User-centered interior finishing material selection: An immersive virtual reality-based interactive approach

Automation in Construction 106 (2019) 102884

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User-centered interior finishing material selection: An immersive virtual reality-based interactive approach

T

Yuxuan Zhanga, Hexu Liub, , Mingjun Zhaoc, Mohamed Al-Husseind ⁎

a

Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI 49008-5316, USA c Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada d Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada b

ARTICLE INFO

ABSTRACT

Keywords: User-centered design Immersive virtual reality Visual aesthetics Interactive particle swarm optimization Interior finishing material

Interior finishing material selection is crucial in creating a desirable living environment. However, the evaluation criteria in the current approach to interior finishing material selection are limited to quantitative indicators such as material energy performance and life expectancy. Qualitative requirements such as visualaesthetics preference, on the other hand, are overlooked. In this regard, this research proposes a novel immersive virtual reality (IVR)-based approach for user-centered interior finishing material selection which incorporates both visual aesthetics and conventional material performance. Conventional material performance is factored into a multi-criteria decision making analysis to determine finishing material type. On this basis, final material products are selected by interactively evaluating homeowner's visual-aesthetics preference during interactive particle swarm optimization algorithm (IPSO)-based material collocation optimization. Here a prototype system is developed within a game engine environment, Unity 3D, and implemented in the form of a head-mounted display device, HTC Vive, in order to provide an interactive and immersive user experience. A typical two-storey residential townhouse is used as a case study to test the developed prototype system. The test results show the proposed approach to be capable of effectively assisting users in selecting their desired interior finishing materials.

1. Introduction Today, the importance of interior design in connecting the built environment, its occupants, and the community is becoming increasingly recognized. Many practitioners within the interior design profession have begun to emphasize the human and environmental aspects of interior design [1]. In this context, the proper choice of finishing material for the building interior environment is critical, because it could bring significant benefits in terms of environmental, economic, and social aspects [2]. As such, extensive research regarding interior material evaluation and selection has been carried out in recent years. However, most studies on building materials for interior design mainly focus on the impacts of the material on environment, occupant health, function, and project cost [3]. There is a deficiency of studies investigating in an analytical manner the personalized preference of endusers with respect to material evaluation and selection. In particular, aesthetic preferences in interior material selection are given little attention in the existing literature.

⁎

In fact, involving end-users in the early product design can increase the value of products to their users. Early end-user involvement in the design phase can lead to a cost reduction in the range of 5–30%, as well as better product quality and performance [4]. As such, early end-user involvement in product design is a common practice in many industries. For example, Nike encourages its customers to design their products online; Dell allows customers to customize their PCs to meet their individual needs. However, interior home design is one of the few domains that has not widely employed the practice of early end-user involvement, partially due to the fact that (a) the housing market has traditionally been a supplier-oriented market [5] and (b) there is a lack of enabling technology for effective end-user engagement in the early design stage [6]. Although home builders, in practice, do engage homebuyers in the early design phase in the form of product catalogs and show home visits, typically the true needs, concerns, and vision of home buyers pertaining to interior finishing materials are not clearly conveyed in an intuitive and effective manner. Home builders usually use guesswork regarding the home buyer's needs in finishing material

Corresponding author. E-mail addresses: [email protected] (Y. Zhang), [email protected] (H. Liu), [email protected] (M. Zhao), [email protected] (M. Al-Hussein).

https://doi.org/10.1016/j.autcon.2019.102884 Received 10 May 2018; Received in revised form 15 May 2019; Accepted 13 June 2019 0926-5805/ © 2019 Elsevier B.V. All rights reserved.

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selection due to the lack of clear communication of homebuyer preferences and the diversity of finishing products available. For this reason, home designers fail to fully leverage the benefits of user engagement, increasing the risk of either low fulfilment of homeowner expectations or numerous design changes during the construction process, which ultimately results in increased costs and negative attitudes among project participants [7]. To address these deficiencies, this research develops an immersive virtual reality (IVR)-based interactive approach for user-centered interior finishing material selection. The proposed approach is intended to engage end-users in material selection in an intuitive and effective manner, thereby improving material selection efficiency while increasing end-user satisfaction. The research presented in this paper contributes to the main body of knowledge by (a) formalizing the interior finishing material selection problem with consideration of both aesthetic criteria, which significantly impacts on occupant experience, and conventional material performance criteria, such as environmental impact, project cost, and operational performance; and (b) proposing an interactive methodology that is capable of empirically assessing enduser preference in interior finishing material selection and optimizing the material collocations in a user-desired manner. The remainder of this paper is organized as follows. In Section 2, the literature pertaining to building material selection and user engagement in building design is reviewed in order to clarify the point of departure. Subsequently, the research methodology is described in Section 3. Section 4 presents the multi-criteria decision making (MCDM) analysis for interior material type selection. Section 5 introduces an optimization model for the aesthetic measurement of material collocation, and an interactive particle swarm optimization (IPSO) algorithm for solving the material collocation selection model. A case study is presented to validate the methodology and the prototype system in Section 6. The final section concludes the paper by highlighting the research contribution.

