Abstract
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 3, 249–259 https://doi.org/10.1080/13467581.2020.1787174 ARCHITECTURAL PLANNING AND DESIGN Ting-Yi Chiang , Chien-Chien Chu , Hsu-Ming Shen and Yu-Fa Chiu Department of Architecture, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan ABSTRACT ARTICLE HISTORY Received 24 October 2019 Due to the natural environment and the consumption of the earth’s resources, the ecological Accepted 29 May 2020 damage and resource depletion have become a serious problem facing the world in recent years. To understand the design process of the interior decoration and its associated environmental KEYWORDS issues, a plan was developed on how to introduce the green interior design concept with a view Indoor environment; interior of creating a safe, healthy, green low-carbon and high-quality living space, where The FDM, AHP renovations; cloud theory; and cloud theory were used to deal with complex interior renovations decision issues mainly due utility-Based Model; green to information uncertainty, time lapse and complexity of the problems, with, therefore, the low-carbon feature cloud model employed to analyse multi-stage risk decision problems. The research results enabled customers to make decisions quickly without relying on the personal experience and ability of designers and experts. The GID decision model verified the feasibility and rationality of the method proposed in this study, which, having high analysis ability was capable of accurately predicting decisions. 1. Introduction programs have raised concerns regarding not only qual- ity but also the understanding of the indoor housing Due to the natural environment and the consumption quality attributes that consumers value. Thus, for a clear of the earth’s resources, the ecological damage and and comprehensive vision of natural resources manage- resource depletion have become a serious problem ment within the building sector, the environmental cost facing the world in recent years. of all phases of building life-cycle should be considered In the last decade, new sustainable building technol- (construction, operation, renovation, recycling/landfill - ogies have been developed and applied to envelop ing) (Andrić et al. 2017; Hwang and Low 2012). renovations, installation of renewable energy systems To understand the design process of interior building stock (Ali and Al-Nsairat 2009; Andrić et al. decoration and the associated environmental pro- 2017). Interior renovation can damage the environment blems, the present study applied the fuzzy Delphi and is among the industries most responsible for envir- method (FDM), analytical hierarchy process (AHP), onmental pollution and waste production (Acre and and cloud theory to develop a space planning deci- Wyckmans 2014). With regard to the issue of severely sion model for green, low-carbon interior design: increased global greenhouse gas emissions, the influence the green interior design (GID) model. Risk con- of the attitudes of individuals and corporate social sciousness was included to verify the feasibility of responsibility on greenhouse gas emissions is worthy of the model’s feasibility. The study presents an intro- exploration (Peloza and Shang 2011). A study pointed out duction and literature review, followed by the data that one person spent about 80–90% of their time in the analysis methods, case study, results, and discus- indoor environment every day (Codinhoto et al. 2009), sion. Moreover, research findings, conclusions, and suggesting the necessity of pursuing a comfortable future research objectives are briefly summarised. indoor space. Building materials is a growing interest in The FDM, AHP, and cloud theory were used to developed countries. At each stage of interior renovation, resolve the decision problems of complex interior practitioners should select environment-friendly materi- renovations caused by information uncertainty, time als, use recyclable products, guide consumers to under- lapses, and problem complexity. Therefore, a feature stand the value of interior decoration, and develop cloud model was employed to analyse multistage a design industry trend that allows safety, health, energy risk decision problems. After a literature review, conservation, and art to coexist. Housing suppliers there- several environmental factors that influence peo- fore continuously seek methods to redesign programs ple’s living decisions, mainly the indoor spaces of and products to meet customer needs. Generally, a house buildings and residential environment plan options, buyer remodels even a newly built dwelling unit in prime were identified (Ali and Al-Nsairat 2009; Liu et al. condition (Hofman, Halman, and Ion 2006; Ozaki 2003). 