Hasil untuk "Details in building design and construction. Including walls, roofs"

Kokou Dowou, Yawovi Nougbléga, Kokou Aménuvéla Toka et al.

Thermal insulation is a reliable strategy for achieving optimal thermal comfort in built environments and is among the most effective energy-saving measures. Currently, environmentally friendly insulation materials produced from plant and animal fibers constitute a significant component of the building industry, largely due to their minimal embodied energy and concerns about certain synthetic insulation materials’ potential adverse health effects. The main objective of the present study is to encourage and facilitate the utilization of environmentally friendly thermal insulation materials derived from biological sources, including vegetal and animal fibers, to improve thermal comfort and consequently reduce energy consumption in buildings. The study attempts to simulate the indoor air temperature profiles of a single building constructed using locally sourced materials and insulated in a series of stages with the aforementioned insulation materials. Firstly, insulation is applied exclusively to the roof. Secondly, the insulation is applied to the remaining wall surfaces. Alternatively, the insulation is applied to both the roof and the wall surfaces simultaneously. The objective is to ascertain the optimal combination of bio- and geo-insulating materials to achieve thermal comfort in buildings constructed with local materials in tropical climates. The Gauss-Seidel iterative method was employed to solve the energy equations that had been written on the walls and roof of the building. The equations were then discretized using the nodal method. To ascertain the thermal comfort of the simulated buildings, a comparison was made of the indoor air temperatures. The results of the simulations demonstrated that the utilization of wood fiber, reed panels, and straw bales as insulation materials led to a notable enhancement in comfort levels across all five building types, with an average increase of 17.5%. Among these materials, wood fiber emerged as the most effective insulation option, reducing temperatures by up to 19%. Its integration into the sheet metal-clad Banco building would be particularly advantageous. The findings demonstrate that the simultaneous insulation of walls and roofs with natural fiber thermal insulation materials markedly reduces indoor air and wall temperatures in buildings by up to 19% in comparison to uninsulated walls and roofs.

F. Ceroni, G.P. Lignola, A. Prota et al.

Ivan Beleacov

Automated construction is one of the most promising areas that can improve efficiency, reduce costs and minimize errors in the process of building construction. In this paper, a comparative analysis of three neural network models for semantic segmentation, U-Net(light), LinkNet and PSPNet, is performed. Two specialized datasets with images of houses built from wooden cubes were created for the experiments. The first dataset contains 4 classes (background, foundation, walls, roof ) and is designed for basic model evaluation, while the second dataset includes 44 classes where each cube is labeled as a separate object. The models were trained with the same hyperparameters and their accuracy was evaluated using MeanIoU and F1 Score metrics. According to the results obtained, U-Net(light) showed the best performance with 78% MeanIoU and 87% F1 Score on the first dataset and 17% and 25% respectively on the second dataset. The poor results on the second dataset are due to the limited amount of data, the complexity of the partitioning and the imbalance of classes, making it difficult to accurately select individual cubes. In addition, overtraining was observed in all experiments, manifested by high accuracy on the training dataset and its significant decrease on the validation dataset. The present work is the basis for the development of algorithms for automatic generation of staged building plans, which can be further scaled to design complete buildings. Future research is planned to extend the datasets and apply methods to combat overfitting (L1/L2 regularization, Early Stopping). The next stage of work will be the development of algorithms for automatic generation of a step-by-step plan for building houses from cubes using manipulators. Index Terms-Deep Learning, Computer vision, CNN, Semantic segmentation, Construction materials.

Bruno Rodrigues, Inês Ochoa, Agostinho Gomes

Calorimeters are a crucial component in modern particle detectors. They are responsible for providing accurate energy measurements of particles produced in high-energy collisions. The demanding requirements set for next-generation collider experiments impose new challenges on the design of new detectors, and a systematic approach to their optimization is increasingly necessary. The performance of calorimeters is primarily characterized by their energy resolution, parameterized by a stochastic and a constant term, related to sampling fluctuations and non-uniformities respectively. To improve the reconstruction quality of physics objects in the calorimeter, both terms need to be taken into account. Changes in a longitudinally constrained design usually result in a trade-off between these terms, making optimization a non-trivial task. This work focuses on the optimization of a hadronic sampling calorimeter, based on the FCC-ee ALLEGRO detector concept. By controlling the absorber layer thickness in a Geant4 simulation, the impact of the passive to active material proportion on the deposited energy distribution and resolution can be analyzed. Our methodology aims at exploring the design space with practical considerations, paving the way for the development of a closed optimization framework that can evaluate multiple designs against physics performance targets.

Ruizhe Chen, Dongyu Xue, Xiangxin Zhou et al.

