Using spatial indicators to predict ventilation and energy performance

In the early design stages, architects are in constant search of a design direction that can determine the success or failure of the final design. However, in real design practice, most of the prediction methods for building performances, in this paper energy and thermal comfort, are utilised in the later design stages. Spatial configuration is one of the most important issues for architectural design in the early design stage. This study investigates the correlations between the spatial indicators connected with architectural design and the building physics indicators ventilation performance and energy performance. The main objective is to explore the potential of applying spatial indicators using space syntax to predict ventilation performance and energy performance in order to support architects for the evaluation of their concept and schemes in early design stage. The layout of a high-rise apartment in China in five different cities is chosen as a case study. The results show that the selected three indicators: connectivity value, air change rate and annual cooling saving ratio are linearly correlated, not just at building level but also at room level. R2, the correlation coefficient of determination, is between 0.53 and 0.90 (except for the case of Chongqing at building level).

Buildings account for nearly 40% of the global process and industrial greenhouse gas emissions, and within the building sector. And within the building sector, nearly 72% of the CO2 emissions are due operational carbon demand in the building sector. To meet the goals set out in the Paris Accord, we need to halve carbon emissions by the end of this decade: an aggressive and ambitious timeline. It has become an immediate and urgent need to design and construct buildings that are minimally intrusive in their impact on the environment and operate efficiently. Improvement of the building's energy performance must begin at the early design stage as the potential for sustainable intervention in the project's early phases is higher. The opportunity to improve building performance as the design progresses, is constantly reducing, while the cost of optimization is constantly increasing. One of the major design variables impacting the performance at the early phase is the building morphology/form and its associated variables. Sensible optioneering of the building form and its variables at the early stages of design - especially the conceptual design stage - can help improve the performance of the design early on in the design process at minimum cost. However, for effective early-phase design optimisation, there is a need to develop tools/methods that allow instantaneous evaluation of a large design sample space without much “lag” between the design and performance evaluation workflows. Also, in regular early phase sustainable design workflows, multiple stakeholders - like the designer, the performance analyst, etc. - are involved, which leads to a cognitive divide in the design process. For instance, while the architect designs the initial conceptual massing with a functional design intuition, the performance analysts suggest design interventions with a performance oriented design intuition. This cognitive divide leads to a dipartite design cognition in early phase design. The underlying premise of this research is to support the notion of blurring the dichotomy between conflicting design intuitions through exploring Human + Artificial Intelligence (AI) synergies and their underlying foundational technological requirements with respect to early phase design, to enable intuitive high performance design scenarios with a centralized design cognition where there are no other stakeholders other than the designer and the design environment. With the paradigm shift of other industries towards Machine Learning (ML) and AI, recent research advances in ML and the increasing availability of Big-data in the AEC industry have bolstered multivariate problems like building performance evaluation to a great extent. Design optioneering with instantaneous performance feedback through a ML method called Surrogate Modelling is an upcoming and promising methodology, which delivers feedback based on knowledge through available data, rather than simulation. Also, paradigms in Concurrent Human Machine Interaction (HMI) have explored avenues of augmenting the architectural design process through data-driven approaches. The type of data (input-output pairs) forming the basis for these paradigms depend on the target problem at hand. For early phase performance optimization - especially energy use optimization - Building Energy Model (BEM) forms the primary data. The US Department of Energy defines BEM as a versatile, multipurpose tool that is used in new building and retrofit design, code compliance, green certification, qualification for tax credits and utility incentives, and even real-time building control. BEM is also used in large-scale analyses to develop building energy-efficiency codes and inform policy decisions. A comprehensive BEM dataset that is large and accessible can support the growing interest in ML and HMI research in the high performance building design field. However, common techniques to acquire large BEM datasets like manual 3D energy modeling and simulation, 3D scanning, etc. are very tedious and time consuming. Also, existing datasets are usually incomplete, inconsistent and very difficult to access, owing to data privacy and open-access issues. And lastly, these datasets are fixed, static and cant be easily reproduced for different use case scenarios, nor scalable to adapt to needs. This research aims to tackle this problem by introducing a novel framework to generate custom problem-specific synthetic BEM dataset that generates a user defined amount of context specific 3D early phase building geometries and their associated BEM models, that is suitable for ML and HMI research. Synthetic datasets generated with this framework offer flexibility and customization in the generation process, making these datasets modular, reproducible and scalable. The framework uses the concepts of Generative Design, Geometry Manipulation, Simulation and Computational Automation to build the dataset. The dataset is qualified through the concepts of Preservationism and Sustainability, which are discussed in further sections of this article. The research identifies the importance and multifacetedness of the impact of building geometry on its performance, and demonstrates the application of generated synthetic BEM datasets by developing an enactive, conversational design environment that allows the designer to make real-time sustainable design decisions based on instantaneous machine feedback, for an intuitive, centralized design intuition.