2012; Zuo and Zhao 2014). Inadequate quality standards of direct-aid interior-design CONTACT Ting-Yi Chiang D10313014@mail.ntust.edu.tw National Taiwan University of Science and Technology, Taipei City 10607, Taiwan © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 250 T.-Y. CHIANG ET AL. � � � This study investigated the design process of inter- cloud ~ y ¼ Ex; Ex ; En; He is named as an interval ior decoration and the associated environmental cloud when the expected value is an interval � � issues. A plan was developed for introducing the con- range Ex; Ex . cept of green interior design for creating a safe, healthy, green low-carbon, and high-quality living 2.2. Linguistic in AHP by cloud models space. Livelihood discussions were analysed with the GID model for residential interior environments to ver- In Saaty’s AHP, the decision-maker is asked to provide ify the feasibility of the model. Furthermore, the pro- his/her ratios a for each pairwise comparison between ij posed hybrid model can be applied to various alternatives A1, A2, A3, . . ., An for each criterion (objec- multicriteria decision-making problems. tive) in a hierarchy and also between the criteria (Saaty 1990). The measurement on a ratio scale of the pair- wise comparison is central in AHP. To make compar- 2. Literature review isons, a scale of numbers should be used to indicate how many times more important or dominant one 2.1. Cloud model theory element is over another element with respect to the The cloud model (Li, Liu, and Gan 2009). Is a new cogni- criterion or property being compared (Huang et al. tion model of uncertainty proposed based on probability 2016; Saaty 2008). Then, decision-maker obtains theory and fuzzy set theory (Chiclana et al. 2007; Zadeh a reciprocal numerical pairwise comparison matrix by 1965). It can be defined as follows: DEFINITION 1: Given selecting a particular numerical scale to quantify the a qualitative concept T defined on a universe of discourse linguistic pairwise comparison data. In practice, U; X � U; letxðx 2 XÞ be a random instantiation of the experts are often highly accustomed to providing concept T and G ðXÞ 2 ½0; 1� be the membership degree their preferences or assessments using linguistic of x belonging to T, which corresponds to a random terms instead of numerical values (Li et al. 1998; number with steady tendency. Then, the distribution of Saaty 2008; Wang et al. 2014). Natural languages the membership over the domain is called a membership usually involve ambiguity and uncertainty, so we cloud, or simply, a cloud (Li, Liu, and Gan 2009; Liu et al. model linguistic terms in AHP using normal cloud 2019). DEFINITION 2: The characters of a cloud y are models which consider both the fuzziness and ran- depicted by three numerical parameters, namely expec- domness. The linguistic terms in AHP and their corre- tation, entropy En and hyper entropy. Here, Ex is the sponding numerical scale model by normal cloud centre value of the qualitative concept domain, En mea- models are listed in Table 1. Their figures are also sures the ambiguity of the qualitative concept and He shown in Figure 2. represents the dispersion degree of cloud droplets and the random variation of the membership (Li, Liu, and Gan 2009; Liu et al. 2019; Wang et al. 2015). The cloud can be 3. Data analysis methods written as ~ y ¼ ðEx; En; HeÞ. Ex expresses the expectation 3.1. Questionnaire design of the interval judgment. ½Ex 3En; Exþ 3He� best represents the qualitative judgment (99.74%, 3En rule), The methodology of constructing an evaluation frame- and a normal cloud is depicted in Figure 1. Note that the work in order to select an interior renovation supplier Figure 1. Normal cloud and its numerical characters. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 251 Table 1. Linguistic in AHP and their corresponding normal cloud 3.2.2. Literature reviews for evidence models. Through a literature review and expert group’s recom- Linguistic terms Normal cloud models mendations, this study developed a questionnaire for equally important (eq) (1, 0.3, 0.02) assessing interior design space planning. After compiling weakly more important (wm) (2, 0.3, 0.02) moderately more important (mm) (3, 0.3, 0.02) useful factors for space planning from relevant literature, moderately plus more important (mpm) (4, 0.3, 0.02) the study applied the FDM to collect experts’ and scho- strongly more important (sm) (5, 0.3, 0.02) lars’ viewpoints; the collected viewpoints were later used strongly plus more important (spm) (6, 