Proteins typically exist in complexes, interacting with other proteins or biomolecules to perform their specific biological roles. Research on single-chain protein modeling has been extensively and deeply explored, with advancements seen in models like the series of ESM and AlphaFold2. Despite these developments, the study and modeling of multi-chain proteins remain largely uncharted, though they are vital for understanding biological functions. Recognizing the importance of these interactions, we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch. It also performs folding and inverse-folding tasks for multi-chain proteins. Moreover, APM demonstrates versatility in downstream applications: it achieves enhanced performance through supervised fine-tuning (SFT) while also supporting zero-shot sampling in certain tasks, achieving state-of-the-art results. We released our code at https://github.com/bytedance/apm.

Jiachen Liu, Yuan Xue, Haomiao Ni et al.

Generating realistic building layouts for automatic building design has been studied in both the computer vision and architecture domains. Traditional approaches from the architecture domain, which are based on optimization techniques or heuristic design guidelines, can synthesize desirable layouts, but usually require post-processing and involve human interaction in the design pipeline, making them costly and timeconsuming. The advent of deep generative models has significantly improved the fidelity and diversity of the generated architecture layouts, reducing the workload by designers and making the process much more efficient. In this paper, we conduct a comprehensive review of three major research topics of architecture layout design and generation: floorplan layout generation, scene layout synthesis, and generation of some other formats of building layouts. For each topic, we present an overview of the leading paradigms, categorized either by research domains (architecture or machine learning) or by user input conditions or constraints. We then introduce the commonly-adopted benchmark datasets that are used to verify the effectiveness of the methods, as well as the corresponding evaluation metrics. Finally, we identify the well-solved problems and limitations of existing approaches, then propose new perspectives as promising directions for future research in this important research area. A project associated with this survey to maintain the resources is available at awesome-building-layout-generation.

Lukas Krupp, Ian O'Connor, Luca Benini et al.

The semiconductor industry is pivotal to Europe's economy, especially within the industrial and automotive sectors. However, Europe faces a significant shortfall in chip design capabilities, marked by a severe skilled labor shortage and lagging contributions in the design value chain segment. This paper explores the role of European universities and academic initiatives in enhancing chip design education and research to address these deficits. We provide a comprehensive overview of current European chip design initiatives, analyze major challenges in recruitment, productivity, technology access, and design enablement, and identify strategic opportunities to strengthen chip design capabilities within academic institutions. Our analysis leads to a series of recommendations that highlight the need for coordinated efforts and strategic investments to overcome these challenges.

Syed Monirul Islam, Fahmida Chowdhury, Faysal Bhuiyan et al.

Even though the manufacturing industry consumes roughly 54% of total available energy globally, little consideration has been devoted to optimizing energy in the early stages of industry design, particularly in densely populated cities. With the increased demand for green buildings, energy performance has a greater influence on design results. As a result, this paper provides an envelope optimization technique that can assist architects and computational designers in analyzing the environmental performance of various alternatives and developing optimal design solutions, especially for Bangladesh and the building of these regions. First, an existing industrial site in Dhaka, Bangladesh's capital was chosen as a case study, and a hypothetical industry building, as well as its surroundings, were parametrically developed. Then, for the optimization method, the design factors linked to the building envelopes were chosen. Finally, a Multi-objective Optimization (MOO) procedure was utilized for defining performance measures including daylighting, energy and comfort measures, UDI, EUI, and PPD. According to the MOO results, the UDI may be enhanced by 25.286% as compared to the least favorable scenario. Consequently, the EUI may decreased by 38.718 kWh/m2 while the PPD can be increased by 41.78%. The geometric configuration of East, West, North, and South played a significant role when designing the industrial building. According to the analysis, the geometric configuration of a South WWR of 50%, a West WWR of 30%, an East WWR of 70%, and a sill height of 0.75m is the most feasible option. A statistical analysis of design factors and performance measures demonstrated that the window-to-wall ratio, particularly on south walls, has the greatest impact on industrial building design in densely populated areas. The proposed approach is expected to be used by architects and municipal planners to develop design metrics based on simulation results.

