Multi-criteria decision-making approach for selecting a structural system of an industrial facility

Structural systems transfer the loads of the structures to the ground safely with their own weight and provide static balance. The purpose of the structural system is to design the ideal system that meets the reliability, economy, functionality and aesthetic demands (Ambrose 1993). The use of new construction technologies and construction materials plays a critical role in structural system design. Therefore, given the aim of selecting the most appropriate structural system for a project, it is important to decide according to the economic, environmental and durability advantages of the systems.

In Turkey, there are two most common construction methods preferred for industrial facilities, namely prefabricated reinforced concrete (PRC) and on-site reinforced concrete (RC) systems. On-site RC structural systems constitute the option that is largely favoured in Turkey, since it is characterised by the advantages of ease of construction, a larger pool of qualified employees having the needed skillset to choose from and fire resistance (Park and Paulay 1975; Nawy 1985). However, the construction process of the RC construction could be adversely affected by weather conditions, and the demolition of the building that has completed its economic life could be expensive (Pun et al. 2006). PRC structural systems are produced by using various production techniques and assembled on site (Evstratova et al. 2021). By choosing PRC building elements, investors save in terms of formwork, scaffolding material and labour costs (Gonzalez-Libreros et al. 2020). In addition, the fast production, the ability to use the structure immediately after the assembly of the building elements and the potential to enlarge the structure in the future are among the advantages of this structural system.

On the other hand, steel structural systems are used in almost all types of buildings, including large-span industrial buildings, roof systems, shopping malls, offices and high-rise residences, depending on the advantage provided by the material. The fact that these systems provide high carrying capacity with small sections is the reason why they are widely used in industrial facilities with wide openings (Trahair et al. 2017). In addition, the advantages of steel systems are rapid and easy-to-control production, lighter weight of the structure and being reusable and recyclable. The disadvantages of using steel material as a structural system in buildings are corrosion and poor fire resistance.

Structural system selection is a complicated decision problem due to the fact that structures could be built with various construction methods/technologies by using different materials. Different criteria are effective in determining the most proper structural system that needs to be used in the construction of a building. Each criterion has an influence on the budget, time, sustainability and aesthetic aspect of the project. Furthermore, the decision problem involves weighting several structural system alternatives against these conflicting criteria, which becomes more complicated when evaluation is done by multiple decision makers. Multi-criteria decision making (MCDM) methods are one of the most promising approaches used to solve these complex decision problems (Jiang et al. 2019).

Literature review

The MCDM approach has been widely used in the construction industry for the selection of alternatives for construction plans, materials and also methods (Ahmed et al. 2019). Dan (2004) implemented MCDM methods for retrofitting existing buildings. Pan (2008) proposed a fuzzy analytic hierarchy process (AHP) model to evaluate the suitability of different bridge construction methods. Caterino et al. (2009) examined the applicability and effectiveness of different MCDM methods for the seismic retrofit of structures. The authors chose the technique for order of preference by similarity to ideal solution (TOPSIS) and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) methods to address the seismic retrofitting problem. Malekly et al. (2010) developed a fuzzy-TOPSIS model to select the most suitable superstructure design for highway bridges. Lombera and Aprea (2010) used Integrated Value Model for Sustainable Assessment (MIVES) method to calculate the sustainable performance of two different industrial buildings. Golestanifar et al. (2011) evaluated the convenience of different rock tunnel excavation methods by using an AHP–fuzzy-TOPSIS hybrid method. Bobylev (2011) carried out a multi-criteria comparison of several underground construction technologies for conduit sewer systems by using Analytic Network Process (ANP) method. Korkmaz et al. (2012) evaluated the active control performance of cables in tensegrity structures by determining the most efficient cable configuration using the Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) method. Pons and Aguado (2012) proposed a combination of MIVES and Life Cycle Assessment (LCA) models to assess the most sustainable design for build schools. Balali et al. (2014) utilised the AHP and PROMETHEE techniques for selection of the appropriate structural system to be applied in the case of 3D panels with light walls in building frames, light steel frame, insulating concrete formwork, tunnel formwork system and TRONCO system in a low-rise multi-housing project. The selected criteria are cost, ease of construction, energy saving, dead load, number of stories and life cycle time. Formisano et al. (2017) examined the seismic retrofitting of an existing RC school building by using TOPSIS method. Fernandes et al. (2017) used AHP method as a structural optimisation method. Alshamrani and Alshibani (2020) developed an automated decision support system (DSS) that would facilitate school districts to select the best envelope and structural systems for new educational facilities.

