Design Decision-making Process Research Articles

Digital Architectural Form Generation Through Pixel System-Driven Image Feature Information

This study analyzes valuable information (image feature information) from pixel-based images to systematize the form generation process utilizing this information. Information in architecture is mainly used as an analytical tool for functional design optimization. However, the Fourth Industrial Revolution and advances in information and communication technology have positioned information as a core value-creating tool. Consequently, there is a growing need to develop methodologies for effectively applying information to architectural design generation. This study combines digital technology with images, which directly influence architectural design, transforming them into numerical information. By systematizing the three-dimensional form generation process using low-level image feature information, it analyzes the relationship between the characteristics of valuable information and the generated forms. This approach helps identify the interrelationships between the characteristics of valuable information and the generated forms, the complementarity needed to mitigate the limitations of valuable information, and the potential for architectural applications of the three-dimensional form generation methodology utilizing image information. This approach reduces labor and enhances architectural aesthetics by instantly generating diverse early design alternatives. Such research can provide a more logical basis for avoiding intuitive judgments during the design decision-making process. It can also lay the groundwork for information-centric design systems in architecture.

Fostering Gender Transformative Cities around the World

In the context of rapid urbanisation and the effects of climate change and conflicts, social inclusion and stakeholder engagements remain a low priority for most decision-makers in shaping the future of cities. The urban development initiative Her City intends to mainstream gender and age perspectives in urban planning and design decision-making processes. Her City offers a toolbox for municipal professionals, urban actors, and local decision-makers to involve girls and young women in urban planning and design processes and challenge exclusionary practices and social inequalities. This viewpoint asserts that integrating girls' perspectives leads to more responsive urban planning and the creation of inclusive and economically viable public spaces across the world. Applying an intersectional approach to urban planning, in a multitude of urban settings with different social, cultural, political and economic prerequisites, have proven to be an efficient shortcut to sustainable cities, Additionally, the initiative's success is underscored by its alignment with the Sustainable Development Goals (SDGs), delivering resilient cities for all. This viewpoint presents lessons learned from the application of Her City in Peru, Jamaica, Mozambique, Uganda, Palestine, Thailand, and Italy. These localised projects in various regions demonstrate how Her City can deliver tangible outcomes, such as the revitalisation of public spaces, community empowerment, and the promotion of gender equality, while fostering social cohesion, economic empowerment, and environmental sustainability. By championing gender-responsive urban development, Her City offers a blueprint for creating inclusive, resilient, and sustainable cities globally.

Developing Positive Design with Innovative Thinking Framework: A design Pedagogical Approach to Enhance Subjective Well-being

Design thinking is the foundation of professional design education, and the shift from zero to professional design skill involves the completion of the transition from single-mindedness to independent and innovative thinking. In this exploratory study on design teaching, we build a novel teaching model by drawing on the connection between students' self-transcendent knowledge and formal knowledge in design thinking. Based on the Design for Happiness framework (DfH), this study uses Positive Emotional Granularity cards (PEG) to stimulate students to identify and categorize various positive emotions. We matched 15 furniture-related positive emotion keywords, summarized the corresponding design strategies, and subsequently generated Design for Happy Furniture cards (DfHF). We looked at new structural reorganizations of design teaching resources and effective teaching models and examples that might be used in course delivery. This study used the USE scale for quantitative analysis and triangulation for qualitative data analysis. The results indicate that this method is crucial for making it easier to convert design ideas into practice and for enhancing the efficiency of instructional techniques in a specific design procedure. The bottom-up approach is beneficial for creating sets of cards and design strategies, as well as for utilizing the cards in innovative ways to enhance students' creativity, engagement, and self-efficacy. DfHF cards are a valuable tool for promoting motivation and facilitating the constructive learning of creative skills. They not only improve students' creative thinking abilities but also expedite the design decision-making process and prioritize emotional integration and user well-being.