[12] proposed a web-based knowledge-intensive manufacturing consulting service system which is intended to help designers to select materials for manufacturing on the basis of information collected from online sources. Rahman et al. [13] employed Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in a knowledge-based decision support system for roofing material selection, in which the background knowledge was collected from domain experts and a literature review. In most knowledge-based strategies, it should be noted, the user is guided through a set of questions, with a built-in knowledge model used to assist users in selecting their desired material. However, since some demands from the user cannot be expressed explicitly, this kind of method is limited in practice. Although a great number of research studies have been directed to material selection, the majority of these focus on the quantitative performance of materials and fail to consider the subjective preferences of the user. Most of the existing literature evaluates and selects materials for interior design from an engineering perspective. Indoor air quality, environmental impact, and energy efficiency are the criteria most frequently investigated [10,14,15], whereas the aesthetic preferences of the homeowner are often neglected. Certainly, these quantitative performance indicators, such as impacts on occupant health, environmental impact, functional performance, and cost, are crucial in interior material selection, but the result of the interior design process means more to the homeowner than a mere engineered product. The designer and the researcher thus have to take subjective performance into account. Accordingly, the interior material selection method proposed in this research seeks to implement the concept of user-centered design (UCD), which focuses on understanding and achieving end-user needs and requirements. Such a method can assist the user in expressing their needs more precisely and efficiently. 2.2. User engagement in building design Traditionally, demographics, social impact assessment, and postoccupancy evaluation have been the main methods through which endusers are engaged in building projects. Builders analyze the information arising from such evaluations in the early building stage in order to better understand the perspective of potential users and solicit feedback [16], and assess the quality and performance of the design during occupancy in order to determine the degree to which the building has met owner expectations [17]. However, these methods demand substantial efforts to obtain the required analysis results, which makes design improvement less feasible. For this reason, there has been a considerable push in recent years to move beyond this traditional approach and find new ways to get users involved in the early design. Accordingly, many researchers have supported the adoption of UCD in architecture, given that the investigation and emphasis of user expectations by design teams during the early design stage can improve overall building performance [18]. Furthermore, a few studies have found improvements in user satisfaction and building performance when UCD methods were integrated into the design of buildings [18,19,20]. These previous studies focused on collecting data pertaining to user requirements through interviews, questionnaires, and on-site observation to establish a user requirement matrix and incorporate it into the design criteria [21]. For instance, Nugroho & Ferdiana [20] improved the design of health status monitoring systems in residential facilities for elderly occupants by identifying the privacy preferences of end-users and gaining better understanding regarding the nature of interactions between occupants and different user interfaces. However, one deficiency of current UCD methods is the ambiguous assessment of users' subjective experiences. Today, technological advances with respect to computer graphics have enabled virtual reality models to provide realistic visual experiences [22,23,24]. In this context, Kuliga et al. [25] and Heydarian et al. [26] conducted experiments to verify whether immersive virtual environments (IVEs) are adequate representations of physical environments in which the user experience and performance can be explored.

2. Literature review 2.1. Building material selection Material selection is a critical and difficult task in engineering design due to the increasing variety of alternative materials available in today's market. However, there is no official guideline to follow, and it could instead be approached as an MCDM problem. Thus, various material decision-making frameworks and searching strategies have been proposed in recent years to assist designers and end-users in selecting the most suitable material. In general, there are three main methods for material selection [8]: (a) free searching strategy based on quantitative analysis, (b) expert questionnaire strategy, and (c) inductive reasoning and analogy strategy. In the architectural domain, the former two strategies are implemented more frequently. For instance, as early as the mid-1990s, Mahmoud et al. [9] developed an expert system for evaluation and selection of floor finishing materials. This system follows a selection process as a quantitative-analysis-based free searching strategy, i.e., screening, ranking, and providing supporting information, in order to assist the designer and end-user in choosing the most appropriate floor material. Similarly, Castro-Lacouture et al. [2] refined the free searching method and developed a mixed integer optimization model to assist the decision maker in selecting the appropriate building material. Many well-documented studies can also be found referring to the free searching method [10,11]. The advantage of this method is that it offers a straightforward, efficient, and rapid application with considerable flexibility. Nevertheless, it requires detailed, analyzable input data, entailing some subjective criteria such as aesthetic satisfaction, which are difficult to estimate. On the other hand, some studies have attempted to build on the knowledge-based approach, implementing the information technology approach to solve the material selection problem. For instance, Zha 2

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Fig. 1. Methodology for user-centered interior finishing material selection.

Both of these studies yielded positive results. Likewise, Luigi et al. [24] applied the analysis of variance (ANOVA) method to analyze users' subjective evaluation ratings of acoustic and visual features in both a real environment and a simulated IVR environment, wherein the statistical results showed there was not a significant difference in the subjective evaluation between the real scenario and the IVR one. Given their realism, immersive virtual environments (IVEs) are increasingly employed in user-centered design, functioning as an efficient medium through which for the designer to incorporate the occupant experience in building design [25,26]. Heydarian et al. [27] developed an IVE platform by which to investigate occupants' lighting preferences and used the collected data to evaluate building design alternatives. Dunston et al. [28] developed an IVE mock-up for hospital patient rooms during the planning and design phases for the purpose of enhancing the impact of design review, and asserted that the IVE mock-up for design review was strengthened by its sense of presence, interactivity, and fullscale demonstration compared to a conventional 2D monitor display. In spite of these contributions, though, there is still a gap in the scholarship when it comes to effective approaches for involving end-users in existing UCD processes. Most of the existing UCD methods in IVE are conducted following a few separate steps [29], including (a) demonstrating design scenarios to users in IVR environment, (b) obtaining user feedback, and (c) improving design as per user feedback. For instance, Heydarian et al. [26,27] explored IVR for user-centered building design; however, their main focus was on collecting end-user lighting preferences using IVR (i.e., the second step in VR-based UCD), which was then manually incorporated into the evaluation of design alternatives. Although these methods do indeed involve the user in the early design stage through IVE by providing the user with a highly realistic, highly detailed fullscale environment to allow them to explore the design alternatives, few design optimizations encompassing evaluation of user preference have been conducted to improve user satisfaction with a home environment and facilitate user-centric interior finishing selection in a holistic and integrated manner. In this regard, a real-time interactive system that can assess the user's subjective preference for finishing material collocation and effectively factor these preferences into the design is needed. Such a system can provide the user not only with three-dimensional spatial awareness, but also with an effortless user engagement design