Identification of design solutions for a built environment that caters to the human needs at all levels, and more specifically, to the needs of the clients and the society, is the main task addressed by architectural design. Architectural design is a prime example of a design task that is characterized by a high degree of complexity. Architectural design problems by definition entail relationships between decisions and objectives that are all but transparent. For the decision-maker to be able to guide design towards fulfilling objectives, a ‘closed-loop’ approach where variations in design solutions are generated and evaluated in an iterative process is employed. Due to the sheer number of alternative solutions to problems of even a moderate scale (due to combinatorial explosion), it is only feasible to iterate over a minuscule fraction of possible solutions. Design intuition of the professionals involved in design is a strong driving force behind the identification of design direction, in which alternatives are explored as part of the preliminary design process. This is an approach that depends on the human cognitive capabilities to navigate the design space and identify potentially promising solutions. Regardless, the complexity associated with architectural design often poses significant challenges to human cognition. Human cognition, while formidable in its ability to flexibly and efficiently navigate challenging environments, is faced with difficulties in addressing the complexity factors outlined previously, namely: the excessive (combinatorially explosive) number of potential solutions to architectural problems, the complex and non-linear relations between objects and their properties and the conflicting nature of design goals that architectural design entails. Thus, design professionals are often faced with the real threat that their decisions may be biased due to the natural limitations of human cognition acting in complex environments.<br/><br/>Due to the reasons highlighted above, a systematic approach to design space exploration must be undertaken, to maximize the potential for discovering optimal solutions to design problems. Due to the nature of such problems that entail multiple conflicting objectives, a single best solution is generally not attainable. Nonetheless, best-tradeoff solutions are distinguished and highly desirable for such multi-objective design problems. The field of Computational Intelligence, and within that in particular Evolutionary Computation-based (EC) intelligent approaches, offer a lucrative option as decision-support tools in design, as they are able to efficiently address the aforementioned proponents of design complexity. EC approaches are able to navigate the design space efficiently and systematically, considering multiple conflicting objectives and hard constraints, and being able to deal with arbitrary relations between design decision variables and design objectives. <br/><br/>In today's setting, products of architecture must lead the way to a sustainable and environmentally friendlier society. As such, the performance of buildings has become the main driving force behind the design process, being referred to as ``performance-driven design''. This initiative emphasizes the quantitative evaluation of a design's function in accordance with established design objectives, related to aspects such as energy performance, visual and thermal comfort, cost and environmental footprint, etc. Simulation-based tools that enable accurate design evaluation are gaining ground and offering valuable insight into the performance of buildings. Nonetheless, making decisions in this multi-objective environment is not trivial, and, as stipulated above, may be challenging to human cognition. Thus, in today’s setting where the quantitative performance of buildings keeps gaining ground, the research on the application of EC in architectural design is high on the scientific agenda.<br/><br/>Recognizing the impact design complexity has on architectural design and the potential that EC-based approaches offer in addressing it, this thesis proposes a comprehensive computational intelligence decision support system that combines components based on intelligence with ones based on cognition, with the ultimate aim of enabling decision-makers manage design complexity and improve decision making. In particular, this thesis adopts the theoretical standpoint that efficient navigation of an unknown environment assumes a fusion of intelligence and cognition. In this sense, and given the already widespread adoption of intelligent approaches (such as EC mentioned above), the main contribution of this thesis is to endow the intelligent approach with cognitive facilities, so as to improve its efficiency to the point that it is readily applicable to the early stages of the architectural design process.