Hamdi Bashir

Reliable cost estimation during the preconstruction phase remains challenging because decisions must be made with limited project information, substantial uncertainty, and project-specific cost drivers. This study proposes and empirically evaluates an AI modelling framework for preconstruction cost estimation (PCE) in power-sector projects that incorporates expert-based predictor selection with predictive and probabilistic modelling. A Modified Delphi process involving 20 experts validated cost predictors for three power-sector project domains: Power Generation, Transmission and Distribution (T&D), and Associated Infrastructure. The resulting domain-specific predictor sets were mapped to a historical database of 300 completed projects and evaluated using Bayesian Network (BN), Multiple Linear Regression (MLR), and Random Forest (RF) models under a common validation framework. The final hold-out evaluation showed that MLR achieved the lowest root mean square error (RMSE) and the highest coefficient of determination (R²) across all three domains. In contrast, RF achieved the lowest observed mean absolute percentage error (MAPE) in all three hold-out samples. Although less accurate for point prediction, the BN provided posterior probability information and interpretable probabilistic dependency structures. Cross-method analysis further showed consistency within the present dataset in identifying Installed Capacity, Transformer Capacity, and Project Size as the principal cost predictors for Power Generation, T&D, and Associated Infrastructure, respectively. Collectively, the results demonstrate a clear trade-off among predictive accuracy, probabilistic information, and interpretability, rather than identifying a universally superior modelling approach. The resulting framework, therefore, moves beyond algorithm comparison by linking expert-based predictor selection with empirical model evaluation to support the assessment of complementary modelling capabilities across project types and decision requirements. This integration provides a transparent and context-sensitive basis for supporting preconstruction cost-estimation decisions under uncertainty.

Reliable preconstruction cost estimation (PCE) is a fundamental stage to project planning and investment decision-making. However, the early-stage uncertainty and limited information make early-stage cost estimation challenging. Artificial intelligence has attracted growing interest as a way out of this impasse, producing a substantial empirical literature worth systematic examination. This study synthesizes the findings of 30 empirical studies published up to December 2025 using PRISMA protocols and examines the AI techniques, project types, dataset characteristics, validation practices, and model interpretability. The results show that artificial neural networks (ANNs) and hybrid approaches dominate the literature, with applications concentrated in building and transportation projects. Reported model performance is generally strong across commonly used evaluation metrics. However, several structural limitations persist: poor generalizability across project contexts, inconsistent validation procedures, limited adoption of explainable AI, and minimal integration of domain expertise. These factors, together, limit the transferability of existing models to real-world practice. This review contributes a structured methodological synthesis, maps the gaps that most limit progress, and proposes a conceptual AI–Expert Integration Framework to support estimation approaches that are more robust, interpretable, and decision-oriented. The findings offer both a current assessment of the field and a practical roadmap for advancing AI-driven PCE research.

Several studies have developed methods to model and analyze dependencies among requirements in software development projects. However, existing approaches provide limited support for decision-making related to software requirement (SR) changes. Extending this line of research, the present study proposes a three-stage decision-support framework to more systematically model, analyze, and manage dependencies among software requirements (SRs). First, Interpretive Structural Modeling (ISM) is employed to organize SRs into a simple hierarchical form. Next, a modified version of the Fuzzy Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis, which considers both the likelihood of occurrence and the impact of change, is employed to categorize SRs based on their criticality. Building on the outputs of the ISM and fuzzy MICMAC analyses, a lexicographic multi-objective optimization model is then developed to support the selection of SR changes that satisfy customer requirements while minimizing the breadth and intensity of change propagation. The applicability of the proposed framework is demonstrated through a real-world software development project involving 28 SRs. This demonstration illustrates that the proposed framework provides a systematic and robust decision-support tool for software requirement change management, helping reducing the risk of project cost and schedule overruns.

