This study introduces a pioneering structural equation modeling (SEM)-based framework to assess BIM-DT adoption readiness in sustainable construction. The research’s approach delves into the intricate correlation between key success factors (KSFs) and sustainable parameters (SPs), fostering BIM-DT deployment. This interdisciplinary perspective provides a holistic understanding of the impact of KSFs on BIM-DT adoption. The research aims to identify KSFs and parameters, prioritize their relative weights for BIM-DT implementation, analyze KSFs and SPs correlations, and offer practical insights into the findings. The results revealed the importance of sustainability considerations in BIM-DT adoption, with cost optimization and resource management playing pivotal roles. The findings also revealed that the readiness of the organization to adopt the technology, availability of technology operators, availability of standards and codes for the new technology, availability of knowledge, skills, and competencies to adopt the technology, and availability of funds and financial investment are the top-ranked KSFs contributing to the successful adoption of BIM-DT.

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R² of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Facility Management (FM) has increasingly focused on integrating Building Information Modeling (BIM) with the Internet of Things (IoT), known as digital twins, in large-scale development projects. Effective BIM integration in FM requires improved cooperation among participants across various project stages. This digital revolution aims to enhance planning, construction, and asset management efficiency, benefiting all parties. However, BIM utilization in FM is limited by incomplete owner understanding, insufficient data accessibility, and stakeholders’ unfamiliarity with BIM procedures and standards. Despite recognizing BIM’s significance, the FM industry faces significant implementation challenges. Facility managers often lack a comprehensive understanding of BIM’s benefits in streamlining operations and enhancing cost efficiency, as well as the necessary skills for its use. Addressing these barriers requires developing an Employer’s Information Requirements (EIR) document at a project’s outset, providing a strategic plan and vision for all involved parties. BIM and IoT are pivotal technologies for transitioning to efficient building operations and crucial for reducing time, costs, and operational challenges throughout any project. This research aims to establish a digital trio workflow, integrating BIM, EIR, and IoT to maximize stakeholder benefits. It explores how preparing the EIR through stakeholder communication can improve design processes, sustainability, efficiency, cost, and time, especially for megaprojects.

Accurate forecasting of wind speed and power is transforming renewable wind farm management, facilitating efficient energy supply for smart and zero-energy cities. This paper introduces a novel low-carbon Sustainable AI-Driven Wind Energy Forecasting System (SAI-WEFS) developed from a promising real-world case study in MENA region. The SAI-WEFS evaluates twelve machine learning algorithms, utilizing both single and ensemble models for forecasting wind speed (WSF) and wind power (WPF) across multiple timeframes (10 min, 30 min, 6 h, 24 h, and 36 h). The system integrates multi-time horizon predictions, where the WSF output is input for the WPF model. The environmental impact of each algorithm is assessed based on CO₂ emissions for each computational hour. Predictive accuracy is assessed using mean square error (MSE) and mean absolute percentage error (MAPE). Results indicate that ensemble algorithms consistently outperform single ML models, with tree-based models demonstrating a lower environmental impact, emitting approximately 60 g of CO₂ per computational hour compared to deep learning models, which emit up to 500 g per hour. This system enhances the Urban Energy Supply Decarbonization Framework (UESDF) by predicting the Urban Carbon Emission Index (UCEI) to illustrate the Urban Carbon Transition Curve.

The Fourth Industrial Revolution (4IR) has accelerated the construction industry’s shift toward digital transformation. This progress is mainly driven by the emergence of innovative technologies, including artificial intelligence (AI) and digital twins (DTs). While global research has extensively explored the benefits and challenges of AI-based DTs, the rapid growth of Saudi Arabia’s construction sector—fueled by substantial local investments and international partnerships—underscores the urgent need to examine their specific impact within this context. To address this gap, this study aims to investigate the potential benefits and challenges of integrating AI-driven DTs into Saudi Arabia’s construction industry. To achieve this, a structured literature review and a survey were conducted among architecture, engineering, and construction (AEC) firms, with 106 complete responses analyzed using correspondence analysis (CA). The findings revealed that AI-driven DTs substantially benefit Saudi Arabia’s construction industry. For example, among the 17 identified benefits, the top-ranked ones include AI capabilities to improve analytics, AI’s facilitation of digital twins in modeling complex real-world systems, and the facilitation of strategic decision making. However, several challenges hinder the realization of these benefits, including a lack of standardization of integrated DT and AI in construction projects, a lack of understanding of AI’s capabilities, a lack of logistics and the limited availability of IT infrastructure, and the complexity of AI algorithms. These findings underscore the transformative potential of integrating AI-driven DTs to optimize construction performance, improve decision-making, and address real-world complexities. This study provides actionable insights for stakeholders and recommends future research exploring strategies for overcoming adoption challenges, fostering technological innovation, and capacity building in Saudi Arabia’s construction sector.

Construction delays are a prevalent issue worldwide, and Saudi Arabia is just a short distance from this experience. Although the Saudi construction industry was booming due to Vision 2030, major delays were recorded in numerous construction projects, resulting in extra costs and schedule overruns. Studies on Building Information Modeling (BIM) have increased recently due to their crucial role in managing construction projects. However, more efforts need to be made to define the impact value of adopting BIM using advanced techniques such as System Dynamics (SD). It also aims to demonstrate the BIM impacts globally to encourage taking advantage of such a system. Limited papers studied BIM impact in-depth using advanced techniques such as SD. Thus, A questionnaire was distributed to fifty-nine experts to measure the extent of BIM’s influence on the ten factors affecting project delay times discovered in the literature. The statistical analysis results were used to calculate each factor’s relative importance index (RII), compare them to the results obtained without BIM use, and calculate the relative difference for each factor. These relative differences were considered in the modified SD. The modified SD was applied to a case study representing an academic building in the King Saud University campus. The main finding revealed that BIM decreased construction project delays by 14.55%. In addition, this study found that BIM has a maximum influence on poor site management and supervision by contractors, with a reduction percentage of 17.65%, and a minimum influence on lack of contractor experience and managerial skills, with 11.76%. The SD analysis results confirmed that BIM has a significant impact on reducing construction project delays.