We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting ‘the right to sunlight’ of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.

How can we assess the ergonomic comfort of a sizeable spatial configuration such as the indoor space of a complex building or an urban landscape when we design, plan, and manage the space? Is there a fundamental difference between indoor [architectural] spatial configurations and outdoor [urban] spatial configurations with respect to ergonomics? Can we have a unified approach to the computational study of spatial ergonomics? This paper addresses these fundamental questions while providing a brief taxonomic review of the scholarly literature on these matters from a mathematical point of view, including a brief introduction to the modelling-based approaches to the computational ways of studying the fundamental effects of spatial configuration on human behaviours. Furthermore, the paper proposes a computational approach for ergonomic assessment of spatial configurations that explicitly allows for combined accessibility and visibility analyses in the built environment. The gist of this approach is the conceptualisation of spatial configurations as rasterised (voxelated) 2D manifold walkable terrains whose voxels have 3D vistas, unifying the simulations and analyses of accessibility and visibility. The paper elaborates on how such a representation of space can provide for conducting various sorts of computational queries, analyses, and simulation experiments for research in spatial ergonomics. The paper concludes with a mapping of the computational modelling approaches pertinent to the study and assessment of spatial ergonomics; and marks avenues of future research on various categories of exploratory, generative, and associative models for ex-ante and ex-post assessment of ergonomic matters at spatial scales.

During the rural [re]vitalization process in China, national strategies required rural public spaces with cultural significance to be identified before planning decision-making. However, places identified as culturally significant by planners and visitors can differ from the ones mostly used and valued by locals. Even if there is a growing interest in integrating local perspectives and experiences in planning, studies seldom discuss and compare openly the adequacy of spatial configuration, cognition and behaviour to support it. This study took Anyi Historic Village Cluster as a case study to empirically investigate rural public spaces with three distinct, yet related approaches: (1) Morphological: spatial network centralities based on space syntax; (2) Cognitive: Lynchian village images with semi-structured interviews; (3) Behavioural: spatiotemporal occupation patterns using Wi-Fi positioning tracking. Significant places valued and used by locals and non-locals were detected with the multi-source data. Furthermore, multivariant regression models managed to characterize the relationship among different aspects of investigated rural public spaces, which also helped diagnose places of interest to prioritize in planning, demonstrating the advantage of integrating the sources of information in practice instead of studying them apart. Results can also assist rural planning on how to identify what to preserve, what to enhance, and how to develop such spaces, without overlooking the local needs or losing the rural identity.

The UNESCO 2011 Recommendation on the Historic Urban Landscape promotes to map cultural significance of urban heritage from the perspectives of the general public in pursuit of social inclusion in heritage management. The user-generated information already available on social media platforms in the form of images, comments, and ratings can be considered a rich source for collecting data concerning the tourists’ image of destinations and their collective perception of urban cultural heritage. Considering the large amount of unstructured data, artificial intelligence (AI) can construct structured feature vectors therefrom and significantly aid the analysis and collation processes compared to the traditional manual approach for mapping public perception of cultural heritage. This paper presents an exploratory case study conducted in the area of Testaccio, Rome, showcasing the use of AI to map the perceived and narrated urban heritage images using social media data. An image-sharing platform, Flickr, is used to collect thousands of posts containing images and comments in the area, which are further analysed with pre-trained image recognition, natural language processing, and dimensionality reduction algorithms. Results as the urban heritage images are visualised, showing the most significant elements from a public perspective. Such a methodology provides an alternative perspective of viewing the urban heritage attributes as a collection of depicted and posted content. It can contribute as a tool for the documentation of collective attention for inclusive heritage management and local development planning during the designing and policy-making processes.

This study presents a systematic review of the literature on decision support for designing hospital layouts using spatial network analysis and/or simulation modelling. The review includes 102 articles, which are classified into five different categories concerning their layout-related challenges. Specifically, the categories include overcrowding, patient waiting times, visibility & staff interaction, wayfinding & walkability, and other issues such as hospital-acquired infections. The main finding is the cross-referenced table of different performance issues related to the hospital layout to different assessment methods, indicators, and quality criteria. The review suggests prospects for associating hospital design problems/challenges with spatial layout, as well as a framework for developing methods for layout representation, aggregation and relativization borrowing from the fields of transport planning and operations research. The main focus of this study lies in the spatial layout. Viewing the spatial complexity of a hospital as an indoor spatial environment is at least as complex as an urban environment, thus justifying a geographical approach; hence we expand the scope of the literature review to papers that may not directly address hospital design but have relations to spatial decision support systems.

