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Architectural Plan Logic and Systematization

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Architectural plan types shape both the expression and the functional efficiency of a building, making them a central subject in architectural research. Traditional classifications often depend on personal experience or historical precedents, creating a fragmented understanding. This study revisits the foundations of architectural plan types and reconstructs them through a logical lens. By establishing a basic plan library derived from regular polygon systems and grid systems, the work proposes 18 universal plan types capable of generating complex forms. A preliminary evaluation method, using indicators such as concentration, symmetry, and complexity, helps analyse these plan types with greater precision. The study’s systematic framework supports design exploration across architecture, urban morphology, visual graphics, and product design. Logical Reconstruction of Plan Types This research proposes a logical pathway for rethinking architectural plan types by moving beyond subjective ...
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Research Topics on Occupant-Centred Space Heating Control Systems

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Traditional space heating systems typically rely on averaged or single-point temperature readings, often neglecting spatial variations within indoor environments. This oversight can cause inefficiencies in maintaining thermal comfort and unnecessary energy consumption. In response, this research introduces an occupant-centred control method that dynamically adjusts heating based on real-time occupant positioning and localized thermal conditions. By integrating advanced localization and thermal modeling, the proposed system enhances both energy efficiency and occupant comfort through intelligent feedback-based control. Adaptive Multi-Target Localization for Occupant Tracking Accurate occupant localization is central to personalized climate control. This study employs an adaptive multi-target localization method using the Density Peak Clustering (DPC) algorithm to detect real-time occupant positions. The algorithm’s strength lies in its ability to identify distinct data clusters witho...
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Breakthrough Research Award

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Description: The Breakthrough Research Award in Architecture honors pioneering studies that redefine the boundaries of design, sustainability, and technology in the built environment. This recognition celebrates researchers whose innovative contributions shape the future of architecture through impactful, data-driven, and visionary exploration.   Architecture Engineers Awards  šŸ”— Nominate now! šŸ‘‰  https://architectureengineers.com/award-nomination/?ecategory=Awards&rcategory=Awardee  šŸŒ Visit:  architectureengineers.com   šŸ“© Contact:  contact@architectureengineers.com   Hashtags: #BreakthroughResearch #ArchitectureInnovation #DesignExcellence #SustainableArchitecture #ResearchAwards #ScienceFather #Scifax  
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Research Topics on Long-Term Behavior of Textile-Reinforced Mortars

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Textile-reinforced mortars are increasingly used to strengthen aging masonry structures, yet their long-term mechanical response under sustained loads remains underexplored. While durability studies exist, creep behavior still holds unanswered questions that affect their use in real reinforcement scenarios. This research focuses on how TRMs deform, stabilize, or fail when exposed to continuous loading over extended periods, using controlled single-lap shear experiments to reveal their true long-term potential. Significance of Creep Behavior in TRM-Reinforced Masonry Understanding creep in TRMs is essential because long-term deformation can reduce load transfer efficiency and compromise structural safety. When old masonry interacts with modern composite overlays, time-dependent strain becomes a critical performance factor. This topic examines why creep contributes to serviceability concerns, how it relates to sustained load levels, and why predicting it accurately is vital for the re...
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Machine Learning Driven Optimisation of Energy Efficiency and Indoor Air Quality in Educational Buildings

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  Educational buildings breathe life into growing minds, yet they often struggle to balance two essential needs: conserving energy and maintaining healthy indoor air quality. The study behind this work steps into that tension, exploring how traditional HVAC control methods fall short when faced with shifting indoor conditions and rising sustainability pressures. By pairing experimental testing with advanced machine learning models, the research aims to create smarter, adaptive systems that reduce energy consumption while protecting occupant health. This introduction frames the need for innovation, outlining why educational spaces demand data-driven solutions capable of reacting in real time. Machine Learning Integration for HVAC Optimization The research explores how models such as RNN, LSTM, GRU, and CNN can interpret vast streams of environmental data and turn them into actionable predictions. With over 35,000 real-world records, the study demonstrates how these models learn ...
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Research Topics on Parametric Modeling and Scan-to-BIM for Historical Vault Geometry

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  The shift from traditional craftsmanship to digital reconstruction has created a new frontier for heritage documentation. As builders of the past shaped vaults directly around existing surfaces, today’s BIM operators face the challenge of translating these irregular geometries into accurate digital models. Modern approaches rely on mathematical descriptions, point-cloud processing, and parametric thinking to bridge this gap. Challenges in Digitizing Historical Vault Geometry Historical vaults rarely present uniform or idealized shapes, which complicates their capture and representation. Surface deformations, construction tolerances, and geometric irregularities require advanced scanning, segmentation, and modeling strategies to achieve BIM-ready outputs. Understanding these constraints is key to advancing reliable Scan-to-BIM workflows. Role of Mathematical Descriptors in Parametric Vault Modeling A descriptive mathematical framework allows researchers to abstract irregular...
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Automating Point Cloud to BIM Transformation Using Intelligent Algorithms

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Building Information Modeling (BIM) has revolutionized the architecture, engineering, and construction (AEC) industry, but the process of creating BIM models from unstructured point cloud data remains highly manual and time-consuming. Traditional methods rely on complex segmentation and calibration techniques, often requiring significant user intervention. Cloud2BIM introduces a breakthrough by automating this transformation process through advanced algorithms that segment walls, slabs, openings, and rooms, delivering a fully automated and accurate workflow for BIM model generation. Background and Motivation The rapid growth of 3D laser scanning and photogrammetry technologies has enabled the collection of detailed geometric data of buildings. However, converting this raw, unstructured point cloud data into usable BIM models continues to be a major challenge due to irregular geometries, noise, and the need for manual processing. The motivation behind Cloud2BIM lies in overcoming thes...
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