0.3, 0.02) demonstratedly more important (dm) (7, 0.3, 0.02) for determining evaluation factors. Compared with non- very, very strongly more important (vvsm) (8, 0.3, 0.02) experts, experts can usually provide more insightful and extremely more important (em) (9, 0.3, 0.02) incisive viewpoints. In particular, the experiences and specialized skills of experts are considerably more valu- in the industry referred to by the same name for this able than those of non-experts (Chiang 2019). study has three phases. In the first phase, suppliers Moreover, experts and scholars who are university with excellent ability to deliver were emphasised. In professors and high-ranking managers and who are the second phase, the consistency of criteria was iden- associated with building design, property manage- tified. In the third stage, the AHP was combined with ment, and hotel management industries were inter- cloud theory to obtain the value of building interior viewed and invited to revise the selected factors and renovation certification levels. Finally, this model was propose potentially overlooked factors. The selected used to certify the building interior renovation certifi - and newly added factors were then compiled and cation level of an illustrious interior renovation com- incorporated into a 16-item questionnaire (Table 2). pany to select the most suitable project. 3.3. FDM method 3.2. Index factor description Copies of the questionnaire were distributed to the parti- 3.2.1. The interior renovations service of evaluation cipating experts through e-mail, postal mail, or personal criteria delivery. The questionnaire completion progress was Sixteen experts were invited in the FDM process to monitored through phone calls. The questionnaire survey express their opinions on identifying the consistency was conducted between July 2018, and October 2019. of evaluation criteria for the selection of suppliers. The FDM technology is used to screen suitable factors for Considering the practical experience in the field of interior renovations. The expert group average was cal- carbon management in the real estate and interior culated for conservative and optimistic values of each design industries, the study identified three managers measure i, which were eliminated, other than the two in their firms, who were responsible for the implemen- standard deviations (Ishikawa et al. 1993; Lee and Seo tation of green procurement and energy management; i i 2016). The minimum C , geometric mean C , maximum L M four suppliers from the interior renovation industry; i i O of the conservative values, as well as the minimum O , U L and nine university professors, whose research were i i geometric mean O , maximum O of the optimistic M U related to green building research and energy man- values, were calculated. agement in the interior renovation industry. Figure 2. Linguistic terms in AHP modeled by normal cloud models. 252 T.-Y. CHIANG ET AL. Table 2. Factors for assessment criteria items. Related Factor Related References Construction projects (Hwang and Low 2012) External sunshade design Proposed by experts Corporate social responsibility (Benavides-Velasco, Quintana-Garcia, and Marchante- Lara 2014; Holcomb, Upchurch, and Okumus 2007; Peloza and Shang 2011) Protection of consumer rights Proposed by experts Indoor temperature control Proposed by experts Interior design (Akadiri et al., 2012; Chiang 2019) Building materials (Andrić et al. 2017; Chiang 2019; Shen et al. 2017) Construction crisis management (Loosemore 1999; Sahin, Ulubeyli, and Kazaza 2015) Installation of renewable energy systems (Andrić et al. 2017; Hsu et al. 2014; Shen et al. 2018) After sales service Proposed by experts Resort Hotel’s Image Proposed by experts Innovative Technology Proposed by experts Waste and pollution prevention (Claver-Cortés et al. 2007) Functional requirements system planning Proposed by experts Environmental awareness (Lutzkendorf 2018) Easiest & Maintain (Chiang 2019) Subsequent to the completion of the data collection 3.4. Approach of GID decision-making process process, the FDM analysis results were useful in refin - In the first stage, the FDM technique was used to select ing the questionnaire items and deleting factors with 10 factors, and in the second stage, cloud model theory insufficient discriminatory power. In general, experts’ analysis was applied to analyse data from the expert consensus regarding a factor is evaluated using the questionnaire survey that were divided into two major i i measures G . A higher G value for a specific factor facets, i.e., interior design and corporate social responsi- indicates