This dissertation conducts a rigorous examination of the capacity of architectural design to mitigate environmental impacts, with a specific focus on the conceptual evolution towards 'positive buildings.' It critically examines whether the concept should be confined to energy positivity, where buildings produce more energy than they consume, or should encompass a broader spectrum of net positive environmental impacts. The research systematically analyzes the role of building geometry and innovative cladding materials in advancing these objectives. Employing a large office building in Boston as a prototype, it illustrates the practical implications and evaluates the effectiveness of the proposed design strategies while questioning the feasibility and practicality of such ambitious goals. The research employs a dual-section methodology to assess the environmental impacts of various building geometries and cladding materials, with a particular focus on operational energy consumption and its influence on indoor comfort conditions. In Section One, the study meticulously examines how different geometric configurations—namely the Cask, Sphere, and Cube—affect environmental sustainability through their energy usage. This analysis leverages advanced design tools such as Rhino and Grasshopper for parametric modeling, complemented by Honeybee and Ladybug for comprehensive energy analysis and climatic adaptation. Additionally, data-driven approaches via OpenLCA, including the life cycle assessment of "Market low-voltage electricity—Cutoff_U", are utilized since the energy for cooling and heating systems in Boston is predominantly supplied through this process. This methodology provides a detailed evaluation of each form's performance during the operational phase. The integration of machine learning with the design process marks the next step in advancing the methodology. The application of Python enhances sensitivity analysis, enabling a deeper understanding of which design variables significantly affect environmental impacts. Notably, the Sphere demonstrates a significant reduction in environmental impact by 30.81% compared to the Cube and Cask, with sensitivity analysis highlighting the critical influence of the sphere's radius on its energy performance. Similarly, the length and height are identified as pivotal design variables for the Cube and Cask, respectively. This process is further enriched by the integration of Python-based machine learning algorithms, specifically Random Forest and Extreme Gradient Boosting, which play a crucial role in identifying and understanding the sensitivity of design variables to environmental impacts. To address the sub question regarding the impact of various environmental categories, normalization based on CO2 equivalence was conducted using TRACI 2.1 normalization factors, calculated from US 2008 inventory data (OpenLCA, ecoinvent 3.6). The analysis identified fossil fuel usage as the most critical category, followed by impacts on global warming, ecotoxicity, and smog in terms of CO2 emissions. This methodology not only showcases the robust application of Python in an architectural context but also underscores the practical implications of machine learning in optimizing building design for reduced environmental impact. By focusing on the strategic manipulation of design variables, this study contributes significantly to the field of sustainable architectural practices, providing actionable insights for the design of energy-efficient and environmentally friendly buildings. The second section of the study shifts focus to material analysis, comparing aluminum, polycarbonate, and hybrid cladding systems using environmental modeling tools. Similar to Section One, this analysis employs Rhino and Grasshopper for parametric modeling, and Ladybug and Honeybee for energy analysis and climatic design. Additionally, OpenLCA is utilized to calculate the environmental impact categories. The materials selected for this analysis are accompanied by Environmental Product Declarations (EPDs) to ensure that other life cycle stages are accounted for, thereby enabling more accurate results. The detailed energy consumption data reveals that polycarbonate cladding consumes significantly more energy than aluminum, with 93.3% higher usage for cooling and 80.7% more for heating, while polycarbonate is substantially more efficient in lighting, using 66.19% less energy than Aluminum. These variations in energy consumption reflect corresponding changes in environmental pollutants during the B6 stage. Alone, neither system met the LEED certification standards for both Annual Sunlight Exposure (ASE) and Spatial Daylight Autonomy (sDA). In response, a hybrid system was developed and optimized using the NSGA-2 algorithm, Wallacei's selection methods, and Design Explorer, achieving a more balanced performance with an sDA of 54.7% and an ASE of 7.3%, suggesting an optimal solution for meeting LEED standards. The findings from both sections are synthesized to formulate architectural design principles that prioritize sustainability and environmental Impacts based on energy usage. The combined findings lead to the formulation of design principles that not only prioritize reducing environmental impacts and energy usage but also explore the cautious and incremental adoption of strategies towards realizing positive buildings. This research proposes a framework for employing data-driven approaches, including machine learning, to refine predictions and enhance building performance, aiming to make architectural design more responsive to environmental sustainability. In conclusion, the thesis engages in a measured discussion on the potential and challenges of achieving positive buildings, questioning the level of optimism surrounding this concept and critically assessing the barriers to its practical realization
Questa tesi conduce un esame rigoroso della capacità del design architettonico di mitigare gli impatti ambientali, con un focus specifico sull'evoluzione concettuale verso gli 'edifici positivi'. Esamina criticamente se il concetto debba essere limitato alla positività energetica—dove gli edifici producono più energia di quella che consumano—o debba includere uno spettro più ampio di impatti ambientali netti positivi. La ricerca analizza sistematicamente il ruolo della geometria degli edifici e dei materiali di rivestimento innovativi nel promuovere questi obiettivi. Impiegando un grande edificio per uffici a Boston come prototipo, illustra le implicazioni pratiche e valuta l'efficacia delle strategie di progettazione proposte, mettendo in discussione la fattibilità e la praticità di obiettivi così ambiziosi. La ricerca impiega una metodologia a doppia sezione per valutare gli impatti ambientali di varie geometrie edilizie e materiali di rivestimento, con particolare attenzione al consumo energetico operativo e alla sua influenza sulle condizioni di comfort interno. Nella Sezione Uno, lo studio esamina meticolosamente come diverse configurazioni geometriche — specificamente il Cask, la Sfera e il Cubo — influenzino la sostenibilità ambientale attraverso l'uso dell'energia. Questa analisi si avvale di strumenti di progettazione avanzati come Rhino e Grasshopper per la modellazione parametrica, integrati da Honeybee e Ladybug per l'analisi energetica e l'adattamento climatico. Inoltre, approcci guidati dai dati attraverso OpenLCA, inclusa la valutazione del ciclo di vita dell'energia elettrica a bassa tensione—Cutoff_U (poiché l'energia per i sistemi di riscaldamento e raffreddamento a Boston è prevalentemente fornita da questo processo), forniscono una valutazione completa delle prestazioni di ciascuna forma durante la fase operativa. L'integrazione dell'apprendimento automatico con il processo di progettazione segna il passo successivo nell'avanzamento della metodologia. L'applicazione di Python migliora l'analisi di sensibilità, consentendo una comprensione più profonda di quali variabili di design influenzano significativamente gli impatti ambientali. In particolare, la Sfera dimostra una riduzione notevole dell'impatto ambientale del 30,81% rispetto al Cubo e al Cask, con un'analisi di sensibilità che evidenzia l'influenza critica del raggio della sfera sulle sue prestazioni energetiche. Analogamente, la lunghezza e l'altezza sono identificate come variabili di design cruciali per il Cubo e il Cask, rispettivamente. Questo processo è ulteriormente arricchito dall'integrazione di algoritmi di machine learning basati su Python, in particolare Random Forest e Extreme Gradient Boosting, che svolgono un ruolo cruciale nell'identificare e comprendere la sensibilità delle variabili di design agli impatti ambientali. Per affrontare la sotto-questione riguardante l'impatto di varie categorie ambientali, è stata condotta una normalizzazione basata sull'equivalenza di CO2 utilizzando i fattori di normalizzazione TRACI 2.1, calcolati dai dati di inventario degli USA del 2008 (OpenLCA, ecoinvent 3.6). L'analisi ha identificato l'uso dei combustibili fossili come categoria più critica, seguita dagli impatti sul riscaldamento globale, l'ecotossicità e lo smog in termini di emissioni di CO2. Questa metodologia non solo mette in evidenza l'applicazione robusta di Python nel contesto architettonico ma sottolinea anche le implicazioni pratiche dell'apprendimento automatico