Conventional concrete is a common building material that is often ridden with cracks due to its low tensile strength. Moreover, it has relatively low shear strength and, unless reinforced, undergoes brittle failure under tension and shear. Thus, concrete must be adequately reinforced to prevent brittle tensile and shear failures. Steel fibres are commonly used for this purpose, which can partially or fully replace traditional steel reinforcement. The strength properties and bond characteristics between reinforcing steel fibres and the concrete matrix are crucial in ensuring the effective performance of the composite material. In particular, the quality of the bond has a significant impact on crack development, crack spacing, and crack width, among other parameters. Hence, the proper application of steel fibre-reinforced concrete (SFRC) requires a thorough understanding of the factors influencing its bond behaviour and strength properties. This paper offers a comprehensive review of the main factors controlling the bond behaviour between concrete and steel fibres in SFRC. In particular, we focus on the effects of the physical and mechanical properties of steel fibres (e.g., geometry, inclination angle, embedded length, diameter, and tensile strength) on the bond behaviour. We find that the addition of up to 2% of steel fibres into concrete mixtures can significantly enhance the compressive strength, tensile strength, and flexural strength of concrete components (by about 20%, 143%, and 167%, respectively). Furthermore, a significant enhancement in the pull-out performance of the concrete is observed with the addition of steel fibres at various dosages and geometries.

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation. Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance.

The study aims to understand to what extent employing A.I. image-generative tools in architectural concept brainstorming demonstrates effectiveness, accuracy, and adherence to text and image inputs, and evaluate the utilization of A.I. image-generative tools in the architectural ideation phase, considering factors such as speed, creativity, and accuracy. Thus, this study will investigate using an A.I. image-generative tool, Midjourney, in the creative brainstorming stage for a Safavid mosque architectural form design process. The study will quantitatively evaluate the form proportions of mosques generated by Midjourney based on certain criteria as they pertain to the Shah Mosque as the inspiration for the design as it encompasses many of the Safavid Mosque’s typical elements and characteristics. The most matching form will then be found. The findings are that Midjourney is reliable in terms of its speed and creativity; however, it lacks accuracy and adherence to inputs. This study is essential as limited literature exists on A.I. image-generative tools in the brainstorming process of architectural projects, particularly mosque architecture. It also provides architects and designers with a framework and the pros and cons of the use of A.I. generative tools, such as Midjourney, in the ideation process for an architectural project.

Purpose: This study analyzes the effect of the techniques of active teaching and learning as a way of delivery on the outcomes of quality learning. Focusing on the courses of architectural science taught in a nontraditional method using various active learning strategies, the study takes the case study of the course Building Illumination and Acoustics (BIA) delivered in the academic year 2019–2020 at the University of Sharjah (UoS)'s Architectural Engineering Department (AED).Design/methodology/approach: Utilizing both quantitative and qualitative research approaches, the study applied a case study and survey as methods. A questionnaire was designed and performed to assess the level of students' satisfaction with the implemented active teaching method.Findings: The vibrant learning setting made the students actively engaged and more motivated and enthusiastic. The active learning practices used, including employing senses as in sight and hearing, reasoning rationally and intuitively, reflecting and acting, working steadily and in fits and starts, creating mathematical models, visualizing and memorizing and drawing analogies, were efficient in boosting their ability to comprehend theoretical concepts more effectively. The delivery style effectively enhances quality learning when various active techniques are used pedagogically beyond being merely a utilitarian instrument to prepare novice students of architectural engineering to fulfill practical challenges.Research limitations/implications: This article focuses specifically on a theoretical, scientific non-studio course in a particular program of architectural engineering in a particular semester before the dramatic changes in styles of teaching delivery that happened due to the COVID-19 pandemic. Future research could further highlight its results by comparing them to statistical evidence of the development of the course, especially for the duration of online teaching during the pandemic and the hybrid teaching period after it.Originality/value: This article contributes to the development of teaching and learning of architectural engineering in the local Emirati context by putting original theories of teaching into practice. This paper further contributes to the field of architectural pedagogy in terms of the effect of active learning in the architecture field in the non-studio courses in higher education in the United Arab Emirates.

This research seeks to gain a deeper understanding of the current ecosystem of BIM technology and its utilization in the UAE. Since the UAE has been an early adopter of BIM, its utilization of BIM is now likely to reach that of developed nations. By adopting the BIM case study analysis framework, two sets of projects of similar categories in terms of functionality and typology were selected and mapped. One set represents projects within the UAE, and the second represents projects from developed nations utilizing BIM during the projects’ lifecycle (LC). Incorporating expert interviews to triangulate the results collected from the case study analysis will further contribute to developing a more in-depth comprehension of BIM use in the UAE. The three significant inferences can be drawn from the findings. Firstly, in the UAE, stakeholders utilize BIM fragmentarily in different stages. Secondly, the full extent of BIM utilization over the project’s LC is not pursued. Thirdly, no effective BIM standards or protocols exist. Even though international firms are the main drivers of UAE BIM adoption and utilization, UAE BIM is still in its infancy, necessitating a more collaborative effort to realize the holistic benefits of the BIM process. The findings could be utilized to identify industry standards and guiding principles for BIM implementation in the UAE and help identify areas for improvement. It also provides valuable insights into how BIM can be used more effectively in the country and how its use can be promoted. Additionally, it could be used as a guide for owners/asset managers and AEC professionals in the UAE looking to implement BIM in their projects.

This study used innovative computational design tools to improve a corridor’s visual and environmental conditions, such as solar radiation exposure and optimal daylighting, at the University of Sharjah’s (UoS) campus in the United Arab Emirates. The research methodology used computational design software to develop two sets of codes. The first set was dedicated to conducting environmental study simulations that assessed the corridor’s performance and classified site-dependent parameters such as sun path analysis and wind rose diagrams, and pattern-dependent parameters such as solar radiation analysis and shadow study diagrams. The second code set generated Islamic geometric patterns, following the design scheme of the University. Varying typologies were produced using the two parameters to change the pattern’s porous size, shape, and gradient.