Values (why to conserve) and Attributes (what to conserve) are essential concepts of cultural heritage. Recent studies have been using social media to map values and attributes conveyed by the public to cultural heritage. However, it is rare to connect heterogeneous modalities of images, texts, geo-locations, timestamps, and social network structures to mine the semantic and structural characteristics therein. This study presents a methodological framework for constructing such multi-modal datasets using posts and images on Flickr for graph-based machine learning (ML) tasks concerning heritage values and attributes. After data pre-processing using pre-trained ML models, the multi-modal information of visual contents and textual semantics are modelled as node features and labels, while their social relationships and spatio-temporal contexts are modelled as links in Multi-Graphs. The framework is tested in three cities containing UNESCO World Heritage properties—Amsterdam, Suzhou, and Venice— which yielded datasets with high consistency for semi-supervised learning tasks. The entire process is formally described with mathematical notations, ready to be applied in provisional tasks both as ML problems with technical relevance and as urban/heritage study questions with societal interests. This study could also benefit the understanding and mapping of heritage values and attributes for future research in global cases, aiming at inclusive heritage management practices. Moreover, the proposed framework could be summarized as creating attributed graphs from unstructured social media data sources, ready to be applied in a wide range of use cases.

The paper proposes a generative design workflow for three major hospital layout planning steps to satisfy multiplex configurational requirements. The initial step is stacking through clustering functional spaces into floor plans, for which a spectral method is presented. Subsequently, a novel simultaneous process of zoning and routing is proposed as a Mixed-Integer Programming problem-solving task; performed on a quadrilateral mesh whose faces and edges are allocated respectively to the rooms and the corridors. The paper situates the workflow in the context of an Activity-Relations-Chart for a general hospital while demonstrating, explaining, and justifying the generated optimal floor plans. The conversion of the hospital layout problem to a Mixed-Integer Programming problem enables the use of existing Operations Research solvers, allowing for the generation of optimal solutions in a digital design environment. The comprehensive problem formulation for a real-world scenario opens a new avenue for utilization of mathematical programming/optimization in healthcare design.

This paper presents ongoing research to define the framework of a computational design approach based on the ideas of spatial analysis and spatial synthesis to implement multi-criteria evaluations and provide evidence of the performance of the design alternatives in the specific case of home adaptation for healthcare at home. The European health systems place among their priority objectives the strengthening of the provision of healthcare at home to guarantee the well-being of elderly people and to limit, at the same time, the unnecessary use of resources. Therefore, existing homes must provide adequate safety, comfort, and accessibility features to ensure a high quality of life for the care receivers and facilitate the carer' tasks. To address the complexity of the requirements to be met, we propose a spatial decision support system (SDSS) to implement multi-criteria assessments to ergonomic design problems at a spatial scale of apartment homes. The system is intended to streamline and assist designers and homeowners in planning interventions for home adaptations for healthcare. Such design problems can be formulated as decision problems with costs and benefits modelled within the constraints of validity and quality criteria. Concerning the specific field of study, the system evaluates the degree of compliance with the accessibility and visibility quality criteria of each design alternative. The reiteration of the evaluation mechanism allows for the classification and supports the selection of satisfactory technical solutions identified with an informed and well-balanced trade-off between the relevant quality criteria.

The Statements of Outstanding Universal Value (OUV) concerns the core justification for nominating and inscribing cultural and natural heritage properties on the UNESCO World Heritage List, ever since 2007. Ten criteria are specified and measured independently for the selection process. The 2008 ICOMOS Report “What is OUV” has been a successful example to interpret OUV as an integral concept by inspecting the associations of the selection criteria in all inscribed properties. This paper presents a novel methodology for interpreting OUV using computational techniques of Natural Language Processing, Machine Learning, and Graph Visualization. Firstly, frequent phrases appearing in Statements of OUV are used to construct a lexicon for each selection criterion; Secondly, three similarity matrices are constructed as graphs to represent the pair-wise associations of the criteria; Lastly, the lexicon and graphs are visualized in 2D. The study shows that the lexicon derived from computational techniques can capture the essential concepts of OUV, and that the selection criteria are consistently associated with each other in different similarity metrics. This study provides a quantitative and qualitative interpretation of the Statements of OUV and the associations of selection criteria, which can be seen as an elaborated computational extension of the 2008 Report, useful for future inscription and evaluation process of World Heritage nominations.

