M. Turrin
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73 records found
1
Conference paper
(2026)
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Eleni Chatzi Nestoros, Pablo Martinez-Alcaraz, Alejandro Fuentes, Juan Diego Blanco Cadena, Michela Turrin, Tiziana Poli, Alessandra Luna-Navarro
Urbanization and climate change have intensified urban heat island effects, significantly affecting outdoor thermal comfort (OTC) in cities. Traditional methods of assessing the influence of urban environments on OTC often rely heavily on computational simulations, neglecting the integration of user-centric data and comprehensive environmental monitoring. This study investigates the relationship between microclimatic conditions, thermal sensation, and human preferences to identify the key factors influencing outdoor thermal comfort. Using the Acquabella district in Milan as a case study, the research adopts a cross-examination approach that integrates qualitative and quantitative assessments to evaluate outdoor thermal conditions. The methodology combines user-centered participatory approaches, including a workshop and an online survey, to identify critical intervention areas and factors contributing to thermal discomfort. Thermal Sensation Votes (TSV) are collected and analyzed in QGIS, highlighting significant environmental and psychological factors influencing user perception. Preliminary simulations using SOLWEIG are employed to cross-examine the results, identifying spaces with varying levels of thermal comfort. This study underscores the value of combining objective and subjective measures to pinpoint areas of critical environmental quality and optimize decision-making processes. By leveraging user data, the methodology reduces computational demands while offering actionable insights for urban health. The approach is scalable to different urban contexts, fostering the development of resilient and climate-adaptive environments.
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Urbanization and climate change have intensified urban heat island effects, significantly affecting outdoor thermal comfort (OTC) in cities. Traditional methods of assessing the influence of urban environments on OTC often rely heavily on computational simulations, neglecting the integration of user-centric data and comprehensive environmental monitoring. This study investigates the relationship between microclimatic conditions, thermal sensation, and human preferences to identify the key factors influencing outdoor thermal comfort. Using the Acquabella district in Milan as a case study, the research adopts a cross-examination approach that integrates qualitative and quantitative assessments to evaluate outdoor thermal conditions. The methodology combines user-centered participatory approaches, including a workshop and an online survey, to identify critical intervention areas and factors contributing to thermal discomfort. Thermal Sensation Votes (TSV) are collected and analyzed in QGIS, highlighting significant environmental and psychological factors influencing user perception. Preliminary simulations using SOLWEIG are employed to cross-examine the results, identifying spaces with varying levels of thermal comfort. This study underscores the value of combining objective and subjective measures to pinpoint areas of critical environmental quality and optimize decision-making processes. By leveraging user data, the methodology reduces computational demands while offering actionable insights for urban health. The approach is scalable to different urban contexts, fostering the development of resilient and climate-adaptive environments.
Sound-absorbing barriers and screens are commonly employed to mitigate one of the most annoying noises in workplaces: intelligible speech. However, isolating their acoustic contribution from all the other elements (ceilings, wall treatment, or carpets) is challenging. This study uses a wave-based room acoustic modeling approach to explore the acoustic function of desk screens in a virtually reconstructed open-plan office. Analytical models, finite-element simulations, and experimental data from 3D-printed samples allowed defining a multi-resonator unit cell, attenuating the voice signal's main formants. The sound-absorbing panels composed of the unit modules iteration are assessed in the full-scale digital model, starting from the calibrated version on in-field measurements. The wave-based engine employed in this study grants the crucial aspect of computing the acoustic performance of the potential multi-resonator screens, including the edge diffraction due to their desk installation. In the virtual workplace, the acoustic role of such screens in increasing the speech level decay is outlined in comparison with the calibrated scenario and the traditional screens' option.
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Sound-absorbing barriers and screens are commonly employed to mitigate one of the most annoying noises in workplaces: intelligible speech. However, isolating their acoustic contribution from all the other elements (ceilings, wall treatment, or carpets) is challenging. This study uses a wave-based room acoustic modeling approach to explore the acoustic function of desk screens in a virtually reconstructed open-plan office. Analytical models, finite-element simulations, and experimental data from 3D-printed samples allowed defining a multi-resonator unit cell, attenuating the voice signal's main formants. The sound-absorbing panels composed of the unit modules iteration are assessed in the full-scale digital model, starting from the calibrated version on in-field measurements. The wave-based engine employed in this study grants the crucial aspect of computing the acoustic performance of the potential multi-resonator screens, including the edge diffraction due to their desk installation. In the virtual workplace, the acoustic role of such screens in increasing the speech level decay is outlined in comparison with the calibrated scenario and the traditional screens' option.
Group work in design education amid pandemic
Observation and reflection
Group work has been used in design-related education programs to train students in handling the complexity of the design process in the built environment through co-operation. Education activities moved on-line when the COVID-19 pandemic started. Students have worked in groups remotely through digital platforms instead of face-to-face on campus. How did design education programs incorporating group work adapt to these changes during the pandemic, and how did the virtual environment influence the design process and outcomes of group work? This paper addresses these questions by examining two master education courses provided at the Faculty of Architecture and the Built Environment, Delft University of Technology. It explores how the group work can be managed online with higher exploitation of digital tools helping interaction and data sharing among group members. The conclusions highlight the distinction between content development via on-line education (and how it can meet certain standards) and the communication dynamics during group work (for example how vocal remarks, separated from body language and shared context, can impact the quality and effectiveness of online group interactions). These findings underscore the importance of considering both aspects when developing group work for online education.
