The construction industry has a huge impact on the environment in terms of noise, water and land pollution, traffic congestion and waste disposal. Another aspect of the construction industry impact on the environment is the increasing energy consumption. According to published research, buildings energy use is expected to increase by 32% by the year 2040. As a result, efforts have been directed toward improving green building awareness and the application of sustainability concepts in the design, construction and building management processes. In this research, using extensive simulations, the integration between Building Information Modeling methodology (BIM) and Building Energy Modeling (BEM) methodologies in order to effectively minimize the overall energy consumption of a residential building in the UAE is investigated by studying several design factors including: building orientation and windows type, size and distribution on the overall building energy consumption. Results show that to increase the modelled building’s energy and financial efficiency, recommended changes to the initial design have to be done including changing the distribution of the southern façade and the type of windows glazing used. More specifically, there was a peak energy reduction of: 8% with a 180 degrees building orientation angle, 2% with a window to wall ratio of 15%, and 2% when double glazing windows were used. This work validates that the combination of BIM and BEM allows to enhance the overall building energy consumption efficiency and to further establish the needed sustainability goals through a generated 3D model.

The vast majority of structures, even in high-risk zones, will not actually be subjected to earthquake during their design lives. It is therefore felt that spiral binding is only likely to be used in practice if it can contribute to the static shear strength of the structure and so replace at least some of the stirrups. The question addressed here is whether such spirals are useful to carry shear in beams and in particular whether they can usefully improve the residual shear strength at hinges after severe cyclic bending.

Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing historical data from archived projects can assist managers in predicting project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average – ARIMùA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next three months. This research provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short-term project progress to take proactive measures on different influencing factors.

The digital transformation of the construction industry has outpaced the evolution of traditional project management competency frameworks, leaving many Project Managers (PMs) underprepared to meet the demands of emerging technologies such as Digital Twins (DTs). This study develops and validates a Next-Gen Digital PM Competency List tailored to the Smart Built Environment (SBE) through a structured two-stage methodology. Stage 1 applied a three-phase process comprising (1) a Systematic Literature Review (SLR), (2) NVivo-based thematic analysis, and (3) Large Language Model (LLM)-driven synthesis. These phases identified, categorised, and defined 55 digital PM competencies, systematically organised into Skills, Knowledge, and Core Personality Traits. Stage 2 evaluates the practical relevance of these competencies by mapping them against six thematic functional requirements of DTs, including interoperability and integration, real-time data management, simulation and predictive modelling, cybersecurity and data governance, stakeholder collaboration, and scalability and lifecycle continuity. DTs were selected as an exemplar because they integrate multiple digital technologies, Building Information Modelling (BIM), Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing, making them a representative test case for evaluating competency alignment in digital transformation. The mapping confirmed strong alignment in digital integration and collaboration while exposing gaps in cybersecurity readiness and lifecycle resilience. The resulting framework provides a validated reference for competency benchmarking, targeted training, and digital leadership development. By equipping PMs with the competencies to lead, manage, and support digitally enabled projects, this research contributes directly to advancing smart and sustainable construction practices in the digital built environment.