experience by embedding algorithms and seamlessly integrating the aforementioned UCD steps. It also follows the essential design principles proposed by Norman [30], especially as they relate to preventing overload from the user perspective and making design alternatives easily understandable through the use of graphics. 3. Methodology In order to tackle these deficiencies in interior finishing material selection, the research presented in this paper has as its aim to design, develop, and verify an IVR-based integrated approach for user-centered interior finishing material selection that incorporates not only conventional material performance indicators such as carbon dioxide emission, moisture resistance, and life span, but also aesthetic preference, a subjective performance indicator that quantifies the user's emotional response to interacting with the indoor environment. In the proposed approach, material selection is carried out in two successive steps, material type selection and material product selection. This is due to the fact that (a) most available data pertaining to the quantifiable performance of interior materials are given at the level of detail of material type, rather than of specific material products; and (b) aesthetics of specific material products is an evaluation criterion that varies from person to person and cannot be measured in the same manner as an objective numeric value. Specifically, the proposed approach uses an MCDM analysis of quantifiable performance indicators in selecting a material type and then formulates an optimization model of visual aesthetics within IVE for selection of specific material products. The evaluation of aesthetics in material product selection takes all interior finishing materials in one room as one study object (referred to as material collocation), in keeping with the universal interior design principle of thinking holistically. Fig. 1 shows an overview of the research methodology. To begin, the available material types for each interior finish (e.g., wall, ceiling, and furniture finish) are investigated and their various performance indicators are collected as inputs through a literature review. Meanwhile, an SQL database is manually developed to store all relevant data (e.g., physical performance indicators, embodied energy consumption, economic indicators, visual character values, and texture images) with the aim of facilitating data management and easing data extraction in 3

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subsequent steps. During material type selection, an MCDM problem is formulated with an analysis of quantifiable material performance indicators. Weights for each performance indicator are taken as inputs in the MCDM analysis. By means of this analysis, an overall performance score is calculated for each alternative material type which is then presented in the form of a radar chart. The radar chart, it should be noted, visually aids user understanding of the benefits and drawbacks of each material type [18]. As a result, it offers the user a more intuitive means of material type selection compared to merely being given a final numeric score for each material type. Subsequently, for material product selection, the user aesthetics criterion, a subjective performance indicator, is investigated as an optimization problem. It is important to note that human preference and visual harmony both influence the measurement of aesthetics [19]. As such, an objective function measuring visual harmony degree and user preference is developed to evaluate the aesthetics criterion for a given interior finishing material. The degree of visual harmony of material collocation is calculated using the dissimilarity (i.e., Hausdorff distance) between the linguistic value distribution of the material collocation being estimated and a user-desired material collocation specified in the form of images. In calculating the dissimilarity, every candidate material product needs to be assigned a linguistic value based on the inputs of “exciting” material and “calm” material datasets, whereas user preference is directly measured when users assign ratings within an IVR environment. Notably, building interior finishes have an intensive interaction with occupants, such that the immersive environment in IVR provides a better approach for users to experience the interior design compared with other advanced technologies such as augmented reality (AR). Although AR is superior to VR in terms of exposing users to a real-world environment whose elements are “augmented” by computer-generated perceptual information, it would be more beneficial to the renovation stage than the design stage [31]. Nevertheless, the goal of this research is to effectively engage homebuyers (i.e., end-users) in the building design stage. As such, IVE is used in the proposed method to enable users to visually evaluate material optical properties in a clear and realistic manner. To obtain optimal material collocation, an IPSO algorithm is employed along with IVR in this research. The general process of how this method finds optimal material collocation in a human-machine

