Floorplan Design Research Articles

Intelligent floor plan design of modular high-rise residential building based on graph-constrained generative adversarial networks

In the context of Modular High-Rise Residential Buildings (MHRBs), designing floor plans involves intricate complexities due to the need for adherence to numerous domain-specific design rules. To address this issue, our research introduces a novel framework based on a Graph-Constrained Generative Adversarial Network (GC-GAN) specialized for generating MHRB floor plans. This enhanced GC-GAN incorporates knowledge graphs that encapsulate domain-specific constraints and guidelines, thereby generating floor plans that exhibit realism, diversity, and conformity to established design principles. Additionally, the framework integrates a sophisticated image-to-vector conversion algorithm that enables seamless alignment with a predefined flat-design standardization library. A salient feature of this framework is the automated generation of Building Information Modeling (BIM) models, which rigorously conform to the modularity specifications essential for efficient modular construction. The efficacy and practical applicability of our approach have been validated through an exhaustive analysis covering fifteen cases across five diverse scenarios.

Artificial intelligence empowering museum space layout design: Insights from China.

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design.

Applying shape grammar and BIM for generating the mass modular housing design in ShanLiChenJia village, ZhaoYuan, China

ABSTRACTThis paper proposes a method for floor plan designs that combines shape grammar with Building Information Modeling (BIM) technology to create plan module libraries by using the mass modular housing project in ShanLiChenJia village as an example. The modular building is composed of several component modules, and shape grammar can manipulate their graphics and functions to enrich the combination relationship between component modules. The rule-making of the combination process can code the size relationship that affects the module form by adding space and size constraints. In the rule system of shape grammar, geometric dimensions, and functional attributes are first embedded in the shape graph. Then, the mapping relationship is established between the shape graph and the attributes of the modules. Finally, the BIM platform can establish the module library using Autodesk Revit. In addition, a spatial relationship in the scheme is selected in Revit software for generative experiments, which confirms the feasibility of shape grammar in generative design and spatial diversity of modular buildings.

Generative design of floor plans of multi-unit residential buildings based on consumer satisfaction and energy performance

Satisfying consumers is an important task in the design of residential floor plans. However, the design of residential floor plans with the aim of improving consumer satisfaction and energy performance has not been fully explored. Also, the preferences of different customer groups regarding customer satisfaction have not been studied. Traditional design and optimization methods still have limitations in terms of performance improvement, while performance-based generative design methods can explore the performance improvement potential of a larger range of solution spaces. Therefore, this study introduces a generative design framework that is grounded on optimizing energy performance and consumer satisfaction for multi-unit residential floor plans, which consists of four main processes: 1) Generation of floor plan schemes. 2) Evaluation of consumer satisfaction and energy performance of the generated schemes. 3) Running a multi-objective optimization to obtain optimal schemes. 4) The final floor plan scheme is determined by the decision maker. The proposed framework was employed in a real design case, and seven optimal solutions were obtained. Compared with the initial scheme, the maximum improvement in consumer satisfaction and energy performance was up to 38.78% and 11.18% respectively. The results also indicated that the preferences of different types of consumers differed significantly. Thus, different evaluation functions have been created to provide solutions with higher satisfaction levels targeting different types of consumers. As such, generative design and optimization of residential floor plans for specific types of consumers can be undertaken at an early design stage to improve consumer satisfaction and reduce energy consumption.

Multi-objective Floorplanning optimization engaging dynamic programming for system on chip

The quest for optimal VLSI Floorplanning (FP) addressing noteworthy challenges namely Area, Wirelength, and Temperature remains an ongoing field of research. Prevailing FP designs individually optimize these constraints that increase the model complexity with reduced efficiency. Given this motivation, this study provides an optimum FP aimed at minimizing the overall chip area by adhering to the standard design requirements. Accordingly, the divide-and-conquer model of Dynamic Programming (DP) adopted in this work simultaneously optimizes the chip area by devising separate analytical cost functions related to the physical design parameters that are then collated to yield the compact Floor plan. The formulated DP optimization coined as Weighted Aggregated DP-Based FP (WADPFP) adaptively tunes the cost function using rank-sum scaled weights and the discount factor thereby reducing congestion and hotspots. This objective is met using two novel recursive functions ensuring temperature and area scalability. Simulation and synthesis of the proposed FP on the Microelectronic Centre of North Carolina (MCNC) and Gigascale Systems Research Center (GSRC) Benchmark circuits demonstrated the viability of the automated FP design by registering 6.05%, 13%, and 4.35% reduction in area, wirelength, and temperature respectively in comparison with the traditional and recent peers. Additional analysis on the AMI49_X benchmark circuits emphasizes the scalable nature of the introduced WADPFP.