nell'ottimizzare la progettazione degli edifici per ridurre l'impatto ambientale. Concentrandosi sulla manipolazione strategica delle variabili di design, questo studio contribuisce significativamente al campo delle pratiche architettoniche sostenibili, offrendo spunti concreti per la progettazione di edifici energeticamente efficienti e ecocompatibili. La seconda sezione dello studio sposta l'attenzione sull'analisi dei materiali, confrontando sistemi di rivestimento in alluminio, policarbonato e ibridi utilizzando strumenti di modellazione ambientale. Similmente alla Sezione Uno, questa analisi impiega Rhino e Grasshopper per la modellazione parametrica, e Ladybug e Honeybee per l'analisi energetica e il design climatico. Inoltre, OpenLCA è utilizzato per calcolare le categorie di impatto ambientale. I materiali selezionati per questa analisi sono accompagnati da Dichiarazioni Ambientali di Prodotto (EPD) per garantire che anche altre fasi del ciclo di vita siano considerate, consentendo così risultati più accurati. I dati dettagliati sul consumo energetico rivelano che il rivestimento in policarbonato consuma significativamente più energia rispetto all'alluminio, con un uso del 93,3% maggiore per il raffreddamento e dell'80,7% in più per il riscaldamento, mentre il policarbonato è sostanzialmente più efficiente nell'illuminazione, utilizzando il 66,19% di energia in meno rispetto all'Alluminio. Queste variazioni nel consumo energetico riflettono cambiamenti corrispondenti negli inquinanti ambientali durante la fase B6. Da soli, nessuno dei sistemi ha soddisfatto gli standard di certificazione LEED per l'Esposizione Solare Annuale (ASE) e l'Autonomia di Illuminazione Spaziale (sDA). In risposta, è stato sviluppato e ottimizzato un sistema ibrido utilizzando l'algoritmo NSGA-2, i metodi di selezione di Wallacei e Design Explorer, ottenendo prestazioni più bilanciate con un sDA del 54,7% e un ASE del 7,3%, suggerendo una soluzione ottimale per soddisfare gli standard LEED. I risultati di entrambe le sezioni sono sintetizzati per formulare principi di progettazione architettonica che danno priorità alla sostenibilità e agli impatti ambientali basati sull'uso dell'energia. I risultati combinati portano alla formulazione di principi di design che non solo danno priorità alla riduzione degli impatti ambientali e all'uso dell'energia ma esplorano anche l'adozione cauta e incrementale di strategie verso la realizzazione di edifici positivi. Questa ricerca propone un quadro per l'impiego di approcci basati sui dati, inclusi l'apprendimento automatico, per affinare le previsioni e migliorare le prestazioni degli edifici, mirando a rendere il design architettonico più responsivo alla sostenibilità ambientale. In conclusione, la tesi si impegna in una discussione misurata sul potenziale e sulle sfide nel raggiungere edifici positivi, mettendo in discussione il livello di ottimismo intorno a questo concetto e valutando criticamente gli ostacoli alla sua realizzazione pratica
ARCHITECTURAL PRINCIPLES FOR DESIGNING SPATIAL STRUCTURES TO ADDRESS POSITIVE BUILDINGS. A COMPARATIVE ENVIRONMENTAL IMPACT ASSESSMENT OF GEOMETRIC FORMS AND SUSTAINABLE CLADDING MATERIALS IN POSITIVE SPATIAL STRUCTURE DESIGN
JAVANMARD, ZINAT
2025
Abstract
This dissertation conducts a rigorous examination of the capacity of architectural design to mitigate environmental impacts, with a specific focus on the conceptual evolution towards 'positive buildings.' It critically examines whether the concept should be confined to energy positivity, where buildings produce more energy than they consume, or should encompass a broader spectrum of net positive environmental impacts. The research systematically analyzes the role of building geometry and innovative cladding materials in advancing these objectives. Employing a large office building in Boston as a prototype, it illustrates the practical implications and evaluates the effectiveness of the proposed design strategies while questioning the feasibility and practicality of such ambitious goals. The research employs a dual-section methodology to assess the environmental impacts of various building geometries and cladding materials, with a particular focus on operational energy consumption and its influence on indoor comfort conditions. In Section One, the study meticulously examines how different geometric configurations—namely the Cask, Sphere, and Cube—affect environmental sustainability through their energy usage. This analysis leverages advanced design tools such as Rhino and Grasshopper for parametric modeling, complemented by Honeybee and Ladybug for comprehensive energy analysis and climatic adaptation. Additionally, data-driven