interactive manner is illustrated in Fig. 2. To start with, initial finishing material collocation solutions are generated, rendered, and demonstrated in an IVR environment. Users can then fully explore the virtual properties of material and give feedback on their degree of preference of the presented material collocation solution. Meanwhile, the proposed approach calculates the visual harmony score of the presented material collocation solution as described above. The sum of these two scores is fed back to the IPSO algorithm as an overall objective value (i.e., aesthetic measure). The IPSO algorithm then generates a new material collocation solution based on the feedback, and sends it to the VR interface for another iteration. It should be emphasized that the rendered visualization of the 3D environment is presented to users in a real-time manner once the material collocation has been generated. The proposed method is encoded within the game engine, Unity, through C# language. When the IPSO algorithm generates new material collocation solutions, the game engine retrieves the corresponding material parameters and textures from the database and renders the interior finish model in real-time. The iteration processes do not stop until the IPSO reaches one of its termination criteria, such as completing the specified number of iterations and seeing no further improvement with respect to user satisfaction of material collocation solutions. 4. Material type selection: multi-criteria decision making As described above, this research applies an MCDM analysis in selecting material type. Three categories of quantifiable material evaluation criteria—environmental factor, cost and time factor, and operation factor—are used in MCDM analysis to determine a material type for each finish in a given room. The material performance indicators in the present study are selected based on the review conducted by Isakov [32] and our market research. Fig. 3 shows the usage frequency of each performance indicator among 33 material studies and material reports reviewed. As illustrated in Fig. 3, environmental indicators such as CO2 emissions potential and embodied energy and lifespan are the most widely used criteria for evaluating interior finishing materials. Based on our market research into the most heavily-weighted material performance indicators for end-users, other performance indicators, including material cost, labor cost, installation time, moisture resistance, and maintenance requirements, are also incorporated into the quantitative

Fig. 2. Optimization of material collocation based on IPSO. 4

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Fig. 3. Usage frequency of material evaluation criteria (adapted from Isakov [22]). Table 1 Quantifiable material evaluation indicators. Factor

Criteria

Environmental impact

Global Warming Potential (kg-CO2-e/sf) Embodied Energy (MJ/sf) Material Cost ($/sf) Labour Cost ($/sf) Installation Time (Labour hrs.) Lifespan (yr.) Moisture Resistance Freedom from Maintenance

Cost/time Consumption Operation effect

Fig. 4. Radar chart of wall finishing material type selection.

0.217 and 0, respectively, for material A (as indicated in Fig. 5.c), and as 0.559 and 0.327, respectively, for material B (as indicated in Fig. 5.d), their visual perceptions significantly differ from one another. In this regard, these five parameters are used to describe the appearance of a given material in the aesthetic evaluation of material collocation: lightness of the color (L∗), color position on the spectrum between red and green (A∗), color position on the spectrum between yellow and blue (B∗), glossiness, and metallicness. To enhance the user experience in interior finishing material product selection, this research develops an optimization model of interior material collocations within an IVR environment which maximizes the satisfaction level of homeowners with respect to aesthetics of material collocations and assists the homeowner and designer in exploring design alternatives in an efficient manner. The proposed objective function, expressed as Eq. (1), aims at maximizing aesthetic measures of material collocations. In fact, both visual harmony and user preference are crucial in measuring the aesthetic effect of material collocation [19]. Hence, the objective function shown as Eq. (1) in the proposed approach consists of two parts. One part, h(Xi), measures the degree of visual harmony of a material collocation, Xi, while the other, p(Xi), measures the homeowner's level of preference of the material collocation, Xi.

evaluation. Table 1 summarizes the quantifiable evaluation indicators employed in this research. The performance data of each material type are collected from multiple sources, including existing literature (e.g., [32]), RSMeans cost data (Edmonton, Canada, 2017), ICE database (Embodied energy and carbon data), and a survey of industry professionals. Several types of material, widely used in the interior design industry, are collected for each finish as candidates (for instance, wallpaper, brick veneer, paint, ceramic tile, and vinyl panel for wall finish). Each material type is evaluated based on eight performance indicators as shown in Table 1, and the weight for each indicator is assigned by experts using an analytic hierarchy process (AHP) method. Subsequently, an overall performance score of each material type is calculated and presented to endusers in the form of a radar chart. Fig. 4 presents one example radar chart for wall finishing material type selection. Theoretically, the material type with the most significant enclosed area within one radar chart (e.g., wallpaper for wall finish) should be selected, as it has the best overall performance with respect to all evaluation indicators. By doing so, a proper material type can be determined for each finish.

Objective (Xi ) = argmax{ 1·h (Xi ) + Xi

2 · p (Xi )}

(1)

th

where Xi represents the i material collocation solution consisting of 5N decision variables and θ1 and θ2 are the weights for sub-objectives as visual harmony and homeowner preference, respectively. It should be noted that, in a given room, each material design solution consists of multiple material products (N), and every material product is determined by the five parameters as mentioned above. As a result, there are 5N decision variables in the material collocation optimization model. In the following sections, the development of two sub-objective functions is discussed in detail.

5. Material product selection: aesthetic evaluation of material collocation The aesthetic preference of end-users in material product selection is evaluated at the level of building room in this research. All interior finishing material products in one room is defined as one material collocation. When the homeowner evaluates the aesthetics of material collocation, not only color but also other attributes such as glossiness and metallicness are taken into consideration. This arises from the fact that materials of similar color but different glossiness and metallicness values could evoke different perceptions and feelings. Fig. 5.a and Fig. 5.b represent the base colors of two materials. Their average CIELAB color space values are found to be similar: (L∗ = 55.4, A∗ = −0.5, B∗ = −0.8) and (L∗ = 57.3, A∗ = −0.5, B∗ = −0.8), respectively. However, when the glossiness and metallicness values are set as

5.1. Visual harmony measurement In practice, emotional terms and adjectives such as soft, cold, and calm are used to describe the optical characteristics of material, underscoring the powerful mental impression color exerts in human perception. Material is able to evoke an array of psychological associations [33,34,35] and can be evaluated by pleasure-related language, of which the aforementioned emotional terms can be applied in visual harmony evaluation [36]. As a result, a pleasure-related linguistic feature 5