The types of spatial adaptability based on user preferences of low-income apartments in erbil city

Spatial adaptability is the ability of floor plans to adapt to families' changing needs and requirements over time. Apartment floor plans that are not designed according to the user's preferences cannot meet his or her changing needs over time. So, an adaptable apartment floor plan is considered a desirable alternative that can provide various solutions to households' changing spatial needs in the present and the future. Future low-income apartment designs will be more effective if users select the most appropriate types of floor plan adaptability for their needs and culture. The aim of this study is to identify user preferences for the types of adaptability of floor plans and assess the adaptability capacity of low-income apartment floor plans based on those preferences. Based on the research criteria, six low-income multi-family residential buildings in Erbil City were chosen as case studies. The questionnaire was conducted through face-to-face interviews with 363 participants. The study used mixed methodologies, including a quantitative questionnaire survey and a qualitative case study analysis method, to answer the research questions. This study concluded that users did not prefer all the types of spatial adaptability; they preferred the ones that most suited their needs, lifestyles, and preferences. Low-income apartment floor plans didn’t support user preferences, and residents of low-income apartments in Erbil City could not adapt their apartment floor plans according to their preferences. According to the findings of this study, there should be a greater emphasis on the future design of low-income apartment floor plans in order to meet the changing space needs and requirements of families. This study can help architects and policymakers improve housing quality in Erbil City by considering user preferences for space adaptation when designing low-income apartment floor plans in the future.

A Survey of Personalized Interior Design

AbstractInterior design is the core step of interior decoration, and it determines the overall layout and style of furniture. Traditional interior design is usually laborious and time‐consuming work carried out by professional designers and cannot always meet clients' personalized requirements. With the development of computer graphics, computer vision and machine learning, computer scientists have carried out much fruitful research work in computer‐aided personalized interior design (PID). In general, personalization research in interior design mainly focuses on furniture selection and floor plan preparation. In terms of the former, personalized furniture selection is achieved by selecting furniture that matches the resident's preference and style, while the latter allows the resident to personalize their floor plan design and planning. Finally, the automatic furniture layout task generates a stylistically matched and functionally complete furniture layout result based on the selected furniture and prepared floor plan. Therefore, the main challenge for PID is meeting residents' personalized requirements in terms of both furniture and floor plans. This paper answers the above question by reviewing recent progress in five separate but correlated areas, including furniture style analysis, furniture compatibility prediction, floor plan design, floor plan analysis and automatic furniture layout. For each topic, we review representative methods and compare and discuss their strengths and shortcomings. In addition, we collect and summarize public datasets related to PID and finally discuss its future research directions.

Architectural space classification considering topological and 3D visual spatial relations using machine learning techniques

ABSTRACT The paper presents a novel method for classifying architectural spaces in terms of topological and visual relationships required by the functions of the spaces (where spaces such as bedrooms and bathrooms have less visual and physical relationships due to the privacy, while common spaces such as living rooms have higher visual relationship and physical accessibility) through machine learning (ML). The proposed model was applied to single and two-storey residential plans from the leading architects of the 20th century Among the five different ML models whose performances were evaluated comparatively, the best results were obtained with Cascade Forward Neural Networks (CFNN), and the average model success was calculated as 93%. The features affecting the classification models were examined based on SHAP values and revealed that width, control, 3D visibility and 3D natural daylight luminance were among the most influential. The results of five different ML models indicated that the use of topological and 3D visual relationship features in the automated classification of architectural space function can report very high levels of classification accuracy. The findings show that the classification model can be an important part of developing more efficient and adaptive floor plan design, building management and effective reuse strategies.