greater consensus among experts with bility, and they were classified as the main aspects basis regard to the factor; therefore, this factor is more sui- the expert group’s recommendations. Using eight eva- table for inclusion in an evaluation factor set. luation factors from the second floor and by establishing i i Furthermore, the values of M and Z were calculated classification of the overall evaluation criteria, our objec- to determine the consistency of expert opinions. The tive is ensuring experts can understand the purpose of differences observed were convergent, and the con- this study when filling a construct GID. Therefore, user sensus value of G was calculated to screen the indica- operation needs are limited but have a positive effect on tors (Lee, Wang, and Lin 2010; Lee and Seo 2016). FDM building materials, installation of renewable energy sys- was applied for factor screening to remove factors with tems, and functional requirements system planning. The a low discrimination index and simplify the question- integration of corporate social responsibility into interior naires further. The analysis results are shown in Table 3; design corporate business practices and operations are there was an expert consensus threshold value (G ) of limited but have a positive effect on waste and pollution 7.0. Six factors, namely, Construction projects, External prevention. Assessing environmental awareness by a firm sunshade design, Indoor temperature control, Resort begins with pursuing a universal definition for CSR and Hotel’s Image, Innovative Technology and Easiest & the corporation’s role in modern society. Using the deci- Maintain, had a value less than 7.0 and were thus sions of the expert group to adjust assessment facets and removed; 10 factors remained. criteria, we identified that the visitor group mainly Table 3. Results of calculation of factors with FDM. Pessimistic value Optimistic value Geometric mean Consensus value i i i i i i i i i Criteria C C O O C O M Z G L U L U M M Construction projects 3.00 8.00 5.00 10.00 5.00 7.38 −0.63 6.33 < 7.0 External sunshade design 2.00 7.00 5.00 8.00 5.56 7.50 −0.06 6.27 < 7.0 Corporate social responsibility 5.00 8.00 7.00 10.00 6.00 8.13 1.13 7.36 > 7.0 Protection of consumer rights 2.00 8.00 6.00 10.00 6.13 8.69 0.56 7.18 > 7.0 Indoor temperature control 2.00 8.00 5.00 10.00 5.44 8.06 −0.38 6.63 < 7.0 Interior design 2.00 8.00 7.00 10.00 6.38 8.94 1.56 7.54 > 7.0 Building materials 3.00 8.00 7.00 10.00 5.75 9.00 2.25 7.47 > 7.0 Construction crisis management 4.00 8.00 6.00 10.00 6.00 8.44 0.44 7.10 > 7.0 Installation of renewable energy systems 2.00 8.00 7.00 10.00 6.19 8.94 1.75 7.52 > 7.0 After sales service 4.00 8.00 6.00 10.00 6.25 8.25 0.00 7.13 > 7.0 Resort Hotel’s Image 2.00 7.00 4.00 9.00 4.69 7.06 −0.63 5.71 < 7.0 Innovative Technology 2.00 8.00 4.00 10.00 5.94 8.75 −1.19 6.79 < 7.0 Waste and pollution prevention 5.00 8.00 7.00 9.00 6.13 8.13 1.00 7.38 > 7.0 Functional requirements system planning 2.00 8.00 6.00 10.00 6.38 9.06 0.69 7.01 > 7.0 Environmental awareness 3.00 8.00 6.00 10.00 6.19 8.38 0.19 7.13 > 7.0 Easiest & Maintain 2.00 7.00 5.00 9.00 5.50 7.44 −0.06 6.24 < 7.0 JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 253 comprised elementary users and occupant family group individual cloud weights were calculated to determine audiences. the weights of the risk factors, and the associated weight The cloud model, propounded by Li, Liu, and Gan vector of the ICOWIA operator was identified as an inter- (2009), is a cognitive model that reflects the uncertain- val cloud. Thus, the utility values predicted using AHP and ties of things in the universe and concepts in knowl- cloud theory show that the optimal and worst case will edge, which, when compared with traditional linear produce a cloud expected utility value. Finally, the final models, has outperformed these models as it has the group AHP, utility values and cloud weight vector are ability to detect both linear and nonlinear relationships obtained by calculating the weight vector of the risk with high predictive accuracy (Leong et al. 2015; Liu factors and analyzing the utility evaluation and ranking et al. 2019). The use of the normal cloud model has of these factors. provided successful results for the construction of AHP analysis was used to calculate the assessed models of linguistic words (Huang et al. 2016; Liu weight (or priority) of each decision element (See et al. 2019; Yang, Yan, and Zeng 2013; Yang et al. 2014). Table 1). A hybrid AHP and cloud theory were used to deal with complex interior renovations the feature cloud model employed to analyse