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Anthropogenic hazards are unrelenting threat to lives and property, with human irresponsibility emerging as a leading source of urban as well as industrial fires. The complexity of urban structures and crowded layouts make these kinds of fires more lethal. This paper presents an Artificial Intelligence (AI) based framework designed for smart buildings as a solution to the devastating obstacles caused by fire crises. Our system creates a 3D model of the building using floor plans and the A* algorithm for escape route identification. The proposed framework includes a YOLO-based smart monitoring system for the identification and counting of people caught in a fire, with the ability to distinguish between conscious and unconscious persons. The proposed system informs inhabitants in the case of a fire and directs them to the closest exit for a safe evacuation. Moreover, fire and rescue officials receive real-time information on affected persons, such as the number and location of adults and children who are conscious and unconscious. Perhaps most significantly, the suggested framework performs exceptionally well, scoring 96% for precision and 98% for recall in the detection of fire and humans. These findings highlight the effectiveness of the model in locating people within infrastructures affected by fire. The framework considerably outperforms the most advanced algorithms in terms of speed and efficiency for shortest path detection, greatly improving the ability of fire rescue teams to quickly find and aid residents who are trapped in a fire. [ABSTRACT FROM AUTHOR]

The prevalence of contemporary architectural designs that feature inclined-form buildings continues to rise, propelled by advances in analytical and drawing techniques, material technology, and construction methods. When viewed from the outside, buildings that make a certain angle to the ground and whose floor plans are horizontally offset could be defined as inclined-form buildings. Although these buildings present unique design opportunities, they present considerable challenges. This study presents an overview of the geometric features and parameters that define inclined buildings, which explores their architectural merits and drawbacks. A thorough analysis of the impact of inclined forms on the structural system is conducted, which encompasses positive and negative effects. Various types of structural systems are exemplified by case studies that showcase buildings with inclined forms. This study delves into the challenges that are encountered during the design and construction of these buildings and scrutinizes the solutions that are devised and implemented by designers. The applicability and sustainability of inclined buildings are considered in compliance with contemporary regulations, which offer a comprehensive evaluation. Therefore, this study aims to provide valuable insights for architects, engineers, and contractors who are involved in the design and execution of inclined buildings, which examines the diverse aspects of their structural behavior and construction processes. [ABSTRACT FROM AUTHOR]

Recent advancements in AI research, particularly in spatial layout generation, highlight its capacity to enhance human creativity by swiftly providing architects with numerous alternatives during the pre-design phase. The complexity of architectural design data, characterized by multifaceted elements and varying representations, presents significant challenges in creating uniform and robust datasets. This study addresses this challenge by developing a robust training dataset specifically tailored for AI-driven spatial layout generation in architecture. An algorithm capable of extracting spatial relationship diagrams from raster-based floor plan images and converting them into vector-based data was introduced. Through extensive web crawling, a dataset comprising 10,000 data rows, categorized into 21 classes and three spatial relationship categories, was collected. When tested with the You-Only-Look-Once (YOLO) model, the detection rate was 99%, the mean average precision was 85%, and the MIoU was 74.2%. The development of this robust training dataset holds significant potential to advance knowledge-based artificial intelligence design automation studies, paving the way for further innovation in architectural design. [ABSTRACT FROM AUTHOR]