Research on the Design Method of Camellia oleifera Fruit Picking Machine

Camellia oleifera fruit pickers are essential for improving picking efficiency and promoting the Camellia oleifera industry. However, it is challenging to develop pickers that meet user needs. Current design tools and methods have limitations, such as a single model, poor synergy between integrated models, and subjective bias when analysing user requirements and translating them into product attributes. To solve these problems, this study proposes a new design decision model based on the Fuzzy Analytic Hierarchy Process (FAHP), Function Analysis System Technique (FAST), Theory of Inventive Problem Solving (TRIZ Theory), and extension transformation theory. The model was developed and applied to design an Camellia oleifera fruit picker. In this paper, an empirical investigation of an Camellia oleifera base in Wuhan was carried out, and multi-level demand analysis was used to identify the design demands in the behavioural process; FAHP was used to calculate the demand weights to clarify the design focus; expert knowledge was used to convert the demands into specific product functional features, and FAST was used to decompose these features to find the contradictory conflicts; TRIZ theory was used to determine the principles of resolving the contradictions, and the extension transformation theory were used to generate the creative design solutions for the products. By integrating FAHP, FAST, TRIZ theory and the extension transformation theory, the subjective bias in product design is eliminated, the design decision-making process is improved, and new methods and ideas are provided for the design of oleaginous tea fruit pickers and similar products. Finally, the conceptual design of an Camellia oleifera fruit picking machine was produced. However, the conceptual design has yet to be subjected to exhaustive simulation experiments and prototype testing. Future research will focus on conducting the necessary simulations, prototypes, and field tests to fully assess the feasibility and effectiveness of the design and make the required iterative improvements accordingly to commercialize the product eventually.

Carbon-mechanic dual-control design decision model of prefabricated structural components

Prefabricated enclosure structures (PES) have become the key technology for low-carbon construction in the engineering field due to their low labor demand, fast construction speed, and low environmental pollution. In the context of low-carbon development, efficiently designing PES that not only possess high-strength but also demonstrate significant greenhouse gas (GHG) reduction benefits has become a critical challenge. The objective of this study is to address the challenges of automated design and scientific decision-making methods for PES components. First, the Carbon-mechanic dual-control assessment model (CDAM) is proposed, providing a novel evaluation dimension for the design of prefabricated components. Subsequently, based on the CDAM, an automated design decision-making process, and model for components are established using an improved genetic algorithm and parameterized method. Finally, under the constraints of actual engineering conditions, adaptive solutions were automatically generated for specific cases. These solutions were comprehensively evaluated and optimized using CDAM, resulting in design guidelines and optimal solutions for PES that balance environmental performance and load-bearing capacity. The case analysis results show that the optimal scheme based on the carbon-mechanic dual-control (CM) index is: the sectional dimension is 830 mm, the reinforcement ratio is 0.9 % and the cavity ratio is 45 %, which shows the ideal characteristics of low-carbon and high-strength. Compared with the single index decision scheme, this scheme achieve a maximum of 42 % GHG emissions reduction. The high comprehensive performance scheme is characterized by small sectional dimension, high reinforcement ratio, and high cavity ratio. Cavity ratio is a key parameter to achieve high performance design. The research findings provide an important theoretical basis and technical support for the low-carbon transformation in the engineering field.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

Simulation-Based Learning and Argumentation to Promote Informed Design Decision-Making Processes within a First-Year Engineering Technology Course

Analyzing the integration of scientific knowledge to guide decision-making processes supporting design challenges in engineering education is critical. However, effectively engaging in informed design decision-making processes is challenging, particularly in the context of online education. Simulation-based learning can bring authentic design practices to online education, but effective guidance and scaffolding must be provided to learners. Therefore, this research investigates the implications of integrating simulation-based learning with an argumentation framework to guide students in incorporating scientific knowledge into their design decisions, particularly in designing energy-efficient housing. This study took place during online learning due to the COVID-19 pandemic and was implemented within a first-year engineering technology undergraduate course. It aimed to analyze students’ decision-making processes when designing a zero-energy home for a Midwestern city using Aladdin, an integrated CAD/CAE platform that can be used to design a structure and simulate its function within a single system. This study investigates how students informed their decision-making processes in design for energy-efficient homes and the recurring trends in students’ designs related to economic decision making and energy science. The overall results show how cost constraints significantly influenced students’ observation and argumentation processes during their design challenge, highlighting the pivotal role of economic considerations in shaping their decision making. Moreover, the findings underscore the importance of holistic approaches in providing insights into teaching strategies for online learning, particularly in navigating the intersection of scientific and economic factors in design challenges.