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Group work has been used in design-related education programs to train students in handling the complexity of the design process in the built environment through co-operation. Education activities moved on-line when the COVID-19 pandemic started. Students have worked in groups remotely through digital platforms instead of face-to-face on campus. How did design education programs incorporating group work adapt to these changes during the pandemic, and how did the virtual environment influence the design process and outcomes of group work? This paper addresses these questions by examining two master education courses provided at the Faculty of Architecture and the Built Environment, Delft University of Technology. It explores how the group work can be managed online with higher exploitation of digital tools helping interaction and data sharing among group members. The conclusions highlight the distinction between content development via on-line education (and how it can meet certain standards) and the communication dynamics during group work (for example how vocal remarks, separated from body language and shared context, can impact the quality and effectiveness of online group interactions). These findings underscore the importance of considering both aspects when developing group work for online education.
The growing population of people living with dementia demands innovative architectural solutions that prioritize wellbeing. Floor layouts significantly affect the quality of life for people living with dementia, but the nuances of perceived user experience remain a challenging criterion to evaluate during the early design stages. Visual sightlines are one of the key aspects for dementia-inclusive design where machine learning (ML) provides decision-support means to validate early-stage designs. In this study, an isovist-based quantification scheme is proposed to capture visual access data to evaluate the extent of compliance of floor layouts with respect to dementia design principles (DDP). The visual access quality labels are determined by the number of isovists that satisfy the visual access requirement for their respective DDPs, thereby ensuring a consistent and objective measurement of visual access. 18 unique spatial features were tested using feature filtering methods to predict visual access quality labels. The framework bridges qualitative design principles with quantitative methods, introducing a scalable approach for evaluating visual access in the context of dementia-friendly design. The ML model was evaluated using individual class output and multi-output evaluation metrics based on 7 feature inputs and 2 class outputs, achieving 84–87% accuracy on an individual class and 72% on the subset accuracy metric. The results suggest a viable pathway for developing ML support tools to provide feedback on DDP compliance at an early design stage.
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The growing population of people living with dementia demands innovative architectural solutions that prioritize wellbeing. Floor layouts significantly affect the quality of life for people living with dementia, but the nuances of perceived user experience remain a challenging criterion to evaluate during the early design stages. Visual sightlines are one of the key aspects for dementia-inclusive design where machine learning (ML) provides decision-support means to validate early-stage designs. In this study, an isovist-based quantification scheme is proposed to capture visual access data to evaluate the extent of compliance of floor layouts with respect to dementia design principles (DDP). The visual access quality labels are determined by the number of isovists that satisfy the visual access requirement for their respective DDPs, thereby ensuring a consistent and objective measurement of visual access. 18 unique spatial features were tested using feature filtering methods to predict visual access quality labels. The framework bridges qualitative design principles with quantitative methods, introducing a scalable approach for evaluating visual access in the context of dementia-friendly design. The ML model was evaluated using individual class output and multi-output evaluation metrics based on 7 feature inputs and 2 class outputs, achieving 84–87% accuracy on an individual class and 72% on the subset accuracy metric. The results suggest a viable pathway for developing ML support tools to provide feedback on DDP compliance at an early design stage.
It is now within reach to use generative artificial intelligence (AI) to autonomously generate full building geometries. However, existing literature utilizing 3D data has focused to a limited degree on architecture and engineering disciplines. A critical first step to expanding the use of generative deep learning models in generative design research is making training data available. This study investigates 3D building model data characteristics that make it suitable for generative AI applications. Key data set attributes are identified through a systematic review of the object-containing datasets currently used to train state-of-the-art 3D GANs. These requirements are then compared to attributes of existing available building datasets. This comparison shows that publicly available data sets of 3D building models lack essential characteristics for generative deep learning. Features that make these building models inadequate for the task include but are not limited to, their mesh formats, low resolution and levels of detail, and inclusion of irrelevant geometry. To achieve the desired properties in this work, necessary transformations of the data are incorporated into a tailored preprocessing pipeline. The pipeline is applied to an existing dataset that contains 3D models of single-family homes. The transformed dataset is tested within state-of-the-art GAN models to assess training performance and document future data requirements for applying deep generative design to buildings. Our experiments show promise for the impact that architectural datasets can make on deep learning applications within the discipline. It also highlights the need for additional 3D building model data to increase the diversity and robustness of new designs.
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It is now within reach to use generative artificial intelligence (AI) to autonomously generate full building geometries. However, existing literature utilizing 3D data has focused to a limited degree on architecture and engineering disciplines. A critical first step to expanding the use of generative deep learning models in generative design research is making training data available. This study investigates 3D building model data characteristics that make it suitable for generative AI applications. Key data set attributes are identified through a systematic review of the object-containing datasets currently used to train state-of-the-art 3D GANs. These requirements are then compared to attributes of existing available building datasets. This comparison shows that publicly available data sets of 3D building models lack essential characteristics for generative deep learning. Features that make these building models inadequate for the task include but are not limited to, their mesh formats, low resolution and levels of detail, and inclusion of irrelevant geometry. To achieve the desired properties in this work, necessary transformations of the data are incorporated into a tailored preprocessing pipeline. The pipeline is applied to an existing dataset that contains 3D models of single-family homes. The transformed dataset is tested within state-of-the-art GAN models to assess training performance and document future data requirements for applying deep generative design to buildings. Our experiments show promise for the impact that architectural datasets can make on deep learning applications within the discipline. It also highlights the need for additional 3D building model data to increase the diversity and robustness of new designs.
