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Stochastic Optimization Framework for Robust Building Performance Under Occupant Behavioral Uncertainty

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Optimizing building performance requires acknowledging the stochastic nature of occupant control behaviors, which significantly influence energy consumption, thermal comfort, and visual comfort outcomes. Traditional building performance models often rely on oversimplified behavioral assumptions and demand extensive computational time for stochastic simulations. This study proposes a novel optimization framework specifically designed to address uncertainty in occupant behavior while improving computational efficiency and solution robustness in building design optimization. Methodological Integration of Stochastic and Intelligent Optimization Techniques The proposed approach integrates Sample Average Approximation (SAA) with Monte Carlo simulations to obtain convergent mean performance estimates under uncertainty. To accelerate optimization, machine learning (ML) models are coupled with a Pareto-based Genetic Algorithm (GA), enabling rapid prediction of building performance metrics acr...
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Comparative Thermal Performance of Advanced Insulation Materials and Hybrid Wall Systems in Cold-Climate Buildings

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Improving building envelope performance is a critical strategy for reducing heating energy demand and enhancing indoor thermal comfort in cold climates. This study evaluates the thermal effectiveness of four advanced insulation materials—phase change materials (PCM), aerogel, vacuum insulated panels (VIP), and autoclaved aerated concrete (AAC)—through dynamic energy simulations. Using 24 years of hourly climate data, five wall configurations were analyzed, including an uninsulated reference case. The research focuses on annual heating demand, surface temperature stability, thermal time lag, and comfort hours to determine how different material properties contribute to energy efficiency and occupant comfort. Methodological Framework and Simulation Approach The investigation employed dynamic building energy modeling using EnergyPlus, incorporating long-term hourly climate data to ensure robust performance assessment. PCM behavior was simulated through an enthalpy–temperature phase ch...
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Seismic Performance Assessment of Monolithic 3D-Printed Housing Units Through Full-Scale Shake Table Testing

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Over the past decade, additive manufacturing has significantly transformed the construction industry, enabling the rapid development of 3D printing systems for building applications worldwide. Despite technological progress and increasing industry adoption, limited research has addressed the seismic behaviour of monolithic 3D-printed structures. This gap presents a critical challenge, particularly for regions exposed to seismic hazards. The present study responds to this need by conducting a systematic experimental investigation into the structural and dynamic performance of a full-scale 3D-printed housing unit, aiming to establish foundational knowledge and contribute to seismic design guidance for this emerging construction technology. Mechanical Characterisation of 3D-Printed Materials The research begins with an extensive mechanical characterisation of the printed material to understand its structural properties and anisotropic behaviour. Preliminary experimental tests, includin...
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Parametric Optimization of Façade Apertures for Enhanced Natural Ventilation in High-Rise Office Buildings

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  High-rise office buildings frequently experience airflow stagnation zones on windward façades, particularly at mid-height levels where wind streams divide upward and downward. These stagnation effects limit natural ventilation potential and increase reliance on mechanical cooling during warm seasons. This study investigates how parametric façade aperture design can strategically enhance airflow distribution and reduce cooling loads in high-rise office buildings. Focus on Stagnation-Level Floor and Design Hypothesis The research concentrates on the floor intersecting the façade stagnation point, where airflow dynamics are most constrained. It is hypothesized that optimized aperture geometry and spatial distribution can redirect pressure differentials to improve indoor ventilation performance and thermal comfort, thereby reducing cooling energy demand without mechanical intervention. Multi-Stage Methodological Framework A multi-stage methodology was implemented integrating com...
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Influence of Drying on the Seismic Performance of Reinforced Concrete Frame Structures

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Drying of concrete is widely recognized as a critical factor contributing to the long-term deterioration of reinforced concrete (RC) structures. While laboratory investigations on individual RC members have provided valuable insights, real structural systems experience more complex boundary and environmental conditions that may alter their mechanical response. This study investigates the structural implications of concrete drying at the building scale under cyclic loading conditions. Experimental Program and Specimen Configuration A quasi-static cyclic loading experiment was conducted on one-third scale, three-story RC frame buildings. Two environmental conditions were considered: a saturated (wet) condition and a two-year natural drying condition. This comparative setup enabled direct evaluation of drying-induced effects on structural stiffness, deformation characteristics, and damage progression. Effect of Drying on Initial Stiffness and Stress Transfer Results reveal a signific...
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Rapid Machine Learning-Based Energy Prediction for BIPV-Integrated Modular Buildings

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The accelerating global transition toward carbon neutrality demands rapid and reliable energy prediction tools for innovative building systems such as Building-Integrated Photovoltaic (BIPV) modular buildings. Conventional physics-based simulation methods, while accurate, are computationally intensive and unsuitable for real-time design optimization. This study proposes a novel machine learning-based rapid energy prediction methodology tailored specifically to the thermal and geometric characteristics of modular BIPV-integrated buildings. Feature Engineering for Modular Building Representation A comprehensive feature engineering framework was developed to capture the distinctive attributes of modular construction. The approach incorporates six-surface thermal property encoding, geometric parameters, and detailed solar irradiance calculations to represent envelope exposure and inter-module interactions. This structured encoding ensures that key thermal behaviors and photovoltaic infl...
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Climate-Specific Optimization of Phase Change Material Glazing for Energy-Efficient Office Buildings

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Buildings remain major contributors to global carbon dioxide emissions, intensifying the urgency of envelope innovations that enhance energy efficiency and climate resilience. As extreme weather conditions increasingly challenge conventional passive design strategies, adaptive façade technologies have gained prominence. Phase Change Material (PCM) glazing offers a promising approach by increasing thermal inertia, reducing cooling demand, and improving indoor comfort. However, its performance under diverse climatic and operational contexts remains insufficiently quantified. Multivariate Design Framework and Simulation Scope This study conducts a comprehensive multivariate analysis of PCM glazing across ten representative climates in Europe and North America. Key design variables include window-to-wall ratio (WWR), façade orientation, and PCM type, evaluated under two internal heat gain scenarios. A validated heat transfer model integrated into EnergyPlus™ was used to perform 4,320 si...
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