Off-site construction has been a crucial part of industrializing the industry to realize higher productivity, better quality, and a more sustainable approach for constructing buildings. Off-site construction requires decomposing a floor plan into modules that can be in the form of either panelized walls or volumetric modules. However, the previous modularization models and approaches are limited due to their inability to consider the topological constraints of the modules, the flexible modularization of varying floor plans, and the mixed use of panelized walls and volumetric modules. As such, this paper proposes a graph-based optimization methodology for the hybrid modularization of building floor plans. The methodology was implemented using a multiobjective genetic algorithm that encodes and decodes the floor plan using novel graph modeling and operations. A visual programming script was developed to extract the wall properties, their adjacencies, and junction information from the building information model (BIM) of the floor plan. Time and cost estimation functions were developed to evaluate the hybrid strategies of panelized-volumetric modularization. The deployment of the methodology was demonstrated using an example floor plan design, which resulted in a spectrum of hybrid modularization plans ranging between fully volumetric and fully panelized solutions. For this specific example, the fully volumetric solution was 23% faster than the fully panelized solution but was 22% more expensive. The main contributions of this study are the topological modeling of module types, their floor plan postdesign flexible utilization, and the ability to explore hybrid modularization strategies. The findings of this study can prove useful for modular and off-site building manufacturers to improve their agility and increase their market share. [ABSTRACT FROM AUTHOR]

Co‐working spaces have emerged as cost‐effective office space management solutions for workers from diverse industries who share space, which generates cost savings and facilitates networking opportunities. Past studies have mainly focused on needs for and benefits of co‐working spaces and range in emphasis from design preferences to potential social and economic impacts. However, limited attention has been given to the temporal clustering and geographical distribution patterns of co‐working spaces. This study examines the spatial distribution patterns of co‐working spaces establishments in terms of building floor plans. We discern the subcategory of 'co‐working office space' from the floor space and employment survey data, spanning 2007, 2012, and 2017 as collected by the City of Sydney, a municipal government in Australia. Both co‐working offices offered by conventional enterprises and co‐working providers are identified, and the spatial associations between co‐working spaces and their relationships with industry composition are analysed. Using establishment analysis, three co‐working spaces are identified: (1) dedicated co‐working enterprises providing office service and well‐design spaces; (2) conventional office rental enterprises diversifying into co‐working style office; and (3) standard enterprises converting fixed office spaces to co‐working office configurations. The spatial analysis reveals an outward diffusion of co‐working spaces from the centre of Sydney CBD, which is of importance in geographical research focused on urban, social, and economic change. [ABSTRACT FROM AUTHOR]

Navigating narrow restrooms is challenging for the elderly using wheelchairs or walkers (EUWW). Existing research focuses on the convenience of furniture use in the restroom and overlooks the convenience of mobility between furniture pieces. However, the floor plans of accessible restrooms determine the mobility path of EUWW, affecting mobility convenience. Thus, to improve mobility, floor plans should be evaluated to shorten movement distances and reduce bends. Experiments in constructed environments were expensive. This study instead proposes a novel path-planning based method for accessible restroom floor plan evaluation, simulating mobility paths in a virtual environment, and evaluating how the floor plans of accessible restrooms affect mobility and convenience. The results showed that: (1) wheelchair users, who approach the toilet from the rear, and walker users find opposite wall toilet-sink arrangements convenient. While Wheelchair users, who approach the toilet from the side or front, find it convenient if the toilet and sink are situated on different walls at a 90° angle. Toilets and sinks on the same wall are inconvenient. (2) Corner sinks and toilets reduce EUWW's turning angles. (3) The farther the sink is from the side wall, the more convenient it is for EUWW. (4) The 45° side approach is the most convenient for moving, followed by the frontal approach, and lastly the 90° approach. Theoretically, this study uses path planning algorithms, simulating the movement of EUWW with varying capabilities and offering a new perspective for barrier-free architectural design. Practically, this study provides recommendations for optimizing accessible restroom floor plans. [ABSTRACT FROM AUTHOR]