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Fig. 5. Appearance of two materials: (a) texture image of material A; (b) texture image of material B; (c) calibrated material A in Unity; (d) calibrated material B in Unity.

described by a linguistic-based image scale is used in this research to represent the mental impression of material and to quantitatively evaluate the degree of visual harmony for material collocation solutions. Specifically, the degree of visual harmony of a material collocation solution is calculated as the normalized dissimilarity between a linguistic value distribution of material collocation being analyzed (i.e., MLDc) and that of the referenced material collocation (i.e., MLDr), as expressed in Eq. (2). The referenced material collocation is specified as visually harmonious collocation by the homeowner in the form of images and models. In the proposed method, rather than calculating the pairwise distance between MLDr and MLDr, a Hausdorff Distance is used to estimate the dissimilarity as per Eq. (3).

h (Xi) = 1

Dissimilarity (MLDr , MLDc ) =

max

min

mlvr MLDr mlvc MLDc

mlvr

mlvc

cj , cj

C}

(4)

IDE (mi , E ) = min { mi

ek , ek

E}

(5)

mt M

IDC (mi , C ) Nm

(6)

mt M

IDE (mi , E ) Nm

(7)

AvgD (M , C ) =

AvgD (M , E ) =

I CG (mi , C ) = 1

IDC (mi , C ) AvgD (M , C ) + AvgD (M , E )

I CG (mi , C ), I CG (mi , C ) ICG (mi , C ) = 1, I CG (mi , C ) > 0

(2)

Normalized (Dissimilarity (MLDr MLDc ))

IDC (mi , C ) = min { mi

[0, 1] (9)

0, I CG (mi , C ) < 0

(3)

where mlvr represents a linguistic value of the reference material collocation solution; mlvc is a linguistic value of the material collocation solution being analyzed; and h(Xi) denotes the degree of visual harmony of the solution being analyzed. Importantly, material linguistic value distributions in this research are generated by following a comprehensive procedure (see Fig. 6). To begin, two material sets— EXCITING material and CALM material—are developed for each material type. These sets consist of material products evoking completely exciting or calm impressions. The EXCITING material set herein contains material products which give impressions of interesting or stimulating emotion, while the CALM material set consists of soothing, serene, and comforting material products. It should be noted that each pair of EXCITING and CALM material sets is extracted from a single material type (e.g., wood). An example of EXCITING and CALM sets for wall painting materials is given in Table 2. As shown in the table, each material product corresponds with one exciting/calm-related linguistic value (e.g., an EXCITING material possesses the exciting grade of 1 and the calm grade of 0) and their appearance is determined by five property parameters. The exciting/calm grade of other material products is estimated based on their distance to the material products in these two sets. The equations of linguistic value distance are given as Eq. (4) and Eq. (5). The average distances of the remainder of materials to CALM set and EXCITING set are calculated using Eq. (6) and Eq. (7), respectively. Based on the distance of each material product to the CALM set and EXCITING set as well as the average distances, exciting and calm grades of materials (represented by IEG and ICG) can be calculated using Eq. (8) to Eq. (11).

I EG (mi , E ) = 1

IDE (mi , E ) AvgD (M , C ) + AvgD (M , E )

I EG (mi , E ), I EG (mi , E ) ICG (mi , E ) = 1, I EG (mi , E ) > 0 0, I EG (mi , E ) < 0

(8)

(10)

[0, 1] (11)

where mi represents the material being analyzed as a five dimentional data point in terms of the aforementioned 5 variables; IDC denotes the distance between material mi and pre-defined CALM set C; IDE represents the distance between material mi and EXCITING set E; cj and ek are the nearest calm material and exciting material to material mi, respectively; M denotes the set of all materials excluding sets C and E; and Nm represents the number of all materials in set M. On the basis of the exciting grade and calm grade of each material, a two-dimensional feature space diagram as illustrated in Fig. 7 can be plotted. For the sake of simplicity only one numeric number is used to represent the linguistic meaning in the proposed objective function, and a two-dimensional exciting/calm grade is projected onto the one-dimensional coordinate system (also called 1-D image scale). The projection line shown in Fig. 8 represents the desired one-dimensional image scale passing through the midpoint (0.5,0.5). Additionally, the normalized distance between each projection point and the midpoint (0.5,0.5) defines the coordinate of the original point on the one-dimensional linguistic-based image scale. The sign (positive or negative) of the value indicates the bias of the mental impression of material toward either exciting or calm. For instance, 1 represents a completely

Fig. 6. Procedures for material linguistic-based image scale development. 6

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Table 2 Examples of EXCITING and CALM material sets.

EXCITING material

CIV LAB

CALM

Metallic-

CIV LAB

material

ness

value

MetallicGlossiness

Glossiness value

ness

80.42

31.70 0.75

38.67

0.13

-7.80

23.44

14.67

44.90

95.56

-36.09

0.86

0

-1.10

7.33

12.06

31.59

96.70

-5.20

0.70

-1.87

0

-28.01

0.09

0

0.05

0

0.22

0

8.53

Fig. 9. Material linguistic value distribution (MLD).

material linguistic distribution (referred to as MLD) can thus be generated, as exemplified in Fig. 9. The x-axis and the y-axis in Fig. 9 represent interior finish indices and material linguistic values, respectively.