Given the urgent sustainability goals, the construction industry is actively seeking renewable and recyclable biobased materials. In this research, cellulose and lignin, the most abundant biopolymers on earth, were studied as fundamental building blocks to create an innovative bio-based material to 3D print elements for the construction industry. Having obtained a 3D printable paste, the study presented in this paper delved into the 3D printing possibilities by using a clay extruder mounted on a robotic arm. A window frame was used as test case, addressing the existing gap in replacing or enhancing current window frames. To better understand the printing process and explore various geometric configurations, a section of a window frame was printed as proof of the concept.
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Given the urgent sustainability goals, the construction industry is actively seeking renewable and recyclable biobased materials. In this research, cellulose and lignin, the most abundant biopolymers on earth, were studied as fundamental building blocks to create an innovative bio-based material to 3D print elements for the construction industry. Having obtained a 3D printable paste, the study presented in this paper delved into the 3D printing possibilities by using a clay extruder mounted on a robotic arm. A window frame was used as test case, addressing the existing gap in replacing or enhancing current window frames. To better understand the printing process and explore various geometric configurations, a section of a window frame was printed as proof of the concept.
Journal article
(2024)
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Charlotte Jeline Kat, Fatemeh Mostafavi, Eleonora Brembilla, Michela Turrin
Optimizing the layout of residential buildings based on daylight performance and view quality is crucial to visual comfort and well-being of building occupants. Machine Learning (ML) methods offer valuable support for performance-based decision-making process at the early-stage building design. In this study, a novel workflow is introduced to integrate ML models into the architectural design process. With the designer’s input floor layout designs, the presented multimodal ML model predicts daylight provision and view quality, which are then translated into practical visual representations by a post-processing step. This approach allows input designs to be evaluated by the ML model, leading to enhanced design decisions while preserving the designer’s autonomy. Results for the best-performing model, implementing ResNet50 and a fully connected network, led to a Mean Square Error (MSE) of 0.0440 and 0.0478, and an R2 score of 0.7411 and 0.7815 for the daylight and view metrics, respectively. The results of the daylight and view predictive models are further interpreted according to different apartment categories and at various resolutions. These results indicate that the method could be viable for predicting daylight provision and view quality in early design tools, providing designers with faster feedback that supports informed decision-making during design iterations. Ultimately, the challenges of the study and further improvements are discussed.
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Optimizing the layout of residential buildings based on daylight performance and view quality is crucial to visual comfort and well-being of building occupants. Machine Learning (ML) methods offer valuable support for performance-based decision-making process at the early-stage building design. In this study, a novel workflow is introduced to integrate ML models into the architectural design process. With the designer’s input floor layout designs, the presented multimodal ML model predicts daylight provision and view quality, which are then translated into practical visual representations by a post-processing step. This approach allows input designs to be evaluated by the ML model, leading to enhanced design decisions while preserving the designer’s autonomy. Results for the best-performing model, implementing ResNet50 and a fully connected network, led to a Mean Square Error (MSE) of 0.0440 and 0.0478, and an R2 score of 0.7411 and 0.7815 for the daylight and view metrics, respectively. The results of the daylight and view predictive models are further interpreted according to different apartment categories and at various resolutions. These results indicate that the method could be viable for predicting daylight provision and view quality in early design tools, providing designers with faster feedback that supports informed decision-making during design iterations. Ultimately, the challenges of the study and further improvements are discussed.
Generative Artificial Intelligence (AI) promises to make a vast impact across disciplines, including transforming the architectural design process by autonomously generating full building geometries. One form of generative deep learning that has been used to create 2D and 3D representations of objects is Generative Adversarial Networks (GANs). Existing literature, however, has limited applications that utilize 3D data for building geometry generation, with previous studies focused on low-scale 3D geometries suitable for objects such as chairs or cars. This paper develops a new GAN architecture to produce high-resolution feasible building geometry. The training dataset used is a selection of 3D models of single-family homes from an existing database, pre-processed for the specific application. State-of-the-art GAN models are initially tested to establish baseline performance and applicability potential. Then, a systematic study is performed to identify the structure and hyperparameters necessary to successfully fit a GAN to this design task. The successful architecture, named 3DBuildingGAN, uses a combination of Wasserstein loss with gradient penalty, leaky rectified linear units for neuron activation in the generator and the critic, and the root mean squared propagation optimizer with a fixed learning rate. The proposed model generates outputs similar in size, shape, and proportion to the training data with minimal noise in the output. Evaluation of memorization properties indicates open research directions, such as incorporating memorization rejection and training on larger data sets. Finally, the study reflects on how AI algorithms can reshape creativity through data-driven design solutions.