multi-stage risk decision problems. At this stage, experts and research- 3.4.1. Introduction of the GID cloud model theory ers were still the objects of investigation. Therefore, framework the various relevant elements, definitions, and criteria In this study, cloud model theory was divided into two are explained to help readers better understand the stages. The first stage involved conducting a consis- goals of this study. As each person had a different tency check on the evaluation results of each expert; attitude towards risk, our calculations utilized this check was conducted to ensure the validity of the a moderate amount of risk to perform the analysis. questionnaire responses and evaluation results of each Let C1 = (3, 0.3, 0.02) (mm), C2 = (4, 0.3, 0.02) (mpm) expert. Initially, a pretest was conducted with nine pro- and C3 = (5, 0.3, 0.02) (sm) be three normal cloud fessors to ascertain the face and content validity of the models in the same universe of discourse. survey questionnaire. The second stage entailed inte- grating expert opinions to determine the weight of each ~ ~ A ¼ Synthetic A ¼ ðEx ; En ; HeÞ s s i i i ; i evaluation factor and consequently improve the relia- ~ ~ A ¼ WeightedAverage A ¼ ðEx ; En ; He Þ wa i wa wa wa bility of the items. The survey results are more objective, ¼ 1; 2; 3; : : : ; m appropriate, and consistent with actual needs. (1) Subsequently, a pilot test was conducted with suppliers to evaluate the questionnaire with respect to the word- The opinion of expert i, whose CI � 0:1; is aban- ing, clarity, relevance, and time spent. This was essential doned to avoid the influence of bad opinions, and the in ensuring that the evaluation results were objective opinions of the rest m experts decision-maker are and applicable and met actual needs. considered (Huang et al. 2016; Yang et al. 2014). The feedback information of the group opinion of m experts contains the synthetic cloud opinion A 3.4.2. Introduction of the GID criteria theory and the weighted average cloud opinion note A . wa framework The synthetic cloud model calculated by cloud The GID analysis is based on three steps. First, a hybrid weights, given any positive reciprocal cloud matrix A;. AHP and cloud theory were used to deal with complex The entropy and hyper-entropy of the synthetic cloud interior renovations, and multi-stage risk decision pro- model are both greater than that of each individual cloud blems were analyzed using the feature cloud model. We model. That there should be more uncertainties when obtain the individual cloud comparison matrix from inter- the AHP’s pairwise comparisons, every two elements val assessments and the opinion of experts I, whose CI � need to compare with each other only once, are provided 0:1 is rejected to avoid the influence of bad opinions. The in practice. (See Equation (2)) is Cs = (4, 0.5613, 0.0327). geometric mean technique is used to compute the cloud ~ ~ ~ Where a ¼ 1=a and a ¼ ð1; 0; 0Þ: The geometric ji ij ii weights of matrix A . Second, the GID utility-based wa mean technique is utilized to compute for the cloud model results from our questionnaire are applied to weights of matrix A : reduce mistakes to meet certain standards. Third, the 2 3 ~ ~ ~ A ðEx ; En ; He ÞA ðEx ; En ; He Þ . . . A ðEx ; En ; He Þ 11 11 11 11 12 12 12 12 1n 1n 1n 1n ~ 4 5 ~ ~ ~ A ¼ A ðEx ; En ; He ÞA ðEx ; En ; He Þ . . . A ðEx ; En ; He Þ (2) 21 21 21 21 22 22 22 22 2n 2n 2n 2n ~ ~ ~ A ðEx ; En ; He ÞA ðEx ; En ; He Þ . . . A ðEx ; En ; He Þ n1 n1 n1 n1 n2 n2 n2 n2 nn nn nn nn 254 T.-Y. CHIANG ET AL. Definition cloud matrix AS (Equation (3)); the prior- Where En ¼ ðEx þ Ex =2Þ and En ¼ ðEx 1 1 1 2 2 ity vector of the positive reciprocal synthetic cloud þEx =2Þ. If En ¼ He ¼ En ¼ He ¼ 0; then the 2 1 1 2 2 matrix in can be calculated as interval cloud reduces to an interval number and � � � �� � � � � ! ! d ~ y ; ~ y Þ ¼ Ex Ex þ Ex Ex :; Let 1 2 1 2 1=n 1=n 1 2 n n n Y X Y � � � ~ ~ y ¼ Ex ; Ex ; En ; He ði ¼ 1; 2; . . . ; nÞ be a set ~ ~ i i i i wi ¼ a = a (3) 1 ij ij j¼1 i¼1 j¼1 of interval clouds in the domain U, then the interval cloud ordered weighted averaging ðICOWAÞ operator Then, we obtain the final cloud weight (score) for is defined as alternative A as X n Si ¼ w e : (4) ik k ICOWA ~ y ; ~ y ; :: : ; ~ y Þ ¼ w ~ y (6) 1 2 n σðiÞ k¼1 i¼1 The final score S is a cloud number where n ¼ ðn ; n ; . . . ; n Þ is the associated 1 2 n S ¼ ðEx ; En ; HeÞ. We can rank the alternatives quickly i i i i by comparing the parameters of cloud numbers: weight vector of the ICOWA, satisfying n 2 If Ex � Ex ; En < En ; and He < He ; then A i j i j i j i absolutely dominates