Brilianty Wijaya, Nurul Jamala, Baharuddin Hamzah

Passive lighting design plays an important role in providing natural lighting to save electricity consumption in buildings. This study aims to investigate the performance of natural lighting and the potential of alternative designs through sidelights with 3 shading device models and light shelves with different sizes in north, west, east, and south orientations. This research method is quantitative, which describes the measurement results in existing conditions and radiance illuminance software simulations. The results showed that at the beginning of field measurements, it was known that there were rooms with lighting intensity too high, too low, and uneven distribution. The simulation results use Radiance Illuminance software to determine the illuminance value of light distribution into space in the morning, afternoon, and evening. Improvised designs are applied with shading device models on the building envelope, namely horizontal, vertical, and egg-crate models in spaces oriented toward the East and West. This study also applied an interior-exterior light self-model measuring 50 cm, 100 cm, and 150 cm of space with the orientation of window openings towards the North and South. The simulation results in this study, show that without a shading device and using a horizontal model, there will be a decrease of 0.4% at a distance of 2 m from window openings oriented to the East, while to the West will be able to increase the illuminance value by 11.5% at a distance of 10.5 m from the window opening. Buildings without using light self and using interior-exterior light shelves measuring 50 cm can increase the illuminance value by 0.5% at a distance of 10.5 m from window openings in a space oriented towards the North, while the orientation towards the south, which can be increased the illuminance value by 0.4% at a distance of 6 m from the window opening. Based on this research analysis, it concluded that the best-improvised design in rooms oriented toward North and South is a 50 cm interior-exterior light shelf and horizontal shading device models are the best for spaces oriented to the East and West. This study, also concluded that the orientation of the building, the passive device model, and the distance of the measuring point in the building envelope, affect the intensity and distribution of daylight entering the building.

Lahari Vishwanath, D Kannamma

Buildings are one of the leading sources of carbon emissions in the world. Most of the carbon emissions are released during the operation phase of the building. It is essential for buildings to provide thermal and visual comfort for the users. In the case of existing buildings, it is necessary to offer retrofit solutions so that the operational carbon emissions can be reduced without compromising on the other essential factors. In this study a Multi-Objective Optimization (MOO) of passive design strategies was conducted for a commercial laboratory in India situated in a moderate climate zone. The design variables considered for the study are wall and roof insulation, glazing material, window-wall ratio (WWR), depth of shading device and the number of shading devices used. The objective functions are: 1. reduced energy use intensity and operational carbon emissions, 2. increased thermal comfort hours and 3. increased daylight autonomy. Rhinoceros and grasshopper software along with Ladybug and Honeybee plug-ins were used for the study which resulted in 1296 iterations. MOO technique namely Pareto front optimization was used to optimize the objective functions. Out of 1296 solutions (excluding base case), 72 solutions were non-dominated. Two methods are described in the study to identify the recommendations for retrofit. The first method describes a Heuristic method of selection using Design Explorer recommending 5 good solutions. In the second method a factor is evolved to identify the 5 best solutions in sequential order. The overall study recommends the use of EPS insulation for the RCC roof, WWR of 20% on all sides, 3 horizontal shading devices of depth 0.75 m for all window openings. When compared with the base case scenario, this solution minimizes the EUI by 3.7%, maximizes average TCH by 106.6% and maximizes average DA by 66.9%. The overall operational carbon emissions are reduced by 7095.6 kgCO2.

Ayşe Mehlika Top, Asena Soyluk, İdil Ayçam

Climate change is an environmental issue that is rapidly escalating due to the effects of global warming. The increase in carbon emissions, along with various human activities such as industrial processes, land use changes, and the reckless consumption of natural resources, are among the primary causes of global warming. Various architectural interventions are being implemented in historical buildings to mitigate the impact of global warming and its consequence, climate change. The study aims to protect the historical building from the harmful effects of climate change by reducing the heating effects of direct sunlight during the summer and enhancing the efficiency of natural lighting throughout the day. In this context, the Erzincan Train Station is located in Erzincan in Eastern Anatolia. Erzincan is one of the most affected by the climate change crisis has been selected for the study. The plans of the Erzincan Train Station were digitized using AutoCAD software and subsequently modeled in three dimensions using Revit software. Daylight analysis was conducted on the created model using the Insight plugin in Revit software. The analysis determined that recommendations should be made for the building's south facade. Based on the climate data of Erzincan province and the conducted analyses, the light shelf system was designed and implemented due to its applicability, shading, and lighting advantages among daylighting systems. The effects of the proposed system were examined using spatial daylight autonomy (sDA) and annual sunlight exposure (ASE) daylight metrics according to LEED v4 EQc7 standards. The study found that the light shelf system resulted in a 5% reduction in ASE values building-wide and a 6% reduction in the average values of specific areas while increasing sDA values. In this context, it has been observed that areas experiencing a decrease in direct sunlight exposure (ASE values) also show an increase in sufficient daylight exposure (sDA values), which could contribute to improving the building's energy performance.

Vishal Batchu, Alex Wilson, Betty Peng et al.

The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by estimating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption.

Luca Collini, Siddharth Garg, Ramesh Karri

High Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for refactoring C code into HLS-compatible formats. We present several case studies by using an LLM to rewrite C code for NIST 800-22 randomness tests, a QuickSort algorithm and AES-128 into HLS-synthesizable c. The LLM iteratively transforms the C code guided by user prompts, implementing functions like streaming data and hardware-specific signals. This evaluation demonstrates the LLM's potential to assist hardware design refactoring regular C code into HLS synthesizable C code.