Fig. 7. Two-dimensional exciting/calm-related feature space diagram.

exciting impression, whereas −1 denotes a completely calm impression. For every material collocation solution, there are N finishing material decisions and N material linguistic values from which a

Fig. 8. One-dimensional image scale coordinate system. 7

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5.2. Material collocation preference measurement

homeowner is prompted to give a preference score to five material collocation solutions. The solution with the highest objective value (preference objective value p(Xik) and visual harmony objective value h (Xik)) is determined as the current gbest. The second iteration then begins. The velocities and positions of every particle are updated according to equations indicated in lines 12 and 13 of Algorithm 1 in Appendix A. All solutions are then evaluated again to identify the new current pbest and gbest. In the interest of brevity, the implementation of three operations and other parameters involved during this optimization are not discussed in detail in this paper. The interested reader may refer to [39].

The preference measurement p(Xi) in the objective function is to evaluate the homeowner's degree of preference of a given material collocation solution. Typically, the homeowner's degree of preference of a given material collocation solution arises from a combination of cultural, societal, educational, and psychological factors. The degree of preference is a subjective standard such that it is difficult to express in a parametric formula. As such, human judgement is incorporated in this research by directly scoring material collocation solutions rather than developing a mathematical equation. The user can employ values −1, 0, and 1 to represent their various attitudes toward a material collocation solution as unacceptable, acceptable, and like, respectively.

w = w min +

5.3. IPSO algorithm for material product selection

k (w max w min) imax

(12)

min

max

where w is the minimal inertia weight, w is the maximal inertia weight, imax is the total number of iterations, and k is the iteration index being analyzed. A distance-based method is applied after the third iteration to predict what scores the user might assign, and to eliminate the need for human-machine interactions in every iteration process. The predicted preference scores for new solutions are calculated using Eq. (13) based on the proportion of distance to all user scoring material solution positions. This method predicts the preference scores of solutions in the next 20 iterations (i.e., the 4th to the 24th iterations), and then the user is prompted to assign scores for the material solutions in the 25th iteration, the results of which are used in the predictive function method in subsequent iterations (i.e., the 26th to the 76th iterations) for the purpose of modifying the direction of solution evolution in subsequent iterations. (The total number of iterations is approximately 180.) The iteration indices pertaining to the preference evaluation conducted by means of either the user scoring method or predictive function method are summarized in Table 3. (It is noteworthy that this predictive method operates under a hypothesis that the user has basically the same degree of preference for similar material collocation solutions.) Finally, the three interior finishing material collocation solutions with the highest objective values throughout 180 iterations are identified as the optimal solutions and are recommended to the user for a final decision.

As described above, the material collocation preference is nonmeasurable and thus cannot be defined by means of a quantitative equation. In this regard, our research employs an interactive optimization algorithm in addressing the optimization model, where the material collocation preference is measured by the user score. More specifically, an interactive particle swarm optimization (IPSO) algorithm is applied to solve the established optimization model. The reason for selecting IPSO rather than other interactive evolutionary computation algorithms is that IPSO has a more robust information sharing mechanism than do other evolutionary algorithms [37]. Moreover, IPSO is more efficient for optimization problems, with simple variablescopes compared to the genetic algorithm and other common evolutionary algorithms [38]. Obtaining a satisfactory result by means of PSO necessitates many iterations and is tedious and time-consuming. However, it is not feasible to use the traditional iterative approach in material collocation optimization, as the material collocation solutions in the traditional approach need be evaluated by the user score, and the user is likely to lose interest or attention after intensive interaction with the system and to be unable to accurately assign scores. In order to balance negative effects resulting from the human fatigue factor, a modified small population-based PSO algorithm proposed by Zhang et al. [39] and a distance-based predictive function of object value requiring less human interaction are adopted in solving the optimization model. The small population-based IPSO uses three operations—mutation operation, DE-acceleration algorithm and migration operation—to enhance the diversity of small population searching and to accelerate the convergence of operational processes as well as keep the crowding diversity of the swarm from causing it to fall short of the desired level of diversity [39]. In addition, a Euclidean distance-based approach is applied to prompt the convergence of optimization. It measures the distance between the current material collocation solution and the historical solutions rated by the user in order to predict unscored solutions and generate more populations without the need for additional user interactions.

p (Xnk ) =

(dn p (Xik )) dn

(13)

where p(Xik) denotes the user preference score of the material solution Xnk;and dn represents the distance between an unmarked material solution Xnk and a user scoring material solution Xik. 6. Validation To test the usability of the proposed framework for user-centered interior finishing material selection, a prototype system is developed through a game engine, Unity 3D, and implemented using a set of headmounted VR display devices (HTC Vives) to create an immersive and interactive environment. Each material record in the developed database corresponds to one existing finishing material product in the real world, and the material models within the IVR environment are developed and calibrated through a visual comparison by a 2D/3D artist with two years of related work experience. Additionally, each material