In contrast to the contemporary aesthetic account, Muqarnas are geometrically complex variations of Squinches used for structural integration of rectilinear geometries and curved geometries. Inspired by the historical functionality of Muqarnas, we present a generalized computational workflow for generating dry-fit stacking modules from two-dimensional patterns in order to construct a dome. Similar to Muqarnas these blocks are modular in nature, complex in geometry, and compression-only in their structural behavior. We demonstrate the design of such structures based on the exemplary Penrose pattern and showcase the variations & potentials of this method in comparison to conventional approaches.

Hospital facilities are known as functionally complex buildings. There are usually configurational problems that lead to inefficient transportation processes for patients, medical staff, and/or logistics of materials. The Quadratic Assignment Problem (QAP) is a well-known problem in the field of Operations Research from the category of the facility's location/allocation problems. However, it has rarely been utilized in architectural design practice. This paper presents a formulation of such logistics issues as a QAP for space planning processes aimed at renovation of existing hospitals, a heuristic QAP solver developed in a CAD environment, and its implementation as a computational design tool designed to be used by architects. The tool is implemented in C# for Grasshopper (GH), a plugin of Rhinoceros CAD software. This tool minimizes the internal transportation processes between interrelated facilities where each facility is assigned to a location in an existing building. In our model, the problem of assignment is relaxed in that a single facility may be allowed to be allocated within multiple voxel locations, thus alleviating the complexity of the unequal area assignment problem. The QAP formulation takes into account both the flows between facilities and distances between locations. The distance matrix is obtained from the spatial network of the building by using graph traversal techniques. The developed tool also calculates spatial geodesic distances (walkable, easiest, and/or shortest paths for pedestrians) inside the building. The QAP is solved by a heuristic optimization algorithm, called Iterated Local Search. Using one exemplary real test case, we demonstrate the potential of this method in the context of hospital layout design/re-design tasks in 3D. Finally, we discuss the results and possible further developments concerning a generic computational space planning framework.

Formalization of knowledge within a scientific paradigm unifies sporadic efforts through converging glossary and notation, thus enabling scientists to identify knowledge gaps and discrepancies easier. Formalization reveals potential bridges to various domain sciences and facilitates the utilization of methods that have proven effective in scientific problem-solving. In the case of Architecture and Built Environment, there is a long history of scattered efforts for identifying and formalizing design problems and design methodologies, but the big picture is yet missing. In this short piece, we name and frame some of these efforts to identify their parallels with Mathematics, Computer Science, and Systems Theory, as well as to illustrate new opportunities that methodical design unlocks.

Focusing on the quality of reuse of buildings with respect to the efficiency of human movement patterns and the effectiveness of allocation of functional spaces with respect to their needs for social encounter and avoidance reveals the configurative nature and the human impact of architectural design. By comparing such functionality assessments before and after a redesign process for retrofitting, a clearer grasp of the effect of the spatial configuration on the efficiency and the effectiveness of a design can be explicated. In configurational analysis the focal point of attention is the structure of the walkable space in the building, detached and abstracted from the shape of the building. In configurational functionality assessment, the focus is to synthesize multiple analyses, aggregate the results, and derive indicators of quality with respect to either quantitative objectives or qualitative ideals. Does the structure of the space and the allocation of functional spaces to various locations in the spatial network of a building have an effect on its overall functionality/utility? How can we study these effects? Are the functional spaces placed in their best-fitting locations? Do the design operations like adding an external staircase or tearing down an interior wall make a difference in the accessibility of functional spaces? By digging into this topic, you will gain a methodology with which you can describe and analyse a spatial configuration in a reproducible way.

