The ILHWLAD-MCDM Framework for the Evaluation of Concrete Materials under an Intuitionistic Linguistic Fuzzy Environment

Chen, Junjie; Zhang, Chonghui; Li, Peipei; Xu, Mingxiao

doi:https://doi.org/10.1155/2020/8852842

Journal of Mathematics

On this page

Abstract Introduction Materials and Methods Conclusions Data Availability Conflicts of Interest References Copyright Related Articles

Special Issue

Decision Making Based on Intuitionistic Fuzzy Sets and their Generalizations

View this Special Issue

Research Article | Open Access

Volume 2020 | Article ID 8852842 | https://doi.org/10.1155/2020/8852842

The ILHWLAD-MCDM Framework for the Evaluation of Concrete Materials under an Intuitionistic Linguistic Fuzzy Environment

Junjie Chen,¹Chonghui Zhang,¹Peipei Li,¹and Mingxiao Xu¹

Academic Editor: Tahir Mahmood

Received26 Sept 2020

Revised15 Oct 2020

Accepted26 Oct 2020

Published16 Nov 2020

Abstract

Concrete, one of the sources of energy consumption and carbon emissions, is widely used in the construction industry. The selection of concrete materials raises the question of energy sustainability and turns it into a complex multicriteria decision-making (MCDM) issue. To address this, we present an MCDM framework based on the intuitionistic linguistic hybrid weighted logarithmic averaging distance (ILHWLAD). To begin with, the intuitionistic linguistic numbers are used to deal with the uncertainty and fuzziness of the decision-making process. In addition, in view of the significance and the ordered position of the input arguments, an intuitionistic linguistic hybrid weighted logarithmic averaging distance (ILHWLAD) operator is defined. We, then, initiate the criteria system and present the MCDM framework based on the ILHWLAD to select the finest concrete. A case study involving four alternative materials, namely, autoclaved aerated concrete (AAC), hollow concrete blocks (HCB), expanded polystyrene (EPS), and lime hemp concrete (LHC), is presented to verify the scientificity of the framework.

1. Introduction

The rapid advance of urbanization has meant that land available for construction is becoming increasingly scarce. However, the number of individuals who crowd into the city to improve their living standards is growing day by day [1]. This has led to an explosion in demand for housing because the existing stock does not meet citizens’ needs. Building materials have become an issue of crucial consideration, and because high-rise buildings accommodate more individuals, safety is critical [2]. Concrete, a mixture of paste and aggregate or rocks, is one of the most widely used building materials. Different kinds are used for building external filled walls, frame structured filled walls, non-load-bearing walls, load-bearing walls, roofs, floors, insulation systems, and so on [3–5]. However, as a result, energy consumption is increasing drastically and so is environmental pollution [6]. The United Nations Environment Program (UNEP) reports that the finest concrete materials account for more than 36% of global energy consumption and up to 40% of energy-related carbon dioxide emissions [7]. Therefore, the choice of suitable concrete for construction is of great significance for energy conservation and emissions reduction, manufacturing, composition, innovation, and so on.

Concrete gives buildings strength and durability. The concrete itself must have great compressive strength. Concrete also affects the thermal performance of buildings. Climatic conditions can change enormously [8]; concrete materials consume heat during the summer season, and this is stored and released in the night in winter, which reduces the effect of external temperature variations. In addition, the residential comfort of the building is not adversely affected because concrete is able to balance variations in outdoor humidity and to avoid excessive variations in humidity within the building. Because concrete is widely used in the construction industry, economy and accessibility are especially important in the selection process of the finest types.

It is difficult to use specific data in the analysis of the performance and functionality of specific concrete materials. For example, the energy of concrete material varies according to levels of technology, production conditions, and so on [9]. The intuitionistic linguistic number (ILN) is an effective way to resolve these issues, and it has been frequently used in different fields [10], e.g., energy performance contracting [11], strategy decisions [12], the selection of offshore wind farms [13], and assessment of green building insulation materials [14].

Compared to the value of language evaluation, ILN increases the degree of membership and nonmembership, which reflects the nonmembership degree of the language evaluation value and the magnitude of hesitation of decision makers [15]. We can utilize ILN to characterize the grades of each criterion for concrete materials because the information is hard to measure with specific values.