Proposed museum service operations framework for addressing overcrowding: A Taipei case study

AbstractAlthough the high growth of the tourism market contributes to a record number of visitors to landmark museums, they also encounter the overcrowding problem. This study reviewed the case of National Palace Museum (NPM) in Taipei to discuss the overcrowding problem and provide suggestions to alleviate this problem. This study adopted document analysis to summarize NPM's policies regarding the overcrowding problem that were identified by performing text mining of visitors' reviews for the NPM. And a content analysis involving semantic analysis and coding of negative reviews for NPM retrieved from TripAdvisor was performed for extracting complaint categories regarding the overcrowding problem. On the basis of the results, a service operation management framework in response to museum overcrowding is proposed. This study suggests that a museum can manage the overcrowding problem on the basis of visitor characteristics by adjusting visitor demand, optimizing the service design, diverting visitors' flow, elaborating the floor plan and exhibition design, or forming a decentralized museum hub to move must‐see exhibits for tourists to a stand‐alone pavilion or branch to disperse the crowd.

The Floor Plan Design Method of Exhibition Halls in CGAN-Assisted Museum Architecture

The floor plan designs of traditional museum exhibition halls are generally developed according to the position and streamlined accessibility of the exhibits. However, there are often many floors in the same building, and multi-story exhibition halls are similar, so architects often spend a large amount of time and energy designing floors individually. Thus, this paper proposes a conditional generative adversarial network (CGAN)-based method for designing the floor plans of museum exhibition halls, which can help architects to work more efficiently. In this study, the basic concepts and structures of CGAN are first introduced; then, the design and training process of the CGAN model used are described in detail, and the datasets and evaluation metrics adopted are briefly described. In the Results and Discussion sections, this paper presents an example of the generated floor plan design of a museum exhibition hall and evaluates and analyzes the floor plan design of a museum exhibition hall generated using the proposed method. Finally, this paper summarizes the advantages of the proposed method, but also notes its shortcomings. If the number of data sets is not sufficient, the scope of the application will be relatively small. For example, museums converted from certain historical buildings are not applicable. The research results show the following: (1) the method proposed in this paper takes advantage of the CGAN model and can generate a museum exhibition hall floor plan design with certain regularity according to the given conditions, rather than pure random generation. (2) This method can automatically generate a variety of plan designs for museum exhibition halls in different schemes, providing designers with more choices and flexibility. (3) This method can carry out design optimization through human–computer interaction, and iterative improvement can be carried out according to user needs and feedback, which improves the practicability of the design.

ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design.

We present a novel two-stage approach for automated floorplan design in residential buildings with a given exterior wall boundary. Our approach has the unique advantage of being human-centric, that is, the generated floorplans can be geometrically plausible, as well as topologically reasonable to enhance resident interaction with the environment. From the input boundary, we first synthesize a human-activity map that reflects both the spatial configuration and human-environment interaction in an architectural space. We propose to produce the human-activity map either automatically by a pre-trained generative adversarial network (GAN) model, or semi-automatically by synthesizing it with user manipulation of the furniture. Second, we feed the human-activity map into our deep framework ActFloor-GAN to guide a pixel-wise prediction of room types. We adopt a re-formulated cycle-consistency constraint in ActFloor-GAN to maximize the overall prediction performance, so that we can produce high-quality room layouts that are readily convertible to vectorized floorplans. Experimental results show several benefits of our approach. First, a quantitative comparison with prior methods shows superior performance of leveraging the human-activity map in predicting piecewise room types. Second, a subjective evaluation by architects shows that our results have compelling quality as professionally-designed floorplans and much better than those generated by existing methods in terms of the room layout topology. Last, our approach allows manipulating the furniture placement, considers the human activities in the environment, and enables the incorporation of user-design preferences.

Semantic-driven Graph Transformations in Floor Plan Design

This paper deals with supporting the design of building floor plans. Floor layouts are represented by labelled and attributed graphs. These graphs are generated using graph grammars, i.e., systems for transforming graphs according to specified rules. The key feature of a graph grammar is that graph representations are obtained by successively adding nodes and edges by applying the rules during the generation process with the use of the so-called embedding principle. The existing embedding principles are often not sufficient to create solutions which fulfill the design criteria. The main contribution of this paper is a generative graph grammar with an innovative semantic-driven embedding where graph attribute values are taken into account while creating new edges. The proposed approach enables automatic generation of graphs corresponding to new layout designs with geometrical properties specified by graph attributes. It is illustrated by examples of apartment floor layouts generated by a prototype application implemented in Python.