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Generative Artificial Intelligence (AI) promises to make a vast impact across disciplines, including transforming the architectural design process by autonomously generating full building geometries. One form of generative deep learning that has been used to create 2D and 3D representations of objects is Generative Adversarial Networks (GANs). Existing literature, however, has limited applications that utilize 3D data for building geometry generation, with previous studies focused on low-scale 3D geometries suitable for objects such as chairs or cars. This paper develops a new GAN architecture to produce high-resolution feasible building geometry. The training dataset used is a selection of 3D models of single-family homes from an existing database, pre-processed for the specific application. State-of-the-art GAN models are initially tested to establish baseline performance and applicability potential. Then, a systematic study is performed to identify the structure and hyperparameters necessary to successfully fit a GAN to this design task. The successful architecture, named 3DBuildingGAN, uses a combination of Wasserstein loss with gradient penalty, leaky rectified linear units for neuron activation in the generator and the critic, and the root mean squared propagation optimizer with a fixed learning rate. The proposed model generates outputs similar in size, shape, and proportion to the training data with minimal noise in the output. Evaluation of memorization properties indicates open research directions, such as incorporating memorization rejection and training on larger data sets. Finally, the study reflects on how AI algorithms can reshape creativity through data-driven design solutions.
Journal article
(2023)
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Miktha Farid Alkadri, Francesco De Luca, Michela Turrin, Muhammad Rafif Cahyadi Agung
This study proposes a novel method of solar geometry by considering the potential application of point cloud data combined with the simulation of solar radiation. With the support of geometric and radiometric information stored in the point cloud such as position information (XYZ) color information (RGB), and reflection intensity (I), architects may compensate for missing information on the existing context during the simulation, especially due to the limited capacity of current 3D modelling sites. However, the dataset often comes in the format of unstructured point cloud data retrieved from merged data scans and as a result, the radiometric information is difficult to occupy due to multiple reference points. Through a 3D subtractive procedure, this study not only examines volumetric samples of the three-dimensional matrix that fulfills the criteria of solar envelopes but also finds the optimal values of the merged data scan for input of solar radiation. In this regard, simulation of solar radiation contributes to identifying the most and the least exposed areas to the sun in existing contexts. This provides information related to visible sun hours that can be used to perform ray tracing analysis between the proposed 3D plot and surrounding contexts. Our proposed method ultimately helps architects not only generate solar geometry based on real contextual settings but also to understand comprehensively the microclimate conditions of the design context.
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This study proposes a novel method of solar geometry by considering the potential application of point cloud data combined with the simulation of solar radiation. With the support of geometric and radiometric information stored in the point cloud such as position information (XYZ) color information (RGB), and reflection intensity (I), architects may compensate for missing information on the existing context during the simulation, especially due to the limited capacity of current 3D modelling sites. However, the dataset often comes in the format of unstructured point cloud data retrieved from merged data scans and as a result, the radiometric information is difficult to occupy due to multiple reference points. Through a 3D subtractive procedure, this study not only examines volumetric samples of the three-dimensional matrix that fulfills the criteria of solar envelopes but also finds the optimal values of the merged data scan for input of solar radiation. In this regard, simulation of solar radiation contributes to identifying the most and the least exposed areas to the sun in existing contexts. This provides information related to visible sun hours that can be used to perform ray tracing analysis between the proposed 3D plot and surrounding contexts. Our proposed method ultimately helps architects not only generate solar geometry based on real contextual settings but also to understand comprehensively the microclimate conditions of the design context.
Wood-based 3D printing
Potential and limitation to 3D print building elements with cellulose & lignin
Journal article
(2023)
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Christopher Bierach, Alexsander Alberts Coelho, Michela Turrin, Serdar Așut, Ulrich Knaack
Under urgent sustainability targets, the building industry craves for renewable and recyclable biomaterials as cellulose is a fiber; Lignin is a plant-derived low-cost polymer with remarkable properties, yet its valorization is in its infancy. Recent studies have shown potentials to combine cellulose and lignin into a renewable bio-based material for the built environment, with the use of additive manufacturing to allow geometric customization and local control of material. However, previous studies also highlighted crucial issues to be solved. One main challenge is the lack of knowledge on combinations of lignin and cellulose with different binders to achieve a paste suitable for 3D printing, leading to a material applicable in the built environment. To contribute overcoming the challenge, this research aimed to explore various combinations of cellulose, lignin, and binders and to study the extrudability of the resulting paste using a clay extruder installed on a robotic arm. Several combinations were explored, evaluated, and compared. The four recipes with the highest scores were used to produce samples for tensile and three-point bending tests, water absorption and retention tests, and microscope analysis. The overall outcome has shown similarities between the mechanical properties of the mixture developed using methylcellulose as the binding agent and rigid polymer foams, such as the ones commonly used as insulation panels. Moreover, the material mix with the highest score in the preliminary assessment was further applied to fabricate samples with varied geometries to assess its potential and limitations combined with the fabrication process. Finally, two demonstrators were produced to explore the printing process for different geometric configurations: conceptual window frame and structural node were designed, and 3D printed as proof of concept.