Background: As supply chains undergo rapid transformation, the concept of supply chain viability (SCV) has gained increasing attention among scholars and industry practitioners. Despite this growing interest, the existing literature lacks a comprehensive and integrated understanding of readiness factors across SCV dimensions. Methods: To address this gap, this study conducted a systematic review of the literature. A total of 86 peer-reviewed articles published between 2015 and 2025 were included, synthesizing evidence across four SCV dimensions: agility, resilience, sustainability, and digitalization. Two-mode network representation was used to analyze cross-dimensional connections between readiness factors and SCV dimensions via degree centrality. Results: The analysis identified 36 readiness factors, 9 of which are cross-dimensional and connected to all four SCV dimensions: leadership and decision-making, stakeholder integration, customer relationship and engagement, partner collaboration and readiness, advanced analytics, technological/digital capabilities, real-time data visibility, information and communication technology infrastructure availability, and digital transformation/smart technologies in supply chain management. Conclusions: Readiness factors vary across SCV dimensions, with some spanning multiple dimensions and others remaining specific. SCV strategies should account for these differences rather than treating factors as uniform.

Public service innovation research often focuses on the private or general public sectors, leaving the distinct challenges government entities face unexplored. An empirical study was carried out to bridge this gap using survey results from the United Arab Emirates (UAE) government entities. This study built on that research by further analyzing the relationships among these challenges through a social network approach, visualizing and analyzing the connections between them by utilizing betweenness centrality and eigenvector centrality as key metrics. Based on this analysis, the challenges were classified into different categories; 8 out of 22 challenges were identified as critical due to their high values in both metrics. Addressing these critical challenges is expected to create a cascading impact, helping to resolve many others. Targeted strategies are proposed, and leveraging open innovation is highlighted as an effective and versatile solution to address and mitigate these challenges. This study is one of the few to adopt a social network analysis perspective to visualize and analyze the relationships among challenges, enabling the identification of critical ones. This research offers novel and valuable insights that could assist decision-makers in UAE government entities and countries with similar contexts with actionable strategies to advance public service innovation.

Challenges in the water supply sector have hindered the advanced implementation of artificial intelligence (AI) compared to other sectors. These challenges have not been sufficiently examined in the existing literature. An empirical study was conducted within a public utilities organization in the United Arab Emirates (UAE) to address this gap. An integrated approach combining interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was utilized to identify the critical challenges and to model and analyze the relationships among them. The ISM model provides significant advantages by enabling decision-makers to visualize complex interactions, supporting the development of an effective AI implementation strategy. The strategy should prioritize four critical challenges: the lack of technical skills and knowledge, the limited availability of ready-to-use AI solutions, inadequate water infrastructure, and concerns regarding privacy and data security. These challenges were identified based on their positioning at the lowest level of the ISM model and their classification as independent in the fuzzy MICMAC analysis. Addressing these four challenges will help to mitigate the remaining six. The study’s findings and implications are expected to offer valuable guidance for decision-makers in implementing AI technologies within water supply organizations, both in the UAE and in countries with similar environments.

Firms in the construction industry are under increasing pressure from regulatory bodies, clients, and the public to integrate sustainability into their business strategies. However, they encounter numerous barriers that hinder the implementation of environmental sustainability practices in construction project management. This study aimed to examine these barriers within the context of the United Arab Emirates’ (UAE’s) construction industry. To achieve this, the research employed a mixed-method approach. Initially, interviews were conducted to identify the prevalent barriers, resulting in the identification of twelve key barriers. Subsequently, a structured questionnaire was distributed to project managers from 90 firms, both ISO 14001-certified and non-certified, to rank these barriers and assess their significance. The findings revealed that “economic benefits placed above meeting environmental sustainability requirements” was the most critical barrier. Through factor analysis, three latent factors were extracted: (1) organizational and policy barriers, (2) compliance and resource efficiency barriers, and (3) sustainable design implementation barriers. Notably, significant differences were observed between ISO 14001-certified and non-certified firms, particularly regarding the importance of “economic benefits placed above meeting environmental sustainability requirements” and “insufficient consultation with stakeholders”. This study highlights the critical barriers to implementing environmental sustainability practices in the UAE’s construction industry and provides actionable suggestions for policymakers and decision-makers to overcome these challenges, with implications for similar environments worldwide.