A ; ~ j ½0; 1�and n ¼ 1; andy is the ith largest one of σðiÞ i¼1 Otherwise : If Ex < En ; and En < En or He < He ; i j i j i j ~ y ; ~ y ; . . . ; ~ y . The aggregated value by means of 1 2 n wa say A averagely dominates A . j i the ICOWA operator is also an interval cloud and can w ¼ ð0:2592; 0:1798; 0:0146Þ; be calculated as follows: ð0:3003; 0:2083; 0:0169Þ; (5) e e e ICOWAðy ; y ; . . . ; y Þ 1 2 n ð0:4405; 0:3056; 0:0248Þ: " # n n X X In Equation (5), w1 ¼ (0.2592, 0.1798, 0.0146), w2 ¼ ¼ w Ex ; w Ex ; i i σðiÞ σðiÞ (7) i¼1 i¼1 (0.3003, 0.2083, 0.0169), w3 ¼ (0.4405, 0.3056, sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! n n 0.0248), then Ex1 ¼ 0.2592, Ex2 ¼ 0.3003, Ex3 ¼ X X � � 2 2 w En ; w He i σðiÞ i σðiÞ 0.4405. As Ex3 > Ex2 > Ex1 using the ranking rules of i¼1 i¼1 cloud models, the ranking of the priority vec- tor wisw3 > w2 > w1. It is noted from Table 6 that Protection of consumer rights perspective has the highest weight with 0.1587 3.4.3. The Proposed GID utility-based Model and the weight of Building materials process perspec- The analysis results (Tables 4 and 5) (Weighting value tive has the 0.1539. Among these criteria, energy man- agement and functional requirements systems plan of overall [Wi = 1]) reveal the GID criteria and the selected decision-making factors. The GID calculation human resources construction costs of real estate process must adhere to a consistency test consisting of developers. Moreover, waste pollution and prevention of pollution are regional environmental problems. two indices, namely, the consistency index “1” ðCIÞ and the consistency ratio (CR). For the CI ; 0 denotes Thus, planning, designing, and environmental aware- that the survey data are consistent, CI ; 1 denotes that ness and novel construction methods must be empha- the data are erroneous, and CI � 1 denotes that the sized in developing housing projects. Further, the data are within an acceptable margin of error. For the interior design (2-1-1 ~ 2-1-4) and corporate social CR, ≤0.1 according to the questionnaire data, the data responsibility (2-2-1 ~ 2-2-4) degree of preference (P) is consistent and credible. This implies that our survey is set as 16. Thereafter, the utility values are predicted by using the AHP and cloud theory (Table 7). The results and conclusions meet a certain criteria (i.e., CR ≤ 0.1) (Chiang 2019; Saaty 1990, 2008). The data value of calculated results show that the worst case will pro- these dimensions were used to calculate the relative duce a cloud expected utility value (CEUV) of 0.53 and the best case will produce a CEUV of 0.9007. weighting values (Liu et al. 2019; Wang et al. 2015). Table 4. Weighting value of interior design. Building materials(2-1-1), Construction crisis management(2-1-2), Installation of renewable energy systems (2-1-3), Functional requirements system planning(2-1-4) Criteria (2-1-1) (2-1-2) (2-1-3) (2-1-4) (2-1-1) (1, 0.3, 0.02) (1.875, 0.327, 0.019) (1.188 0.185, 0.011) (1.063, 0.159, 0.009) (2-1-2) (0.533, 0.048, 0.003) (1, 0.3, 0.02) (1.063, 0.159, 0.009) (1.063, 0.159, 0.009) (2-1-3) (0.842, 0.113, 0.007) (0.941, 0.133, 0.008) (1, 0.3, 0.02) (1.313, 0.211, 0.012) (2-1-4) (0.941, 0.133, 0.008) (0.941, 0.133, 0.008) (0.762, 0.096, 0.006) (1, 0.3, 0.02) Eigenvector (3.317, 0.627, 0.037) (4.757, 0.927, 0.054) (4.012, 0.772, 0.045) (4.438, 0.860, 0.050) Consistency Index(CI) = 0.019, Consistency Ratio(CR) = 0.021 JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 255 Table 5. Weighting value of corporate social responsibility. Protection of consumer rights(2-2-1), After sales service(2-2-2), Waste and pollution prevention(2-2-3), Environmental awareness(2-2-4) Criteria (2-2-1) (2-2-2) (2-2-3) (2-2-4) (2-2-1) (1, 0.3, 0.02) (2.125, 0.379, 0.022) (1.188, 0.185, 0.011) (1.063, 0.159, 0.009) (2-2-2) (0.471, 0.036, 0.002) (1, 0.3, 0.02) (1.125, 0.172, 0.01) (1.125, 0.172, 0.01) (2-2-3) (0.842, 0.113, 0.007) (0.889, 0.123, 0.007) (1, 0.3, 0.02) (1.156, 0.178, 0.01) (2-2-4) (0.941, 0.133, 0.008) (0.889, 0.123, 0.007) (0.865, 0.118, 0.007) (1, 0.3, 0.02) Eigenvector (3.254, 0.614, 0.036) (4.903, 0.957, 0.056) (4.177, 0.806, 0.047) (4.344, 0.841, 0.049) Consistency Index(CI) = 0.026, Consistency Ratio(CR) = 0.029 Table 6. Weighting values of criteria and cloud eigenvalue. Exi Criteria Level Wi Overall Wi Overall Sequence Ex1 Ex2 Ex3 ICOWA (2-1-1) 0.3078 0.1539 2 0.040 0.046 0.068 0.355 (2-1-2) 0.2188 0.1094 8 0.028 0.033 0.048 0.001 (2-1-3) 0.2492 0.1246 3 0.032 0.037 0.055 0.118 (2-1-4) 0.2242 0.1121 6 0.029 0.034 0.049 0.026 (2-2-1) 0.3174 0.1587 1 0.041 0.048 0.070 0.396 (2-2-2) 0.2192 0.1096 7 0.028 0.033 0.048 0.001 (2-2-3) 