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Under urgent sustainability targets, the building industry craves for renewable and recyclable biomaterials as cellulose is a fiber; Lignin is a plant-derived low-cost polymer with remarkable properties, yet its valorization is in its infancy. Recent studies have shown potentials to combine cellulose and lignin into a renewable bio-based material for the built environment, with the use of additive manufacturing to allow geometric customization and local control of material. However, previous studies also highlighted crucial issues to be solved. One main challenge is the lack of knowledge on combinations of lignin and cellulose with different binders to achieve a paste suitable for 3D printing, leading to a material applicable in the built environment. To contribute overcoming the challenge, this research aimed to explore various combinations of cellulose, lignin, and binders and to study the extrudability of the resulting paste using a clay extruder installed on a robotic arm. Several combinations were explored, evaluated, and compared. The four recipes with the highest scores were used to produce samples for tensile and three-point bending tests, water absorption and retention tests, and microscope analysis. The overall outcome has shown similarities between the mechanical properties of the mixture developed using methylcellulose as the binding agent and rigid polymer foams, such as the ones commonly used as insulation panels. Moreover, the material mix with the highest score in the preliminary assessment was further applied to fabricate samples with varied geometries to assess its potential and limitations combined with the fabrication process. Finally, two demonstrators were produced to explore the printing process for different geometric configurations: conceptual window frame and structural node were designed, and 3D printed as proof of concept.
This paper discusses a novel, compact sound absorption solution with high performance at various frequencies, including low frequencies, achieved through the effective use of Computational Design and Additive Manufacturing (AM). Sound absorption is widely applied for reducing noise and improving room acoustics; however, it is often constrained by conventional design, material properties and production techniques, which offer limited options for customising performance. This research highlights that AM, in combination with computational design tools, can support the development of novel sound-absorbing products with high performance based on the principle of viscothermal wave propagation in prismatic tubes. The potential of these designs was explored via two studies of customised sound-absorbing panels whose performance was measured in a reverberation room. A custom measurement technique was used based on logarithmic sweeps with high-resolution FFT analysis. A comparison of the measurement results with the theory of viscothermal wave propagation indicated good agreement; thus, this study demonstrates the possibility of developing new concepts and design methods for novel room acoustic devices.
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This paper discusses a novel, compact sound absorption solution with high performance at various frequencies, including low frequencies, achieved through the effective use of Computational Design and Additive Manufacturing (AM). Sound absorption is widely applied for reducing noise and improving room acoustics; however, it is often constrained by conventional design, material properties and production techniques, which offer limited options for customising performance. This research highlights that AM, in combination with computational design tools, can support the development of novel sound-absorbing products with high performance based on the principle of viscothermal wave propagation in prismatic tubes. The potential of these designs was explored via two studies of customised sound-absorbing panels whose performance was measured in a reverberation room. A custom measurement technique was used based on logarithmic sweeps with high-resolution FFT analysis. A comparison of the measurement results with the theory of viscothermal wave propagation indicated good agreement; thus, this study demonstrates the possibility of developing new concepts and design methods for novel room acoustic devices.
Conference paper
(2023)
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S. de Groot, E. Brembilla, A. Dahlmann-Noor, L. Price, M. Khazova, A. Eijkelenboom, M. Turrin
Myopia is on the rise worldwide and its onset can be triggered by environmental factors, including light exposure. Guaranteeing an adequate exposure to daylight is particularly important for young children, whose eyes need the best conditions for a healthy development. Monitoring and assessing light levels in school buildings is therefore paramount for studies on myopia but it can prove challenging. This paper paves the way for the use of climate-based daylight simulation as a tool to complement field studies on myopia. Existing simulation tools were found to characterise vertical spot illuminance with an acceptable error range (rMBE=8,7%; rMAE=35,4%), as well as cumulative daily illuminance (rMBE=-10%; rMAE=12,6%). Spectral simulation resulted instead in larger errors, mostly for wavelengths at the edges of the visible range, previously found to be important factors in the onset of myopia. Despite current limitations, simulation tools could become an essential support for future research on myopia.
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Myopia is on the rise worldwide and its onset can be triggered by environmental factors, including light exposure. Guaranteeing an adequate exposure to daylight is particularly important for young children, whose eyes need the best conditions for a healthy development. Monitoring and assessing light levels in school buildings is therefore paramount for studies on myopia but it can prove challenging. This paper paves the way for the use of climate-based daylight simulation as a tool to complement field studies on myopia. Existing simulation tools were found to characterise vertical spot illuminance with an acceptable error range (rMBE=8,7%; rMAE=35,4%), as well as cumulative daily illuminance (rMBE=-10%; rMAE=12,6%). Spectral simulation resulted instead in larger errors, mostly for wavelengths at the edges of the visible range, previously found to be important factors in the onset of myopia. Despite current limitations, simulation tools could become an essential support for future research on myopia.
CAAD Futures is a biennial international conference on Computer-Aided Architectural Design under the umbrella of the CAAD Futures Foundation, and it is active world-wide in advancing and documenting related research. On 5–7 July 2023, the 20th CAAD Futures conference was hosted at Delft University of Technology. The CAAD Futures Foundation was established in 1985, holding the first conference on 18–19 September of that year at the very same University. The return of the conference to Delft for its 20thedition offered a chance to reflect on the past, present and future role of Computation in Architecture and the Built Environment. With reference to the theme of “INTERCONNECTIONS: Co-computing beyond boundaries”, CAAD Futures 2023 reflected on the role of computation to interconnect in and for Architectural Design.
