Design Decision-making Research Articles

Comparing BIM-Based XR and Traditional Design Process from Three Perspectives: Aesthetics, Gaze Tracking, and Perceived Usefulness

With technological development and industrial transformation, the architecture, engineering, and construction (AEC) industry, comprising architecture, engineering, and construction, has shifted from a traditional drawing-based design mode to a digital and computer-based mode. In recent years, the application of extended reality (XR) technology, including virtual reality (VR), augmented reality (AR), and mixed reality (MR) technology, emphasizes the immersive and interactive experiences between reality and virtuality, bringing breakthrough developments to architectural projects. This study proposes a new design process mode—the BIM-based XR system—and compares it with the traditional design process mode through an actual stadium design project. Three evaluation perspectives including aesthetics, gaze tracking, and perceived usefulness assessment are used to compare the differences between the two modes. The result showed that the use of the BIM-based XR system could bring users more immersive experience and aesthetic assessment preference, and perceived usefulness in design decision-making, communication, and spatial cognition. The gaze tracking result also revealed that the BIM-based XR system can implement the design process more efficient. It is expected that XR and BIM technologies can be effectively integrated to enhance the integrity of industrial applications and establish a new design collaboration mode for the AEC industry.

Framework for the Evolution of Heuristics in Advanced Manufacturing

Abstract This study works toward addressing a knowledge gap in understanding how heuristics are developed, retrieved, employed, and modified by designers. Having a better awareness of one’s own set of heuristics can be beneficial for relaying to other team members, improving a team’s training processes, and aiding others on their path to design expertise. The ability to understand and justify the use of a heuristic should lead to more effective decision-making in systems design. To do this, the heuristics and their characteristics must be extracted using a repeatable scientific research methodology. This study describes a unique extraction and characterization process compared to prior literature. It includes some of the first work towards documenting heuristics for both designers and operators in a hybrid manufacturing setting. Eight participants performed a series of two design journals, two interviews, and one survey. Heuristics were extracted and refined between each method and then verified by participants in the survey. The surveys produced novel statistically significant findings in regard to heuristic characterizations, impacting how participants view how often a heuristic is used, the reliability of the heuristic, and the evolution of the heuristic. Lastly, an alternate perspective of heuristics as an error management bias is highlighted and discussed.

InDEStruct: engineering advanced heat transfer systems

InDEStruct: engineering advanced heat transfer systems Within the University of Southampton, Atul Singh – also referred to as ESR1 – works on his PhD within a Horizon 2020 Programme project InDEStruct. His scope within this project is to work on optimisation and design methods to improve decision-making in heat exchanger conceptual design, in other words, a more effective design taking into account multidisciplinary aspects of such designs and reducing the experimental cost required to make decisions on new heat exchanger topologies. Open Access Government interviews members of the team of the InDEStruct project, a Horizon 2020 project which works toward inter-disciplinary design approaches for advanced heat transfer systems.

A Design-Based Research Study Exploring Pre-service Teachers’ Instructional Design Decision-Making for Technology Integration

A Design-Based Research Study Exploring Pre-service Teachers’ Instructional Design Decision-Making for Technology Integration

On the robustness of thermal comfort against uncertain future climate: A Bayesian bootstrap method

Climate change mitigation and adaptation warrants their synergetic consideration in the building design process, yet past decades have witnessed an unbalanced focus on the mitigation of energy and carbon. In redressing the imbalance, the major challenge lies in the accurate prediction of future building performance via building energy modelling, which is considerably hindered by uncertainties in future climate data. Robustness analysis is a promising technique to inform uncertainty-based decision-making, but its application to future thermal comfort has yet to be sufficiently explored in the built environment. From the perspective of domestic overheating, this paper represents an initial investigation into the implementation of the Bayesian bootstrap method, to quantitatively evaluate the robustness of thermal comfort against uncertain future climate. This is demonstrated using a case study of two typical post-retrofit dwellings in England, where the Bayesian bootstrap also enables the statistical comparison of their expected future overheating risk with climate uncertainty considered. The main findings reveal the magnitude of both overheating risk and its variability experienced during nocturnal occupancy in regulation-compliant dwellings, respectively comprising nearly 15 and 12 times greater than during daytime in extreme cases. Results also imply that adaptive ventilation is potentially the key measure to enhance the robustness of thermal comfort against climate uncertainty. Overall, the Bayesian bootstrap is shown to provide a systematically consistent approach to the robustness assessment of future thermal comfort, which can facilitate the comparability of design alternatives that is vital to the building design decision-making process integrating both mitigation and adaptation strategies.

Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree

Energy Efficient Building (EEB) design decisions that have traditionally been made in the later stages of the design process now often need to be made as early as the feasibility analysis stage. However, at this very early stage, the design frame does not yet provide sufficient details for accurate simulations to be run. In addition, even if the decision-makers consider an exhaustive list of options, the selected design may not be optimal, or carefully considered decisions may later need to be rolled back. At this stage, design exploration is much more important than evaluating the performance of alternatives, thus a more transparent and interpretable design support model is more advantageous for design decision-making. In the present study, we develop an EEB design decision-support model constructed by a transparent meta-model algorithm of simulations that provides reasonable accuracy, whereas most of the literature used opaque algorithms. The conditional inference tree (CIT) algorithm exhibits superior interpretability and reasonable classification accuracy in estimating performance, when compared to other decision trees (classification and regression tree, random forest, and conditional inference forest) and clustering (hierarchical clustering, k-means, self-organizing map, and Gaussian mixture model) algorithms.

Holistic Quality Model and Assessment—Supporting Decision-Making towards Sustainable Construction Using the Design and Production of Graded Concrete Components as an Example

This paper describes a holistic quality model (HQM) and assessment to support decision-making processes in construction. A graded concrete slab serves as an example to illustrate how to consider technical, environmental, and social quality criteria and their interrelations. The evaluation of the design and production process of the graded concrete component shows that it has advantages compared to a conventional solid slab, especially in terms of environmental performance. At the same time, the holistic quality model identifies potential improvements for the technology of graded concrete. It will be shown that the holistic quality model can be used to (a) consider the whole life cycle in decision-making in the early phases and, thus, make the complexity of construction processes manageable for quality and sustainability assessments and (b) make visible interdependencies between different quality and sustainability criteria, to help designers make better-informed decisions regarding the overall quality. The results show how different quality aspects can be assessed and trade-offs are also possible through the understanding of the relationships among characteristics. For this purpose, in addition to the quality assessment of graded concrete, an overview of the interrelations of different quality characteristics is provided. While this article demonstrates how a HQM can support decision-making in design, the validity of the presented evaluation is limited by the data availability and methodological challenges, specifically regarding the quantification of interrelations.

Investigating effects of group model building on sustainable design decision-making

This research seeks to investigate the effects of a Group Model Building approach on the sustainability knowledge base prior to the weighting of design requirements. Current practice shows that the knowledge about sustainability impacts and implications on other design objectives often is limited and dispersed among decision-makers. Including sustainability criteria in needs and requirements, therefore, tend to rely on limited knowledge and decision-support, leading to decision-makers often prioritizing design objectives that they are more experienced with. The aim of this research was to capture and discuss effects on team alignment of contextual sustainability understanding from co-modeling impacts and implications of sustainability on other design objectives. The research question guiding the study is ‘what are the effects of a group model building approach on early sustainability design decisions?’. A mixed-methods research, structured according to four assumptions, was designed to analyze effects quantitatively and qualitatively. The assumptions were that the proposed approach helps design teams foster an improved contextual sustainability understanding, align the view of the relative importance of sustainability, identify suitable sustainability improvement, and increase the knowledge maturity of their design decisions. The results triangulation showed that the participants showed signs of improved sustainability understanding of their design projects and that improvement actions were identified from applying the approach. A statistically relevant converging effect could be found on the team alignment of the weighting of sustainability criteria but not on the knowledge maturity. These findings are discussed to conclude on main contributions, limitations, and areas for future work.

Multi-criteria decision making for adaptive façade optimal design in varied climates: Energy, daylight, occupants’ comfort, and outdoor view analysis

Adaptive façades can enhance interior environmental quality and reduce building energy expenditure. To this end, counterbalancing numerous objectives must be considered during the early stage. However, there remain challenges in this high-performance access, leading to difficulties in obtaining the final design solution. Therefore, this study aims to establish a robust design decision-making process for adaptive façade systems in actual commercial building scales. Firstly, the parametric simulation was implemented in varied climates, simultaneously integrating daylight, energy, and occupants' view analysis. Five comprehensive objectives of maximising daylighting and view quality whilst minimising energy use, thermal discomfort, and visual discomfort were assessed, in which the most challenging criterion, outdoor view, was built precisely upon three all-inclusive facets: view context, view access, and view clarity. Then, the robust multi-criteria decision-making framework was applied to determine the unique optimal design for each selected region with preferences sensitivity analysis. Simulation results revealed that climatic conditions, materials, and configurations significantly impact adaptive façade performance. Meanwhile, optimal design results showed that building energy can save as much as 17% compared to the average design values. Although daylighting and outdoor view criteria meet current building standards, it suggested proper architectural layouts to accomplish the most occupants’ comfort. • The parametric simulation was integrated for adaptive façades in varied climates. • Maximising daylight and view whilst minimising energy use and occupants' discomfort. • The proposed outdoor view factor comprised view context, access, and clarity. • Multi-criteria decision making was investigated by preferences sensitivity analysis. • The optimal designs found complied with current building performance standards.

