https://doi.org/10.14311/APP.2022.33.0219 Acta Polytechnica CTU Proceedings 33:219–225, 2022 © 2022 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague THE ROLE OF CONCEPTUALIZATION IN THE EVALUATION OF SUSTAINABLE CONCRETE Michael Henrya, ∗, Joel Oponb a Shibaura Institute of Technology, Toyosu 3-7-5, Koto-ku, Tokyo, Japan b MSU - Iligan Institute of Technology, Iligan City 9200, Philippines ∗ corresponding author: mwhenry@shibaura-it.ac.jp Abstract. Improvement of the sustainability of concrete materials will be realized through the development of analytical tools that facilitate sustainable design and evaluation. However, these processes may be dependent on how sustainability is conceptualized for concrete. Conceptualization is the means by which sustainability is operationalized by creating a structure that connects a qualitative goal to its quantitative indicators. As there exists no established definition of sustainability for the concrete field, conceptualization is a source of uncertainty in the sustainability evaluation of concrete. This paper explores the role conceptualization plays in the evaluation of concrete material sustainability by analyzing its effects using multicriteria analysis and a sustainability indicator framework to quantify sustainability for concrete materials. Six analytical scenarios are explored using frameworks based on direct loading, the three pillars of sustainability, and the Sustainable Development Goals, together with two aggregation methods. It was found that the most sustainable concrete mix varied by scenario, but one concrete mix combining blast furnace slag and high grade recycled aggregates could be judged as the most sustainable due to its highest mean score and lowest variance across all analytical scenarios, which suggests it as the mix least sensitive to methodological choices on conceptualization and aggregation. Overall, however, the sustainability scores were highly correlated between the different scenarios. Keywords: Conceptualization, multi-criteria analysis, sustainable design. 1. Introduction The use of concrete incurs significant impacts to envi- ronmental systems, including climate change, ecosys- tem degradation, resources depletion, and pollution [1]. To address these issues, it is necessary to adopt more sustainable materials in construction, which re- quires evaluation of the sustainability of concrete con- sidering the multi-dimensional nature of sustainable development. Literature presents a variety of indi- cators and evaluation methods for tackling the sus- tainability evaluation of concrete [2]. Examples in- clude comparative analysis of the trade-off between environmental impact and other performances, such as safety and cost [3], and the application of life cy- cle assessment (LCA) to quantify the impacts across the life cycle [4]. Multicriteria decision-making tech- niques, such as Analytical Hierarchy Process, have also been explored to identify the most sustainable construction material [5]. However, these approaches are non-equivalent and lead to uncertainty in the eval- uation result, as differing conclusions may be reached when adopting different methods. The lack of a formalized conceptual paradigm for concrete sustainability may contribute to the multi- plicity of evaluation methods, which introduces un- certainty into the sustainability evaluation. Concep- tualization is the process whereby a complex system is decomposed into increasingly smaller components until they can be measured by individual indicators. This creates a structure that rationally links quanti- tative measures to a qualitative objective. One of the most well-known conceptualizations of sustainable development is the "three pillars" model, wherein the nebulous goal of "sustainability" is first decomposed into the three dimensions of society, environment, and economy. These dimensions are then further broken down into relevant themes and sub-themes, which are ultimately measured by specific indicators [6]. The three pillars, however, represent just one conceptual paradigm for sustainability, and there exist other, equally