0.2364 0.1182 4 0.031 0.035 0.052 0.072 (2-2-4) 0.2270 0.1135 5 0.029 0.034 0.050 0.031 Total 2 1 - 0.259 0.300 0.441 1 4. Case study 20 years old and located in the Taipei City Shilin District with less traffic and low taxes, suitable Surveying related articles and site visits, we have cho- space, and quiet and scenic environmental con- sen a space with optimal indoor environments. To ditions; however, the building quality is verify the feasibility of this model, we used the Taipei unsatisfactory. City “Green Interior Decoration Project” as an example House Case H4: The estimated renovation costs application. Of the 40 “Green Interior Decoration are about US$7,330/m . Installment plans and Project” site visits conducted in Taipei City, we selected bank financing are allowed. The house is over six projects as subjects for the empirical case study. 14 years old and located at the Taipei City Neihu Four selected cases were decorated with environmen- District with satisfactory traffic and taxies; how- tally friendly materials and sound insulation materials ever, space is rather small, and the environmental and basis statistical evidence, they should promote condition and building quality are moderate. environmental protection and energy education. By applying the design technique environment and When comparing the four case studies, Case Study energy-saving skills, people can experience and under- H1 is a better model (See Tables 8 and 9 and Figure 3). stand how to save energy. To facilitate judging interior Thus, the model will be able to adjust the quantized environment, a brief description of the houses has utility function values according to our respondents’ been presented below. attitude toward risk. When adjusting these values, it is necessary to also update Table 7. A change in the House Case H1: The estimated renovation costs decision elements will affect the relationship between are about US$8,080/m . Installment plans and different CEUVs. Thus, decisions can be made to max- bank financing are allowed. The house is over imize utility. 16 years old and located at the city center, in a noisy environment, but the building quality is moderate. 5. Results and discussion Research findings House Case H2: The estimated renovation costs The decision GID Utility-Based Model we used to are about US$7,930/m . Installment plans and choose residential interior renovations is based on bank financing are allowed. The house is over scientific calculations and empirical case studies. With 10 years old and located at the Taipei City Daan the proposed approach, the initial residential design of District with satisfactory traffic and taxies; how- a project can be effectively intervened, so that users or ever, space is a rather small, and the environmen- consumers can truly participate in the design, and the tal condition and building quality are moderate. ● residential construction service can be provided in House Case H3: The estimated renovation costs a unique, but non-universal way. Considering the glo- are about US$6,500/m ; however, payment is on bal trend of promoting green energy nowadays, when a non-installment basis. The house is over 256 T.-Y. CHIANG ET AL. Table 7. Cloud expected utility value for criteria. Exi CEUV Criteria Overall Wi Ex min Ex max Ex t Min Max Threshold (2-1-1) 0.1539 3.99% 6.78% 4.62% 9.82% 16.69% 10.43% (2-1-2) 0.1094 2.84% 4.82% 3.29% 4.96% 8.44% 5.27% (2-1-3) 0.1246 3.23% 5.49% 3.74% 6.44% 10.94% 6.84% (2-1-4) 0.1121 2.91% 4.94% 3.37% 5.21% 8.86% 5.53% (2-2-1) 0.1587 4.11% 6.99% 4.77% 10.45% 17.75% 11.09% (2-2-2) 0.1096 2.84% 4.83% 3.29% 4.98% 8.47% 5.30% (2-2-3) 0.1182 3.06% 5.21% 3.55% 5.79% 9.85% 6.07% (2-2-4) 0.1135 2.94% 5.00% 3.41% 5.34% 9.08% 5.75% Total 1 25.92% 44.05% 30.03% 53.00% 90.07% 56.28% Table 8. AHP assessment results for criteria and CEUVs. Exi CEUV- Min/Max Criteria Overall Wi- Min Overall Wi- Max H1 H2 H3 H4 H1 H2 H3 H4 (2-1-1) 9.82% 16.69% 9.27 9.27 7.21 9.27 0.88/1.5 0.88/1.5 0.69/1.17 0.88/1.5 (2-1-2) 4.96% 8.44% 6.12 7.14 6.12 6.12 0.3/0.51 0.35/0.59 0.3/0.51 0.3/0.51 (2-1-3) 6.44% 10.94% 5.05 5.05 4.04 5.05 0.32/0.55 0.32/0.55 0.26/0.44 0.32/0.55 (2-1-4) 5.21% 8.86% 8.16 9.18 6.12 8.16 0.42/0.71 0.47/0.8 0.31/0.53 0.42/0.71 (2-2-1) 10.45% 17.75% 8.08 9.09 5.05 9.09 0.94/1.6 0.94/1.6 0.52/0.89 0.94/1.6 (2-2-2) 4.98% 8.47% 5.88 6.86 5.88 6.86 0.35/0.59 0.35/0.59 0.3/0.51 0.35/0.59 (2-2-3) 5.79% 9.85% 5.88 7.84 5.88 6.86 0.41/0.69 0.46/0.79 0.35/0.59 0.41/0.69 (2-2-4) 5.34% 9.08% 5.88 6.86 4.90 6.86 0.37/0.64 0.37/0.64 0.27/0.45 0.37/0.64 Total 53.00% 90.07% 54.32 61.29 45.20 58.27 - - - - Table 9. Assessment results for cases H1–H4. Criteria