The ultimate goal of this paper is to select the finest concrete material. Its contribution to the field can be summarized as follows:(1)The intuitionistic linguistic weighted logarithmic average distance (ILWLAD) operator and the intuitionistic linguistic ordered weighted logarithmic average distance (ILOWLAD) operator are projected by combining the weighted logarithmic and the ordered weighted logarithmic average methods. Furthermore, to address the defects of ILWLAD and ILOWLAD, we introduce a new intuitionistic linguistic hybrid weighted logarithmic average distance (ILHWLAD) operator for better handling of the data.(2)As mentioned previously, the selection of concrete materials is a comprehensive decision-making problem. To improve the scientificity of the decision-making process, a multidimensional examination of concrete materials is required. Thus, a six-criterion evaluation system for concrete material selection has been developed, and four concrete materials have been graded using ILNs.(3)The HWLAD-MCDM framework of concrete material selection is presented [16,17], and the ILHWLAD operator is used to select the most suitable material by using the fuzzy concept. To illustrate the rationality of the ILHWLAD operator, we compare the results obtained by using the ILHWLAD operator with those obtained using the ILOWLAD operator, ILOWAD operator, and ILWLAD operator.

The remainder of this paper is arranged as follows: Section 2 introduces the evaluation criteria of concrete materials and briefly reviews the related concepts. Section 3 presents the new ILHWLAD operator and introduces the HWLAD-MCDM framework used to select the most suitable and finest concrete material. In Section 4, we apply the framework to the four alternative concrete materials and display the results. In addition, some comparisons are made and discussed to explain the rationale behind the ILHWALD operator. The conclusions, limitations, and recommendations for further applications are presented in Section 5.

2. Materials and Methods

2.1. Criteria for Concrete Materials Selection

Constructing the evaluation criteria system is a key step in the concrete materials selection process. In accordance with the available literature, four main aspects and six criteria were selected (see Table 1) [18, 23].

(1)Embodied energy (A1): the embodied energy of the concrete materials refers to the exact total energy consumed in the entire process of concrete material production. It includes production, processing, transportation, and construction, that is, is the sum of direct and indirect energy consumption. The lower the energy content, the lower the energy consumption of the concrete material and the more the energy that is saved.(2)Embodied carbon (A2): the concrete material in production, processing, and other processes expel carbon dioxide into the atmosphere. Carbon dioxide contributes to air pollution and provokes the global greenhouse effect, which is the cause of global climate change. Hence, the less the carbon contained in the concrete material, the lower the carbon dioxide expelled.(3)Purchase cost (A3): the finest concrete materials are frequently used in the construction business. The purchase cost of concrete changes greatly, and a reduction contributes to economies in construction costs. Therefore, the purchase cost can be regard as an economic criterion for measurement.(4)Thermal performance (A4): the outdoor temperature of buildings is generally high in the summer. Good thermal performance enables indoor temperatures to reach a lower level than the day’s peak temperature, thus improving the comfort of indoor living. In contrast, outdoor temperatures are generally lower in winter and reach their lowest point at the night. Therefore, the indoor temperature is capable of reaching a higher level at the lowest external night-time temperature as a result of the concrete’s outstanding thermal performance. In this paper, the highest indoor temperature of the building in summer and the lowest indoor temperature in winter at night-time are combined to measure the thermal performance of the concrete materials.(5)Ability to balance outdoor humidity fluctuations (A5): a criterion that measures the ability of certain concrete materials to balance outdoor humidity fluctuations is the variation range of indoor humidity within a day. The smaller the range, the better the ability to balance outdoor humidity fluctuations. We combine the indoor humidity variation of the building in the summer and in the winter to measure the ability of the concrete materials to balance fluctuations in outdoor humidity.(6)Compressive strength (A6): the compressive strength of concrete refers to the strength limit applied by external forces, which is obtained by a test of a cube specimen with a side length of 150 mm under the strength of C60. As has been noted, it is one of the factors that influence the stability of a building. The greater the compressive strength, the greater the maximum pressure the material can withstand.

In terms of energy sustainability, embodied energy and embodied carbon are the two main criteria that reflect the energy consumed and carbon dioxide emitted [19,20]. When taking the comfort of the building’s interior into account, the thermal performance of the concrete materials and the ability to balance outdoor humidity fluctuations are especially significant [34]. For the economy of the concrete materials, the purchase cost is the critical criterion by which to estimate economic possibilities, given concrete’s widespread use in the construction industry. Finally, the criterion of its compressive strength has to be considered because this is what makes buildings strong [30].

2.2. Weighting Method of Criteria

In this section, we briefly review some concepts related to the linguistic approach and the intuitionistic linguistic set (ILS) and present a programming model for calculating the weights of the criteria.