valid conceptualizations derived from alter- native perspectives on sustainable development [7]. Formalizing a conceptual framework for concrete sustainability is essential to unify sustainability eval- uation for the concrete field. In this paper, the ef- fect of conceptualization on the sustainability eval- uation of concrete materials is explored through an exploratory evaluation using multicriteria analysis. Three conceptual frameworks for sustainable develop- ment are examined: direct loading, wherein sustain- ability is measured directly from individual indica- tors; the aforementioned three pillars paradigm; and the Sustainable Development Goals (SDGs), which represent the latest global framework for pursuing sustainable development through a set of 17 goals. Compensability, or the treatment of trade-offs be- tween indicators, pillars, or SDGs, is also examined. 219 https://doi.org/10.14311/APP.2022.33.0219 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en Michael Henry, Joel Opon Acta Polytechnica CTU Proceedings � )LJXUH����6HWXS�IRU�VXVWDLQDELOLW\�HYDOXDWLRQ�E\�0&$�Figure 1. Setup for sustainability evaluation by MCA. Mix OPC BFS FA W NG RG S W/B BFS/B FA/B f ′ c−28 [kg/m3] [−] [−] [−] [MPa] OPC-NG 325 0 0 184 1063 0 783 0.57 − − 30.6 OPC-RG 325 0 0 184 0 1063 783 0.57 − − 30.6 FA-NG 238 0 94 187 950 0 847 0.56 − 0.28 30.2 FA-RG 238 0 94 187 0 950 847 0.56 − 0.28 30.2 BFS-NG 200 133 0 192 965 0 806 0.58 0.40 − 30.4 BFS-RG 200 133 0 192 0 965 806 0.58 0.40 − 30.4 OPC: ordinary portland cement, BFS: blast furnace slag, FA: fly ash, W: water, NG: normal coarse aggregate, RG: high grade recycled coarse aggregate, S: fine aggregate, B: total binder materials Table 1. Mix proportions and 28-day compressive strengths of the concrete mixes. The results and discussion are expected to provide insights into how to address uncertainty in conceptu- alization for improving the transparency and robust- ness of sustainability evaluation for concrete. 2. Methodology 2.1. Multicriteria analysis setup Sustainability evaluation was carried out using mul- ticriteria analysis (MCA). MCA is widely utilized to support decision-making by identifying the most sus- tainable option among a set of potential alternatives [8]. The mix proportions of a set of concrete mixes were used as the inputs. The conventional stages of MCA are: indicator selection (I), data normalization (N), weighting assignment (W), and aggregation (A). The output of this process is typically a sustainabil- ity score (S) or rank (R) for each alternative, which can then be used for quantitative comparison and decision-making. In this paper, the selection of conceptual frame- work (C) for linking the indicators to sustainability is treated as an additional stage in the MCA process. The setup for sustainability evaluation by MCA is illustrated in Figure 1. Only a single indicator set, normalization method, and weighting scheme were adopted for their respective stages. However, three conceptual frameworks and two aggregation methods were utilized for their stages for a total of six analyt- ical scenarios leading to six sustainability scores for each mix alternative. These scores are then statisti- cally examined to explore how a plurality of perspec- tives on concrete sustainability may affect the evalu- ation result. 2.2. Input concrete mix data The concrete mix alternatives were sampled from a database assembled by Noguchi et al. [9]. A target compressive strength of 30 MPa at 28 days was set, and three concrete mixes with different binder con- tents were chosen. An additional three mixes were then generated assuming 100% replacement of normal coarse aggregates with high grade (class H) recycled coarse aggregates. The Japan Industrial Standard (JIS A 5021) indicates that high grade recycled ag- gregates should be equivalent to normal aggregates, so the concrete compressive strengths are assumed to remain unchanged. However, the energy consump- tion and emissions footprints of high grade recycled aggregates are higher than that of normal aggregates due to the recycling process, so the inclusion of these mixes will explore the tradeoff between increased con- sumption of recycled materials and increased environ- mental impacts in the sustainability evaluation. The mix proportions are summarized in Table 1. 