approaches via OpenLCA, including the life cycle assessment of "Market low-voltage electricity—Cutoff_U", are utilized since the energy for cooling and heating systems in Boston is predominantly supplied through this process. This methodology provides a detailed evaluation of each form's performance during the operational phase. The integration of machine learning with the design process marks the next step in advancing the methodology. The application of Python enhances sensitivity analysis, enabling a deeper understanding of which design variables significantly affect environmental impacts. Notably, the Sphere demonstrates a significant reduction in environmental impact by 30.81% compared to the Cube and Cask, with sensitivity analysis highlighting the critical influence of the sphere's radius on its energy performance. Similarly, the length and height are identified as pivotal design variables for the Cube and Cask, respectively. This process is further enriched by the integration of Python-based machine learning algorithms, specifically Random Forest and Extreme Gradient Boosting, which play a crucial role in identifying and understanding the sensitivity of design variables to environmental impacts. To address the sub question regarding the impact of various environmental categories, normalization based on CO2 equivalence was conducted using TRACI 2.1 normalization factors, calculated from US 2008 inventory data (OpenLCA, ecoinvent 3.6). The analysis identified fossil fuel usage as the most critical category, followed by impacts on global warming, ecotoxicity, and smog in terms of CO2 emissions. This methodology not only showcases the robust application of Python in an architectural context but also underscores the practical implications of machine learning in optimizing building design for reduced environmental impact. By focusing on the strategic manipulation of design variables, this study contributes significantly to the field of sustainable architectural practices, providing actionable insights for the design of energy-efficient and environmentally friendly buildings. The second section of the study shifts focus to material analysis, comparing aluminum, polycarbonate, and hybrid cladding systems using environmental modeling tools. Similar to Section One, this analysis employs Rhino and Grasshopper for parametric modeling, and Ladybug and Honeybee for energy analysis and climatic design. Additionally, OpenLCA is utilized to calculate the environmental impact categories. The materials selected for this analysis are accompanied by Environmental Product Declarations (EPDs) to ensure that other life cycle stages are accounted for, thereby enabling more accurate results. The detailed energy consumption data reveals that polycarbonate cladding consumes significantly more energy than aluminum, with 93.3% higher usage for cooling and 80.7% more for heating, while polycarbonate is substantially more efficient in lighting, using 66.19% less energy than Aluminum. These variations in energy consumption reflect corresponding changes in environmental pollutants during the B6 stage. Alone, neither system met the LEED certification standards for both Annual Sunlight Exposure (ASE) and Spatial Daylight Autonomy (sDA). In response, a hybrid system was developed and optimized using the NSGA-2 algorithm, Wallacei's selection methods, and Design Explorer, achieving a more balanced performance with an sDA of 54.7% and an ASE of 7.3%, suggesting an optimal solution for meeting LEED standards. The findings from both sections are synthesized to formulate architectural design principles that prioritize sustainability and environmental Impacts based on energy usage. The combined findings lead to the formulation of design principles that not only prioritize reducing environmental impacts and energy usage but also explore the cautious and incremental adoption of strategies towards realizing positive buildings. This research proposes a framework for employing data-driven approaches, including machine learning, to refine predictions and enhance building performance, aiming to make architectural design more responsive to environmental sustainability. In conclusion, the thesis engages in a measured discussion on the potential and challenges of achieving positive buildings, questioning the level of optimism surrounding this concept and critically assessing the barriers to its practical realizationFile | Dimensione | Formato | |
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PhD Thesis_Javanmard Zinat_XXXVII ciclo.pdf
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https://hdl.handle.net/20.500.14242/210485
URN:NBN:IT:UNIRC-210485