A decision-making design framework for the integration of PV systems in the urban energy planning process

Researchers typically use performance analysis tools to support the design decisions of PV systems on urban scales. However, performance simulation tools are mostly used for analysis only, providing feedback to confirm the system performance rather than influencing the system design to respond to the intended performance goals. Hence, it is essential to use simulation for synthesis and generation instead of analytical simulation in designing PV systems. Employing evolutionary algorithms (AEs) seems to be a great way to solve multi-objective optimization problems that aim to design PV systems in accordance with the electricity demand and peak times of neighborhoods. This study proposes a novel design decision-making framework to develop, evaluate, and interact with the design of the PV system at the neighborhood scale in one interface to better achieve the intended objectives. The design objectives of this study include increasing the self-consumption (SC), decreasing the payback period (PB), maintaining higher self-sufficiency (SS) of the PV system, and reducing the net load variance over the grid. The study aims to increase the correlation between the PV system's electricity production and the aggregate electricity demand of buildings in neighborhoods. This is done by optimizing the range of orientation of the PV panels and the number of panels on each orientation. The design decision-making framework is guided by multiple scenarios, and a residential neighborhood located in Los Angeles, California, is considered a case study. The results show how the PV production pattern differs when various design objectives are considered in the optimization process. In general, the optimized PV system in all tested scenarios has more PV panels in the south-west orientations as a response to the late demand and the higher electricity prices in the evening in the studied neighborhood.

Nonlinear fusion method for multistage product design decision-making using plant growth simulation algorithm

Design decision-making is a vital activity for selecting an optimal scheme for product development. Owing to the uncertainty and ambiguity of design requirements and constraints, several product design phases are often deployed for concept refinement, which makes multistage product design decision-making (MPDDM) and the effective fusion of MPDDM data indispensable. However, few existing methods have considered the nonlinear relationships among the MPDDM information. Therefore, a nonlinear fusion method for MPDDM was proposed in this study. This method applies a three-parameter interval grey number to depict decision-makers’ judgement about product design schemes. Based on converting linguistic judgements into interval scales, an interval analytic hierarchy process (AHP) method was employed to calculate the weights of the design criteria, decision-makers, and decision-making stages. Considering the advantage of integrating multiple matrices without requiring external control parameters, a multistage decision-making fusion process using a plant growth simulation algorithm (PGSA) was proposed to aggregate multistage decision-making data for product design. A case study was conducted to collect multistage decision-making data, and the PGSA was developed. Through comparison with the extant method, the effectiveness and feasibility of the fusion of MPDDM was verified. The results indicate that (1) uncertainty perceived by decision-makers at three stages accounted for 96.7%, 95%, and 97.2%, respectively. The “center of gravity” of a three-parameter interval grey number, which reflects the largest possibility of decision-makers’ preferences, is not always equally distant from its maximum or minimum value (73.9%). (2) The optimization model using interval AHP to calculate the weights of decision-making indicators and stages is conducive to reducing the decision-maker’s uncertainty. (3) The global search mechanism of the PGSA can effectively realize the nonlinear fusion of MPDDM.

Automatic coloration of pattern based on color parsing of Sung porcelain

In order to improve the coloration efficiency of fashion design, an automatic color matching mechanism based on adaptive color clustering of image scenes is proposed for clothing patterns. Taking images of Sung porcelain as an example, 300 porcelain images from six different kilns were collected as testing samples. The porcelain area of each sample image was detected by image segmentation and denoising. The bipartite K-means clustering algorithm was utilized to adaptively extract the main colors of each sample. Also, the secondary clustering was carried out to obtain the main color values, proportions and co-occurrence ratios of each kiln’s image. A dynamic color matching mechanism integrating the number of color clusters, co-occurrence ratio, and structure characteristics of the target pattern, was designed and automatic color parsing and matching software was developed. The experiment results show that the color matching control parameters, source images, pattern shape, and other factors affect the main color selection sequence and the final matching effect. The time consumed for single sample color extraction and pattern automatic color matching are all less than 1 s, which can quickly realize the pattern color migration based on image scenes and further provide auxiliary decision-making for clothing pattern color design.