On reviewing the literature, it is learnt that the use of MCDM for the selection of the structural system is limited. Furthermore, the application of MCDM methods in the selection of structural system for an industrial facility is a novel application for the construction industry. In the present study, an approach that integrates AHP and TOPSIS has been implemented for the ranking of structural system alternatives using the technical characteristics and the performance indicators.

Research methodology

In this study, the selection of the structural system of an industrial facility project in Manisa, Turkey was carried out by using MCDM methods. Eight evaluation criteria – project cost, project duration, project lifetime, the need for labour and equipment, recycling, fire resistance, suitability for installation and natural lighting needs – were determined from the literature for the selection process. PRC, on-site RC and steel structural system alternatives were evaluated according to each criterion by a survey study conducted by 193 civil engineer participants. The weights of criteria were determined by using AHP method, and structural system alternatives were ranked according to these criteria. TOPSIS method was also applied to rank the alternatives according to these weights. Finally, the results were compared with two MCDM methods. Figure 1 illustrates the methodology used in this research.

3.1

Analytic hierarchy process method

AHP was introduced by Saaty (1980), and it is an effective method for complex decision-making problems (Zhou et al. 2018). In this method, the decision maker could evaluate their preferences by considering multiple qualitative or quantitative criteria. The relationships between the objective, evaluation criteria and alternatives are shown in a hierarchical structure. The AHP method provides a systematic and scientific solution to complex decision problems (Chen and Deng 2018). In general, the process involved in the AHP method could be conducted in three stages:

Step 1. The pairwise comparison judgement matrix: The comparison judgement matrix (A) is constituted from positive values according to the pairwise comparisons of the criteria and alternatives (a_ij > 0, i, j = 1, 2, …, n) [Eq. (1)]. (1) $A = [\begin{matrix} a_{11} & a_{12} & \dots & a_{1 n} \\ a_{21} & a_{22} & \dots & a_{2 n} \\ \dots & \dots & \dots & \dots \\ a_{n 1} & a_{n 2} & \dots & a_{nn} \end{matrix}] = [\begin{matrix} 1 & a_{12} & \dots & a_{1 n} \\ 1 / a_{12} & 1 & \dots & a_{2 n} \\ \dots & \dots & 1 & \dots \\ 1 / a_{1 n} & \dots & \dots & 1 \end{matrix}]$ A = \left[ {\matrix{ {{a_{11}}} & {{a_{12}}} & \cdots & {{a_{1n}}} \cr {{a_{21}}} & {{a_{22}}} & \cdots & {{a_{2n}}} \cr \cdots & \cdots & \cdots & \cdots \cr {{a_{n1}}} & {{a_{n2}}} & \cdots & {{a_{nn}}} \cr } } \right] = \left[ {\matrix{ 1 & {{a_{12}}} & \cdots & {{a_{1n}}} \cr {1/{a_{12}}} & 1 & \cdots & {{a_{2n}}} \cr \cdots & \cdots & 1 & \cdots \cr {1/{a_{1n}}} & \cdots & \cdots & 1 \cr } } \right]

Each value of a_ij represents the importance of the i^th element on the j^th element. The relative importance between two decision elements is compared by using Table 1 (Saaty 1980): Tab. 1

Pairwise comparison scale (Saaty 1980).

Value of a_ij	Definition
1	i and j are equally important
3	i is slightly more important than j
5	i is more important than j
7	i is strongly more important than j
9	i is absolutely more important than j
2, 4, 6, 8	Intermediate values between the two adjacent judgements

Step 2. Derivation of the Eigen value and Eigen vector: The values of pairwise comparison matrix should be normalised and the Eigen vector is acquired by determining the mean values of each line in the normalised comparison matrix. The Eigen matrix is computed by multiplying the Eigen vector and the pairwise comparison matrix. The Eigen vector corresponding to the largest Eigen value could be seen as the final criterion for ranking.

