The limited research on obstruction-driven daylight reduction continues to hinder efforts to optimize natural daylight in compact mid-rise residential buildings. This study systematically examines how features of nearby obstructions, such as height, surface reflectance, and distance, along with street width, affect indoor daylighting in tropical residences. It employs an integrated approach combining climate-based simulations with occupant perception surveys for validation. Using IES-VE with RadianceIES, climate-based daylight modeling was conducted at the Sri Aksalaya mid-rise apartment complex in Tirupur, India. A total of 1,152 simulation scenarios were performed, varying the room layout orientation (north-east and south-west), road width (4–10 m), obstruction height (G to G+3), and façade reflectance (30–65%). Daylight performance was assessed using two metrics: Spatial Daylight Autonomy (sDA300/50%) and Daylight Glare Probability (DGP). The results were validated through structured surveys of 57 residents across all floors. Findings indicate that external obstructions are the primary factors impacting daylight performance; those located closest to the building (4 m from the building) reduce sDA by up to 67% compared to open conditions. The proximity of obstructions results in insufficient daylight (sDA < 50%) on the lower floors, whereas the upper floors experience excessive glare (DGP > 0.40). The middle floors are most affected by façade reflectivity, with the probability of glare increasing by 250% as reflectance rises from 30% to 65%. Statistical analysis revealed a strong correlation between simulation metrics and occupant satisfaction (R²= 0.84, p < 0.001). Window performance was orientation-dependent; from the selected room layouts, 1, 3, and 4 performed best for north-east, while layouts 2, 3, and 6 were ideal for south-west. Overall, urban morphology greatly influences daylight access and visual comfort in tropical homes. The study highlights the importance of context-specific fenestration design, façade reflectance, and floor-level strategies to optimise daylight and minimise glare in multi-floor residences in tropical settings.
Details in building design and construction. Including walls, roofs
Floriberta Binarti, Nimas Sekarlangi, Meita Kasianus Virgin Brilianto
et al.
This study examines the neurophysiological responses of students to different classroom window views - forest, park, and city - within energy-efficient, green campus environments. Ten architecture students participated in EEG recordings while experiencing six virtual reality-simulated classroom conditions featuring varied window orientations and external views. The classroom models were designed to meet green building standards, with energy and daylighting performance validated using OTTV calculations and simulations in EnergyPlus and Radiance via DesignBuilder. EEG data were recorded using a 14-channel Emotiv EPOC X headset and analysed across delta to gamma frequency bands through power spectral and topographic mapping. Results revealed that forest views consistently evoked the most restorative neural responses, indicated by increased alpha and theta activity and decreased beta and gamma power. City views triggered neural patterns associated with cognitive load and overstimulation, while park views supported balanced attentional engagement. These findings highlight the importance of integrating biophilic elements - particularly dense greenery - into classroom design to enhance students’ cognitive performance and psychological well-being in sustainable educational settings.
Details in building design and construction. Including walls, roofs
Light influences human physiology and psychology through visual and beyond-visual effects, collectively termed ‘integrative lighting.’ Human responses depend on luminous (quantity, spectrum, directionality) and temporal (timing, duration, history) factors, yet no studies examined their combined influence on integrative lighting. Therefore, this study evaluates representative metrics integration by designing, implementing, and testing a comprehensive lighting simulation framework incorporating luminous and temporal factors to address integrative lighting needs while assessing energy consumption. A quantitative approach was employed, integrating multiple criteria through computational simulations using Rhinoceros/Grasshopper, Lark, ClimateStudio, and Ladybug/Honeybee. Simulations are performed in a single office with nine control points, four vertical viewing directions, and one horizontal, each testing eight window sizes and different electric lighting combinations of ceiling panels and wall-washers with varying melanopic-content across four seasonal days. Including beyond-visual effects in multi-criteria optimisation introduces complexity due to the interplay between luminous and temporal aspects. Results show that beyond-visual effects depend on light quantity, spectral composition, and spatial distribution. Increasing window-to-wall ratio or melanopic-content lighting alone does not ensure uniform beyond-visual performance. Instead, directing wall washers at opaque surfaces enhances background luminance, reduces glare, and improves retinal exposure. Beyond-vision criteria are challenging due to temporal dependencies, often requiring window size and lighting energy use trade-offs. These findings highlight the need for lighting designs that optimise light levels, spectrum, and directionality at the right time. Future approaches should use multi-objective optimisation to balance visual and non-visual outcomes, automate adjustments, and enhance well-being while maintaining energy efficiency.
Details in building design and construction. Including walls, roofs
Natural disasters are increasing in frequency and severity, causing hundreds of billions of dollars in damage annually and posing growing threats to infrastructure and human livelihoods. Accurate data on roofing materials is critical for modeling building vulnerability to natural hazards such as earthquakes, floods, wildfires, and hurricanes, yet such data remain unavailable. To address this gap, we introduce RoofNet, the largest and most geographically diverse novel multimodal dataset to date, comprising over 51,500 samples from 184 geographically diverse sites pairing high-resolution Earth Observation (EO) imagery with curated text annotations for global roof material classification. RoofNet includes geographically diverse satellite imagery labeled with 14 key roofing types -- such as asphalt shingles, clay tiles, and metal sheets -- and is designed to enhance the fidelity of global exposure datasets through vision-language modeling (VLM). We sample EO tiles from climatically and architecturally distinct regions to construct a representative dataset. A subset of 6,000 images was annotated in collaboration with domain experts to fine-tune a VLM. We used geographic- and material-aware prompt tuning to enhance class separability. The fine-tuned model was then applied to the remaining EO tiles, with predictions refined through rule-based and human-in-the-loop verification. In addition to material labels, RoofNet provides rich metadata including roof shape, footprint area, solar panel presence, and indicators of mixed roofing materials (e.g., HVAC systems). RoofNet supports scalable, AI-driven risk assessment and serves as a downstream benchmark for evaluating model generalization across regions -- offering actionable insights for insurance underwriting, disaster preparedness, and infrastructure policy planning.
Ying-Kuan Tsai, Vispi Karkaria, Yi-Ping Chen
et al.
Control Co-Design (CCD) integrates physical and control system design to improve the performance of dynamic and autonomous systems. Despite advances in uncertainty-aware CCD methods, real-world uncertainties remain highly unpredictable. Multi-generation design addresses this challenge by considering the full lifecycle of a product: data collected from each generation informs the design of subsequent generations, enabling progressive improvements in robustness and efficiency. Digital Twin (DT) technology further strengthens this paradigm by creating virtual representations that evolve over the lifecycle through real-time sensing, model updating, and adaptive re-optimization. This paper presents a DT-enabled CCD framework that integrates Deep Reinforcement Learning (DRL) to jointly optimize physical design and controller. DRL accelerates real-time decision-making by allowing controllers to continuously learn from data and adapt to uncertain environments. Extending this approach, the framework employs a multi-generation paradigm, where each cycle of deployment, operation, and redesign uses collected data to refine DT models, improve uncertainty quantification through quantile regression, and inform next-generation designs of both physical components and controllers. The framework is demonstrated on an active suspension system, where DT-enabled learning from road conditions and driving behaviors yields smoother and more stable control trajectories. Results show that the method significantly enhances dynamic performance, robustness, and efficiency. Contributions of this work include: (1) extending CCD into a lifecycle-oriented multi-generation framework, (2) leveraging DTs for continuous model updating and informed design, and (3) employing DRL to accelerate adaptive real-time decision-making.
Climate change and improving building energy performance are significant contemporary concerns. Conversely, climate-adaptive building envelopes (CABEs) offer promising solutions to enhance structural performance amidst fluctuating environmental conditions. Despite extensive research, few studies have compared the general movement strategies of climate-specific CABEs. Thus, this study examines common movement methods—Changing Opening Percentage (COP), Changing Shading Angle (CSA), Changing Fraction Axis (CFA), and Changing Pattern Geometry (CPG)—in terms of their energy and daylight performance in Mashhad, Iran's cold semi-arid climate (BSk). Simulation using LBT 1.6.1, a Grasshopper plugin in Rhinoceros, assessed Energy Usage Intensity (EUI), Spatial Daylight Autonomy (sDA), and Annual Sun Exposure (ASE). The results highlight the COP-CSA integrated model as optimal, achieving a 4-8% reduction in energy usage intensity, thus demonstrating its efficacy amid climate change.
Details in building design and construction. Including walls, roofs
As smart schools increasingly rely on technology, achieving energy efficiency becomes crucial for cost reduction and sustainability. This study investigates energy efficiency strategies in smart schools, focusing on the integration of renewable energy technologies. A quantitative approach using numerical simulations and literature reviews establishes benchmarks for energy-efficient smart schools. The Design Builder software is employed to evaluate system performance, with validation achieved through analysis in the System Advisor Model software. The modeled smart school building in Design Builder consumes 75,385.63 kWh annually, based on the weather conditions of specific location. Further studies indicate that integrating photovoltaics and hot water collectors can generate approximately 86,635 kWh annually. This not only offsets the energy consumption of building but also produces an excess of 11,249 kWh, which can be transferred back to the grid for additional revenue. Validation using SAM software demonstrated a minimal difference (3.2%) in annual energy outputs, confirming the accuracy of the model. The findings suggest that photovoltaics and hot water collectors can significantly contribute to achieving net-zero energy consumption in smart school buildings. Additionally, a focus on rooftop installations promotes sustainability by minimizing land use.
Details in building design and construction. Including walls, roofs
The Gross-Neveu model is a quantum field theory model of Dirac fermions in two dimensions with a quartic interaction term. Like Yang-Mills theory in four dimensions, the model is scaling critical (i.e. renormalizable but not super-renormalizable) and asymptotically free (i.e. its short-distance behavior is governed by the free theory). We give a new construction of the massive Euclidean Gross-Neveu model in infinite volume. The distinctive feature of the construction is that it does not involve cluster expansion, discretization of phase-space or a tree expansion ansatz and is based solely on the renormalization group flow equation. We express the Schwinger functions of the Gross-Neveu model in terms of the effective potential and construct the effective potential by solving the flow equation using the Banach fixed point theorem. Moreover, we construct a random field in the probability space of the free field such that its moments coincide with the Schwinger functions of the Gross-Neveu model. This is the first construction of a strong coupling between the free and interacting fields for a scaling-critical QFT. Since we use crucially the fact that fermionic fields can be represented as bounded operators our construction does not extend to models including bosons. However, it is applicable to other asymptotically free purely fermionic theories such as the symplectic fermion model.
Christopher Langrenez, Stephan De Bièvre, David R. M. Arvidsson-Shukur
Given two observables $A$ and $B$, one can associate to every quantum state a Kirkwood-Dirac (KD) quasiprobability distribution. KD distributions are like joint classical probabilities except that they can have negative or nonreal values, which are associated to nonclassical features of the state. In the last decade, KD distributions have come to the forefront as a versatile tool to investigate and construct quantum advantages and nonclassical phenomena. KD distributions are also used to determine quantum-classical boundaries. To do so, one must have witnesses for when a state is KD nonpositive. Previous works have established a relation between the uncertainty of a pure state with respect to the eigenbases of $A$ and $B$ and KD positivity. If this $\textit{support uncertainty}$ is large, the state cannot be KD positive. Here, we construct two witnesses for KD nonpositivity for general mixed states. Our first witness is the convex roof of the support uncertainty; it is not faithful, but it extends to the convex hull of pure KD-positive states the relation between KD positivity and small support uncertainty. Our other witness is the convex roof of the total KD nonpositivity, which provides a faithful witness for the convex hull of the pure KD-positive states. This implies that the convex roof of the total nonpositivity captures the nonpositive nature of the KD distribution at the underlying pure state level.
The cultural tradition of tie cloth is a typical Indonesian tradition which is the origin of the cultural ecosystem of a region. Ikat cloth has even become an integral part of traditional ceremonial ceremonies, traditional marriage ceremonies, and religious ceremonies. Tie fabrics have different motifs in each region according to the peculiarities of the region itself. Lewokluok Village is a village in East Flores Regency, which has a variety of ikat motifs. The diversity of the ikat motifs is a symbol of unifying the culture of Lewokluok village from the many tribes that inhabit Lewokluok village. The provision of an tie craft center is an effort to overcome the problems faced by tie weaving craftsmen in preserving the tie culture, promoting tie weaving, and marketing the existing tie weaving works. The craft center is a place that is used as a center for production, promotion, tourism, and cultivation activities as well as the cultural development of an area. The design of the Lewokluok tie craft center uses the application of fractals. Fractal is a geometric shape that is irregular in shape, contorted but has similarities with itself. This fractal formation uses the principle of self-similarity from the basic pattern, namely the Lewokluok tie motif which is transformed into the form of building mass. This study uses a descriptive analysis method, starting from data collection consisting of primary data and secondary data. This data is obtained from the results of observations, interviews, and documentation as well as from literature studies and comparative studies of similar objects and themes. This data is then analyzed with various alternatives to produce the final design concept. In the end, it can produce a design center for Lewokluok tie weaving according to its function and purpose and increase the potential for cultural tourism in Lewokluok by applying fractals to the shape of the building that shows the characteristics of Lewokluok tie motifs.
Details in building design and construction. Including walls, roofs, Urban renewal. Urban redevelopment
This is a numerical study of wind pressure loads on low rise buildings in which three different types of roofs were analyzed which are the flat, gable and circular roof at different wind speed. The numerical analysis was performed using FLUENT package based on values of k (turbulence kinetic energy) and (dissipation rate of turbulence) based on partial differential equation. Also, flat, and shallow escarpment terrains were considered during the simulation to determine the coefficient of pressure at different wind speed for different roof types. For the shallow escarpment terrain, a flat roof was considered at different velocities and for the flat terrain, three different types of roofs are considered which are the flat, gable and circular roof. It is observed that as the wind speed increases, the coefficient of drag decreases. It also shows the effect of vortex formed at the leeward direction of the building which implies the higher the wind speed, the larger the vortex formed and the lower the building ventilation and higher the damage on the roof of the building. Based on the analysis, it is preferable to use a circular roof based on the aerodynamic characteristics of wind around building walls and roofs.
In this chapter, we argue that it is highly beneficial for the contemporary construction grammarian to have a thorough understanding of the strong relationship between the research fields of construction grammar and artificial intelligence. We start by unravelling the historical links between the two fields, showing that their relationship is rooted in a common attitude towards human communication and language. We then discuss the first direction of influence, focussing in particular on how insights and techniques from the field of artificial intelligence play an important role in operationalising, validating and scaling constructionist approaches to language. We then proceed to the second direction of influence, highlighting the relevance of construction grammar insights and analyses to the artificial intelligence endeavour of building truly intelligent agents. We support our case with a variety of illustrative examples and conclude that the further elaboration of this relationship will play a key role in shaping the future of the field of construction grammar.
Harish Tayyar Madabushi, Laurence Romain, Petar Milin
et al.
Recent progress in deep learning and natural language processing has given rise to powerful models that are primarily trained on a cloze-like task and show some evidence of having access to substantial linguistic information, including some constructional knowledge. This groundbreaking discovery presents an exciting opportunity for a synergistic relationship between computational methods and Construction Grammar research. In this chapter, we explore three distinct approaches to the interplay between computational methods and Construction Grammar: (i) computational methods for text analysis, (ii) computational Construction Grammar, and (iii) deep learning models, with a particular focus on language models. We touch upon the first two approaches as a contextual foundation for the use of computational methods before providing an accessible, yet comprehensive overview of deep learning models, which also addresses reservations construction grammarians may have. Additionally, we delve into experiments that explore the emergence of constructionally relevant information within these models while also examining the aspects of Construction Grammar that may pose challenges for these models. This chapter aims to foster collaboration between researchers in the fields of natural language processing and Construction Grammar. By doing so, we hope to pave the way for new insights and advancements in both these fields.
Pirouz Nourian, Shervin Azadi, Roy Uijtendaal
et al.
This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in Artificial Intelligence for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.
Let $h$ be a connective homology theory. We construct a functorial relative plus construction as a Bousfield localization functor in the category of maps of spaces. It allows us to associate to a pair $(X, H)$ consisting of a connected space $X$ and an $h$-perfect normal subgroup $H$ of the fundamental group $π_1(X)$ an $h$-acyclic map $X \rightarrow X^{+h}_H$ inducing the quotient by $H$ on the fundamental group. When $h$ is an ordinary homology theory with coefficients in a commutative ring with unit $R$, this provides a functorial and well-defined counterpart to a construction by cell attachment introduced by Broto, Levi, and Oliver in the spirit of Quillen's plus construction. We also clarify the necessity to use a strongly $R$-perfect group $H$ in characteristic zero.
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. This paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully reconstructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.
Rapid renovation of Europe's inefficient buildings is required to reduce climate change. However, analyzing and evaluating buildings at scale is challenging because every building is unique. In current practice, the energy performance of buildings is assessed during on-site visits, which are slow, costly, and local. This paper presents a building point cloud dataset that promotes a data-driven, large-scale understanding of the 3D representation of buildings and their energy characteristics. We generate building point clouds by intersecting building footprints with geo-referenced LiDAR data and link them with attributes from UK's energy performance database via the Unique Property Reference Number (UPRN). To achieve a representative sample, we select one million buildings from a range of rural and urban regions across England, of which half a million are linked to energy characteristics. Building point clouds in new regions can be generated with the open-source code published alongside the paper. The dataset enables novel research in building energy modeling and can be easily expanded to other research fields by adding building features via the UPRN or geo-location.