주거공간의 유닛모듈 조합 방법을 이용한 설계개선방안 연구

In the domestic construction industry, the number of manpower available for construction is decreasing. As a result, the productivity of the construction site gradually decreases and the quality of construction deteriorates. In order to solve this situation, a new construction method such as a modular construction method is being introduced in Korea. However, research is lacking in Korea due to the lack of standards and cases for modular housing. Therefore, the purpose of this study was to lay the groundwork for the study of floor plan development and design standardization for the effective application of modular architecture in small houses. In this study, module specifications were proposed by analyzing domestic laws and regulations, and 75 types of unit modules were developed, and the process of verifying the practicality of unit modules was presented by making a flat surface. This study proposed a plan for modular housing with insufficient cases and provided a basic study on the type and standardization of modular housing design. The study was intended to contribute to the future use of modular construction methods in various buildings.

Efforts to Preserve Woven Art by Utilizing Pet Rope Waste at “Cemoro Pitu Nganyam Budoyo” Event in Dayu Village, Karanganyar

This research was conducted in the village of Dayu, Gondangrejo District, Karanganyar Regency by promoting regional culture. The activities carried out were themed with the name Cemoro Pitu Nganyam Budoyo which aims to promote the tourism potential of Dayu village to the public. In addition, this activity is an increase in the strategy or efforts of the Dayu village government to develop cultural potential, which at the same time provides space for the community to improve the economy through marketing local culture such as culinary, crafts, agricultural products, and others. The methods carried out are in the form of committee formation, location determination, floor plan design, and event concept, progress of activity design, marketing and publication, implementation, and sustainable empowerment programs. KKNT students also participated in the event committee along with youths from Dayu village, the head of Grenjeng hamlet. Determining the plan design needs to pay attention to several things related to the concept of the event including the location of the main stage, invited guests, culinary tourism spots, visitor selfie spots, children's play spots (traditional toys), off road spots, free spots. Cultural market activities and pet rope workshops provide sustainable benefits for the Dayu community in addition to Cemoro Pitu which can become a new tourist destination, pet ropes can open up opportunities to increase the community's economy and new jobs. The work of pet ropes made by the Dayu community can support the village program, namely Dayu as a tourist village by making pet rope creations as typical souvenirs.

Differentiable Constraints’ Encoding for Gradient-Based Analog Integrated Circuit Placement Optimization

Analog IC design is characterized by non-systematic re-design iterations, often requiring partial or complete layout re-design. The layout task usually starts with device placement, where the several performance figures and constraints to be met escalate its complexity immensely, and, due to the inherent tradeoffs, an “optimal” floorplan solution does not usually exist. Deep learning models are now establishing for the automation of the placement task of analog integrated circuit layout design, promising to bypass the limitations of existing approaches based on: time-consuming optimization processes with several constraints; or placement retargeting from legacy designs/templates, which rely heavily on legacy layout data. However, as the complexity of analog design cases tackled by these methodologies increases, a broader set of topological constraints must be supported to cover the different layout styles and circuit classes. Here, model-independent differentiable encodings for regularity, boundary, proximity, and symmetry island constraints are formulated for the first time in the literature, and an unsupervised loss function is used for the artificial neural network model to learn how to generate placements that follow them. The use of a deep learning model makes push-button speed placement generation possible, additionally, as only sizing data are required for its training, it discards the need to acquire legacy layouts containing insights into this vast set of, often neglected, constraints. The model is ultimately used to produce floorplans from scratch at push-button speed for real state-of-the-art analog structures, including technology nodes not used for training. A case-study comparison with a floorplan design made by a human-expert presents improvements in the fulfillment of every constraint, reaching an overall improvement of around 70%, demonstrating the approach’s value in placement design.

Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes

The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process.

Is there a need for new kitchen design? Assessing the adaptative capacity of space to enable circularity in multiresidential buildings