Step 3. Calculation of consistency ratio (CR): Consistency index (CI) shows the consistency within each pairwise comparison judgement matrix, which is defined as Eq. (2), where λ_max represents the maximum Eigen value and n is matrix size: (2) $CI = \frac{(I_{\max} - n)}{(n - 1)}$ {\rm{CI}} = {{\left( {{{\rm{I}}_{\max }} - n} \right)} \over {\left( {n - 1} \right)}}

CR could be estimated by using Eq. (3), where Random Index (RI) is the random CI taken from Table 2 (Saaty and Özdemir 2003). The CR must be <0.10 (CR < 0.10), and if not, the calculation is considered as not consistent. (3) $CR = CI / RI$ {\rm{CR}} = CI/RI

Tab. 2

Random CI (Saaty and Ozdemir 2003).

n	1	2	3	4	5	6	7	8	9	10	11	12
RI	0.00	0.00	0.52	0.89	1.11	1.25	1.35	1.40	1.45	1.49	1.52	1.54

CI, consistency index.

3.2

Technique for order of preference by similarity to ideal solution

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, developed by Hwang and Yoon (1981), is based on selecting the alternative having the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution by using the Euclidean distance to calculate the relative proximity (Ding et al. 2016). The application steps of the TOPSIS method are as follows:

Step 1. Normalisation of the decision matrix: The values of the decision matrix are normalised by using Eq. (4), where a_ij is the deterministic value of alternative i for criterion j, and x_ij is the normalised score of decision matrix (i = 1, 2, …, m; and j = 1, 2, …, n). (4) $x_{ij} = \frac{a_{ij}}{\sqrt{\sum_{i = 1}^{m} a_{ij}^{2}}}$ {x_{ij}} = {{{a_{ij}}} \over {\sqrt {\sum\nolimits_{i = 1}^m {a_{ij}^2} } }}

Step 2. Obtaining the weighted normalised matrix: The weights of the evaluation criteria are determined by the decision makers and represented as w_j. By multiplying the normalised matrix with the criterion weights (w_j), the weighted normalised matrix (V_ij) is obtained.

Step 3. Determining the ideal and non-ideal solution matrices: The ideal and negative ideal solution matrices are determined by using Eqs (5) and (6). (5) $A^{*} = {(\max_{i} v_{ij} | j \in J), (\min_{i} v_{ij} | j \in J^{'}}$ {A^*} = \left\{ {(\mathop {max }\limits_i \;{v_{ij}}|j \in J),(\mathop {min}\limits_i \;{v_{ij}}|j \in J'} \right\} (6) $A^{-} = {(\min_{i} v_{ij} | j \in J), (\max_{i} v_{ij} | j \in J^{'}}$ {A^-} = \left\{ {(\mathop {min }\limits_i \;{v_{ij}}|j \in J),(\mathop {max}\limits_i \;{v_{ij}}|j \in J'} \right\}

Step 4. Calculation of separation distances: S* is an alternative distance from the ideal solution and defined as Eq. (7). S⁻ is an alternative distance from the negative ideal solution and is defined as Eq. (8). (7) $S_{i}^{*} = \sqrt{\sum_{j = 1}^{n} {(v_{ij} - v_{j}^{*})}^{2}}$ S_i^* = \sqrt {\sum\nolimits_{j = 1}^n {{{\left( {{v_{ij}} - v_j^*} \right)}^2}} } (8) $S_{i}^{-} = \sqrt{\sum_{j = 1}^{n} {(v_{ij} - v_{j}^{-})}^{2}}$ S_i^ - = \sqrt {\sum\nolimits_{j = 1}^n {{{\left( {{v_{ij}} - v_j^ - } \right)}^2}} }

Step 5. Calculation of proximity according to the ideal solution: The ideal and negative ideal separation distances are used to calculate the proximity (C_i^*) of each decision point to the ideal solution by Eq. (9). An alternative with the largest value of C_i^* is selected as the most suitable alternative for the decision problem. (9) $C_{i}^{*} = \frac{S_{i}^{-}}{S_{i}^{-} + S_{i}^{*}}$ C_i^* = {{S_i^ - } \over {S_i^ - + S_i^*}}

Application of the MCDM methods

4.1

Case study

In order to apply AHP and TOPSIS methods to the structural system selection problem, an industrial facility was selected for the case study, which is located in Manisa, Turkey. The industrial facility is going to be used for an olive oil production factory and is established on an area of approximately 6,300 m² with a constructional area of 2,818 m². The facility has a rectangular building area that measures 40.40 m × 68.40 m (Figure 2). The building is designed for PRC, on-site RC and steel structural system on ETABS. The static and dynamic analyses of the building for each structural system alternative are conducted, and quantity take-offs arising from these calculations are made within the scope of the study.