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CAAD Futures is a biennial international conference on Computer-Aided Architectural Design under the umbrella of the CAAD Futures Foundation, and it is active world-wide in advancing and documenting related research. On 5–7 July 2023, the 20th CAAD Futures conference was hosted at Delft University of Technology. The CAAD Futures Foundation was established in 1985, holding the first conference on 18–19 September of that year at the very same University. The return of the conference to Delft for its 20thedition offered a chance to reflect on the past, present and future role of Computation in Architecture and the Built Environment. With reference to the theme of “INTERCONNECTIONS: Co-computing beyond boundaries”, CAAD Futures 2023 reflected on the role of computation to interconnect in and for Architectural Design.
Topologically optimized cast glass
A new design approach for loadbearing monolithic glass components of reduced annealing time
Up to now, fabricating cast glass components of substantial mass and/or thickness involves a lengthy and perplex annealing process. This has limited the use of this glass manufacturing method in the built environment to simple objects up to the size of regular building bricks, which can be annealed within a few hours. For the first time, structural topological optimization (TO) is investigated as an approach to design monolithic loadbearing cast-glass elements of substantial mass and dimensions, with significantly reduced annealing times. The research is two-fold. First, a numerical exploration is performed. The potential of reducing mass while maintaining satisfactory stiffness of a structural component is done through a case-study, in which a cast-glass grid shell node is designed and optimised. To achieve this, several design criteria in respect to glass as a material, casting as the manufacturing process and TO as a design method, are formulated and applied in the optimisation. It is concluded that a TO approach fully suited for three-dimensional glass design is as of yet not available. For this research, strain- or compliance based TO is selected for the optimization of the three-dimensional, cast glass grid shell node; in our case, we consider that a strain based TO allows for a better exploration of the thickness reduction, which, in turn, has a major influence on the annealing time of cast glass. In comparison, in a stress-based optimization, the considerably lower tensile strength of glass would become the main restrain, leaving underutilized the higher compressive strength. Furthermore, it is determined that a single, unchanging and dominant load-case is most suited for TO optimisation. Using ANSYS Workbench, mass reductions of up to 69% compared to an initial, unoptimized geometry are achieved, reducing annealing times by an estimated 90%. Following this, the feasibility of manufacturing the resulting complex-shaped glass components is investigated though physical prototypes. Two manufacturing techniques are explored: lost-wax casting using 3D-printed wax geometries, and kiln-casting using 3D-printed disposable sand moulds. Several glass prototypes were successfully cast and annealed. From this, several conclusions are drawn regarding the applicability and limitations of TO for cast glass components and the potential of alternative manufacturing methods for making such complex-shaped glass components.
...
Up to now, fabricating cast glass components of substantial mass and/or thickness involves a lengthy and perplex annealing process. This has limited the use of this glass manufacturing method in the built environment to simple objects up to the size of regular building bricks, which can be annealed within a few hours. For the first time, structural topological optimization (TO) is investigated as an approach to design monolithic loadbearing cast-glass elements of substantial mass and dimensions, with significantly reduced annealing times. The research is two-fold. First, a numerical exploration is performed. The potential of reducing mass while maintaining satisfactory stiffness of a structural component is done through a case-study, in which a cast-glass grid shell node is designed and optimised. To achieve this, several design criteria in respect to glass as a material, casting as the manufacturing process and TO as a design method, are formulated and applied in the optimisation. It is concluded that a TO approach fully suited for three-dimensional glass design is as of yet not available. For this research, strain- or compliance based TO is selected for the optimization of the three-dimensional, cast glass grid shell node; in our case, we consider that a strain based TO allows for a better exploration of the thickness reduction, which, in turn, has a major influence on the annealing time of cast glass. In comparison, in a stress-based optimization, the considerably lower tensile strength of glass would become the main restrain, leaving underutilized the higher compressive strength. Furthermore, it is determined that a single, unchanging and dominant load-case is most suited for TO optimisation. Using ANSYS Workbench, mass reductions of up to 69% compared to an initial, unoptimized geometry are achieved, reducing annealing times by an estimated 90%. Following this, the feasibility of manufacturing the resulting complex-shaped glass components is investigated though physical prototypes. Two manufacturing techniques are explored: lost-wax casting using 3D-printed wax geometries, and kiln-casting using 3D-printed disposable sand moulds. Several glass prototypes were successfully cast and annealed. From this, several conclusions are drawn regarding the applicability and limitations of TO for cast glass components and the potential of alternative manufacturing methods for making such complex-shaped glass components.
Journal article
(2022)
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T. Du, M. Turrin, S.C. Jansen, A.A.J.F. van den Dobbelsteen, Francesco De Luca
Architectural space layout has proven to be influential on building energy performance. However, the relationship between different space layouts and their consequent energy demands has not yet been systematically studied. This study thoroughly investigates such a relationship. In order to do so, a computational method was developed, which includes a method to generate space layouts featuring energy-related variables and an assessment method for energy demand. Additionally, a design of experiments was performed, and its results were used to analyse the relationship between space layouts and energy demands. In order to identify their relationship, four types of design indicators of space layout were proposed, both for the overall layout and for each function. Finally, several optimisations were performed to minimise heating, cooling and lighting demands. The optimisation results showed that the maximum reduction between different layouts was up to 54% for lighting demand, 51% for heating demand and 38% for cooling demand. The relationship analysis shows that when comparing the four types of design indicators, the façade area-to-floor area ratio showed a stronger correlation with energy demands than the façade area ratio, floor area ratio and height-to-depth ratio. Overall, this study shows that designing a space layout helps to reduce energy demands for heating, cooling and lighting, and also provides a reference for other researchers and designers to optimise space layout with improved energy performance.