5.3.1. Procedure of small-population-based IPSO algorithm The pseudocode of the detailed implementation of the IPSO algorithm to solve the material collocation model is included in Appendix A (refer to Algorithms 1 to 4). It is of note that a time-decreasing inertia weight w from 0.9 to 0.4 is specified here (Eq. 12), since a large inertia weight at the beginning helps with finding the good seeds (initial solutions) whereas a small inertia weight later in the implementation facilitates a fine search [40]. To begin, five material collocation solutions, Xi, are initialized randomly. Every material collocation solution consists of 5N decision variables, x(i, n, j)k; i is the index of solutions; n is the index of interior finishes (e.g., floor, wall, ceiling, and countertop); j is the index of material visual characteristics (e.g., LAB color value, glossiness, and metallicness); and k is the iteration index being analyzed. In the first iteration, all particles are set as gbest. Then, the

Table 3 Iterations of predictive function implementation. Iteration no. 1 2 3 4–24 25

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Evaluator Human Human Human Predictive Function Human

Iteration no. 26–76 77 78–178 179

Evaluator Predictive Function Human Predictive Function Human

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model is developed through a shading model as physically-based rendering in the game engine which is capable of simulating photorealism and of presenting accurate optical properties of materials, such as glossiness and metallicness. Moreover, previous research [31] has asserted that an IVR model is a valid environmental representation in terms of visual perception, where users have similar perception and performance to that in a real environment. In this context, the material model within the IVR environment can achieve a satisfying level of realistic optical properties and evoke a similar user experience as finishing materials would do in the real world to help the user select interior finishing material collocation for their home. Additionally, the illuminance setting of the room in the IVR environment is designed to mimic real daylight in order to more accurately reflect the optical properties of materials. The prototype system is developed to facilitate holistic and integrated user-centric interior finish selection, allowing for direct and effective collaboration between builders and homebuyers by seamlessly integrating the UCD steps described in Section 2. An experiment is conducted in which participants are asked to select finishing materials for a kitchen using the prototype system in order to evaluate its usability and efficiency. A computer with the following technical specifications was used in testing the prototype system: Intel CoreTM i7-7820 processor, NVIDIA GeForce® GTX 1080, 16 GB RAM.

algorithm. The experiment involves 25 participants (n = 25, 12 females; Median age = 28.16, SD = 7.75), 20 university students and 5 university staff members. The sample size in this experiment is determined in consideration of guaranteeing sufficient quality of the experimental results while avoiding experimental resource waste. Given that the purpose of the experiment conducted in this research is to verify the usability of the proposed prototype system, a summative sample size estimation method from Sauro & Lewis [41] is used to estimate the number of participants with 95% confidence. Moreover, since the target user of this system is the homeowner, who is usually not an expert in interior design, most of the participants selected have little experience or knowledge of interior finishing material selection. The finishing material decision-making process begins with the material type selection, where the participants are asked to assign importance to various material criteria through either an encoded AHP method based on their knowledge, or by following the default settings proposed by the authors. Accordingly, a radar chart of the overall material performance indicators is generated in which participants are able to determine the desired material type for each finish. Then, participants turn to the IVR for the aesthetic evaluation of the finishing materials and select specific material products for each finish within the IVR environment. For the purpose of visual harmony evaluation of finishing material collocations during the IPSO process, a series of images of kitchen design scenarios is provided, from which participants are asked to choose the most visually harmonious as a reference. Meanwhile, according to the decision results from the previous material type selection, corresponding data pertaining to the visual characteristics of each selected material type (such as material texture, LAB value, glossiness, and metallicness) are automatically uploaded to the IVR platform. The average LAB color value for each material is obtained by transforming the material texture image into one pixel using the Bicubic method, while the glossiness and the metallicness values are assigned to materials based on the physical properties of materials. To ensure a consistent perception of users toward virtual reality material, the dedicated visual calibration for each material IVR model against its corresponding physical material is conducted prior to the experiments (see Fig. 11). Fig. 11.a is a photograph of the gravel oak laminate flooring, while Fig. 11.b shows the corresponding virtual material in Unity with the edited material asset shown in the right corner prepared by an experienced 2D/3D artist. It should be noted that high dynamic range rendering (HDR) and physical based rendering (PBR) techniques in Unity engine are enabled to enhance the IVEs.

6.1. Overview of experiment An IVR model for a kitchen in a two-storey residential townhouse is selected as the case study for the usability test. The area of the kitchen on the main floor is approximately 223.41 sq. ft. and its floor plan is outlined in Fig. 10. During the material collocation decision-making process, the layout of the kitchen does not change, but the finishing materials of walls, floor, ceiling, countertops, cabinets, and backsplash are continually updating in real time by means of the embedded IPSO

Fig. 11. Calibration of floor material in IVR against real gravel oak laminate flooring: (a) physical material; (b) virtual material.

Fig. 10. Floor plan of the kitchen.

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Fig. 12. Participant using the prototype system with HTC Vive.