Enhancing life cycle product design decision-making processes: Insights from normal accident theory and the satisficing framework

Life Cycle Assessment (LCA), a computational tool for enabling sustainable product design decision making, faces challenges in the interpretation phase, where conclusions are drawn for improvement recommendations. This necessitate the need to incorporate into LCA management-relevant theoretical underpinnings to strengthen decision-making processes. Comparative LCA case studies of lead-based piezoelectric material (lead zirconate titanate – PZT) and lead-free alternatives (potassium sodium niobate – KNN, sodium bismuth titanate – NBT), was employed to demonstrate how two theoretical lenses, namely Normal Accident Theory (NAT) and the Satisficing Framework, are used inductively to enhance decision making regarding unintended consequences in the value chain. By operationalising NAT, which has hitherto focused on the consequences of physical accidents, as a life cycle engineering-based methodology, NAT attributes of interactive complexity and tight coupling was revealed in piezoelectric materials, based on environmental systems’ predictability, observability, and applicability. This led to the introduction of Environmental Impact Accident (EIA) as a new concept, facilitating an early assessment of the associated complexities influencing the sustainability credentials of piezoelectric materials whilst informing mitigation strategies. However, when considering multiple objectives that conflict or trade-off between alternative piezoelectric materials with different environmental and health impacts across the value chain, a conundrum is created but resolved using the Satisficing Framework. The paper concludes by proposing theoretical and practical policy options for incorporating LCA into product life cycle decision making.

Model of IoT design decision-making processes in Flow Based Programming systems

ABSTRACT The aim of the article is to present a model of decision-making processes in the design of flow-based Internet of Things systems. This is an attempt to fill the research gap in the construction of decision-making systems for designing both infrastructure and the application development environment. Therefore, the aim of the article is to show a decision-making model for selecting infrastructure and design environment for the development of IoT systems. In order for these processes to be implemented, it is necessary to use containerization processes and Flow Based Programming systems, both at the stage of system prototyping and its subsequent implementation. The model verification was carried out on the example of design in accordance with the assumptions of the parallel design process model proposed in the article, using the infrastructure as code approach in infrastructure design, the Docker process for creating containers, publisher and subscriber design in Node Red, and Postgress database design.

BIM4EarlyLCA: An interactive visualization approach for early design support based on uncertain LCA results using open BIM

To meet the European climate goals in the building sector, a holistic optimization of embodied greenhouse gas (GHG) emissions using the method of life cycle assessments (LCA) are necessary. The early design stages have high impact on the final performance of the buildings and are characterized by high uncertainty due to the lack of information and not yet taken decisions. Furthermore, most current LCA approaches based on Building Information Models (BIM) require high expertise and experience in both BIM and LCA and do not follow an intuitive visualization approach for other stakeholders and non-experts. This paper presents a novel design-decision-making approach for reducing embodied GHG emissions by interactive, model-based visualizations of uncertain LCA results. The proposed workflow is based on open BIM data formats, such as Industry Foundation Classes (IFC) and BIM Collaboration Format (BCF), and is developed for decision support for non-LCA experts in the early design stages. With the help of a user study, the prototypical implementation is tested by 103 participants with different levels of experience in BIM and LCA based on a case study. We evaluate the proposed approach regarding the support of open BIM data formats, different LCA visualization strategies, and the intuitiveness of different approaches to visualizing uncertain LCA results. The user study results show a broad acceptance and need for open BIM data formats and model-based LCA visualization but less for visualizing uncertainties, which needs further research. In conclusion, this interactive, model-based visualization approach using color coding supports non-LCA experts in the design decision-making process in early design stages.

Synergistic cotreatment of cooling tower blowdown and produced waters: Modeling strategies for a comprehensive wastewater treatment simulation

Cooling tower blowdown from thermoelectric power plants and produced water from hydraulic fracturing may soften when mixed due to the complimentary chemistry of the two wastewaters. The potential benefit of low effort softening warrants the investigation of a cotreatment process concerning these industrial wastewaters. The synergy is expected to lessen chemical demand for softening and further enable sustainable practices such as designing for a zero-liquid discharge (ZLD) process. The treatment process may then provide a treated effluent suitable for use as blowdown makeup. In this study, the feasibility and effectiveness of a cotreatment train is explored through simulation of chemical softening, filtration, reverse osmosis (RO), multi-effect distillation (MED), and electrolysis unit operations within a comprehensive process model. Innovative modeling strategies are necessary to result in a meaningful process model when considering the significant complexity of the water chemistry for this treatment train. Through the methodology described in this work, a feasible process design is presented as a base case for treatment of experimentally characterized blowdown and produced water samples obtained in the northern Appalachian region. Thereafter, the base case also establishes a platform for more detailed process analyses of different scenarios in future work. Detailed descriptions of modeling methodologies, process design decisions, and simulation results are provided as a perspective on comprehensive modeling of multi-technology treatment trains for complex feedwaters. With the results of the cotreatment simulation, it is determined that the total water recovery of the process may serve to completely makeup the blowdown rate and partially make up the evaporative losses of a theoretical thermoelectric plant's water demand. This complete recovery is evaluated to require 18,343.531 kW of a chemical energy equivalent, 1184.971 kW of electricity, and 68,126.291MJh−1 of power plant steam to result in 408.532m3h−1 of treated water effluent at a quality of approximately 0.135gL−1 total dissolved solids for the base case design.