This paper aims to contribute to the development of spatial criteria for adaptive capacity, which is identified as one important factor for the transition towards more circular housing design. The paper focuses on the kitchen, as an important function of the home which is connected to large resource flows and is exposed to frequent renovations and replacements. This paper identifies spatial characteristics of the kitchen and evaluates their potential to accommodate circular solutions focusing on adaptive capacity. As a first step, previous literature on the spatial characteristics of kitchens and indicators that support adaptability is reviewed. These are then used to develop an analytical framework to assess the adaptive capacity and circularity potential of 3624 kitchens in contemporary Swedish apartments. A qualitative approach in combination with quantitative methods is employed to analyse the selected sample. The main contributions of this paper include its spatial analytical framework, its descriptive presentation of contemporary kitchen and apartment designs, and its adaptive capacity assessment of the studied kitchens. The results point out that although the over-capacity of the floor area of kitchens and apartments can have significance for adaptability, it is not the only determinative spatial characteristics. The windows' location and distribution, the number of door openings and traffic zones, the shafts’ location and accessibility from multiple rooms, the room typology and the kitchen typology can improve the adaptive capacity and circularity potential of kitchens and dwellings. The findings show that in contemporary floorplans advantageous design solutions connected to the identified spatial characteristics are not applied in a systematic way. Further research is necessary to define the exact measures of the individual spatial characteristics and their combined application in multiresidential floorplan design.

A case study on a flexible floor plan design for South Korean apartments using two types of margins

PurposeThe purposes of this study are to prove that the content of floor flexibility can be designed objectively with the use of margins, which are an architectural vocabulary visualizing the possibility of formal change, and to conduct a case study on how the existing typical floor plans of South Korean apartments change with the extension of flexibility: the usability of margins and the results.Design/methodology/approachThis study reviews two housing projects with different methods of flexible housing design: one is the flexibility to change the sizes of spaces and the other is the flexibility to change the topology of spaces. In this paper, the architectural term “area margin” is used for the former and “linear margin” for the latter. These were applied to the case of current apartments in South Korea to transform them into floor plans with the two types of flexibility and investigated whether the proposed floor plans satisfy the required efficacy structurally and functionally.Findings This case study shows that margins can be used as architectural vocabularies representing flexible sizes of rooms and flexible boundaries with neighbors. The final form of the structural framework became homogeneous, even though it conserved its indigenous spatial characteristics of abundant natural sunlight and airflow. In addition, the transformed structural framework has higher rigidity than the original one, even though the transversal wall was cut off with a margin, as shown by the schematic representations in this paper. Research limitations/implicationsThe alternative plan, designed as a modification process, is not representative as a flexible floor plan. Rather, it is thought more important to make a range of variations rather than prototyping a model. This study starts from the premise that it is desirable for apartment house plans to share and encourage variations rather than aiming at typical sizes and shapes. Furthermore, this study exemplified the process to modify the existing typical floor plan into a flexible one using margins. Through this modification, it is thought that the typology of the South Korean apartment, which has succeeded in gaining social consensus for half a century, can be preserved while accommodating social changes in the future.Social implicationsThe control of future variations of floor plans will extend the socioeconomic and physical life of a building, enabling a reasonable reinvestment of resources.Originality/valueThis paper deals with a design method applying distinct visual symbols to different contexts of flexibility and using those as architectural vocabularies.

Factors Influencing the Sustainability of Wood-Based Constructions’ Use from the Perspective of Users

Traditional construction solutions face increasing competition from more ecological materials such as construction systems based on wood. Thanks to the numerous favourable properties of wood, wood construction enjoys great popularity and allows building economical and modern constructions that are durable and contribute to an ecological future. This study is motivated by the need for innovative solutions in construction and offers numerous findings based on examining actual constructions based on wood. By examining the interactions among selected factors of constructions and their users, the study reacts to the global challenges that call for increased efficiency and sustainability in construction. The examination of the interactions among the selected factors offers more extensive knowledge in the field of constructions based on wood and points towards possible innovative approaches for more sustainable housing and for a more efficient construction industry. The analyses showed that the key aspects that determine the sustainability of housing from the perspective of users are the standard of construction workmanship and construction time, which depend on the choice of construction system, cost-efficiency of use, and material composition and floor plan design. These aspects also interacted with other technical and design aspects, which also played an important role in the perception of housing sustainability.

Evolutionary Methods in House Floor Plan Design

In this paper, an evolutionary technique is proposed as a method for generating new design solutions for the floor layout problem. The genotypes are represented by the vectors of numerical values of points representing endpoints of room walls. Equivalents of genetic operators for such a representation are proposed. A case study of the design problem of one-story houses is presented from the initial requirements to the best solutions. An evaluation method using requirement-weighted fitness function for evolved plans is also proposed. The obtained results as well as the advantages and issues related to such an approach are also discussed.