<br/><br/>Fusion of intelligent with cognitive approaches, as outlined in the approach proposed by this thesis, offers the unique advantage of a decision support approach that is both powerful, owing to the extensive capabilities of intelligent search algorithms, and flexible, owing to the extensive knowledge modeling capabilities of cognitive approaches. As such, it is uniquely suited to the early conceptual design stage where the need to explore large design spaces, flexibly redefine the design problem, and satisfy preferences that are not included in the primary design goals, are all paramount.<br/><br/>Thus, the word ``comprehensive'' as it appears on this thesis' title obtains a twofold meaning: On one hand comprehension as in the combination of computational intelligence and cognition in a single approach; on the other hand, as in \\textit{comprehension} of the environment, the result of an intelligent and cognitive approach to understanding.<br/><br/>Firstly, it seeks to address the excessive computational burden associated with the use of modern high-fidelity simulation software in architecture, to render computational optimization more approachable. There is a clear trend in modern design practice to employ accurate simulation-based performance assessment tools from the very early stages of design. The use of such tools provides a valuable advantage to the decision-maker, in endowing objective awareness regarding the performance of a design solution. On the other hand, such tools are associated with a heavy computational burden, which may limit their application to the conceptual design stage. There exist methods to alleviate the computational burden through the use of computational cognitive machine learning tools, also known as surrogate modeling. However, training of surrogate models can be time-consuming itself, thus limiting the application. This thesis proposes a surrogate model that is modular in that it considers each space of the building in question as a separate entity, encoded through generic variables, and as such promotes model reuse in different design cases.<br/><br/>Secondly, it seeks to advance the state of the art on post-Pareto decision support by proposing a cognitive machine-learning based approach that enables the decision-maker to combine near-optimality with preferences regarding concrete features of the design solution. Post-Pareto decision making is an important step of the decision-making process, that seeks to identify a best-tradeoff solution among the possible ones that best matches the decision-maker's preferences in terms of performance. Such preferences are termed second-order because they follow design objectives in terms of importance. Nonetheless, it is often in architectural design that preferences are expressed in terms of design properties and not performance. Due to the non-linearity between the objective function space and the decision variable space that dictates object properties, it is challenging to exercise decision making using second-order preferences. Here the contribution of this thesis is a machine cognitive approach that learns the underlying relationships between object properties, distinguishing those that are relevant when the object is optimal with respect to design objectives. In other words, only imposing relations that are relevant to achieve optimality, it enables the expression of preferences by the decision-maker that are minimally constrained.<br/><br/>The main output of this thesis is a comprehensive decision support framework; it is a framework, in the sense that it comprises a set of methods and implemented tools that seek to augment decision making in architectural design; it is termed comprehensive in that it employs computational cognition and machine learning to augment the intelligent decision support capabilities throughout the design decision support process. It is also generic and applicable as-is to a wide spectrum of architectural design problems. In the context of this thesis, validation of the proposed approach is performed mainly in case studies relevant to facade design, recognizing this design topic as a complexity-exhibiting exemplar in architectural design practice.