A practical decision process for building façade performance optimization by integrating machine learning and evolutionary algorithms

ABSTRACT With the development of building design parameterization, more and more designers want to explore design options that optimize performance. However, the enormous time and costs that accompany optimization exploration are often beyond the reach of design practices. The application of machine learning in the construction field in recent years has offered potential solutions. Training predictive models through machine learning enables the rapid assessment of built environment performance and thus brings the optimization process closer to reality. In this paper, we mainly developed a process that integrates machine learning predictive model and multi-objective algorithms to achieve rapid evaluation and obtain optimal solutions. Using façade design as a case study, we demonstrate the design decision process with regard to the optimized solutions. The results showed that, through the proposed visual design decision process, designers can easily compare the performance and design appearance of different solutions and make informed decisions. In addition to saving 87% of time compared to the traditional simulation process, the integrated process also introduced the predictive model, which can achieve optimization exploration in one day. These results all demonstrate that the use of an integrated approach boasts considerable time advantages and potential feasibility in design practice.

Architectural criticism: A review of types and context

Given its wide scope, architectural criticism as a discipline has had a long history of discussions and debates over its uses, contexts, methods, and even boundaries. Authors have alluded to its complex and virtually indefinable nature. The term architectural criticism itself is diverse in its usage, as it is used to describe the activities of critics, academic architectural treatises, and the architectural design decision-making process. Beyond its architectural origins, architectural criticism itself also approaches architecture from an interdisciplinary standpoint, often blending and adapting the methods and ideas of other disciplines to architecture. Despite its long history, not too many review papers approach the subject from a broad and general perspective. The majority of previous works have tended to focus on gaps or more specialized matters within the subject area. This thematic review therefore focuses on the amalgamation of existing literature on the nature of architectural criticism. A keyword search, followed by a filter using inclusion criteria from the Google Scholar Database, identified 52 articles. After a review of these articles, only 26 were found useful. This paper synthesizes and discusses the works of different authors in the areas of types and contexts of architectural criticism. The discussions will benefit future studies on the general nature or characteristics of architectural criticism and can be used as a guideline for highlighting and understanding the key issues in architectural criticism. Keywords: Architectural Criticism, Criticism Context, Criticism Types

Lyocell fibre production using NMMO – A simulation-based techno-economic analysis

The demand for man-made cellulosic fibres is expected to grow in the future. One commercially-available concept to supply fibres is Lyocell manufacturing from dissolving wood pulps using N-Methylmorpholine N-oxide (NMMO) as the solvent. The literature qualitatively indicates that NMMO recycling efficiency is a key factor for profitable operation. Process design information and parameter data are however poorly available publicly to illustrate the cost factors. Therefore, systematic techno-economic analysis of a 50 kt/year Lyocell plant was conducted using steady-state process simulation and cost modeling. With the simulation models, the underlying technical process design and modelling decisions, and economic assumptions were studied. NMMO makeup need is an important cost item. The simulated makeup need is very dependent on the design of the solvent recovery system and the vapor-liquid equilibrium thermodynamic model selection. On the other hand, water use, fibre washing process design, and washing model parameterization have relatively lower impact on the cost of production. Raw material cost and capital expenses are most critical cost items when the NMMO recycling efficiency is high.

SYSTEM VALUE ANALYSIS: MODEL AND EXAMPLE

AbstractSystems design, being a socio-technical discipline, is highly affected by available technologies, the global economy, the state of the environment, and social issues. Hence, in recent years, the ultimate objective of design started to shift from best-performance systems to ones providing value to customers, enterprises, and society. This paper presents a holistic concept of system value, equipping the stakeholders participating in the design process with a broad view of this measure. The contribution of this paper includes a proposal for general system value taxonomy, which can be used as a foundation for a comprehensive, case-specific, system value model (SVM). As an all-round perspective of value is possible only when all stakeholders are represented, we suggest deploying the PSI framework for mapping the relevant stakeholders. The system value analysis of Tesla's Model Y electrical vehicle is demonstrated, as a test case for SVM application. We conclude that a detailed analysis of SVM, performed by a carefully chosen group of diverse stakeholders, highlights less conventionally discussed aspects of the system during design decision processes, hence is expected to improve the system's overall value.