Study of industrial interactive design system based on virtual reality teaching technology in industrial robot

Abstract Across numerous disciplines, virtual reality (VR) had been used to aid decision-making in training, design, and evaluation processes. Both the educational and industrial groups have contributed to a vast knowledge based on a variety of VR topics during the last two decades. VR has been expanded to industry in recent years, but the majority of its applications do not involve industrial robots. To study the application of VR technology in industrial design, it is better to combine the design activities with computer-integrated manufacturing system and bring new opportunities for the innovation of industrial design. Therefore, in this article, an application of industrial interactive design system based on VR technology in the education domain is explored. First, the function and scheme design of industrial robot assembly and adjustment system are designed, and the model is established. Finally, SolidWorks and 3DsMAX are selected as three-dimensional model development tools. Unity 3D is used as the VR development engine; HTC VIVE is used as VR equipment. The study shows that the design of the machine motion instruction interpreter is effective, and the specific steps of the system to realize real-time control are also given. The feasibility of the system is verified through the analysis of typical applications of industrial robots.

Green Infrastructure and Urban-Renewal Simulation for Street Tree Design Decision-Making: Moderating Demands of Stormwater Management, Sunlight and Visual Aesthetics.

The design of green infrastructure in urban renewal sites is complex, requiring engagement with existing communities and future sustainable development goals, consideration of existing and future urban forms, changing climatic conditions, and the sites often being in low-lying and flood-prone areas. Traditional street tree decision-making approaches are inadequate for addressing the scale, environmental complexity, and mutability of decisions involved in urban renewal projects—new tree selection approaches that consider complex competing criteria for tree selections addressing stormwater management systems, visual assessment and solar amenity are needed. This paper describes a new method of multi-criteria street design decision modelling that combines outputs from hydrology modelling, digital procedural tree modelling and urban form analysis, with animation and gaming technologies. We evaluate our approach through application to the design of a large-scale, urban renewal project underway in Melbourne, Australia. The results of the study demonstrate the functionality of our model, which allowed the simultaneous output of streetscape visualisation, with tree selection responding to integrated stormwater management infrastructure and flooding, along with the likely overshadowing conditions of urban renewal built-form. Our multi-criteria approach makes a significant contribution to the tools available to urban designers, planners and landscape architects in their pursuit of smarter streetscape design decisions that respond to complex spatial, cultural and climatic urban challenges.

Evaluation of Emerging Product Design Scheme Based on Multicriteria Decision-Making

With the development of China’s economy and the affluence of people’s material life, there are more and more kinds of products around us. People have more and more kinds of products to choose from in similar needs, so the competition between similar products is becoming more and more fierce. In order to better win the competition, the current emerging products not only cover the basic functions of the same kind at the beginning of design, but also include many additional functions that can help the product position among consumers. The focus of product design has gradually changed from functional needs to emotional needs, so the product design method based on emotional needs has gradually attracted more and more attention. However, the traditional product design methods have some limitations in data research, product design scheme visualization and decision-making, and lack of theoretical support and data support. Aiming at the background of this problem, this paper studies and evaluates the design scheme of emerging products based on multi-criteria decision-making technology and big data analysis.

GİYSİ UYGULAMALARINDA TASARIM İŞBİRLİKLERİNİN ÖNEMİ

The purpose of this study is to investigate the importance of design collaborations that allow common action, sharing and development in clothing practices that are collaboratively carried out by different designers. For this purpose, the study is carried out at two stages. The first stage of the study involves a literature review that allows the understanding of the topic from a theoretical and conceptual perspective, source interviews and personal photography archives. At the second stage, a ‘capsule collection’ where the designers conducting the study use their independent ideas in design-related processes but act collaboratively in the process of design decision-making is prepared. Three authentic clothing designs included in the collection are discussed in a manner where art acts as a source for fashion, and the theme of the study is structured on the topic of “sustainability”. The traditional fabric called ‘Şile bezi’ is used as the material in the collection, and thus, the sustainability aspect of this weave is also examined. Based on the implementation results where the common conclusions of the designers who participate in the study, it is observed that design collaborations enrich the elements that are used in the design, new and unique ideas emerge, and by sharing the professional knowledge that is gained, existing talents are effectively used, and potential design risks are minimized. According to the result emerging from the design processes of the collection and the common opinions of the designers conducting the study, it is emphasized that new collections may be developed by expanding the scope of the study with new collaborations in which different designers will participate with regional-local brands.