Definition 1. The linguistic approach is an approximate technique that expresses qualitative aspects as linguistic values through the use of linguistic terms [35]. For ease of calculation, let K = {k_a|a = 1, 2, …, t} be an ordered linguistic term set, where t is the positive odd value and k_a represents a possible value for a linguistic variable.
For instance, taking t = 7, a set K could be expressed as follows: = {extremely bad, quite bad, bad, medium, good, quite good, extremely good}.
Any label k_α should satisfy the following operational laws:(1)(2)(3)(4)Considering two linguistic terms k_α, k_β ∈ K, and μ > 0, the operations are defined as follows:(1)(2)

Definition 2. Let X be a nonempty set. An ILS A in X is, then, expressed aswhere and the and indicate the membership degree and nonmembership degree of the element to the set A, respectively. Hence, we have and , for all . For convenience, the ILN is generally denoted as .
More specifically, for each ILS A in X, we havewhere is called the hesitation degree of x to linguistic variable .
After the notion of the ILN has been presented, we then calculate the weights of criteria. The weight calculation methods commonly used to determine the weights when the information is completely unknown include the analytic hierarchy process (AHP) [36, 37] and entropy method [38, 39]. Here, the criterion information is partially known, so the programming model is preferred to calculate the weights. The steps are as follows: Step 1: establish a matrix with ILNs (u × ), which includes u different experts and different criteria Step 2: calculate the positive ideal solution (PIS) and negative ideal solution (NIS) for each criterion by using the following equations: where represents the value of PIS for the j-th criterion, and represents the value of NIS for the j-th criterion Step 3: determine an objective function by using the PIS and NIS values:where and represents the distance between two ILNs
For example, for the two ILNs A₁ and A₂, we have

2.3. The ILOWAD and the ILWALD Operators

In this section, some related operators are briefly reviewed, including the OWAD [40], the WLAD [41], the OWLAD [42], and the ILOWAD [43] measures.

Definition 3. Let A and B be two intuitionistic linguistic sets; the normalized hamming distance between A and B is given by the mathematical form:where and are the i-th ILN of A and B, respectively, and represents the i-th linguistic value of A or B.

Definition 4. Let and be two crisp sets and the distance between a_i and b_i. The OWAD measure is, then, defined aswhere is the j-th largest value of is the associated weighting vector of OWAD.

Definition 5. The WLAD operator of dimension n is a mapping has a relative weighting vector , with and .where represents the individual distance between x_i and y_i.

Definition 6. Let and be two crisp sets and be the distance between xi and yi. The OWLAD operator of dimension n is a mapping that has a relative weighting vector , with and , satisfyingwhere has the same meaning and value range as Definition 4.

Definition 7. The ILOWAD operator of dimension n is a mapping ILOWAD: Ωⁿ × Ωⁿ ⟶ Ω that is defined by an associated weighting vector W. Hence, the sum of weights is equal to 1 and . Then, we havewhere is the j-th largest value among the intuitionistic linguistic distance and and are the i-th ILN of A and B, respectively.

3. Proposed Method

3.1. Intuitionistic Linguistic Weighted Logarithmic Distance Measures

In this section, we present the intuitionistic linguistic weighted logarithmic average distance (ILWLAD) operator, the ILOWLAD operator, and the ILHWLAD operator.

Definition 8. The ILWLAD operator of dimension n is a mapping . This operator can be formulated as:where is the relative weighting vector of ILWLAD, , .

Definition 9. The ILOWLAD operator maps the parameter vector of dimension n to a real number, which has a relative weighting vector , with and . Hence, we define this operator as follows:where represents the j-th largest value of all intuitionistic linguistic distances .
In the aggregation process, the ILWLAD measure examines the importance of criteria and the ILOWLAD measure examines the importance of the ordered deviation. However, the ILWLAD is unable to perform the aggregation function in order, while the ILOWLAD fails to integrate the criteria in a way that the ILWLAD can. To compensate for this disadvantage, we present the ILHWLAD measure.

Definition 10. Let and two sets of ILN. An ILHWLAD operator of dimension n is a mapping . The ILHWLAD measure is given as follows:where represents the j-th largest value among , which is defined as is the weight vector corresponding to , and is the associated weight of the unordered value D_j, satisfying and . N is used as a balancing factor to compensate for the double weighting. Moreover, we can explore a wide range of special cases of the ILHWLAD operator utilizing the similar methods proven in [16, 44].

3.2. The MCDM Framework Based on the ILHWLAD Measure

The selection of concrete materials is a multicriteria decision-making problem. Based on the ILHWLAD operator, this section uses a weight programming model to calculate weight, and an MCDM framework is used to select the finest concrete material. The specific steps are shown in Figure 1.