2.3. Sustainability indicators A subset of 14 quantitative indicators was adopted from a comprehensive set of sustainable concrete ma- terial indicators (SCMIs) [2], with descriptions and characteristics given in Table 2. Measurement was carried out using available inventory data from the Japan Society of Civil Engineers (for NG, RG, S) and the Life Cycle Assessment Society of Japan (for OPC, BFS, FA), characterization values from the 220 vol. 33/2022 Evaluation of Sustainable Concrete SCMI ID Description Unit Loading by concept Three pillars SDGs 1 Energy consumption, renewable and non-renewable MJ All 7 2 Raw materials consumption, primary and secondary kg All 12 3 Water consumption kg All 6 4 Recycled materials consumption, primary and secondary kg All 12 5 CO2 emissions kg-CO2 Env 9 6 SOx emissions kg-SOx EnvSoc 11 7 NOx emissions kg-NOx EnvSoc 11 8 Particulate matter emissions (PM) kg-PM Env 11 28 Global warming potential (GWP) kg-CO2 eq Env 13 29 Photochemical ozone creation potential (POCP) kg-C2H4 eq Env 11 30.2 Acidification potential, aquatic (AP) kg-SO2 eq Env 14 31.1 Eutrophication potential, terrestrial (EP) kg-PO4 eq Env 2 34 Human toxicity potential (HP) kg-C6H4Cl2 eq Soc 3 40 Material cost of concrete yen Eco 8 All: environmental, social, and economic, EnvSoc: environmental and social, Env: environmental, Soc: social, Eco: economic Table 2. Summary of the adopted sustainable concrete material indicators. Mix SCMI 1 SCMI 2 SCMI 3 SCMI 4 SCMI 5 SCMI 6 SCMI 7Energy Raw mats. Water Recyc. mats. CO2 SOx NOx OPC-NG 1213.0 2225.8 184.0 123.8 255.1 0.053 0.513 OPC-RG 1563.8 1162.8 184.0 1186.8 270.9 0.053 0.539 FA-NG 956.6 2075.1 186.7 184.8 190.2 0.043 0.379 FA-RG 1270.1 1125.2 186.7 1134.7 204.3 0.043 0.402 BFS-NG 861.8 2005.3 192.0 209.2 162.6 0.038 0.320 BFS-RG 1180.4 1039.9 192.0 1174.6 176.9 0.039 0.344 Mix SCMI 8 SCMI 28 SCMI 29 SCMI 30.2 SCMI 31.1 SCMI 34 SCMI 40PM GWP POCP AP EP HTP Cost OPC-NG 0.015 255.1 0.017 0.412 0.067 0.620 5775 OPC-RG 0.016 270.9 0.018 0.430 0.070 0.652 5775 FA-NG 0.012 190.2 0.013 0.308 0.049 0.458 5266 FA-RG 0.012 204.3 0.013 0.324 0.052 0.487 5266 BFS-NG 0.010 162.6 0.011 0.262 0.042 0.388 5679 BFS-RG 0.011 176.9 0.011 0.279 0.045 0.416 5679 Table 3. Mix proportions and 28-day compressive strengths of the concrete mixes. LCA database of Leiden University (for GWP, POCP, AP, EP, HP), and constituent material costs reported by Henry et al [10]. The cost of high grade recycled aggregates was assumed the same as that of normal aggregates due to lack of data. The raw indicator values for the six mixes were calculated as shown Ta- ble 3. The loadings of the indicators within the three pil- lars and SDGs concepts, as established by Opon and Henry [2], are also shown in Table 2. The environ- mental pillar is most represented among the three pillars, with 12 of the 14 indicators loading to the environment. Ten of the 17 goals are evaluated un- der the SDG concept, with SDG 11 (sustainable cities and communities) receiving the most representation with four indicators highly relevant to this goal. 2.4. Normalization method As the adopted indicators exhibit differing charac- teristics and behavior, their data were normalized by standardization. Standardization was carried out through a combination of z-scores, which convert the data based on a mean of zero and standard deviation of one (Eq. 1), and t-scores, which shift the data to a non-negative scale of zero to 100 (Eq. 2). z = x − µ s (1) t = (z × 10) + 50 (2) Where z: z-score, t: t-score, x: raw value, µ: av- erage, and s: standard deviation,. In some cases, the t-score is divided by 100 to scale the data from zero to one. 221 Michael Henry, Joel Opon Acta Polytechnica CTU Proceedings �D��'LUHFW�ORDGLQJ� �E��7KUHH�SLOODUV�FRQFHSWXDO�IUDPHZRUN� �F��6'*V�FRQFHSWXDO�IUDPHZRUN� Figure 2. Conceptual frameworks for sustainability with indicator loadings. 