Over the past decade, biophilic architecture has been widely developed across the Asia–Pacific region. However, there is a notable lack of research based on online public reviews focusing on mid- to high-rise biophilic buildings, especially quantitative studies combining traditional architectural design features. This study aims to fill this gap by analyzing the typical floor plans and online public reviews of nine renowned biophilic buildings in the Asia–Pacific region. Using space syntax and natural language processing tools, the design features of typical floor plans and public feedback will be analyzed separately, and their correlation will be evaluated. Additionally, the content of negative and low-score reviews will be categorized to identify issues in current biophilic building designs. The findings suggest that biophilic design can stimulate widespread public discussion, with large direct blue–green elements receiving overwhelming attention. However, biophilic elements can also lead to negative sentiments due to factors like humidity, high temperatures, mold, and insects. This study provides insights and design recommendations for future biophilic buildings, demonstrating the value of biophilic design in public reviews and emphasizing the need to balance these factors to enhance public satisfaction and acceptance. [ABSTRACT FROM AUTHOR]

Floor planning, as one of the most important considerations in building design, often involves intensive trial-and-error processes with many constraints considered simultaneously. Artificial intelligence (AI) generative design solutions being developed are hampered by two shortcomings. Firstly, the vast topological knowledge embedded in existing floor plans has been largely unexplored and is thus wasted. Secondly, an efficient methodological instrument to learn the topological knowledge for generative design is lacking. This paper aims to develop a graph learning methodology to learn graph knowledge from floor plans and generate knowledge ready for building generative design. A discrete denoising diffusion model (D3M) that can learn topology graphs via its bi-directional structure of 'corruption and denoise' is developed and trained using more than 80,000 floor plans from a large-scale dataset. It is found that the D3M can learn the knowledge from floor plans and present it as various building floor topologies, which are evaluated in a preliminary case study as reliable and useful for generating real-life building floor plans. The research provides a design knowledge management framework that can be further implemented in academic works and design practices through some mainstreaming or commercializing efforts. [ABSTRACT FROM AUTHOR]

Around 1526, Emperor Charles V decided to build a new Renaissance palace in addition to the Nasrid palaces in the Alhambra of Granada, a monumental site currently included in the United Nations Educational, Scientific and Cultural Organisation (UNESCO) World Heritage List. In that period, a large floor plan, which is preserved today at the Library of the Royal Palace of Madrid, was drawn to represent the building and its surroundings. Although this anonymous drawing has attracted considerable historiographic interest, a study of the graphical aspects analysed here, namely, paper assemblage, drawing technique, representation system, metrology, graphical scale, dimensioning, and labelling, is lacking. To accomplish this analysis, the original document was carefully examined and digitalised with high definition. This process allowed a comprehensive graphic analysis, utilising other drawings from the same period as a comparative reference and studying for the first time the major characteristics of one of the most relevant architectural drawings of the 16th century. [ABSTRACT FROM AUTHOR]

The present study proposes a fiducial marker for location systems that uses computer vision. The marker employs a set of tape-shaped markers that facilitate their positioning in the environment, allowing continuous reading to cover the entire perimeter of the environment and making it possible to minimize interruptions in the location service. Because the marker is present throughout the perimeter of the environment, it presents hierarchical coding patterns that allow it to be robust against multiple detection scales. We implemented an application to help the user generate the markers with a floor plan image. We conducted two types of tests, one in a 3D simulation environment and one in a real-life environment with a smartphone. The tests made it possible to measure the performance of the tape-shaped marker with readings at multiple distances compared to ArUco, QRCode, and STag with detections at distances of 10 to 0.5 m. The localization tests in the 3D environment analyzed the time of marker detection during the journey from one room to another in positioning conditions (A) with the markers positioned at the baseboard of the wall, (B) with the markers positioned at camera height, and (C) with the marker positioned on the floor. The localization tests in real conditions allowed us to measure the time of detections in favorable conditions of detections, demonstrating that the tape-shaped-marker-detection algorithm is not yet robust against blurring but is robust against lighting variations, difficult angle displays, and partial occlusions. In both test environments, the marker allowed for detection at multiple scales, confirming its functionality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for service robots to perform delivery tasks in indoor environments, including map-building and the automatic renewal of destinations for navigation. The first step involves converting the available floor plan (i.e., CAD drawing) of a new space into a grid map that the robot can navigate. The system then segments the space in the map and generates movable initial nodes through a generalized Voronoi graph (GVG) thinning process. As the second step, we perform room segmentation from the grid map of the indoor environment and classify each space. Next, when the delivery object is recognized while searching the set space using the laser and RGB-D sensor, the system automatically updates itself to a position that makes it easier to grab objects, taking into consideration geometric relationships with surrounding obstacles. Also, the system supports the robot to autonomously explore the space where the user's errand can be performed by hierarchically linking recognized objects and spatial information. Experiments related to map generation, estimating space from the recognized objects, and destination node updates were conducted from CAD drawings of buildings with actual multiple floors and rooms, and the performance of each stage of the process was evaluated. From the quantitative evaluation of each stage, the proposed system confirmed the potential of partial automation in performing location-based robot services. [ABSTRACT FROM AUTHOR]