An MCDM problem includes j different alternatives, denoted as , and a total of t experts are invited to evaluate the alternatives under k finite criteria . The process can be summarized into the following steps; Table 2: Step 1: each expert (the corresponding weight is τ_q, which meets and ) measures his or her performance using criteria from the ILNs. Afterwards, the individual decision matrix is obtained, where r_ij(q) is the evaluation of the alternative C_i by q-th experts with regard to criterion E_j. Step 2: calculate the collective decision matrix to aggregate individual evaluations, where . Step 3: establish the ideal alternative by setting the ideal performances for each criterion (see Table 2). Step 4: use the programming model presented to obtain the weights of criteria according to equation (5). Then, the weights of operators can be determined by experts. Step 5: the distances between the alternative and the ideal alternative I are computed by utilizing the ILHWLAD measure: Step 6: based on the value of the distances obtained in the previous steps, we can order the alternatives and select the finest one.

4. Case Study

4.1. Description of Concretes

The concrete materials used in the construction industry are mainly autoclaved aerated concrete (AAC) [10, 45], hollow concrete blocks (HCB) [46, 47], expanded polystyrene (EPS) [48, 49], and lime hemp concrete (LHC) [50].

LHC (C₁) is a new composite material of lime and hemp, which maintains an excellent thermal and moisture processing performance. Because of its lower embodied energy (EE) and embodied carbon (EC), the energy consumption and emission of carbon dioxide are compact during its manufacture. Its low mechanical property means that it is widely used in roofs, walls, slabs, and insulation.

AAC (C₂) forms numerous small air holes in the interior during the production process, so it possesses good heat and sound insulation functions. Moreover, it is relatively light, and the density is about 1/3 of clay brick. It is generally used in the outside filled walls of buildings and non-load-bearing internal partitions.

HCB (C₃) is of low density and possesses a good thermal performance, which are advantageous for masonry. It is commonly used in industrial and civil buildings, particularly in bearing walls and frame structure fill walls of multistorey buildings. It is also frequently adopted to construct fences, flower beds, bridges, and so on.

EPS (C₄) is a lightweight polymer with good thermal insulation. It is commonly used in the heat insulation system of external walls, roofs, and floors of buildings.

4.2. Decision Procedure

In this section, we use the framework to deal with selection problems under IL environments. Four possible concretes are evaluated from the following criteria: embodied energy (E1); embodied carbon (E2); purchase cost (E3); thermal performance (E4); ability to balance outdoor humidity fluctuation (E5); and compressive strength (E6). Four experts (expert’s weight ) utilize IL information to evaluate four candidate concretes under six criteria, where the linguistic term set is assumed to be ; Tables 3–9 and Table 10. Step 1: let each expert express evaluation of four concrete materials under given criteria through ILNs. The intuitionistic linguistic individual decision matrixes are shown in Tables 3–6. Step 2: on the basis of the individual decision matrices and weights of the experts, we can obtain the collective decision matrix; see Table 7. Step 3: having acquired the relevant information on the four concrete materials, the experts construct the ideal concrete with good performance in each criterion; see Table 8. Step 4: according to Table 7 and equations (2)−(4), the PIS and NIS of each criterion are determined; see Table 9. The distance matrix is calculated in accordance with equation (6); see Table 10. In many real-world situations, the information about criteria weights is incomplete, so we should first determine the criteria weights in Step 4 if the known weight information on the criteria is set as in the following set: . The objective function and the constraints are determined by using equation (5). The objective function is given by Finally, the weighting vector of the criteria is obtained as ω = (0.15, 0.2, 0.25, 0.15, 0.1, 0.15)^T using the Python programming language. In the meantime, the weighting vectors of the ILHWLAD are set to be = (0.2, 0.15, 0.25, 0.1, 0.15, 0.15)^T. Step 5: in accordance with equation (15) and the available information, the distances between the alternatives and the ideal concrete are computed by using the ILHWLAD as follows:(1)ILHWLAD (C₁, I) = 0.3651(2)ILHWLAD (C₂, I) = 0.3717(3)ILHWLAD (C₃, I) = 0.4319(4)ILHWLAD (C₄, I) = 0.5338 Step 6: the smaller the value of the ILHWLAD (C_i, I), the closer the C_i to the ideal concrete. Therefore, we can rearrange the order of C_i : C₁ ≻ C₂ ≻ C₃ ≻ C₄

Hence, the best alternative is C₁.