Shiva Farivar, Shabnam Teimourtash

Window design affects the building's appearance. Besides, it has a significant impact on daylight performance and the visual comfort of interior spaces. Therefore, choosing the shape and position of windows can be a challenge for architects. This research aims to investigate the impact of window design on dynamic daylight performance to enhance visual comfort. The research examines five common window shapes that are located in two different positions on the southern-facing side. The most common dynamic daylight metrics of LEED v4.1 were used to investigate the spatial daylight autonomy (sDA), and annual sunlight exposure (ASE). Furthermore, useful daylight illuminance (UDI) was considered a complementary approach to assess useful daylight levels. The metrics are examined in three cities including Mashhad, Isfahan, and Bandar Abbas, which are located in the northeast, center, and south of Iran, respectively. Thirty simulations in each city are conducted by Grasshopper Graphical editor as a parametric interface and its plugins, ladybug, and honeybee for dynamic daylight analysis. The results emphasize that window design has a significant impact on dynamic daylight performance. The square window meets the LEED needs in three cities by achieving maximum sDA and minimum ASE by up to 68.8% and 20% in both positions, respectively. Moreover, the centrally positioned square window presents the lowest ASE level of 14.4% among other cases. However, the windows in a higher position, especially horizontal windows obtain the highest values of sDA, UDI, and ASE by up to 77%, 59%, and 30%, respectively. Therefore, the ASE rates deteriorate by increasing the sill height and head height of windows. This paper can provide window design recommendations based on the comparison of dynamic daylight metrics for five common window shapes.

Maurice Funk, Simon Hosemann, Jean Christoph Jung et al.

We present a method for automatically constructing a concept hierarchy for a given domain by querying a large language model. We apply this method to various domains using OpenAI's GPT 3.5. Our experiments indicate that LLMs can be of considerable help for constructing concept hierarchies.

Isabelle Tingzon, Nuala Margaret Cowan, Pierre Chrzanowski

Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the automated classification of roof characteristics from very high-resolution orthophotos and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the fusion of multimodal earth observation data performs better than using any single data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and 0.92 for roof type and roof material classification, respectively. This work is intended to help governments produce more timely building information to improve resilience and disaster response in the Caribbean.

Ghadie Tlaiji, Fabienne Pennec, Salah Ouldboukhitine et al.

Paolo Zazzini, Alessandro Di Crescenzo, Luigi Pio Rossitti et al.

This paper is focused on the daylighting system named Modified Double Light Pipe (MDLP) designed by the authors as an evolution of the Double Light Pipe to eliminate the drawbacks due to its encumbrance and the high luminance of its upper portion. They cut off its lower part and applied a circular light shelf to the system to prevent people from seeing the upper brightest portion of it. An experimental activity under real sky has been carried out for four months on a reduced scale model of the MDLP. The dynamic metrics DA, cDA, DR, and U0 have been calculated on monthly basis. The results can be considered satisfactory both in terms of illuminance distribution and daylight autonomy, as well as illuminance uniformity, taking into account that the tests have been carried out in winter, under unfavorable climatic conditions. The results show that, even in winter climatic conditions, the MDLP gives a contribution to delivering daylight in the intermediate room both on sunny and cloudy days with good uniformity of illuminance distribution on the horizontal work plane.

Mahshid Amani Zanganeh, Iman Sheikh Ansari

An advanced complex fenestration system can utilize uniform daylight. Nonetheless, an inefficient design would increase solar heat gain and indoor temperatures, besides uneven light distribution that would cause the "cave effect." Prismatic panels are widely used as complex fenestration systems, providing uniform daylight. This paper proposes a computational model that integrates optical principles like Snell's law with environmental variables and visualizes the performance of prismatic panels in terms of redirection angle while encountering the prism refractive index and geometry at the specified geographic location. The proposed model entails a prismatic panel as a daylight system for redirecting daylight. In contrast to detailed modeling needed for simulation in software programs like Radiance, this computational tool provides a more straightforward and efficient solution for the initial design of light redirection panels that rely on the principle of refraction and evaluate their annual performance based on the angle of deviation. The model's applicability has been demonstrated by utilizing various triangular prism design examples with diverse materials in Frankfurt and Helsinki.

Qihao Zhu, Xinyu Zhang, Jianxi Luo

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a novel form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a computational approach to bridge the gap. This paper proposes a generative design approach based on the pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the correlation between the domains within the generated BID concepts. The approach is then tested via a case study in which the fine-tuned models are applied to generate and evaluate light-weighted flying car concepts inspired by nature. The results show our approach can generate BID concepts with good performance.