Digital audio–tactile maps (DATMs) on touchscreen devices provide valuable opportunities for people who are visually impaired (PVIs) to explore the spatial environment for engaging in travel activities. Existing solutions for DATMs usually require extensive training for the PVIs to understand the feedback mechanism. Due to the shortage of human resources for training specialists, as well as PVIs' desire for frequent practice to maintain their usage skills, it has become challenging to widely adopt DATMs in real life. This paper discusses the use of large language models (LLMs) to provide a verbal evaluation of the PVIs' perception, which is crucial for the independent practice of DATM usage. A smartphone-based prototype providing DATMs of simple floor plans was developed for a preliminary investigation. The evaluation results have proven that the interaction with the LLM could help the participants better understand the DATMs' content and could vividly replicate them by drawings. [ABSTRACT FROM AUTHOR]

Torsionally flexible buildings (that have torsional mode as fundamental mode) twist during earthquake shaking, and may collapse partially or completely depending upon the direction and level of shaking. The problem is aggravated when the building is torsionally unsymmetrical. Some design codes (like Eurocode, Indian Code) explicitly prohibit the design of such buildings. This paper presents a simple approximate method to identify torsionally flexible RC Moment Frame and RC Structural Wall buildings at the initial proportioning stage itself without carrying out a detailed structural analysis. It is possible to identify whether or not the first mode is torsional mode of a building (i.e., torsionally flexible building) if Natural Period Ratio τ>1 by modelling the building with rigid diaphragm and distribution of mass & stiffness along the height of building, and estimating: (1) radius of gyration of rotational mass rm of each floor plan geometry by lumping the masses of slabs, beams and all vertical elements at each nodes, (2) radius of gyration of twisting stiffness rKθ of all vertical elements using their translational and torsional stiffnesses (considering flexibility of adjoining beams and vertical elements accounting for both flexural and shear deformations), and (iv) τ (=rm/rKθ). The method is validated with 3D Modal Analysis (using τ =Tθ/T, where Tθ is Uncoupled Torsional Natural Period and T Uncoupled Translational Natural Period) of hypothetical buildings using a commercial structural analysis software. Also, parameters are identified that lead to τ>1, and solutions suggested to avoid torsional flexibility in buildings. Further, the method helps identify vertical stiffness irregularity in buildings. Draft provisions are suggested for inclusion in seismic codes. Also, poor performance of multi-storey building (with τ>1) is demonstrated using nonlinear static and nonlinear time history analyses. [ABSTRACT FROM AUTHOR]

