial Intelligence (AI) in the post-pandemic era. The use of smart devices and IoT will allow warehouse managers to have real-time visibility of the location and inventory tracking, as well as enabling warehouse workers to access information without being physically present. Cutting-edge drone technology can quickly perform inspections to make inventory control more economical and efficient. While they are certainly not a perfect fit for every building surveillance task, drones have many advantages for probing buildings in search of leaks, performing aerial surveys, and dealing with security issues more cost-effectively than manual procedures, thereby leading to improved communication and collaboration between different stakeholders. This paper includes a real-life case study and dynamic mathematical model to demonstrate how this approach results in a project’s materials becoming visible, traceable, and easily tracked from end to end.