Wi H1 H1*Wi H2 H2*Wi H3 H3*Wi H4 H4*Wi (2-1-1) 15.39% 9.27 1.39 9.27 1.39 7.21 1.08 9.27 1.39 (2-1-2) 10.94% 6.12 0.66 7.14 0.77 6.12 0.66 6.12 0.66 (2-1-3) 12.46% 5.05 0.62 5.05 0.62 4.04 0.50 5.05 0.62 (2-1-4) 11.21% 8.16 0.90 9.18 1.01 6.12 0.67 8.16 0.90 (2-2-1) 15.87% 8.08 1.27 9.09 1.43 5.05 0.79 9.09 1.43 (2-2-2) 10.96% 5.88 0.66 6.86 0.77 5.88 0.66 6.86 0.77 (2-2-3) 11.82% 5.88 0.71 7.84 0.95 5.88 0.71 6.86 0.83 (2-2-4) 11.35% 5.88 0.68 6.86 0.79 4.90 0.57 6.86 0.79 Total 54.32 6.89 61.29 7.73 45.20 5.63 58.27 7.39 ICOWA 0.3401 0.2018 0.2151 0.2430 e 0.3401, 1.234, 0.072 0.2018, 1.391, 0.081 0.2151, 0.998, 0.058 0.2430, 1.328, 0.077 S ¼ ðEx ; En ; HeÞ. i i i i Using the ranking rules of cloud models, the ranking of the priority vector ICOWA H1 > H4 > H3 > H2 Figure 3. Schematic of the assessment results for case study H1-H4. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 257 in pursuit of endless high-end material life, practi- The findings are summarized as follows. The FDM was tioners should also fully recognize their social respon- used to select key factors to widen and deepen proces- sibilities, and endeavour to achieve the goal of green sing according to the opinions of decision-makers. The design capable of coexisting with the environment AHP and cloud theory were then used to determine (Akadiri et al., 2012; Chiang 2019; Shen et al. 2017). the weight of each criterion and decision-making fac- The use of construction waste management techni- tor. Additionally, the utility-based GID model was used ques which rely on recycle and reuse of materials has to rank alternatives and to select the most favorable been proven to have economic benefits for the con- cloud service. struction industry (Amponsah et al. 2012; Andrić et al. From the perspective of green design principles, we 2017). However, buyers’ participation in the design determined that the government currently requires process is highly limited, and developers usually deter- developers to research and implement environmen- mine their planning and initial residential design stra- tally friendly plans and corporate social responsibility tegies based on experience and intuition. The in many building development cases. construction and renovation plans can be designed in At the same time, developers must cater to the a way that promotes environmental protection. Our needs of residents of all ages, and integrate common experimental results provide a practical and unique design principles in addition to green ones during model for deciding on an indoor environment. In the planning and design stages in order to increase response to environmental changes such as climate the comfort and safety of residents. While pursuing and lifestyle, its form, function, and arrangement economic development, it is important to make deci- have constantly evolved. The comfort of living envir- sions that are humane and promote environmental onments can be achieved only through the planning sustainability. and design of spaces, in which real designers and The GID Utility-Based Model is a critical decision estate developers should consider the health of home- support tools that can be utilized to promote and buyers and corporate social responsibility, thus estab- facilitate application in the industry. lishing a corporate image. Some distinguished This study only considered interior-design compa- contributions of this study are as follows: nies. Follow-up researchers may expand the scope of Based on literature review and experts survey in real respondents to contractors, clients, and engineers to estate and interior design industrial domains, we fina - compare the diverse concerns regarding green building lize the 16 decision-making factors for company’s users materials, automated industries, and smart systems. The under each GID perspective. In addition, we also select government’s responsibility is to first provide structured the critical and meaningful decision-making factors by training support to obtain a comprehensive view of FDM. Initially, 10 decision-making factors under AHP industrial development. Although the ultimate goal is perspective are considered for cloud service selection. similar, the challenges and problems encountered by This paper proposed a hybrid GID Utility-Based various teams differ. Model for a cloud service selection using FDM, AHP and Cloud theory. 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Journal
Journal of Asian Architecture and Building Engineering
– Taylor & Francis
Published: May 4, 2021
Keywords: Indoor environment; interior renovations; cloud theory; utility-Based Model; green low-carbon