4.3. Comparisons and Discussion

In this section, to verify the superiority and rationality of the ILHWLAD method, and the results of the ILOWLAD, the ILOWAD, and the ILWLAD measures are compared with those of the ILHWLAD measures in the selection of the concrete materials. According to equation (13), the distances between the alternatives and the ideal concrete are calculated by using the ILOWLAD operator as follows:(1)ILOWLAD (C₁, I) = 0.3786(2)ILOWLAD (C₂, I) = 0.3991(3)ILOWLAD (C₃, I) = 0.4445(4)ILOWLAD (C₄, I) = 0.5476

By the ILOWAD measure, we have(1)ILOWAD (C₁, I) = 0.3963(2)ILOWAD (C₂, I) = 0.4579(3)ILOWAD (C₃, I) = 0.4516(4)ILOWAD (C₄, I) = 0.5499

The results obtained by the ILWLAD measure are(1)ILWLAD (C₁, I) = 0.3563(2)ILWLAD (C₂, I) = 0.3387(3)ILWLAD (C₃, I) = 0.4237(4)ILWLAD (C₄, I) = 0.5455

Thus, the final ranking of the four alternatives according to the ILOWLAD, the ILOWAD, and the ILWLAD measures are C₁ ≻ C₂ ≻ C₃ ≻ C₄, C₁ ≻ C₃ ≻ C₂ ≻ C₄, and C₂ ≻ C₁ ≻ C₃ ≻ C₄, respectively. The final results are shown in Table 11.

The ranking results obtained by the abovementioned four methods are contrasting. As Table 11 shows, LHC is the finest concrete material measured by the ILHWLAD, ILOWLAD, and ILOWAD measures. LHC performs very well in terms of thermal performance, embodied energy, and embodied carbon; it is a high-quality environmentally friendly insulating material. A building constructed with LHC not only reduces carbon dioxide emissions but also cuts down energy consumption. However, according to the measurement results of the ILWLAD operator, AAC is the finest concrete material. EPS performs poorly under all four measures, mainly because, according to the evaluation criteria system designed for this paper, its compressive strength is low, it consumes more energy, and produces more carbon dioxide in the manufacturing process than the other three materials.

The reasons for the inconsistent results can be summarized as follows:

First, ILOWLAD and ILOWAD take the ordering mechanism of the parameters into account and pay more attention to the importance of ordered deviation. But, ILWLAD considers the importance of the criteria. Under this measure, AAC is superior to the other three concrete materials in the economic aspect. Hence, the distance between AAC and the ideal solution is lowest in terms of the purchase cost criterion. However, during the aggregation process of the ILOWLAD and the ILOWAD operators, the higher weights are coordinated by those larger intuitionistic linguistic distance values, in which LHC performs best. Hence, the distance between it and the ideal solution is the smallest.

Second, unlike the ILOWAD operator, the ILOWLAD operator performs logarithmic transformation of distance. If the evaluation of an alternative is closer to the ideal solution under a certain criterion, the advantage of logarithmic transformation will be clearer. In this case, AAC is the closest to the ideal solution under the purchase cost criterion, which increases the gap between AAC and HCB, so that the second and third place results obtained by the two measures are different.

As has been noted, ILWLAD prioritizes the criteria, whereas the ILOWLAD measure only accounts for the importance of the ordered deviation. Therefore, ILWLAD and ILOWLAD take into account different stages in the assembly process. ILHWLAD makes up for this deficiency by taking input arguments and the ordered position into account simultaneously. Hence, it is the modest among the four operators.

5. Conclusions

Concrete plays a vital role in the construction industry; it helps to determine the strength, thermal performance, and relative humidity of buildings. At the same time, it is one of the major sources of energy consumption and carbon emissions, though certain types of concrete contribute to energy sustainability more than others. In this paper, we proposed an HWLAD-MCDM framework for the selection of the finest concrete materials under an intuitionistic linguistic fuzzy environment. The four alternative concrete materials LHC, AAC, HCB, and EPS were evaluated using six criteria (embodied energy, embodied carbon, purchase cost, thermal performance, ability to balance outdoor humidity fluctuations, and compressive strength), which encompass energy sustainability, economic performance, comfort, and safety. The OWLAD, WLAD, ILOWLAD, and ILWLAD operators were used in combination with an ILS. We, then, presented the new ILHWLAD operator, which addresses the limitations of the latter two. In addition, instead of using the traditional AHP and entropy method, we used a programming model to calculate the weight under incomplete information. The MCDM method based on the ILHWLAD operator was proposed for the selection of the finest concrete material, and the results are compared with those obtained by ILOWLAD, ILOWAD, and ILWLAD.