We present a modular generative design framework for design processes in thebuilt environment that provides for the unification of participatory design andoptimization to achieve mass-customization and evidence-based design. Thepaper articulates this framework mathematically as three meta proceduresframing the typical design problems as multi-dimensional, multi-criteria,multi-actor, and multi-value decision-making problems: 1) space-planning, 2)configuring, and 3) shaping; structured as to the abstraction hierarchy of thechain of decisions in design processes. These formulations allow for applyingvarious problem-solving approaches ranging from mathematical derivation &artificial intelligence to gamified play & score mechanisms and grammaticalexploration. The paper presents a general schema of the framework; elaborateson the mathematical formulation of its meta procedures; presents a spectrum ofapproaches for navigating solution spaces; discusses the specifics of spatialsimulations for ex-ante evaluation of design alternatives. The ultimatecontribution of this paper is laying the foundation of comprehensive SpatialDecision Support Systems (SDSS) for built environment design processes. (3) (PDF) GoDesign: A modular generative design framework for mass-customization and optimization in architectural design. Available from: https://www.researchgate.net/publication/353931074_GoDesign_A_modular_generative_design_framework_for_mass-customization_and_optimization_in_architectural_design [accessed Aug 27 2021].

Social inclusion has grown as an important goal for heritage planning over the past decades. Whilst the document Recommendation on the Historic Urban Landscape called a decade ago for novel tools for civic engagement and knowledge documentation, social media already functions as a platform for online communities to actively get involved in heritage-related activities by sharing their ideas. Especially when radical events occur around heritage properties, either positive or negative, emotions and opinions would spread rapidly across the globe via the internet to reach online communities of interested or concerned citizens. This paper presents a theoretical framework defined to classify social inclusion of online communities in heritage planning processes through differentiating the everyday baseline scenarios from the event-triggered activated ones. A preliminary systematic literature review shows that research integrating and comparing both scenarios is still scarce, and that specific tools and algorithms to handle large datasets are needed to identify the structure of communication networks underpinning the spread of information on social media. This framework is the first step on future research to investigate the different focal attention points, mechanisms, and patterns of social inclusion of online communities in heritage planning, towards transforming it to a more socially inclusive practice.

The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of OUV are not mutually exclusive. Furthermore, manual annotation of heritage values and attributes from multi-source textual data, which is currently dominant in heritage studies, is knowledge-demanding and timeconsuming, impeding systematic analysis of such authoritative documents in terms of their implications on heritage management. This study applies state-of-the-art NLP models to build a classifier on a new dataset containing Statements of OUV, seeking an explainable and scalable automation tool to facilitate the nomination, evaluation, research, and monitoring processes of World Heritage sites. Label smoothing is innovatively adapted to improve the model performance by adding prior interclass relationship knowledge to generate soft labels. The study shows that the best models fine-tuned from BERT and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable. The study is promising to be further developed and applied in heritage research and practice.

This article explains the motivation and the theoretical underpinnings of a master's level course on generative design for earth and masonry architecture.

As one of the most important areas in the Palace Museum, Beijing, China, the Hall of Mental Cultivation had suffered from overcrowding of visitors before it was closed in 2016 for conservation. Preparing for the reopening in 2020, the Palace Museum decided to take the chance and initiate finer-grained tourism management in the Hall. This research intends to provide an audio-guided touring program by dynamically evaluating the Tourism Carrying Capacity (TCC) with the highlight spots in the Hall, to operate the touring program spatiotemporally. Framing an optimization problem for the touring program, an agent-based simulator, Thunderhead Pathfinder, originally developed for evacuation in the emergency, is utilized to verify the performance of the touring system. The simulation shows that the proposed touring program could precisely fit all the key requirements to improve the visitors' experience, to guarantee heritage safety, and to ensure more efficient management.

This paper introduces a Discrete-Event Simulation (DES) tool developed as a parametric CAD program for validating a program of requirements (PoR) for hospital space planning. The DES model simulates the procedures of processing of patients treated by doctors, calculating patient throughput and patient waiting times, based on the number of doctors, patient arrivals, and treatment times. In addition, the tool is capable of defining space requirements by taking hospital design standards into account. Using this tool, what-if scenarios and assumptions on the PoR about space planning can be tested and/or validated. The tool is ultimately meant for reducing patient waiting times and/or increasing patient throughput by checking the match of the layout of a hospital with respect to its procedural operations. This tool is envisaged to grow into a toolkit providing a methodological framework for bringing Operations Research into Architectural Space Planning. The tool is implemented in Python for Grasshopper (GH), a plugin of Rhinoceros CAD software using the SimPy library.