2.5. Conceptual frameworks To represent different conceptual approaches to sus- tainability, three frameworks linking the individual sustainability indicators to the overall objective of concrete sustainability were constructed (Figure 2). The first framework, direct loading, assumes no prior- ity areas or sub-categories; that is, all indicators load directly to the sustainability performance of the con- crete materials. The second framework decomposes concrete sustainability into the three pillars of soci- ety, environment, and economy, and each indicator then loads to its related pillar, or pillars (for multi- dimensional indicators), following the loadings given in Table 2. The final framework sets the ten relevant SDGs as the intermediate priority areas, with each indicator loading to its respective SDG. 2.6. Weighting scheme As no information was available to establish the com- parative importance of the indicators, pillars, and SDGs, equal weighting was applied for all loadings between levels of the conceptual framework. This is consistent with the United Nations resolution adopt- ing the SDGs, which states that they will give "equal priority" to all goals in their implementation efforts [11]. 2.7. Aggregation methods Two aggregation methods were used to produce the sustainability scores for the concrete mixes: linear and geometric. Linear aggregation is an additive op- eration (Eq. 3), and is frequently the default method in MCA, whereas geometric aggregation is a multi- plicative operation (Eq. 4). Sin = n! i=1 (wi × SCM Ii) (3) Sgeo = n" i=1 (SCM Ii) wi (4) Where S: sustainability score, n: total number of indicators, SCM Ii: sustainability indicator i, and wi: weighting applied to indicator i. The choice of aggregation method represents an- other perspective on sustainability in the evaluation process. Linear aggregation allows for full compens- ability between indicators; that is, an increase in one performance (such as CO2 emissions) can be balanced by an equivalent decrease in another performance (such as cost). Geometric aggregation, however, is less compensatory [12], meaning that trade-offs be- tween indicators have a more pronouned effect on the sustainability score. The impact of compensability on the sustainability evaluation result may differ ac- cording to the conceptual framework, so these two 222 vol. 33/2022 Evaluation of Sustainable Concrete �D��'LUHFW�ORDGLQJ��OLQHDU�DJJUHJDWLRQ �E��'LUHFW�ORDGLQJ��JHRPHWULF�DJJUHJDWLRQ �F��7KUHH�SLOODUV��OLQHDU�DJJUHJDWLRQ �G��7KUHH�SLOODUV��JHRPHWULF�DJJUHJDWLRQ �H��6'*V��OLQHDU�DJJUHJDWLRQ �I��6'*V��JHRPHWULF�DJJUHJDWLRQ� ���� ���� ���� ���� ���� ���� � �� �� �� �� �� �� �� 23&�1* 23&�5* )$�1* )$�5* %)6�1* %)6�5* 6 XV WD LQ DE LOL W\ �V FR UH ���� ���� ���� ���� ���� ���� � �� �� �� �� �� �� �� 23&�1* 23&�5* )$�1* )$�5* %)6�1* %)6�5* 6 XV WD LQ DE LOL W\ �V FR UH ���� ���� ���� ���� ���� ���� � �� �� �� �� �� �� �� 23&�1* 23&�5* )$�1* )$�5* %)6�1* %)6�5* 6 XV WD LQ DE LOL W\ �V FR UH ���� ���� ���� ���� ���� ���� � �� �� �� �� �� �� �� 23&�1* 23&�5* )$�1* )$�5* %)6�1* %)6�5* 6 XV WD LQ DE LOL W\ �V FR UH ���� ���� ���� ���� ���� ���� � �� �� �� �� �� �� �� 23&�1* 23&�5* )$�1* )$�5* %)6�1* %)6�5* 6 XV WD LQ DE LOL W\ �V FR UH ���� ���� ���� ���� ���� ���� � �� �� �� �� �� �� �� 23&�1* 23&�5* )$�1* )$�5* %)6�1* %)6�5* 6 XV WD LQ DE LOL W\ �V FR UH Figure 3. Sustainability scores by analytical scenario. aggregation methods were included in the MCA pro- cess. 3. Results and discussion 3.1. Sustainability scores Figure 3 shows the sustainability scores for the con- crete mix alternatives by analytical scenario. For di- rect loading, the two BFS mixes exhibited the best sustainability performance, with BFS-NG slightly higher for linear aggregation, and BFS-RG slightly higher for geometric aggregation. In the case of the three pillars, FA-RG possessed the highest sustain- ability score regardless of aggregation method, fol- lowed by BFS-RG. Finally, for the SDG concept, BFS-NG demonstrated the best sustainability for both aggregation methods, but the gap between the top four mixes was relatively small. Overall, the four concrete mixes containing