Research on the Decision Model of Product Design Based on a Deep Residual Network

An artificial intelligence (AI) design decision model is constructed to improve the efficiency of design decision evaluation and avoid the influence of the decision preference on product design and development. Using the concept of AI, the proposed model is based on a data set of product modeling design schemes, and the data set is marked with product modeling semantics. The deep learning residual network (ResNet) algorithm is used to train the data set to improve the accuracy of design decisions, transform the general design decision problem into the semantic recognition problem of design scheme images, and eliminate the design decision preference to the greatest extent. The validity and the feasibility of the proposed AI design decision-making method based on the ResNet algorithm are verified via an example of motorcycle modeling design decision-making.

Sports Facilities Investment Based on Multi-objective Optimization and Attribute Decision-Making

With the development of the social spiritual civilization, people pay more and more attention to physical health. The construction of sports facilities is an important way to improve the national physical quality, which helps to promote the national exercise. So, the demands of sports facilities around the world are in the climax. The blind and crazy expansion of sports facilities can alleviate people’s demand for physical exercise to a certain extent, but there are also risks of environmental damage and economic waste. At present, there is still a lack of scientific guiding theory for the investment decision-making of sports design. In order to improve the quality and efficiency of the sports facility’s construction, its investment decision-making research was combed. According to the requirements, construction goal, and the restriction of the sports facility’s construction, the method of multi-objective optimization and attribute decision was studied and adopted to provide guidance for sports facilities investment decisions-making in this article. And sports facilities construction plan in a given area was taken as a study example to verify the effectiveness of the results studied in this paper. The results showed that the application of multi-objective optimization and attribute decision-making methods to guide the investment and construction of sports facilities can improve its economy and reduce environmental pollution.

Conceptual Framework to Support Personalized Indoor Space Design Decision-Making: A Systematic Literature Review

COVID-19 has forced people to spend more time indoors due to lockdown and social distancing, and clients demand personalized indoor spaces designed to increase individual satisfaction indoors. Consequently, various fourth industrial revolution technologies have been applied to support construction spaces to satisfy those clients lacking architectural knowledge and experience by reflecting individual tendencies and perceptions to build personalized indoor spaces. Therefore, it is crucial to understand how users evaluate the space according to behaviors and emotions felt in the space. A systematic review is performed to integrate significant categories from multiple disciplines to investigate the various decision-making aspects. In this study, 124 papers were selected, applying the PRISMA checklist to conduct a systematic literature review with scientometric analysis to propose a conceptual framework by reflecting the research trend related to indoor space decision-making. Accordingly, research on indoor space decision-making is increasing with pursuing convergence with various fields of study. The research is focused on the following four clusters: indoor space components, human tendencies, technology, and spatial evaluation. The framework proposed by integrating these trends could be utilized by clients as a practical tool to support people-centered indoor space decision-making post-COVID-19. Moreover, a framework should be developed to expand effectiveness in indoor spaces through convergence and collaboration research with psychology, physiology, and the medical field.

An in-Depth Analysis of Learners’ Environmental Education Knowledge in Undergraduate Colleges of Khammam District, Telangana: A Quantitative Approach

This research endeavors to employ a robust quantitative methodology to systematically analyze the depth of learners' knowledge in the realm of environmental education within undergraduate colleges situated across the expanse of Khammam district, Telangana. The study's central focus is on an extensive assessment encompassing awareness, understanding, and practical application of environmental concepts among undergraduate students. The utilization of advanced statistical calculations serves as a powerful tool to unravel intricate patterns and glean nuanced insights from the collected data. The research approach includes a meticulous examination of not just the surface-level awareness but also the depth of understanding and the real-world application of environmental knowledge. By leveraging statistical methodologies, such as descriptive statistics, correlation analyses, inferential statistics, and regression analysis, the study aims to go beyond a superficial comprehension of the learners' environmental education status. Initial findings gleaned from mean scores unveil the average levels of awareness, understanding, and application within the student population. The correlation analyses delve into the interplay between these dimensions, unveiling potential associations that contribute to a richer understanding of the environmental education landscape. Additionally, inferential statistics provide insights into variations based on demographic factors, while regression analysis helps identify key predictors influencing higher levels of environmental education knowledge. The significance of these findings extends beyond mere quantification. The results obtained are poised to form a foundational basis for informed decision-making in educational policy formulation and curriculum design. By discerning specific strengths and weaknesses in the learners' environmental education journey, educators and policymakers can tailor interventions that address precise knowledge gaps. The data-driven insights not only quantify the existing state of environmental education in Khammam district but also pave the way for strategic enhancements, ensuring that future curricula align with the evolving needs of learners and the broader goals of environmental sustainability.