Neural radiance fields have emerged as a dominant paradigm for creating complex 3D environments incorporating synthetic novel views. However, 3D object removal applications utilizing neural radiance fields have lagged behind in effectiveness, particularly when open set queries are necessary for determining the relevant objects. One such application area is in architectural heritage preservation, where the automatic removal of non-architectural objects from 3D environments is necessary for many downstream tasks. Furthermore, when modeling occupied buildings, it is crucial for modeling techniques to be privacy preserving by default; this also motivates the removal of non-architectural elements. In this paper, we propose a pipeline for the automatic creation of cleaned, architectural structure only point clouds utilizing a language embedded radiance field (LERF) with a specific application toward generating suitable point clouds for the structural integrity assessment of occupied buildings. We then validated the efficacy of our approach on the rooms of the historic Sion hospital, a national historic monument in Valais, Switzerland. By using our automatic removal pipeline on the point clouds of rooms filled with furniture, we decreased the average earth mover's distance (EMD) to the ground truth point clouds of the physically emptied rooms by 31 percent. The success of our research points the way toward new paradigms in architectural modeling and cultural preservation. [ABSTRACT FROM AUTHOR]

This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase. [ABSTRACT FROM AUTHOR]

The here presented Virtual Campus project developed a 3D geodata modelling toolchain, and an open source software based technology stack for creating a Digital geoTwin of the University of Cologne (UoC). We also develop and present a reactjs based web application, a flutter and Unity based mobile app with AR features, as well as an UnrealEngine based VR application to access, browse and visualize the geoTwin data and models in the UoC CAVE 3D environment. The 3D models of the Universities Buildings will include indoor models based on 2D floorplans, which will facilitate indoor positioning in the mobile application of the project through geometry matching and simultaneous localization and mapping (SLAM) technology. For distribution and streaming the 3D building models, we utilze the OGC Standard 3D Tiles, for which we present a tool to georeference the glTF 3D models and assets accurately on the globe. [ABSTRACT FROM AUTHOR]

Physical design is major key processes in integrating the circuit into the chip area in VLSI circuit design. A netlist is accepted as input by a floorplanning stage, provided by the physical design phase of splitting the circuits. The floor planner creates the best positions for the circuit module. The dimensions, size, and connectivity details of the modules are contained in the netlist. It is crucial to create an optimum floorplan by creating metaheuristic optimization algorithms to increase the circuit performance by reducing chip size, wire length, and peak temperature. This scheme always has a complexity to allocate the weights and might consequence in adverse bias with a specific intention. Here, the non-slicing floor plan is studied through the sequence pair (SP) method. SP is a method for packing the blocks using a pair of modules termed sequences. Consequently, optimal parameter (area and wire length) tuning is performed by a new hybrid Integrated Bald Eagle and SMO (IBE-SMO) scheme that combined the approaches such as BES and SMO for optimal FP by means of tuning the wire length and area, taking into consideration as multi-objective. This validates the optimization of wire length and area utilizing IBE-SMO to reduce the length, width and height of the floor plan area. The standard MCNC benchmark circuits were used to test the suggested technique, and the findings show that it decreases the area by 0.7 × 104, and wire length by 0.84 × 105, compared to the existing methods. [ABSTRACT FROM AUTHOR]

Designing modular housing is a complex task that necessitates a thorough understanding of the diverse needs of clients in terms of both design aesthetics and floor plan layout. Furthermore, adhering to design for manufacture and assembly (DfMA) principles adds to the complexity, as these are essential modular home requirements. Traditional construction methods frequently fail to meet the specific needs of both clients and DfMA, potentially resulting in suboptimal design solutions. Incorporating client requirements during the design phase necessitates the use of an effective system and framework to reduce changes in subsequent project stages. Existing literature lacks a suitable approach, particularly in the context of modular housing. To address this gap, this paper introduces an artificial intelligence–building information modeling recommender system (RS) for detached modular housing design. The system processes client requirements entered as text utilizing the Word2vec algorithm with the GloVe dataset, refined through transfer learning using surveyed client data of housing needs. The system recommends three distinct modular building design alternatives sourced from a building information modeling models database using cosine and Euclidean similarity functions. A sensitivity analysis ensures that client needs are considered fairly, increasing the robustness of the RS. By incorporating natural language processing, this system transforms the construction industry by making initial designs more client-centric compared with traditional methods. Furthermore, it promotes improved collaboration among clients, design, and construction teams, reducing modifications to design in later stages of construction. [ABSTRACT FROM AUTHOR]