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Architectural space layout has proven to be influential on building energy performance. However, the relationship between different space layouts and their consequent energy demands has not yet been systematically studied. This study thoroughly investigates such a relationship. In order to do so, a computational method was developed, which includes a method to generate space layouts featuring energy-related variables and an assessment method for energy demand. Additionally, a design of experiments was performed, and its results were used to analyse the relationship between space layouts and energy demands. In order to identify their relationship, four types of design indicators of space layout were proposed, both for the overall layout and for each function. Finally, several optimisations were performed to minimise heating, cooling and lighting demands. The optimisation results showed that the maximum reduction between different layouts was up to 54% for lighting demand, 51% for heating demand and 38% for cooling demand. The relationship analysis shows that when comparing the four types of design indicators, the façade area-to-floor area ratio showed a stronger correlation with energy demands than the façade area ratio, floor area ratio and height-to-depth ratio. Overall, this study shows that designing a space layout helps to reduce energy demands for heating, cooling and lighting, and also provides a reference for other researchers and designers to optimise space layout with improved energy performance.
Phase change materials (PCMs) have already been used in buildings and building services for several decades, mostly integrated into walls or ceilings to passively increase the building’s thermal inertia, or integrated into the HVAC system for (pre-)heating or (pre-)cooling fresh air. More recently, the use of PCMs in facades is being explored for solar heating. This paper presents the results of a several years of research into the use of PCMs in rotatable Trombe walls and sun-shading for passive heating and cooling purposes. Simulations used a custom-made model of a room in Matlab/Simulink, in which all relevant heat transfer paths and mass components are accounted for. Once the behaviour of PCM was modelled, the model was connected with the optimisation platform modeFRONTIER to study the (best) performances under different scenarios. The results show that a significant reduction in the energy demand for heating and cooling can be achieved in different climates. The results also show that the shading and insulating effect of the solar wall have the highest impact on the reduction of the cooling respectively heating demand, followed by the thermal mass effect. The paper ends with the development of a prototype of a Trombe wall which was installed in an office at the Green Village (a living lab in Delft).
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Phase change materials (PCMs) have already been used in buildings and building services for several decades, mostly integrated into walls or ceilings to passively increase the building’s thermal inertia, or integrated into the HVAC system for (pre-)heating or (pre-)cooling fresh air. More recently, the use of PCMs in facades is being explored for solar heating. This paper presents the results of a several years of research into the use of PCMs in rotatable Trombe walls and sun-shading for passive heating and cooling purposes. Simulations used a custom-made model of a room in Matlab/Simulink, in which all relevant heat transfer paths and mass components are accounted for. Once the behaviour of PCM was modelled, the model was connected with the optimisation platform modeFRONTIER to study the (best) performances under different scenarios. The results show that a significant reduction in the energy demand for heating and cooling can be achieved in different climates. The results also show that the shading and insulating effect of the solar wall have the highest impact on the reduction of the cooling respectively heating demand, followed by the thermal mass effect. The paper ends with the development of a prototype of a Trombe wall which was installed in an office at the Green Village (a living lab in Delft).
Optimising High-Rise Buildings for Self-Sufficiency in Energy Consumption and Food Production Using Artificial Intelligence
Case of Europoint Complex in Rotterdam
Journal article
(2022)
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B. Ekici, Okan Türkcan, M. Turrin, I.S. Sariyildiz, Mehmet Fatih Tasgetiren
The increase in global population, which negatively affects energy consumption, CO2 emissions, and arable land, necessitates designing sustainable habitation alternatives. Self-sufficient high-rise buildings, which integrate (electricity) generation and efficient usage of resources with dense habitation, can be a sustainable solution for future urbanisation. This paper focuses on transforming Europoint Towers in Rotterdam into self-sufficient buildings considering energy consumption and food production (lettuce crops) using artificial intelligence. Design parameters consist of the number of farming floors, shape, and the properties of the proposed façade skin that includes shading devices. Nine thousand samples are collected from various floor levels to predict self-sufficiency criteria using artificial neural networks (ANN). Optimisation problems with 117 decision variables are formulated using 45 ANN models that have very high prediction accuracies. 13 optimisation algorithms are used for an in-detail investigation of self-sufficiency at the building scale, and potential sufficiency at the neighbourhood scale. Results indicate that 100% and 43.7% self-sufficiencies could be reached for lettuce crops and electricity, respectively, for three buildings with 1800 residents. At the neighbourhood scale, lettuce production could be sufficient for 27,000 people with a decrease of self-sufficiency in terms of energy use of up to 11.6%. Consequently, this paper discusses the potentials and the improvements for self-sufficient high-rise buildings.