The MCDM framework based on the ILHWLAD operator allows a new way of making decisions, and its field of application is not limited to the selection of concrete materials. In further research, both methodological developments and new areas of application should be considered. With regard to the former, the expert weighting scheme could be employed in a more objective manner. In addition, we should try to expand further the new operator and implement it in more complex areas.

Data Availability

All data generated and analyzed during this study are included in the published paper.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

C. Zhu, X. Zhang, K. Wang, S. Yuan, L. Yang, and M. Skitmore, “Urban-rural construction land transition and its coupling relationship with population flow in China's urban agglomeration region,” Cities, vol. 101, Article ID 102701, 2020.
View at: Publisher Site | Google Scholar
T. T. Moghadam and M. Feizabadi, “Increasing ecological capacity by designing ecological high rise buildings,” Open House International, vol. 43, pp. 94–104, 2018.
View at: Google Scholar
K. Gourav, S. N. Ullas, and B. V. Venkatarama Reddy, “Studies on properties of flowable earth mix concrete for monolithic load bearing walls,” Construction and Building Materials, vol. 250, Article ID 118876, 2020.
View at: Publisher Site | Google Scholar
A. Cardoni and G. P. Cimellaro, “The role of reinforced concrete roofs in the seismic performance of masonry buildings,” Journal of Building Engineering, vol. 28, Article ID 101056, 2020.
View at: Publisher Site | Google Scholar
S. F. Seyyedalipour, D. Yousefi Kebria, and M. Dehestani, “Effects of recycled paperboard mill wastes on the properties of non-load-bearing concrete,” International Journal of Environmental Science and Technology, vol. 12, no. 11, pp. 3627–3634, 2015.
View at: Publisher Site | Google Scholar
W. Su, Y. Ye, C. Zhang, T. Baležentis, and D. Štreimikienė, “Sustainable energy development in the major power-generating countries of the European Union: the Pinch analysis,” Journal of Cleaner Production, vol. 256, Article ID 120696, 2020.
View at: Publisher Site | Google Scholar
W. Z. Taffese and K. A. Abegaz, “Embodied energy and CO2 emissions of widely used building materials: the Ethiopian context,” Buildings, vol. 9, Article ID 9060136, 2019.
View at: Publisher Site | Google Scholar
R. Haik, A. Peled, and I. A. Meir, “The thermal performance of lime hemp concrete (LHC) with alternative binders,” Energy and Buildings, vol. 210, p. 109740, 2020.
View at: Publisher Site | Google Scholar
N. Emami, H. Giles, and P. von Buelow, “Structural, daylighting, and energy performance of perforated concrete shell structures,” Automation in Construction, vol. 117, Article ID 103249, 2020.
View at: Publisher Site | Google Scholar
X. Gou, Z. Xu, H. Liao, F. Herrera, and F. Herrera, “Consensus model handling minority opinions and noncooperative behaviors in large-scale group decision-making under double hierarchy linguistic preference relations,” IEEE Transactions on Cybernetics, vol. 42, no. 1, pp. 1–14, 2020.
View at: Publisher Site | Google Scholar
Y. Wu and J. Zhou, “Risk assessment of urban rooftop distributed PV in energy performance contracting (EPC) projects: an extended HFLTS-DEMATEL fuzzy synthetic evaluation analysis,” Sustainable Cities and Society, vol. 47, Article ID 101524, 2019.
View at: Publisher Site | Google Scholar
W. Su, W. Li, S. Zeng, and C. Zhang, “Atanassov's intuitionistic linguistic ordered weighted averaging distance operator and its application to decision making,” Journal of Intelligent & Fuzzy Systems, vol. 26, no. 3, pp. 1491–1502, 2014.
View at: Publisher Site | Google Scholar
J. Gao, F. Guo, Z. Ma, X. Huang, and X. Li, “Multi-criteria group decision-making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators,” Energy, vol. 204, Article ID 117899, 2020.
View at: Publisher Site | Google Scholar
D. Streimikiene, V. Skulskis, T. Balezentis, and G. P. Agnusdei, “Uncertain multi-criteria sustainability assessment of green building insulation materials,” Energy and Buildings, vol. 219, pp. 1–8, 2020.
View at: Publisher Site | Google Scholar