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods. [ABSTRACT FROM AUTHOR]

In the area of very large-scale integrated circuit design, optimizing floorplans for area, wirelength, and temperature poses a daunting challenge. This study introduces an innovative floorplanning approach that amalgamates the rectangular packing method with linear programming techniques to ensure optimal module placement in the core area. Leveraging linear programming, this model optimizes three vital layout floorplanning objectives namely area, wirelength, and temperature through an analytical cost function termed linear programming-based multi-objective floorplanning. To enhance the optimization process, adaptive weights are employed for each constraint, effectively preventing congestion and hotspots. To validate the efficacy of the proposed automated floorplanning approach, extensive simulations and synthesis were conducted on benchmark circuits sourced from the Microelectronic Centre of North Carolina and Gigascale Systems Research Center. Comparative analysis with existing solutions demonstrates a significant improvement of 13%, 12% in area, and wirelength, respectively. These outcomes underscore the superior performance and practicality of the proposed floorplanning methodology, showcasing its potential for widespread application in chip design. [ABSTRACT FROM AUTHOR]

Recognizing the detailed information embedded in rasterized floorplans is at the research forefront in the community of computer graphics and vision. With the advent of deep neural networks, automatic floorplan recognition has made tremendous breakthroughs. However, co‐recognizing both the structures and semantics of floorplans through one neural network remains a significant challenge. In this paper, we introduce a novel framework Raster‐to‐Graph, which automatically achieves structural and semantic recognition of floorplans. We represent vectorized floorplans as structural graphs embedded with floorplan semantics, thus transforming the floorplan recognition task into a structural graph prediction problem. We design an autoregressive prediction framework using the neural network architecture of the visual attention Transformer, iteratively predicting the wall junctions and wall segments of floorplans in the order of graph traversal. Additionally, we propose a large‐scale floorplan dataset containing over 10,000 real‐world residential floorplans. Our autoregressive framework can automatically recognize the structures and semantics of floorplans. Extensive experiments demonstrate the effectiveness of our framework, showing significant improvements on all metrics. Qualitative and quantitative evaluations indicate that our framework outperforms existing state‐of‐the‐art methods. Code and dataset for this paper are available at: https://github.com/HSZVIS/Raster-to-Graph. [ABSTRACT FROM AUTHOR]

• Mortar and bricks from four constructive phases of Santalla de Boveda monument were dated. • Radiocarbon and OSL dating was performed on lime mortars to cross-check. • Two Bayesian chronological models were built with dating results. • The building was constructed in the 4th century and transformed in the 7th century. Santalla de Bóveda Monument (Lugo, NW Spain) is a small semi-buried building, under the Bóveda parish church, built in the 18th century, with an apsidal quadrangular floor plan, which was divided into three naves, a small vaulted apse and a rectangular floor plan vaulted, where paintings depicting birds and plant elements have been well preserved. The monument has aroused great interest since its discovery, but its chronology and functionality has always been the subject of controversy. Several hypotheses have suggested that the monument was built in the Roman period and some others around the 5h, 8th or 9th century AD. However, any direct evidence or absolute age has been provided until today. The stratigraphic analysis of the building suggests five constructive phases. Bricks and mortars of all phases were sampling to perform absolute dating techniques and to get a precise and accurate chronology. Radiocarbon dating of mortars and Optically Stimulated Luminescence (OSL) dating of bricks and mortars have been used to this purpose. A total of 34 samples were taken, being 20 used for luminescence (mortars and quartz aggregates) and 15 used for 14C ages (charcoal fragments of lime mortars). 5 brick samples were also dated by Thermoluminescence (TL). The obtained ages have allowed, for the first time, stablishing an accurate and precise chronology being the first phase assigned to the 4th Century AD. Alterations of the building and the vault paintings were performed in the early 7th century, while the construction of the second floor and occasional repairs were performed during the 10th-12th centuries. [ABSTRACT FROM AUTHOR]