The structural design of the industrial facility.

4.2

Selection of evaluation criteria

In order to find the optimum solution to the structural system selection problem, evaluation criteria were determined according to the literature review. In the literature, it is seen that there are plenty of different criteria selected for this decision problem. Yildirim (2003) preferred the evaluation criteria for the structural system selection as: the resistance to external conditions, earthquake safety, fire safety, wind resistance, construction energy, material production energy, reuse of the material, number of floors, openings to be passed, usable interior volume, external appearance, interchangeability, disassembly, construction cost, operating costs, labour and construction machine requirement, construction period and service life. Kuzman and Grošelj (2012) determined the evaluation criteria as: construction time, construction cost, depreciation costs, design and embedded energy. Balali et al. (2014) chose the evaluation criteria for the selection of a suitable structural system for a housing project as: cost, dead load, feasibility, number of coefficients, conservation of energy and lifecycle time. Dagilgan (2019) chose the evaluation criteria for the selection of a suitable structural system for crossing wide gaps as: place of use, passable span, suitability for prefabrication, acoustic effect, suitability for installation, natural illumination possibility, system section according to the opening (h/l), joint details, fire resistance, resistance to environmental influences, energy, workmanship and construction equipment requirements, manufacturing and assembly process, lifecycle and disassembly and recycling facilities.

In order to select the evaluation criteria to be used in this study, all the criteria in the literature were presented to the project stakeholders such as the client and the main contractor. The project stakeholders selected the evaluation criteria they wanted to prioritise for the industrial facility project through brainstorming by considering the Turkish construction sector dynamics. Consequently, eight different evaluation criteria were determined for the structural system selection problem, which are project cost (C1), project duration (C2), project lifetime (C3), labour and equipment requirement (C4), recycling opportunities (C5), resistance to environmental effects (C6), suitability for installation (C7) and natural lighting needs (C8) (Figure 3).

Decision hierarchy of the selection problem.

4.3

Data collection

The decision hierarchy of the structural system selection problem for the industrial facility consists of quantitative and qualitative criteria. The quantitative criteria are project cost, project duration and project lifetime. The project costs were determined according to the quantity take-off calculations corresponding to each alternative. The total construction cost (C1) of the PRC structural system was 60,403.60 Euro, that of the on-site RC structural system 66,631.47 Euro and that of the steel structural system 71,249.16 Euro. The durations of projects (C2) were determined by using a project management software, which provides construction scheduling. According to the results, it is assumed that the total construction time of the steel structural system is 80 days, that of the PRC structural system 100 days and that of the on-site RC structural system 120 days. The project lifetime (C3) was evaluated in the light of the data obtained from the literature. It is seen that the average useful life of the on-site RC structural system is 50 years, that of the PRC structural system 60 years and that of the steel structural system 80 years (Masters and Brandt 1987).

A consolidated survey study was developed to assist decision makers to compare both structural system alternatives and criteria for the selection of the structural system of the industrial facility. The questionnaire was answered by 193 civil engineers working in various positions. In the survey study, 42% of civil engineers work in a project office, 38.3% on a construction site, 6.2% in a public institution, 3.6% in the university and 9.9% in another construction-related organisation. Among the participants, 35% have an experience of ≥15 years, 13% 10–15 years, 18% 5–10 years, 14% 3–5 years and 20% of them have 0–3 years of experience.

The qualitative evaluation criteria were evaluated by using the results of this survey study. Participants were asked to score each alternative on a 1–5 scale for the application of TOPSIS method. The structural system alternatives evaluated according to each criterion are presented below (Table 3).

Tab. 3

Collected data for structural system alternatives.

Alternatives	C1 (Euro)	C2 (day)	C3 (year)	C4 (score)	C5 (score)	C6 (score)	C7 (score)	C8 (score)	Cα
On-site RC structural system	66,631.47	120	50	3.54	1.82	3.90	3.39	2.98	0.826
PRC structural system	60,403.60	100	60	3.31	2.71	3.41	3.35	3.49
Steel structural system	71,249.16	80	80	3.85	4.27	2.72	3.74	3.96

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

The reliability of the questionnaire survey used in this study was tested using Cronbach's alpha coefficient (Cα) calculated by Statistical Package for the Social Sciences (SPSS) IBM Inc., (NY, USA). For the entire questionnaire, the value of Cronbach's alpha was 0.826 (Cα > 0.70), which indicates the internal consistency and reliability of the questionnaire.