CLUSTERING OF SEQUENTIAL CAD MODELLING DATA

AbstractAutomating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

Refining urban design governance: an investigation of the urban design assessment processes in Aotearoa New Zealand

ABSTRACT This article investigates the urban design decision-making processes within Aotearoa New Zealand. It analyses the documents and terminologies in urban design assessments of residential developments and maps the professions that prepare them. It draws data from four cities: Auckland, Tauranga, Wellington, and Christchurch. The findings reveal a core of referenced documentation but uncertainty regarding supplementary documents, a variety of terminologies, and a range of expertise and qualifications of those preparing the assessments. These findings suggest that further guidance and prescribed professional standards are desirable to refine the governance of the urban design sector in Aotearoa New Zealand.

Building Performance Optimization throughout the Design–Decision Process with a Holistic Approach

Building performance optimization throughout the design–decision process has not been well defined, especially in holistic approaches that require multilevel integration. For this, it is essential to ensure coexistence between different analysis programs such as three-dimensional (3D), energy, and computational fluid dynamics (CFD) models that can be applied on a single platform such as building information modeling (BIM). This research aims, first, to define and document a holistic design approach to building performance optimization concerning energy usage by employing the BIM platform and, second, to underline interoperability and data exchange between current technology and commercially available software from different vendors. Therefore, the novelty of this study is to serve a systematic, multiobjective, and multicriterion optimization of building performance with a holistic approach in the BIM platform. The holistic design approach is for a multidisciplinary design process including three stages (conceptual, schematic, and detailed) that cover different optimization criteria (building envelope design, occupants’ comfort conditions, and building’ energy consumption). The proposed method aims to generate, evaluate, develop, and utilize a 3D model during the different design stages concerning the model’s level of details and included information, which underlines the usage of 3D model-related software and sharing data among the design stages. A BIM-based method is developed through the case study of a hypothetical, two-story building (domestic dwelling) by identifying the activities of each design stage. With the implementation of the proposed method, the result shows a predicted energy saving of up to 75%, with a detailed energy analysis placing the building energy performance in the “A” class (annual energy consumption 17,865 kW · h). At last, based on the authors’ experience during the study, it is time- and effort-saving to have interoperability between BIM tools.

Joint Pilot Spacing and Power Optimization Scheme for Nonstationary Wireless Channel: A Deep Reinforcement Learning Approach

Pilot-assisted channel estimation techniques are essential for wireless communication systems. Most studies focus on the estimation algorithms and interpolation techniques. However, the design of pilot pattern is often neglected. In this letter, we propose a joint pilot spacing and power optimization scheme based on deep reinforcement learning (DRL) to address the mismatch problem of pilot configuration for nonstationary wireless channel. First, we model the adaptive pilot design decision-making process as a Markov Decision Process (MDP) to reduce pilot overhead and power loss. Then a deep Q-network (DQN) based learning algorithm is proposed to optimize the spacing and power of pilots so as to maximize estimation performance while reducing system cost. Simulation results show that the performance of the proposed approach is better than conventional pilot configuration algorithms. Moreover, we analyze the key factors that affect the performance of the proposed scheme.

A generalized disjunctive programming model for the optimal design of reverse electrodialysis process for salinity gradient-based power generation

Reverse electrodialysis (RED) is an emerging electro-membrane technology that generates electricity out of salinity differences between two solutions, a renewable source known as salinity gradient energy. Realizing full-scale RED would require more techno-economic and environmental assessments that consider full process design and operational decision space from the RED stack to the entire system. This work presents an optimization model formulated as a Generalized Disjunctive Programming (GDP) problem that incorporates a finite difference RED stack model from our research group to define the cost-optimal process design. The solution to the GDP problem provides the plant topology and the RED units’ working conditions that maximize the net present value of the RED process for given RED stack parameters and site-specific conditions. Our results show that, compared with simulation-based approaches, mathematical programming techniques are efficient and systematic to assist early-stage research and to extract optimal design and operation guidelines for large-scale RED implementation.