This study explores the spatiotemporal evolution of urban landscapes in 19 Chinese historic water towns in the northern Zhejiang plain. Utilising historical maps and remote sensing images, we derived 2-D morphological patterns from town ground plans in 1918, 1969, 2000, and 2021 to represent urban landscape fractions (buildings, lands, and waters). Morphology-based landscape metrics reveal three distinct periods of urban landscape dynamics over the past century: stabilisation (1918–1969), accelerated growth (1969–2000), and high-speed growth (2000–2021). Our findings present a diminishing role of rivers in shaping land fragments and urban riverscapes, behind which is the weakening conventional water-human relationship during water towns' modern urbanisation. The results offer insights into shifting water town landscape patterns and regional landscape heterogeneity, prompting further considerations of hydrology-oriented urban design and planning to conserve historic urban landscapes. [ABSTRACT FROM AUTHOR]

In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural plan is designed in different dimensions. While designing all these modes takes considerable time, there are numerous potential applications for artificial intelligence (AI) in this domain. This study aims to compute and use an adjacency matrix to generate architectural residential plans. Additionally, it develops a plan generation algorithm in Rhinoceros software, utilizing the Grasshopper plugin to create a dataset of architectural plans. In the following step, the data was entered into a neural network to identify the architectural plan's type, furniture, icons, and use of spaces, which was achieved using YOLOv4, EfficientDet, YOLOv5, DetectoRS, and RetinaNet. The algorithm's execution, testing, and training were conducted using Darknet and PyTorch. The research dataset comprises 12,000 plans, with 70% employed in the training phase and 30% in the testing phase. The network was appropriately trained practically and precisely in relation to an average precision (AP) resulting of 91.50%. After detecting the types of space use, the main research algorithm has been designed and coded, which includes determining the adjacency matrix of architectural plan spaces in seven stages. All research processes were conducted in Python, including dataset preparation, network object detection, and adjacency matrix algorithm design. Finally, the adjacency matrix is given to the input of the proposed plan generator network, which consequently, based on the resulting adjacency, obtains different placement modes for spaces and furniture. [ABSTRACT FROM AUTHOR]

The demand for compact housing is on the rise, driven by the need for floor plans that accommodate stakeholders' preferences. However, clients frequently struggle to convey their spatial needs to professionals, such as architects, due to a lack of means to present evidence, such as spatial configurations or cost projections. This study seeks to develop a methodology that translates sketched, data-driven spatial requirements into 3D building components within BIM (Building Information Modeling) to enhance spatial comprehension and offer building performance analysis, assisting in budget considerations during the initial design stages. The research methodology encompasses the formulation of a process model, its implementation, and subsequent validation. The process model outlines the data flow within the system and delineates necessary functionalities. Implementation includes the creation of systems and user interfaces for the integration of various components. Validation confirms the system's capability to automatically transform sketched spatial requirements into BIM model elements, such as walls, floors, and roofs, and to autonomously compute material and energy expenses based on the BIM model. This system enables clients to effectively generate 3D building components from sketches, aiding stakeholders in spatial understanding and building performance evaluation through the generated BIM models. [ABSTRACT FROM AUTHOR]

This article discusses various artists and their collections throughout European history from the 15th to the 19th century. It explores the relationship between artists and their collections, highlighting the importance of valets de chambre and ducal collections in the 15th century, as well as the influence of friendship on artists' collections. The article also delves into the collections of specific artists, such as Pablo de Céspedes, Bernardino Poccetti, Francisco de Solís, Vincenzo Carducci, and others. It concludes by examining the role of artists as collectors and scholars, particularly in relation to the Spanish court and the growth of Philip IV's collection. The publication is well-produced and includes contributions in both English and Spanish. [Extracted from the article]