4.4

AHP application

The proposed methodology was applied in selecting an apt structural system for the industrial facility. The AHP method is preferred primarily due to its capability to compute the weights of evaluation criteria. For calculation of the criteria weights, the pairwise comparisons from the questionnaire survey that was administered to the civil engineers were used (Table 4). For the pairwise comparison of each alternative, the participants’ responses, in terms of the 1–9 scale employed in the questionnaire survey, were used.

Tab. 4

The pairwise comparison of the evaluation criteria.

Criteria	C1	C2	C3	C4	C5	C6	C7	C8
C1	1	2	2	3	2	2	2	5
C2		1	1/2	2	4	1/2	2	6
C3			1	3	4	3	2	7
C4				1	2	1/5	1/3	2
C5					1	1/6	1/4	2
C6						1	2	8
C7							1	4
C8								1

Expert Choice Inc., (Arlington, VA, USA) software was used for the application of the method. The project cost, project duration and labour/equipment requirement criteria – which are cost criteria – were minimised, while other criteria were maximised and defined as benefit criteria. The obtained criteria weights are shown in Table 5.

Tab. 5

Weights of the evaluation criteria.

No.	Criteria	Weights
1	Project cost	0.218
2	Project duration	0.133
3	Project lifetime	0.212
4	Labour and equipment requirement	0.058
5	Recycling opportunities	0.047
6	Resistance to environmental effects	0.191
7	Suitability for installation	0.114
8	Natural lighting needs	0.027

According to the results, the three most important criteria are the project cost (21.8%), project lifetime (21.2%) and resistance to environmental effects (19.1%). The results demonstrate that the obtained CR (0.01) is less than the threshold CR (i.e. CR < 0.1), which proves that the computations are consistent. After determining the weights of the evaluation criteria, the pairwise comparisons of the alternatives were performed according to the data obtained from the survey study (Tables 6–13).

Tab. 6

The pairwise comparison of alternatives with respect to the project cost.

Project cost	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	1/3	2
PRC structural system		1	4
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 7

The pairwise comparison of alternatives with respect to the project duration.

Project duration	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	1/3	1/5
PRC structural system		1	1/3
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 8

The pairwise comparison of alternatives with respect to the project lifetime.

Project lifetime	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	1/2	1/5
PRC structural system		1	1/3
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 9

The pairwise comparison of alternatives with respect to the labour and equipment requirement.

Labour and equipment requirement	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	2	1/3
PRC structural system		1	1/2
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 10

The pairwise comparison of alternatives with respect to the recycling opportunities.

Recycling opportunities	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	1/2	1/5
PRC structural system		1	1/3
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 11

The pairwise comparison of alternatives with respect to the resistance to environmental effects.

Resistance to environmental effects	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	2	3
PRC structural system		1	2
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 12

The pairwise comparison of alternatives with respect to the suitability for installation.

Suitability for installation	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	2	1/3
PRC structural system		1	1/2
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Tab. 13

The pairwise comparison of alternatives with respect to the natural lighting needs.

Natural lighting needs	On-site RC structural system	PRC structural system	Steel structural system
On-site RC structural system	1	1/3	1/5
PRC structural system		1	1/2
Steel structural system			1

PRC, prefabricated reinforced concrete; RC, reinforced concrete.

Comparison of PRC, on-site RC and steel structural system alternatives was performed for each evaluation criterion. The results are given in Table 14.

Tab. 14

Results of the AHP method.

Alternatives	Results (%)	Rankings
On-site RC structural system	25.0	3rd
PRC structural system	32.9	2nd
Steel structural system	42.1	1st

AHP, analytic hierarchy process; PRC, prefabricated reinforced concrete; RC, reinforced concrete.

According to the results of the AHP method, the steel structural system with 42.1% has been determined as the most suitable alternative. The second most suitable structural system is PRC with a rate of 32.9%, and the third is the on-site RC structural system with a rate of 25.0%.

4.5

TOPSIS application

In order to apply the TOPSIS method to the structural system selection problem, it is necessary to construct a decision matrix that includes the alternatives, evaluation criteria, criteria weights and the relevant data for each of them. The decision matrix is shown in Table 15. The criteria weights were obtained using the AHP method, since they could not be calculated using the TOPSIS method.