Fig. 14. Convergence process of IPSO.

asked to complete a questionnaire and comment on their experience with respect to the testing result and usability. 6.2. Experiment results In order to verify and validate the developed prototype system, the system outputs are analyzed for user satisfaction level and material selection time consumed. User satisfaction is investigated in order to confirm that the prototyped system is capable of generating a material allocation that is well-aligned with users' expectations so as to eliminate design changes, while the material selection time is examined to ensure that the proposed approach is capable of improving the material selection efficiency. The average duration for the interactive material selection is found to be approximately 13 min, deemed acceptable by the participants. It should be noted that this research advances the state of the art in interior finishing material selection research by accounting for visualaesthetic preferences and material quantitative performance, even though the research in this paper is not directly comparable to other published research. Given this, the results of the prototyped system in terms of material selection time are compared with the industry benchmark in order to validate the proposed system. The time for finishing material selection can exceed 1 h on average, according to the benchmark data of our industry partner. In comparing the time consumed with these benchmarks, material selection time when using the prototyped system is found to be below the benchmark. Fig. 14 shows the objective value of gbest (i.e., aesthetic measure) in every iteration for one example experiment. As it illustrates, the objective value (i.e., the user satisfaction level in the form of a user score) in the experiment is found to be 0.4, and it gradually approaches 0.68 over 175 optimization iterations, meaning that the user satisfaction level has increased by 0.28 (appropriately 40%). This finding suggests that the proposed method is capable of gradually leading the participant toward their desired design in an effective manner. Additionally, this points to significant improvement with respect to the user satisfaction and visual harmony score of the resulting solution compared to the initial one. One material collocation solution example during the evolutionary process of IPSO is presented in Fig. 15. In this example, the participant confirmed that the resulting solution precisely represented their desired design. The results of the interior material selection process show that the method proposed in this paper does improve the efficiency of interior

Fig. 13. GUI examples of the prototype system within IVR environment: (a) scenario 1; (b) scenario 2; (c) scenario 3.

When performing the aesthetics assessment on finishing materials, participants engage in an IVR environment through an HTC Vive headset as shown in Fig. 12. A dialogue box prompt as shown in the GUI (see Fig. 13) is used to allow end-users to assign ratings to a given material collocation based on their subjective preference. The system records this preference score and feeds it back to the IPSO algorithm, which then updates the material collocation solution accordingly for the next generation. The total number of material collocations for each participant scoring is approximately 40 on average. After completing the interactive material collocation solution selection, the participant is

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Fig. 15. Evolutionary process of material collocation plan.

finishing material selection for the homeowner while enhancing the user's aesthetic satisfaction with material collocation. During the usability test, the IVR model is able to represent the physical world to a great extent and provide a more realistic and immersive impression than could a two-dimensional image. This, in turn, influences the emotional experience of the user interacting with the built environment and effectively captures how the finishing materials interact with one another. In addition, this method allows participants to explore innovative interior finishing material collocations that they had not previously considered, and inputs more diversity and innovation to the interior design process.

a material type for each interior finish is determined based on a MCDM analysis for quantifiable criteria, such as environmental performance, project cost, and operational performance. Then, an optimization model incorporating two measurements—visual harmony and human preference—is established by which to evaluate the aesthetic performance of material collocations. In addition, a pleasure-related linguistic value assignment method is used to estimate the visual harmony score of material collocations. To solve the proposed optimization model, a small-population-based IPSO algorithm is applied which factors user preference of material collocation by means of human judgement during the optimization. Accordingly, a prototype system is developed and implemented under an IVR environment to verify the feasibility and efficiency of the proposed framework. Applying such a system provides the user with a more realistic understanding of the interior finishing material design compared to traditional approaches. The experimental results prove that the proposed interactive material selection method is effective in assisting end-users in identifying their desired material collocations as well as expediting the decision-making process. It also facilitates the discovery of creative interior material design options which the enduser or even the interior designer may not have come up with following traditional interior design practice. The main contribution of this research is that it captures a broad range of material selection issues in formulating a structured material selection framework that considers in a scientific manner visual aesthetics as well as other material performance indicators. Moreover, the proposed approach emphasizes the importance of the homeowner's emotional response to materials by allowing users to select their desired interior finishing material on the basis of visual aesthetics in an intuitive and effective manner. The IVR-based integrated framework for interior finishing material selection thus fills an important gap in usercentered interior design and material selection.

6.3. Discussion and future work Overall, the proposed framework is found to be capable of assisting homeowners in developing a finishing material collocation based on their emotional response to various finishing designs. On the other hand, the prototype system, it should be noted, is limited in the following respects: (a) the predictive function used in the IPSO algorithm is a distance-based method to predict the unrated material collocation solution. This method is premised on the hypothesis that the user has a similar degree of preference for similar material collocation solutions. Thus, further study of the relationship between the visual characteristics of materials and human preference will be carried out; (b) this research also shares a common limitation with all other VR-related research in terms of the difference between IVR and the physical environment [42]. Future research efforts will be directed toward the improvement of the IVEs. Additionally, it should be noted that the prototype system was verified and validated by graduate students and research staff members. In future research, targeted homebuyers will be invited to further test and validate the developed prototype system. 7. Conclusion

Acknowledgments

With the goal of efficiently increasing user satisfaction level with respect to the interior finishing environment, this research incorporates the end-user's aesthetic judgement in finishing material selection by proposing an IVR-based integrated framework. This method involves two steps to accommodate traditional quantitative material performance and subjective preference of users in the material selection. First,

The authors wish to thank all anonymous reviewers for their valuable comments and suggestions that improve the quality of this paper. The authors also would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for financial support (Grant File No. CRDPJ 500475-16).

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Appendix A

Algorithm 1. IPSO Algorithm.

Algorithm 2. Mutation Operation.

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Algorithm 3. DE-acceleration Operation.

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Algorithm 4. Migration Operation.

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