Daylight quality and ambiences characterization in patient room. A parametric workflow and genetic algorithms approach (Case of Biskra city).

Abstract

This dissertation focuses on attaining a balance between Architecture, Daylight exposure and Well-being of the hospitalized patients. This is achieved through the implementation of a modern methodology derived from the realm of Artificial Intelligence. This approach integrates the paradigm of parametric-based method of evolutionary algorithms. The complexity of the issue becomes even more pronounced in healthcare facilities, where it becomes crucial to maintain suitable equilibrium between natural light, energy usage, and the well-being of patients. The realm of daylight and health has experienced significant transformations and garnered heightened recognition within architectural planning in recent years. However, it remains a relatively nascent domain, necessitating dedicated efforts and research to comprehensively define the various health dimensions of light. The objective is to leverage these insights to enhance the integration of daylight principles into architectural practices effectively. Contemporary research has uncovered novel and advantageous aspects of daylight, complementing and reinforcing the notions of healthy architecture that emerged in the early century. The findings of recent studies not only validate but also expand upon the principles and concepts of creating architectural spaces that promote well-being through the appropriate utilization of daylight. Daylight plays a crucial role in patients' recovery process and has been shown to reduce hospital stays. It serves as a pivotal element that substantially contributes to patients' recovery by potentially shortening their hospitalization period. It wields a substantial capacity to impact energy usage either positively or negatively via lighting control tactics. Consequently, healthcare facilities warrant special attention and sensitivity, particularly in regions characterized by extreme climatic conditions such as hot and arid areas. Numerous research endeavors have concentrated on devising optimization strategies and methodologies to address a multitude of concerns associated with optimizing building performance. Given the intricate nature of optimizing daylight performance, it becomes imperative to account for an array of factors and explore diverse algorithmic approaches. Genetic algorithms have emerged as a suitable choice within this domain. In recent years, advancements in computational technology have yielded an abundance of building performance simulation tools accessible to designers and engineers. By integrating parametric design and building performance assessment tools, the potential to generate design alternatives based on performance criteria is realized. The utilization of building performance ii simulation spans various phases of building design and construction. The process of building optimization generally entails an automated sequence that leverages a building simulation program alongside an optimization engine encompassing optimization algorithms. The suggested method is built upon a combination of two approaches; conceptual and quantitative approaches. In the initial stage, an extensive literature review was conducted to provide insights and establish the basis for the conceptual and analytical methodology. The subsequent component of the research methodology involved the application of a quantitative approach, which encompassed two pivotal elements: an empirical study involving on-site measurements taken within actual patient rooms. The hospital ward and the selected rooms for this study were classified based on their orientations and the types of wards. Various factors were considered, including variations in daylight conditions such as illuminance, luminance ratio, daylight factor, and illuminance diversity and uniformity, as well as the physical characteristics of the patient rooms and the indoor environments characterization. Subsequently, a numerical study encompassing the development of simulation-based optimization algorithms to gauge the daylight performance within these spaces. The utilization of the parametric simulation workflow and optimization strategy facilitated the creation and evaluation of diverse daylight conditions and indoor environments in relation to visual comfort. The analysis of daylight was carried out employing the Grasshopper software, a parametric modeling tool that automated the daylight simulation procedure. Furthermore, the ladybug, honeybee, Diva for-Rhino plugin, integrated with Radiance software, were employed to development of simulation-based optimization algorithms to evaluate the performance of daylight in these rooms. A link between Grasshopper and ArchiCAD software was established, enabling the real-time modification of various variables until the optimal solutions were attained through the utilization of the Octopus plugin integrated with Grasshopper. The findings initially offer a deeper understanding of how various parameters of the building envelope interact within hot and arid climates. They also provide cues to investigate a range of potential combinations and selections for optimizing facade devices, along with determining the most suitable attributes of the openings surface that align with the specific climatic conditions being considered. Then results demonstrates the effectiveness of utilizing reliable strategies to attain optimal solutions for building performance challenges and emphasizes that the adaptive facade system, compared to the conventional shading system, improved indoor daylight levels and energy performance simultaneously. The performance optimization method proposed in this study incorporates a range of tools and technologies iii such as parametric design, building simulation modeling, and Genetic Algorithms. Within this method, parametric design is employed to thoroughly explore different design alternatives for the building. Genetic Algorithms are utilized to identify design options that exhibit optimal energy and daylighting performance. The results were analyzed, and the potential impact of design decisions in various environments was discussed. The framework presented in this study can serve as a reference model in architecture, offering opportunities to address complex design challenges during the early design and providing recommendations for sustainable building design with combination of current approach used in Artificial Intelligence.