J. Q. Wang and H. B. Li, “Multi-criteria decision-making method based on aggregation operators for intuitionistic linguistic fuzzy numbers,” Control and Decision, vol. 25, pp. 1571–1574, 2010.
View at: Google Scholar
S. Zeng, D. Luo, C. Zhang, and X. Li, “A correlation-based TOPSIS method for multiple attribute decision making with single-valued neutrosophic information,” International Journal of Information Technology & Decision Making, vol. 19, no. 1, pp. 343–358, 2020.
View at: Publisher Site | Google Scholar
S. Zeng, Y. Hu, T. Balezentis, and D. Streimikiene, “A multi‐criteria sustainable supplier selection framework based on neutrosophic fuzzy data and entropy weighting,” Sustainable Development, vol. 28, no. 5, pp. 1431–1440, 2020.
View at: Publisher Site | Google Scholar
G. P. Hammond and C. I. Jones, “Embodied energy and carbon in construction materials,” Proceedings of the Institution of Civil Engineers-Energy, vol. 161, no. 2, pp. 87–98, 2008.
View at: Publisher Site | Google Scholar
K. I. Praseeda, B. V. V. Reddy, and M. Mani, “Embodied energy assessment of building materials in India using process and input-output analysis,” Energy and Buildings, vol. 86, pp. 677–686, 2015.
View at: Publisher Site | Google Scholar
R. Kumanayake, H. Luo, and N. Paulusz, “Assessment of material related embodied carbon of an office building in Sri Lanka,” Energy and Buildings, vol. 166, pp. 250–257, 2018.
View at: Publisher Site | Google Scholar
W. Zhu, W. Feng, X. Li, and Z. Zhang, “Analysis of the embodied carbon dioxide in the building sector: a case of China,” Journal of Cleaner Production, vol. 269, Article ID 122438, 2020.
View at: Publisher Site | Google Scholar
P. S. M. Thilakarathna, S. Seo, K. S. K. Baduge, H. Lee, P. Mendis, and G. Foliente, “Embodied carbon analysis and benchmarking emissions of high and ultra-high strength concrete using machine learning algorithms,” Journal of Cleaner Production, vol. 262, Article ID 121281, 2020.
View at: Publisher Site | Google Scholar
M. Rohan, “Cement and concrete industry integral part of the circular economy,” Revista Romana De Materiale-Romanian Journal of Materials, vol. 46, pp. 253–258, 2016.
View at: Google Scholar
C. Butean and B. Heghes, “Cost efficiency of a two layer reinforced concrete beam,” Procedia Manufacturing, vol. 46, pp. 103–109, 2020.
View at: Publisher Site | Google Scholar
M. Bravo, J. Brito, and L. Evangelista, “Thermal performance of concrete with recycled aggregates from CDW plants,” Applied Sciences-Basel, vol. 7, Article ID 707740, 2017.
View at: Publisher Site | Google Scholar
A. Solomon and G. Hemalatha, “Characteristics of expanded polystyrene (EPS) and its impact on mechanical and thermal performance of insulated concrete form (ICF) system,” Structures, vol. 23, pp. 204–213, 2020.
View at: Google Scholar
C. Zhang, Q. Wang, S. Zeng et al., “Probabilistic multi-criteria assessment of renewable micro-generation technologies in households,” Journal of Cleaner Production, vol. 212, pp. 582–592, 2019.
View at: Publisher Site | Google Scholar
Y. Jin, F. Wang, M. Carpenter, R. B. Weller, D. Tabor, and S. R. Payne, “The effect of indoor thermal and humidity condition on the oldest-old people's comfort and skin condition in winter,” Building and Environment, vol. 174, Article ID 106790, 2020.
View at: Publisher Site | Google Scholar
C. Zhang, C. Chen, D. Streimikiene, and T. Balezentis, “Intuitionistic fuzzy MULTIMOORA approach for multi-criteria assessment of the energy storage technologies,” Applied Soft Computing, vol. 79, pp. 410–423, 2019.
View at: Publisher Site | Google Scholar
S. B. Shaik, J. Karthikeyan, and P. Jayabalan, “Influence of using agro-waste as a partial replacement in cement on the compressive strength of concrete - a statistical approach,” Construction and Building Materials, vol. 250, Article ID 118746, 2020.
View at: Publisher Site | Google Scholar
J. Xie, H. Zhang, L. Duan et al., “Effect of nano metakaolin on compressive strength of recycled concrete,” Construction and Building Materials, vol. 256, Article ID 119393, 2020.
View at: Publisher Site | Google Scholar
A. Kandiri, E. Mohammadi Golafshani, and A. Behnood, “Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm,” Construction and Building Materials, vol. 248, Article ID 118676, 2020.
View at: Publisher Site | Google Scholar