After applying the related steps, ideal (A*) and negative ideal (A⁻) solution sets were found as:

A* = {0.114766; 0.060627; 0.151695; 0.031017; 0.037338; 0.127307; 0.070377; 0.017639}

A⁻ = {0.135372; 0.090941; 0.094809; 0.036077; 0.015915; 0.088789; 0.063038; 0.013274}

Tab. 15

The decision matrix of the TOPSIS method.

Alternatives	C1 (Euro)	C2 (day)	C3 (year)	C4 (score)	C5 (score)	C6 (score)	C7 (score)	C8 (score)
W_i (weights)	0.218	0.133	0.212	0.058	0.047	0.191	0.114	0.027
On-site RC structural system	66,631.47	120	50	3.54	1.82	3.90	3.39	2.98
PRC structural system	60,403.60	100	60	3.31	2.71	3.41	3.35	3.49
Steel structural system	71,249.16	80	80	3.85	4.27	2.72	3.74	3.96

PRC, prefabricated reinforced concrete; RC, reinforced concrete; TOPSIS, Technique for Order of Preference by Similarity to Ideal Solution.

The separation distances (Si* and Si⁻) were calculated by using the values of ideal solution sets. The proximity of each alternative to the ideal solution (Ci*) was calculated and is shown in Table 16.

Tab. 16

The results of the TOPSIS method.

Alternatives	Si⁺	Si⁻	Ci*	Normalised Ci*	Rankings
On-site RC structural system	0.068417	0.043787	0.390	0.274	3rd
PRC structural system	0.048043	0.035565	0.425	0.299	2nd
Steel structural system	0.043976	0.068460	0.609	0.427	1st

PRC, prefabricated reinforced concrete; RC, reinforced concrete; TOPSIS, Technique for Order of Preference by Similarity to Ideal Solution.

In the TOPSIS method, the alternatives are listed starting from the highest Ci^* value. According to the results, steel structural framing system is determined as the most suitable structural system, PRC is the second and on-site RC structural system is in the third place for the industrial facility.

Results and discussion

The results obtained from employing the AHP and TOPSIS methods suggest that the steel structural system is the most suitable among the various alternatives evaluated for selecting an apt structural system for an industrial facility. This is an interesting result, because the steel structural system is the least preferred system for industrial facilities in Turkey. It could be assumed that Turkish design firms and contractors of industrial projects generally choose their structural systems based on the cost criterion. However, this study reveals that there are lots of other criteria as important as cost, such as project lifetime and resistance to environmental effects, which should be considered in the planning phase of an industrial facility project.

The results also show that the AHP and TOPSIS methods generate the same ranking for the structural system selection problem. The overall ranking of the alternatives is obtained as: steel structural framing system > PRC structural system > on-site RC structural system. The results show that both methods are consistent and rated the alternatives in the same order. Since the result does not change according to the methods, the decision-makers might choose to use the simplest method. Accordingly, the TOPSIS method could be the preferred one, since it requires less computation compared to the AHP method. In the TOPSIS method, the solution to the problem is arrived at using Microsoft (MS) Excel without the need for any other software, and additionally, the problem is only handled mathematically, without pairwise comparison; on the other hand, the AHP method requires evaluations based entirely on pairwise comparison matrices.

The weakest aspect of the AHP method is the use of priorities determined by the decision maker instead of numerical data. Another weakness is that if a new criterion is added to the analysis, the whole process needs to be repeated from the beginning. Also, due to the random CI used in the evaluation, a decision problem could only be solved with a limited number of alternatives and criteria.

Another essential issue involved in MCDM methods is the vulnerability of the analysis. The results rely on the decision-maker's judgements. Selecting different criteria, criteria weights and criteria values could affect the entire result of the study. To identify the vulnerability of the case study, the TOPSIS method has been applied with different criteria and criteria weights.

In the first case, the criteria weights were selected equally to 0.125. In this situation, the ranking result did not change; however, the normalised Ci^* values were determined as 0.507, 0.298 and 0.195.

In the second case, analysis was executed by using only the first three criteria, which are project cost (C1), project duration (C2) and project lifetime (C3). In this case, the ranking results were still the same, and therefore the gap between normalised Ci^* values have increased to 0.560, 0.289 and 0.150.