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The increase in global population, which negatively affects energy consumption, CO2 emissions, and arable land, necessitates designing sustainable habitation alternatives. Self-sufficient high-rise buildings, which integrate (electricity) generation and efficient usage of resources with dense habitation, can be a sustainable solution for future urbanisation. This paper focuses on transforming Europoint Towers in Rotterdam into self-sufficient buildings considering energy consumption and food production (lettuce crops) using artificial intelligence. Design parameters consist of the number of farming floors, shape, and the properties of the proposed façade skin that includes shading devices. Nine thousand samples are collected from various floor levels to predict self-sufficiency criteria using artificial neural networks (ANN). Optimisation problems with 117 decision variables are formulated using 45 ANN models that have very high prediction accuracies. 13 optimisation algorithms are used for an in-detail investigation of self-sufficiency at the building scale, and potential sufficiency at the neighbourhood scale. Results indicate that 100% and 43.7% self-sufficiencies could be reached for lettuce crops and electricity, respectively, for three buildings with 1800 residents. At the neighbourhood scale, lettuce production could be sufficient for 27,000 people with a decrease of self-sufficiency in terms of energy use of up to 11.6%. Consequently, this paper discusses the potentials and the improvements for self-sufficient high-rise buildings.
As technology advances, architects often employ innovative, non-standard shapes in their designs for the fast-growing number of high-rise buildings. Conversely, climate change is bringing about an increasing number of dangerous wind events causing damage to buildings and their surroundings. These factors further complicate the already difficult field of structural wind analysis. Current methods for calculating structural wind response, such as the Eurocode, do not provide methods for unconventional building shapes or, in the case of physical wind tunnel test and in-depth computational fluid dynamics (CFD) simulation, they are prohibitively expensive and time-consuming. Thus, wind load analysis is often relegated to late in the design process. This paper presents the development of a computational method to analyze the effect of wind on the structural behavior of a 3D building model and optimize the external geometry to reduce those effects at an early design phase. It combines CFD, finite-element analysis (FEA), and an optimization algorithm in the popular parametric design tool, Grasshopper. This allows it to be used in an early design stage for performance-based design exploration in complement to the more traditional late-stage methods outlined above. After developing the method and testing the timeliness and precision of the CFD, and FEA portions on case study buildings, the tool was able to output an optimal geometry as well as a database of improved geometric options with their corresponding performance for the wind loading.
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As technology advances, architects often employ innovative, non-standard shapes in their designs for the fast-growing number of high-rise buildings. Conversely, climate change is bringing about an increasing number of dangerous wind events causing damage to buildings and their surroundings. These factors further complicate the already difficult field of structural wind analysis. Current methods for calculating structural wind response, such as the Eurocode, do not provide methods for unconventional building shapes or, in the case of physical wind tunnel test and in-depth computational fluid dynamics (CFD) simulation, they are prohibitively expensive and time-consuming. Thus, wind load analysis is often relegated to late in the design process. This paper presents the development of a computational method to analyze the effect of wind on the structural behavior of a 3D building model and optimize the external geometry to reduce those effects at an early design phase. It combines CFD, finite-element analysis (FEA), and an optimization algorithm in the popular parametric design tool, Grasshopper. This allows it to be used in an early design stage for performance-based design exploration in complement to the more traditional late-stage methods outlined above. After developing the method and testing the timeliness and precision of the CFD, and FEA portions on case study buildings, the tool was able to output an optimal geometry as well as a database of improved geometric options with their corresponding performance for the wind loading.
Numerous studies have shown that architectural design affects energy performance significantly. However, the effect of space layouts on building energy performance has not been fully analysed. In this paper, we aim to study the effect of space layouts on energy performance. An office building was used as the reference, and 11 layout variants were proposed and compared for energy performance. Three climates (temperate, cold and tropical) were inspected, with three typical cities (Amsterdam, Harbin and Singapore). Dynamic simulation was conducted for the energy performance assessment integrating daylighting simulation with energy simulation. For each layout, two situations were simulated: one has no shading system, and the other one has an exterior screen for shading. Based on the simulation results, it is found that lighting demand is affected the most by the layout variance, and the resulting maximum difference (difference divided by the highest demand) happens in Harbin, being 46% without shading and 35% with shading. Regarding the sum of the final energy for heating, cooling and lighting, using a heat pump system, the maximum difference is 8% for the layouts both without and with shading system occurring in Amsterdam.
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Numerous studies have shown that architectural design affects energy performance significantly. However, the effect of space layouts on building energy performance has not been fully analysed. In this paper, we aim to study the effect of space layouts on energy performance. An office building was used as the reference, and 11 layout variants were proposed and compared for energy performance. Three climates (temperate, cold and tropical) were inspected, with three typical cities (Amsterdam, Harbin and Singapore). Dynamic simulation was conducted for the energy performance assessment integrating daylighting simulation with energy simulation. For each layout, two situations were simulated: one has no shading system, and the other one has an exterior screen for shading. Based on the simulation results, it is found that lighting demand is affected the most by the layout variance, and the resulting maximum difference (difference divided by the highest demand) happens in Harbin, being 46% without shading and 35% with shading. Regarding the sum of the final energy for heating, cooling and lighting, using a heat pump system, the maximum difference is 8% for the layouts both without and with shading system occurring in Amsterdam.
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1
Background, methodology, setup, and machine learning results
Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase.
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Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase.