Martijn B. J. Brummelhuis, Nathan F. Lepora, Salua Hamaza
Operating drones in urban environments often means they need to land on rooftops, which can have different geometries and surface irregularities. Accurately detecting roof inclination using conventional sensing methods, such as vision-based or acoustic techniques, can be unreliable, as measurement quality is strongly influenced by external factors including weather conditions and surface materials. To overcome these challenges, we propose a novel unmanned aerial manipulator morphology featuring a dual-arm aerial manipulator with an omnidirectional 3D workspace and extended reach. Building on this design, we develop a proprioceptive contact detection and contact localization strategy based on a momentum-based torque observer. This enables the UAM to infer the inclination of slanted surfaces blindly - through physical interaction - prior to touchdown. We validate the approach in flight experiments, demonstrating robust landings on surfaces with inclinations of up to 30.5 degrees and achieving an average surface inclination estimation error of 2.87 degrees over 9 experiments at different incline angles.
Ewa Rokita-Magdziarz, Barbara Gronostajska, Marcin Magdziarz
In this paper, we develop a rigorous mathematical framework for the optimization of hip roof house geometry, with the primary goal of minimizing the external surface of the building envelope for a given set of design constraints. Five optimization scenarios are systematically analyzed: fixed volume, fixed footprint ratio, fixed slenderness ratio, fixed floor area, and constrained height. For each case, explicit formulas for the optimal dimensions are derived, offering architects and engineers practical guidelines for improving material efficiency, reducing construction costs, and enhancing energy performance. To illustrate the practical relevance of the theoretical results, case studies of real-world hip roof houses are presented, revealing both inefficiencies in common practice and near-optimal examples. Furthermore, a freely available software application has been developed to support designers in applying the optimization methods directly to architectural projects. The findings confirm that square-based footprints combined with balanced slenderness ratios yield the most efficient forms, while deviations toward elongated or flattened proportions significantly increase energy and material demands. This work demonstrates how mathematical modeling and architectural design can be integrated to support sustainable architecture, providing both theoretical insight and practical tools for shaping energy-efficient, cost-effective, and aesthetically coherent residential buildings.
This study introduces a comprehensive computational framework integrating image-based simulations, spatial frequency analysis, and multi-objective optimization to evaluate and optimize passive solar shading devices from an occupant-centric perspective. While traditional façade optimization primarily addresses daylight performance and glare control, critical gaps remain in objectively and simultaneously quantifying visual comfort and preference, as well as external view content and quality—both essential to user satisfaction and psychological well-being. To bridge these gaps, spatial frequency metrics, historically utilized in image classification and visual assessments, are proposed as quantitative indicators for evaluating shading devices. The methodology employs first-person interior views analyzed through advanced computational techniques—daylight glare probability, image segmentation and power spectrum analysis—to objectively assess visual comfort, view content and spatial frequency composition. The proposed framework employs an adaptive optimization algorithm that iteratively generates and refines shading device configurations, effectively balancing glare reduction, external visibility, and visual complexity. Two experimental studies validate the approach: the first systematically evaluates multiple predefined shading patterns to identify optimal characteristics, while the second demonstrates that algorithmic optimization of highly irregular shading configurations can simultaneously improve multiple visual comfort metrics, significantly outperforming regular shading patterns in terms of glare reduction, view preservation, and spatial frequency performance.
Details in building design and construction. Including walls, roofs
Efficient energy use is vital in architecture, and the building envelope plays a key role in aesthetics, thermal comfort, energy efficiency, and natural lighting. Traditional architecture has employed innovative solutions for building envelopes to address climatic challenges and ensure suitable indoor thermal conditions. One prominent example is the use of Girih tile patterns and colored Orosi windows, which have successfully met the functional and climatic needs of traditional buildings. This study investigates various Girih tile patterns and colored glass combinations in Orosi windows of traditional buildings in Isfahan. Their layout and wood-to-glass ratio were analyzed. Using a semi-experimental, quantitative approach with software simulations, 60 Girih tile patterns were assessed in Rhino7 with Honeybee and Ladybug, and 120 colored glass combinations were optimized via Octopus in Grasshopper. Results show that the optimal pattern (N50), a twelve-sided Safavid design with 58.14% wood density, achieved 88.51% useful daylight illuminance, 99.03% visual comfort, and 99.47% thermal comfort. This yielded the highest overall performance (95.67% average, SD = 6.20). Patterns with 51–57% wood density maintained high optimization, while densities above 59% reduced daylight penetration. For colored glass, combinations with higher yellow content reached up to 87.89% UDI, whereas patterns dominated by colorless and blue glass dropped to 4.58% UDI. These findings provide evidence-based guidelines for designing openings that balance daylighting, visual comfort, and thermal efficiency in Isfahan’s cold semi-arid climate. It is noteworthy that the results of analyzing the performance level of single colors differ significantly from the arrangement of various colors alongside each other.
Details in building design and construction. Including walls, roofs
To balanced multi-criteria's daylighting performance in indoor spaces, several dynamic metrics have been proposed, but so far there is no convention on which daylight metrics thresholds are preferred and which objective weights are given priority in optimization of daylighting under certain climate. This study explores uncertainty and sensitivity analyses to identify and prioritize the most influencing critical objective weights and appropriate useful daylighting illuminance (UDI) threshold for designing glazed system with Insulated Glazed Units that include Parallel Slat Transparent Insulation Materials (IGUs/PS-TIMs) for office buildings in sub-tropics areas for achieving best level of daylighting availability with enhanced thermal and visual comfort. To ascertain which objectives and threshold have significantly impact the optimized IGUs/PS-TIMs parameters, the study performed a framework of three statistical methods-based multi-objective optimization (MOO) of Objective’s weights (OWs), Sensitivity analysis (SA) and uncertainty analysis (UA), were used. The framework comprises multi-optimization stages based on two main steps. In the first step, MOO based-multi statistical analysis was run for ranking UDI threshold control setpoint (UDI-TCS) and of critical objective weights (PC-OWs), meanwhile the second step, MOO based-optimal UDI-TCS and PC-OWs was run for final ranking solutions. The results showed that if considering the priority of PC-OWs in ranking final solutions based best UDI thresholds, the different schemes can be obtained, which is of great significance to the early design stage of buildings. Overall, the results showed that when considering PC-OWs in ranking final UDI thresholds, it can be clearly noticed that the UDI, ASE, and QV respectively, are the most PC-OWs, and UDI500-2000lux is the optimal threshold because it has significantly improved the UDI and ASE in all optimal cases compared to optimal cases resulted from UDI500-1000lux/50%. the total average percentage of UDI between 22.63% to 27.80%, meanwhile, the total average percentage of ASE improved between 2.14%. to 11.52%. For QV, the optimal cases result from UDI500-1000lux/50% threshold working better by slightly improving QV by 1.16% and 10.56%. Notwithstanding, these two pairs of UDI thresholds with given priority to UDI, ASE and QV as PC-OWs are suggested as the most appropriate conflicted objectives for optimizing daylight under sub-tropical climate conditions.
Details in building design and construction. Including walls, roofs
Population aging, extreme weather conditions, and rising energy costs present significant challenges, especially in developing Asian countries like India. A key sustainability goal for future age-friendly cities is to ensure that older adults live in housing tailored to their specific needs. However, existing thermal comfort studies in India often treat the adult population as homogeneous, overlooking the unique thermal comfort requirements of the older people. This study addresses this gap by examining seasonal variations in indoor thermal comfort and associated behavioural adjustments among older adults in their residences of warm and humid climate of India. Data were collected from 1,480 respondents through a longitudinal survey and simultaneous measurements of indoor environmental variables. Linear regression analysis revealed that comfort temperatures (TSV ±1) for the older people are 30°C in summer, 28.1°C in winter, and 29.2°C overall. The steeper slope of the summer regression indicates that even minor increases in indoor temperature significantly affect thermal sensations, highlighting the difficulties older adults face in warmer months. At a prevailing mean outdoor temperature (PMOT) of 25°C, the comfort band for older individuals ranges from 26°C to 31°C. Under ASHRAE 55-2020 conditions (TSV ±0.5), the comfort range is 26.9°C to 29.7°C. Notably, the comfort range for older individuals in this study is narrower compared to IMAC R, IMAC NV, and ASHRAE 55-2020 models, with a regression slope of 0.56 indicating heightened sensitivity to temperature variations. Additionally, relative importance index analysis shows that older males favour active cooling measures in summer, such as wearing lighter clothing and seeking airy spaces, while females prioritize modifying daily routines. In winter, both genders adopt warming strategies, with males emphasizing hot showers and females choosing heavier clothing.
Details in building design and construction. Including walls, roofs
For any fixed dimension $d \geq 3$ we construct a Nikodym set in $F_q^d$ of cardinality $q^d - (\frac{d-2}{\log 2} +1+o(1)) q^{d-1} \log q$ in the limit $q \to \infty$, when $q$ is an odd prime power. This improves upon the naive random construction, which gives a set of cardinality $q^d - (d-1+o(1)) q^{d-1} \log q$, and is new in the regime where $F_q$ has unbounded characteristic and $q$ not a perfect square. While the final proofs are completely human generated, the initial ideas of the construction were inspired by output from the tools \texttt{AlphaEvolve} and \texttt{DeepThink}. We also present a simple construction of Nikodym sets in $F_q^2$ for $q$ a perfect square that is a special case of known unital-based constructions, and matches the existing bounds of $q^2 - q^{3/2} + O(q \log q)$, assuming that $q$ is not the square of a prime $p \equiv 3 \pmod{4}$.
Many enterprise software systems provide complex Graphical User Interfaces (GUIs) that need robust architectural patterns for well-structured software design. However, popular GUI architectural patterns like Model-View-ViewModel (MVVM) often lack detailed implementation guidance, leading GUI developers to inappropriately use the pattern without a comprehensive overview of design variants and often-mentioned trade-offs. Therefore, this paper presents an extensive review of MVVM design aspects and trade-offs, extending beyond the standard MVVM definition. We conducted a multivocal literature review (MLR), including white and gray literature, to cover essential knowledge from blogs, published papers, and other unpublished formats like books. Using the standard MVVM definition as a baseline, our study identifies (1) 76 additional design constructs grouped into 29 design aspects and (2) 16 additional benefits and 15 additional drawbacks. These insights can guide enterprise application developers in implementing practical MVVM solutions and enable informed design decisions.
Construction robotics increasingly relies on natural language processing for task execution, creating a need for robust methods to interpret commands in complex, dynamic environments. While existing research primarily focuses on what tasks robots should perform, less attention has been paid to how these tasks should be executed safely and efficiently. This paper presents a novel probabilistic framework that uses sentiment analysis from natural language commands to dynamically adjust robot navigation policies in construction environments. The framework leverages Building Information Modeling (BIM) data and natural language prompts to create adaptive navigation strategies that account for varying levels of environmental risk and uncertainty. We introduce an object-aware path planning approach that combines exponential potential fields with a grid-based representation of the environment, where the potential fields are dynamically adjusted based on the semantic analysis of user prompts. The framework employs Bayesian inference to consolidate multiple information sources: the static data from BIM, the semantic content of natural language commands, and the implied safety constraints from user prompts. We demonstrate our approach through experiments comparing three scenarios: baseline shortest-path planning, safety-oriented navigation, and risk-aware routing. Results show that our method successfully adapts path planning based on natural language sentiment, achieving a 50\% improvement in minimum distance to obstacles when safety is prioritized, while maintaining reasonable path lengths. Scenarios with contrasting prompts, such as "dangerous" and "safe", demonstrate the framework's ability to modify paths. This approach provides a flexible foundation for integrating human knowledge and safety considerations into construction robot navigation.
João Francisco Walter Costa, Cláudia Naves David Amorim, Joara Cronemberger Ribeiro Silva
The employment of electrochromic glazing can be a solution to balance circadian lighting and avoid glare. This can be achieved by controlling daylight entering the room and may be useful within the context of highly glazed facades in buildings in hot climates. Nevertheless, the use of this technology is rarely discussed in this context. In this regard, the aim of this study is to investigate the electrochromic glazing for the lighting conditions, including visual and non-visual effects within the luminous context of Brasilia, Brazil. The method consisted of computer simulations of a representative highly glazed non-residential room with the comparison of electrochromic glazing and conventional clear glass. Climate Studio was used to evaluate the visual effects of light for the entire year, and ALFA for the evaluation of melanopic daylight illuminance, vertical illuminance, and melanopic daylight efficacy ratio (mel-DER) in four days, including two solstices and two equinoxes encompassing the beginning of the four seasons. Results for the electrochromic glazing showed a better balance between a minimum threshold of 250 lux of mel-EDI without exceeding 1,500 lux of vertical illuminance in comparison with the clear glass. This was achieved in 33.33% of the hours for the north, against 27.78% of the hours for the east, 29.17% for the west, and 24.72% for the south. For the clear glass, this balance was achieved in only 9.17% of the hours for north, 10.28% for east, 12.22% for west, and 15% for south. Regarding the spectrum, higher results of melanopic daylight efficacy ratio were observed for the clear glass over the four simulated days. The main conclusion was that the electrochromic glazing was capable of providing a better balance between visual and non-visual requirements and can be a suitable solution for highly glazed facades in Brasilia. Nevertheless, particularly for the north orientation, the supply of circadian lighting can be jeopardized when the electrochromic glazing remained at the dark state.
Details in building design and construction. Including walls, roofs
Omnya Saleh Zekry, Ahmed Ahmed Fekry, Reham El Dessuky Hamed
Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.
Details in building design and construction. Including walls, roofs
Sustainability-focused design research is witnessing a change in approach with the emergence of More-than-human Design (MTHD), which challenges human-centered thinking by incorporating nonhuman perspectives into the design process. However, implementing MTHD presents challenges for design researchers and practitioners, such as understanding non-verbal species. Despite the techniques developed to facilitate such an understanding (e.g. contact zone), the growing literature on MTHD lacks studies reflecting on how these techniques are utilized in the design process. In this paper, we present a case study on designing olive harvesting tools from a MTH lens, where designers used contact zone, plant interviews, plant persona, and experience map to explore the perspectives of olive trees and incorporate them into ideas in collaboration with farmers and agricultural engineers. The results indicate the significance of reconsidering decentralization in MTHD from the standpoint of entanglements among techniques and incorporating various knowledge types to manage tensions arising from perspective shifts.
We provide a construction method for biharmonic submanifolds in cohomogeneity one manifolds. In particular, we give new examples of biharmonic submanifolds and study the normal index of these submanifolds. We use this strategy to construct metrics on the sphere admitting biharmonic non-minimal hypersurfaces with three distinct principal curvatures. Finally, we perform a similar study of $r$-harmonic submanifolds of cohomogeneity one manifolds.
The majority of hotels in Balikpapan use a modern look that does not accommodate tropical climate conditions. In addition, hotels in Balikpapan are also somewhat inconsistent with local architecture, so the buildings do not have a character that reflects local culture. This is one of the main reasons for designing hotels in Balikpapan with a tropical architectural approach. The character of this Tropical Architecture is the approach that will be applied to this design to anticipate tropical climate conditions which have quite high temperatures and humidity. The design approach that will be applied to the building is the determination of spatial orientation, shading, cross ventilation (air circulation), lighting and the use of traditional Dayak motifs. The method used in this design is the rational method. It is hoped that the Hotel Design in Balikpapan with a Tropical Architectural Approach can become a design that can accommodate the tropical climate, support the needs of people who are on business trips and become buildings that have local cultural characteristics in Balikpapan.
Details in building design and construction. Including walls, roofs, Urban renewal. Urban redevelopment
Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management.
The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades. To tackle these challenges, we present a multi-task guided change detection network model, named as MTGCD-Net. The proposed model approaches the specific BCD problem by designing three auxiliary tasks, including: (1) a pixel-wise classification task to predict the roofs and facades of buildings; (2) an auxiliary task for learning the roof-to-footprint offsets of each building to account for the misalignment between building roof instances; and (3) an auxiliary task for learning the identical roof matching flow between bi-temporal aerial images to tackle the building roof mismatch problem. These auxiliary tasks provide indispensable and complementary building parsing and matching information. The predictions of the auxiliary tasks are finally fused to the main building change detection branch with a multi-modal distillation module. To train and test models for the BCD problem with off-nadir aerial images, we create a new benchmark dataset, named BANDON. Extensive experiments demonstrate that our model achieves superior performance over the previous state-of-the-art competitors.
The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and development competition. TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/machine learning (AI/ML) algorithms on implantable devices. The challenge problem of TDC'22 is to develop a novel AI/ML-based real-time detection algorithm for life-threatening ventricular arrhythmia over low-power microcontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs). The dataset contains more than 38,000 5-second intracardiac electrograms (IEGMs) segments over 8 different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC'22, which is open to multi-person teams world-wide, attracted more than 150 teams from over 50 organizations. This paper first presents the medical problem, dataset, and evaluation procedure in detail. It further demonstrates and discusses the designs developed by the leading teams as well as representative results. This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.