This study estimated availability of an air compression system and a nitrogen generation system in liquefied natural gas—floating production storage and offloading unit (LNG-FPSO) with different design stages to investigate the gap between the availability at the early design stage and that at the late design stage. Although availability estimation in the early design stage is more important than the late design stage, it is difficult to estimate the availability accurately in the early design stage. The design stage was divided into three depending on the design progress. Monte Carlo simulation technique was employed for the availability estimation. The results of the availability estimation showed that there was 0.434% difference between the early and late design stages. This meant that the availability in the early design stage was underestimated due to limited information. A sensitivity analysis was performed to investigate critical factors affecting the results. The investigated factors were failure rate, repair time, redundant equipment, and modified preventive maintenance schedule. The most critical factor was redundant equipment. It increased 0.486% availability.

Effective design collaboration during the early design stages in architecture is a condition for effective overall design and construction. This can be facilitated, supported and promoted through visual technological means. Collaborative design protocols implemented within the engineering fields (electrical and mechanical) can be adapted to the built environment design with emerging augmented reality technologies being the enablers for that shift. Architectural design requires a strongly visual approach to communication and means through which multiple disciples can collaborate effectively. Therefore, the aim of the research is to investigate principles and protocols to assist early design stage collaborative design among multidisciplinary teams, through visual and tactile user interfaces. This paper explores the current paradigm in AEC (architecture, engineering and construction), design collaboration and multidisciplinary group dynamics during the early design and conceptual stages in architecture. Afterwards, a design protocol is suggested, literature review regarding collaborative design and design protocols is summarized and further research is proposed on optimization and standardization of early design collaborative design in AEC.

This paper explores the effect of outdoor microclimatic environment upon indoor conditions for different urban block types in hot-humid climate. The main focus here is on courtyard patterns, considering its potentials for hot-humid climate is not fully understood yet. Courtyard spaces have been examined in conjunction with the internal spaces of surrounding buildings with the aim to create a link between both. Based on theoretical models, it intends to devise strategies to optimise both indoor-outdoor thermal comfort and building energy performance while enabling the building designers and urban professionals to consider these essential issues at the early design stage. For this study, four simplified archetypal urban arrays are selected, primarily developed by Martin and March. These are: pavilions, enclosed courtyard pavilions, open-square and open-rectangular courtyard pavilions. Firstly, it has observed the microclimatic characteristics of the geometric patterns through a high resolution CFD microclimatic model: ENVI-met. Thermal comfort in the adjacent and enclosed outdoor spaces was assessed against Physiological Equivalent Temperature (PET) index with the aid of Rayman 1.2. Secondly, the energy performance of the surrounding buildings was analysed by IES-VE: a building performance modelling tool. The methodology and results from the current study can be integrated in the future urban planning processes in a high-density warm-humid context.

Visualized strategy for predicting buildings energy consumption during early design stage using parametric analysis

In chapter 9, it was found that the spatial indicators can reflect the airflow performance. There is a positive or linear correlation between the spatial indicators (connectivity, integration and depth) and the airflow indicator (airflow rate). The indicators that reflect the accessibility of the spatial configuration, i.e. connectivity, integration and depth, reflect the potential of achieving natural ventilation of a particular spatial configuration. In the other words, a high degree of connectivity, integration and low depth value mean a high accessibility of the spatial configuration and a high potential of obtaining natural ventilation. This result is useful for the architectural design practice, especially in the early design stage. In chapter 10, the extended space syntax methods in the program of Depthmap for natural ventilation potential analysis were proposed by the author. In this chapter 11, the proposed methods for spatial analysis will be used for design practice. The spatial configurations of a number of Chinese rural house designs in the area studied will be evaluated in terms of natural ventilation potential for thermal summer comfort by the proposed spatial analysis methods. The rural houses as a case study in this research is chosen because the Chinese rural houses normally using passive ways to achieve thermal comfort in summer, therefore the spatial configuration for natural ventilation is important, as the author concluded in part 1 in this thesis. In addition, the rural population in China accounts for 40% of the total population with a total amount of approximately 560 million at the end of 2018 (NBSC, 2018). At the end of 2014, there were more than 585,451 villages in China and the rural housing area is at present 22.6 billion m², within a total area of more than 40 billion m² of China’s urban and rural housing together. The amount of rural housing is constantly rising. According to 2010 data, the total floor space of newly built houses is 1.6 billion m², and half of them are rural residential buildings (NBSC, 2018). Therefore, improving the living environment for Chinese rural residential buildings is important for the sustainable development of China. Moreover, previous studies have been carried out on the sustainable development of Chinese rural residential building. However, many investigations and studies related to energy conservation and indoor thermal comfort have been proposed for northern China’s rural houses (Jin & Zhou, 2008; Lai, Zhang, Wei, & Zhang, 2011; Sun, 2003; Yang, Yang, Yan, & Liu, 2011; Zhao & Jin, 2007; Zheng, Li, & Yang, 2008), focusing on winter comfort. Studies of the Chinese rural residential building in the hot humid climate regions are scarce (Han, Zhang, & Zhou, 2009; Jin, Meng, Zhao, Zhang, & Chen, 2013; Liu, Tan, Chen, Chu, & Zhang, 2013; Xie & Shi, 2012). Studies of spatial configuration for passive cooling of rural residential building design are very scarce.

