Architecture Engineers Award

                Spectral geometry bridges mathematics and design by linking geometric properties to the eigenvalues of differential operators on surfaces. While well established in geometry processing, its adoption in architectural geometry and structural engineering has been limited. This research explores how spectral methods can provide new opportunities for shape modeling and design in architecture, opening pathways for more efficient and innovative structural solutions. Spectral Methods in Shape Modeling The use of spectral methods in shape modeling introduces new possibilities for architectural geometry by providing mathematically grounded tools for analyzing and optimizing forms. By applying eigenvalue-based techniques to surfaces and meshes, architects and engineers can unlock alternative workflows for modeling complex structures, enabling both precision and creativity. Anisotropic Laplacian Operators for Design Flexibility A key inn...

Smart buildings are rapidly becoming a vital part of sustainable development, offering solutions that enhance energy efficiency, improve user comfort, and reduce environmental impact. However, their adoption in Nigeria and other developing nations remains limited due to cost constraints, lack of expertise, and insufficient awareness. This study highlights the urgent need to analyze the financial implications of smart building adoption, focusing particularly on Abuja as a case study. Cost Implications of Smart Building Implementation The study emphasizes that the most significant challenge in smart building development is the high cost of hardware components, which often discourages investors and builders. These financial barriers create a widening gap between traditional construction practices and smart building adoption, particularly in markets with limited financial incentives. Cost analysis becomes an essential step in identifying realistic approaches to reducing expenses without ...

🧪🏆 Outstanding Scientist Awards The Outstanding Scientist Award honors exceptional researchers who have demonstrated remarkable contributions to scientific innovation, advancing knowledge, and creating a lasting impact in their field through dedication, creativity, and excellence Architecture Engineers Awards 🔗 Nominate now! 👉 https://architectureengineers.com/award-nomination/?ecategory=Awards&rcategory=Awardee 🌐 Visit: architectureengineers.com 📩 Contact: contact@architectureengineers.com #OutstandingScientist #ScientificExcellence #InnovationAward #ResearchExcellence #ScienceLeadership #GlobalScientists #ScientificImpact #InnovationInScience #FutureOfScience #ScienceAward #ResearchLeaders #ExcellenceInResearch #ScienceForFuture #InnovatorsInScience #TopScientists #ResearchInnovation #ScientificAchievement #ScienceRecognition #InnovationLeaders #AwardOfExcellence  

This study investigates the application of six machine learning regression models to predict building performance in a residential unit located in Sari, Iran. Using a calibrated EnergyPlus model and three years of utility data, the research evaluates primary energy consumption, emissions, indoor air quality, thermal comfort, and visual discomfort. The aim is to enhance sustainable design decision-making by comparing the efficiency and accuracy of different models. Machine Learning Models in Building Performance The research evaluates Random Forest, K-Nearest Neighbors, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Linear Regression. Each model is tested against real-world performance indicators, highlighting their predictive strength and weaknesses in handling multidimensional building datasets. Dataset and Methodology A synthetic dataset of 1,826 configurations with 25 input variables was developed using EnergyPlus. The dataset was split in...

Traditional building energy simulation models often overlook the stochastic behavior of occupants and the complex interactions between multiple devices. This limitation creates a significant gap between predicted and actual building energy consumption. Addressing this issue requires behavior-sensitive frameworks that integrate both human and technical dimensions of building performance. The proposed study bridges this gap by introducing a fuzzy multi-criteria decision-making (FMCDM) approach coupled with evolutionary optimization to ensure realistic and adaptive performance predictions. Significance of Occupant Behavior in Energy Modeling Occupant behavior is one of the most influential yet uncertain factors in determining building energy performance. Stochastic patterns, such as irregular use of appliances, varying thermal preferences, and diverse daily routines, make deterministic models insufficient. Incorporating behavioral diversity through FMCDM provides more accurate results, ...

  Sustainable hospital design plays a pivotal role in reducing the ecological footprint of healthcare infrastructure while ensuring effective service delivery. In resource-constrained regions such as Jordan, the need for a balanced approach that integrates environmental, economic, and sociocultural dimensions has become increasingly vital. This study employs the Analytic Hierarchy Process (AHP) to evaluate multiple sustainability criteria, thereby offering a systematic framework for decision-making in hospital planning and development. Role of AHP in Sustainable Healthcare Planning The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making tool that allows experts and policymakers to prioritize sustainability factors based on structured comparisons. In this study, AHP enabled the evaluation of seven primary sustainability criteria, capturing both quantitative and qualitative aspects. By ensuring logical consistency, AHP proved effective in aligning expert judgment ...

Energy efficiency in buildings is a critical research priority as global demand for cooling and heating continues to rise. This study focuses on improving building envelope systems by integrating phase change materials (PCMs) with regenerative water flow. While PCMs are known for their latent heat storage capacity that helps reduce peak thermal loads, their ability to lower average loads remains limited. To overcome this drawback, the research explores an active cooling strategy where PCMs are regenerated through controlled water circulation, leading to significant thermal performance enhancement. Role of PCM in Thermal Load Management PCMs are widely recognized for their ability to absorb and release heat during phase transitions, making them suitable for moderating indoor temperature fluctuations. However, when compared to conventional insulation of identical thermal resistance, PCMs alone do not significantly improve average cooling loads. This limitation highlights the necessity ...