U. Anyaoha, A. Zaji, and Z. Liu, “Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal,” Construction and Building Materials, vol. 257, Article ID 119472, 2020.
View at: Publisher Site | Google Scholar
F. Pittau, F. Krause, G. Lumia, and G. Habert, “Fast-growing bio-based materials as an opportunity for storing carbon in exterior walls,” Building and Environment, vol. 129, pp. 117–129, 2018.
View at: Publisher Site | Google Scholar
X. Gou, Z. Xu, and W. Zhou, “Managing consensus by multi-stage optimization models with linguistic preference orderings and double hierarchy linguistic preferences,” Technological and Economic Development of Economy, vol. 26, no. 3, pp. 642–674, 2020.
View at: Publisher Site | Google Scholar
T. L. Saaty, “Decision making with the analytic hierarchy process,” International Journal of Services Sciences, vol. 1, no. 1, pp. 83–98, 2008.
View at: Publisher Site | Google Scholar
B. D. Rouyendegh, “Developing an integrated AHP and intuitionistic fuzzy TOPSIS methodology,” International Journal of Services Sciences, vol. 21, pp. 1313–1320, 2014.
View at: Google Scholar
M. Xia and Z. Xu, “Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment,” Information Fusion, vol. 13, no. 1, pp. 31–47, 2012.
View at: Publisher Site | Google Scholar
D. A. Wood, “Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting,” Journal of Natural Gas Science and Engineering, vol. 28, pp. 594–612, 2016.
View at: Publisher Site | Google Scholar
J. M. Merigó and A. M. Gil-Lafuente, “New decision-making techniques and their application in the selection of financial products,” Information Sciences, vol. 180, no. 11, pp. 2085–2094, 2010.
View at: Publisher Site | Google Scholar
L. G. Zhou and H. Y. Chen, “Generalized ordered weighted logarithm aggregation operators and their applications to group decision making,” International Journal of Intelligent Systems, vol. 25, pp. 683–707, 2010.
View at: Google Scholar
V. G. Alfaro-García, J. M. Merigó, A. M. Gil-Lafuente, and J. Kacprzyk, “Logarithmic aggregation operators and distance measures,” International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1488–1506, 2018.
View at: Publisher Site | Google Scholar
J. F. Wang, S. Z. Zeng, and C. H. Zhang, “Single-valued neutrosophic linguistic logarithmic weighted distance measures and their application to supplier selection of fresh aquatic products,” Mathematics, vol. 8, Article ID 8030439, 2020.
View at: Publisher Site | Google Scholar
S. Zeng, X. Peng, T. Baležentis, and D. Streimikiene, “Prioritization of low-carbon suppliers based on Pythagorean fuzzy group decision making with self-confidence level,” Economic Research-Ekonomska Istraživanja, vol. 32, no. 1, pp. 1073–1087, 2019.
View at: Publisher Site | Google Scholar
Z. Owsiak and A. Soltys, “The influence of a halloysite additive on the performance of autoclaved aerated concrete,” Ceramics-Silikaty, vol. 59, pp. 24–28, 2015.
View at: Google Scholar
G. H. Santos, M. A. Fogiatto, and N. Mendes, “Numerical analysis of thermal transmittance of hollow concrete blocks,” Journal of Building Physics, vol. 41, pp. 7–24, 2017.
View at: Google Scholar
F. Zhu, Q. Zhou, F. Wang, and X. Yang, “Spatial variability and sensitivity analysis on the compressive strength of hollow concrete block masonry wallettes,” Construction and Building Materials, vol. 140, pp. 129–138, 2017.
View at: Publisher Site | Google Scholar
F. Giuliani, F. Autelitano, E. Garilli, and A. Montepara, “Expanded polystyrene (EPS) in road construction: twenty years of Italian experiences,” Transportation Research Procedia, vol. 45, pp. 410–417, 2020.
View at: Publisher Site | Google Scholar
Y. A. Y. Ali, E. H. A. Fahmy, M. N. AbouZeid, Y. B. I. Shaheen, and M. N. A. Mooty, “Use of expanded polystyrene in developing solid brick masonry units,” Construction and Building Materials, vol. 242, Article ID 118109, 2020.
View at: Publisher Site | Google Scholar
R. Walker, S. Pavia, and R. Mitchell, “Mechanical properties and durability of hemp-lime concretes,” Construction and Building Materials, vol. 61, pp. 340–348, 2014.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Junjie Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

302

Downloads

614

Citations