Multivariate regression as an energy assessment tool in early building design

It is widely accepted that the concentration of people living in high-density city centers offers greater operational energy efficiency and lower greenhouse gas emissions than lower-density expanded suburbs. The prevailing assumption is that lower-density suburbs are dominated by larger low-rise buildings that have higher building energy use requirements and greater per-person automobile travel requirements than high-density city centers dominated by medium- and high-rise buildings located in close proximity to a variety of public transit systems. However, very few studies to date have utilized empirical data at an individual household scale to evaluate differences in the operational energy (OE) footprints for both building and transportation energy end-uses between high-rise urban and low-rise suburban households. Therefore, this work collects empirical data on building and transportation OE consumption by individuals and households living in two economically similar groups: existing high-rise residential buildings in downtown Chicago, IL, USA and existing low-rise residential buildings in suburban Oak Park, IL, USA. Data were collected from over 500 households via an online survey. We considered the following components of residential living: (1) building OE (BOE), which includes electricity and/or natural gas use for all building energy end-uses; and (2) transportation OE (TOE), which includes the OE for multiple modes of transportation (i.e., automobile, bus, subway, regional train, etc.) based on average travel behavior in each location, as well as the OE for supporting transportation infrastructure. We estimate that downtown high-rise living in this sample of residences in Chicago, IL accounts for approximately 427 GJ of primary OE per household per year, on average, which was 14% lower than the average for suburban low-rise living in the Oak Park, IL homes (499 GJ per household per year). However, on a per-person basis, downtown high-rise living accounts for approximately 246 GJ of primary OE per person per year, which was approximately 61% higher than suburban low-rise living (153 GJ per person per year). In both building types, building OE was the single largest contributor to total OE use. This study accurately captured the energy requirements associated with realistic behaviors and lifestyles of occupants of both low-rise suburban and high-rise urban households, and found that building OE dominates the total OE, which suggests that efforts to reduce building OE should be given high priority in building design and management as well as urban planning.

A performance-based generative design framework based on a design grammar for high-rise office towers during early design stage

Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation

Supplier involvement in automotive component design: are there really large US Japan differences?

Sustainable High-rises

Life Cycle Assessment (LCA) is used and accepted as a method to assess environmental impacts and resource use of buildings. In practice, LCA is typically used in stages where the design of the building is already finalized. However, LCA-calculations from early design stages can be used actively in design and optimization of the building. One of the obstacles to early stage LCA is that extensive data input on precise material types and amounts is needed, which is limited in early design stages. The simplifications needed for a designer in an early design LCA is addressed in a research project, where an extensive library of predefined building components and installations were developed and integrated into the existing Danish LCAbyg tool. The library assists the user in establishing a full building inventory by simple inputs of geometry of the building and a selection from the library of building element layers. However, the simplified approach to LCA of a building at early design stages inevitably affects results compared with results of a calculation made at later design stages where more, specific data is available. This paper presents an evaluation of building cases, modelled with the same background database and life cycle stages, using the simplified early design LCA approach and a detailed LCA approach. The evaluation includes testing of how well the predefined components in the early design approach fit with the case buildings and comparisons of the total material input and precision of the final LCA results.