mineral admixtures consistently exhibited higher sustainabil- ity scores than the two OPC mixes across all analyt- ical scenarios. These mixes tended to have lower raw values for environment-related indicators compared to OPC; and, since 12 indicators loaded to the envi- ronment, the effect of reduced environmental impacts was amplified through the MCA process. While con- sidering the limations of this analysis, these results nonetheless support the general perception that the application of alternative cementitious materials to concrete construction is a critical technological solu- tion for improving sustainability in the concrete in- dustry. 3.2. Statistical examination It remains unclear which concrete mix is actually the most sustainable among the alternatives, as the re- sults of each analytical scenario may be considered as equally valid due to the lack of an established stan- 223 Michael Henry, Joel Opon Acta Polytechnica CTU Proceedings Mix Sustainability scores RanksMin. Mean Max. Std. dev. Variance Mean Variance OPC-NG 34.3 37.0 38.5 1.6 2.2 6 3 OPC-RG 35.8 38.1 42.1 2.6 5.8 5 4 FA-NG 53.4 55.0 56.7 1.3 1.4 4 2 FA-RG 54.6 56.8 61.2 3.1 8.0 2 5 BFS-NG 50.2 55.5 58.2 3.7 11.4 3 6 BFS-RG 56.3 57.6 58.4 1.0 0.8 1 1 Table 4. Summary statistics of the concrete mixes sustainability performance. Analytical scenario (a) (b) (c) (d) (e) (f) (a) Direct loading, linear aggregation 1.00 (b) Direct loading, geometric aggregation 1.00* 1.00 (c) Three pillars, linear aggregation 0.89 0.89 1.00 (d) Three pillars, geometric aggregation 0.88 0.89 0.99 1.00 (e) SDGs, linear aggregation 0.99 0.99 0.86 0.86 1.00 (f) SDGs, geometric aggregation 0.99 0.99 0.86 0.86 1.00* 1.00 *Actual coefficients were less than 1.00, but appear as 1.00 due to rounding Table 5. Correlation coefficients for sustainability scores between analytical scenarios. dard for concrete sustainability evaluation. To judge the sustainability under the uncertainty introduced by multiple concepts and aggregation methods, sta- tistical properties of the sustainability score distri- butions for each material were examined (Table 4). The mix with the highest mean sustainability score was BFS-RG; however, per Figure 3, this mix was the most sustainable alternative in only one analyt- ical scenario. On the other hand, BFS-NG was the most sustainable material in three scenarios, but its mean sustainability score was ranked third out of the six alternatives. Similarly, FARG was the most sus- tainable in two scenarios, but was ranked second by mean score. This result may be explained by the variance of the sustainability scores. The variance of BFS-RG is the lowest among all alternatives, suggesting it is rela- tively insensitive to the uncertainty introduced by dif- ferent conceptual frameworks and aggregation meth- ods, whereas the variance of BFS-NG is the highest, and thus the alternative most affected by the choice of concept and aggregation method (and their inter- actions). Considering both the highest mean sustain- ability score and accompanying lowest variance, BFS- RG may be judged as the most sustainable material when there is no consensus on the conceptual frame- work or aggregation method. Finally, the Pearson correlation coefficient matrix for the sustainability scores was calculated between analytical scenarios. It can be seen that all coeffi- cients are equal to or greater than 0.86; in partic- ular, the results of the linear and geometric aggre- gation scenarios were almost perfectly correlated for all three conceptual frameworks. This result suggests a very highly correlated set of sustainability scores regardless of the chosen conceptual framework or ag- gregation method, and despite the rankings of the mix alternatives shifting between scenarios. 