The results of the analyses were argued with the design team for the industrial facility project, and they confirmed that the MCDM methods could be easily integrated in selecting the most appropriate structural system.

Conclusion

In this study, on-site RC, prefabricated reinforce concrete and steel structural system alternatives were evaluated by using MCDM methods for the industrial facility, while considering project cost, project duration, project lifetime, labour and equipment requirement, recycling opportunities, resistance to environmental effects, suitability for installation and natural lighting needs. Among the MCDM methods, the AHP and TOPSIS methods were selected. The methodology was applied on an industrial facility located in Manisa, Turkey in order to guide industrialists and practitioners, whose role is to invest in the industrial zones or who are planning on constructing an industrial building, in selecting the most apt structural system using MCDM methods.

It is not an appropriate approach to select a structural system after considering only one criterion, such as the project cost. All structural systems have their own superior aspects compared to others. Before making a selection, it is necessary to examine all available structural system options with an unbiased and scientific approach according to the priority of the investors. Especially, integrating criteria such as recycling opportunities, natural lighting needs, etc. into the evaluation process is extremely valuable in terms of sustainability. It is also important that companies, industrialists and practitioners construct the selected structural system without compromising on quality and with due regard to the factors of occupational health and adherence to safety standards.

A limitation of this study is that the obtained solution has not been compared with other MCDM methods, in the way in which a comparison has been made between solutions to the structural system selection problem obtained using the AHP and TOPSIS methods. The same problem could be solved using other MCDM methods, and the obtained solutions could be compared. In future studies, other methods could be preferred to solve the problem, such as Complex Proportional Assessment (COPRAS), fuzzy-TOPSIS, Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Decision Making Trial and Evaluation Laboratory (DEMATEL), VIKOR, etc.

eISSN:: 1847-6228
Język:: Angielski

Częstotliwość wydawania:: Volume Open
Dziedziny czasopisma:: Engineering, Introductions and Overviews, other

Kanał RSS czasopisma

Multi-criteria decision-making approach for selecting a structural system of an industrial facility

Article Category: Research Paper

Data publikacji: 15 paź 2022

Zakres stron: 2656 - 2665

Otrzymano: 16 sty 2021

Przyjęty: 28 lip 2022

DOI: https://doi.org/10.2478/otmcj-2022-0010

Słowa kluczowe
structural system selection, industrial facility, multi-criteria decision-making methods, AHP, TOPSIS, prefabricated reinforced concrete, on-site reinforced concrete

© 2022 Irem Bayram Zumrut et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Criteria	C1	C2	C3	C4	C5	C6	C7	C8
C1	1	2	2	3	2	2	2	5
C2		1	1/2	2	4	1/2	2	6
C3			1	3	4	3	2	7
C4				1	2	1/5	1/3	2
C5					1	1/6	1/4	2
C6						1	2	8
C7							1	4
C8								1

Criteria	C1	C2	C3	C4	C5	C6	C7	C8
C1	1	2	2	3	2	2	2	5
C2		1	1/2	2	4	1/2	2	6
C3			1	3	4	3	2	7
C4				1	2	1/5	1/3	2
C5					1	1/6	1/4	2
C6						1	2	8
C7							1	4
C8								1

Multi-criteria decision-making approach for selecting a structural system of an industrial facility

Article Category: Research Paper

Data publikacji: 15 paź 2022

Zakres stron: 2656 - 2665

Otrzymano: 16 sty 2021

Przyjęty: 28 lip 2022

DOI: https://doi.org/10.2478/otmcj-2022-0010

Słowa kluczowestructural system selection, industrial facility, multi-criteria decision-making methods, AHP, TOPSIS, prefabricated reinforced concrete, on-site reinforced concrete

© 2022 Irem Bayram Zumrut et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Słowa kluczowe
structural system selection, industrial facility, multi-criteria decision-making methods, AHP, TOPSIS, prefabricated reinforced concrete, on-site reinforced concrete

Criteria	C1	C2	C3	C4	C5	C6	C7	C8
C1	1	2	2	3	2	2	2	5
C2		1	1/2	2	4	1/2	2	6
C3			1	3	4	3	2	7
C4				1	2	1/5	1/3	2
C5					1	1/6	1/4	2
C6						1	2	8
C7							1	4
C8								1