4. Conclusion This paper examined the effect of conceptualization on the sustainability evaluation of concrete using mul- ticriteria analysis and a set of six concrete mix alter- natives with varying binder contents and aggregate types. It was found that the most sustainable con- crete mix varied according to conceptual framework and aggregation method, with three different mixes achieving the highest score among six scenarios. Sta- tistical examination of the distributions of the mixes’ sustainability scores revealed that the concrete mix with the highest mean sustainability score, BFS-RG, was only ranked first in one analytical scenario. How- ever, the variance of this mix’s scores was the lowest among the alternatives, suggesting that its evalua- tion was comparatively less sensitive to methodolog- ical choices regarding conceptualization and aggre- gation. Despite the different rankings for each an- alytical scenario, the sustainability scores were still very highly correlated between all scenarios, indicat- ing that different methodological choices produced closely related results. It should be noted that the results reported here cannot simply be generalized to the greater concrete industry, as they are dependent on the sample and conditions of this specific analysis. However, it is ex- pected that the demonstrated evaluation method will provide concrete industry stakeholders with an exam- ple of how to consider methodological uncertainties in sustainability evaluation to increase the robustness of their decision-making. 224 vol. 33/2022 Evaluation of Sustainable Concrete References [1] K. Sakai, T. Noguchi. The Sustainable Use of Concrete, CRC press, pp. 188, 2017. [2] J. Opon, M. Henry. An indicator framework for quantifying the sustainability of concrete materials from the perspectives of global sustainable development. Journal of Cleaner Production 218:718-37, 2019. https://doi.org/10.1016/j.jclepro.2019.01.220. [3] H. Yokota, S. Goto, K. Sakai. Proc. of Second International Conference on Concrete Sustainability, CINM E, Barcelona, p 1046, 2016. [4] H. S. Müller, M. Haist, M. Vogel. Assessment of the sustainability potential of concrete and concrete structures considering their environmental impact, performance and lifetime. Construction and Building Materials 67:321-37, 2014. https: //doi.org/10.1016/j.conbuildmat.2014.01.039. [5] P. O. Akadiri, P. O. Olomolaiye, E. A. Chinyio. Multi-criteria evaluation model for the selection of sustainable materials for building projects. Automation in Construction 30:113-25, 2013. https://doi.org/10.1016/j.autcon.2012.10.004. [6] UNDESA. CSD Theme Indicator Framework from 2001, United Nations, New York, 2001. https://www.un.org/esa/sustdev/natlinfo/indica tors/isdms2001/table_4.htm. [7] L. Janeiro, M. K. Patel. Choosing sustainable technologies. Implications of the underlying sustainability paradigm in the decision-making process. Journal of Cleaner Production 105:438-46, 2015. https://doi.org/10.1016/j.jclepro.2014.01.029. [8] M. Cinelli, S. R. Coles, K. Kirwan. Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators 46:138-48, 2014. https://doi.org/10.1016/j.ecolind.2014.06.011. [9] T. Noguchi, F. Tomosawa, K. M. Nemati, et al. A practical equation for elastic modulus of concrete. ACI Structural Journal 106(5):690, 2009. [10] M. Henry, G. Pardo, T. Nishimura, et al. Balancing durability and environmental impact in concrete combining low-grade recycled aggregates and mineral admixtures. Resources, Conservation and Recycling 55(11):1060-9, 2011. https: //doi.org/10.1016/j.resconrec.2011.05.020. [11] UNDESA. Resolution adopted by the General Assembly on. A/RES/70/1, United Nations, New York, 2015. https://www.un.org/en/development/de sa/population/migration/generalassembly/docs/g lobalcompact/A_RES_70_1_E.pdf. [12] M. J. Dobbie, D. Dail. Robustness and sensitivity of weighting and aggregation in constructing composite indices. Ecological Indicators 29:270-7, 2013. https://doi.org/10.1016/j.ecolind.2012.12.025. 225 https://doi.org/10.1016/j.jclepro.2019.01.220 https://doi.org/10.1016/j.conbuildmat.2014.01.039 https://doi.org/10.1016/j.autcon.2012.10.004 https://www.un.org/esa/sustdev/natlinfo/indicators/isdms2001/table_4.htm https://doi.org/10.1016/j.jclepro.2014.01.029 https://doi.org/10.1016/j.ecolind.2014.06.011 https://doi.org/10.1016/j.resconrec.2011.05.020 https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_70_1_E.pdf https://doi.org/10.1016/j.ecolind.2012.12.025