Presentation FYP-Final Self Compacting Concrete.pptx

Self Compacting Concrete

1. Presented by: Jawad Ahmad (2020-CIV-70) Hafiza Mushata Nazir (2020-CIV-62) Mudasir Bashir (2020-CIV-71) Shahzaib Rafi (2020-CIV-86) Application of Multi-Criteria Decision Analysis in Civil Engineering Thesis Supervisor: Dr. Muhammad Rizwan Riaz Associate Professor Civil Engineering Department (UET Lahore)

2. 2 TABLE OF CONTENTS Introduction Literature Review Problem Statement Research Objective Research Methodology Excel-Based Tools Application of MCDA to different examples 01 02 03 04 05 06 07 References 08

3. INTRODUCTION Multi-criteria Decision Making (MCDA) Why we need MCDA? Evaluation of multi- criterian situations using conventional approaches may be difficult. It helps civil engineers make well-informed choices that lead to sustainable, cost effective, and socially responsible infrastructure development. It is the process of determining the best solution for given problem according to established criteria. 3 Applications of MCDA: Nowadays MCDA is using in almost all fields such as: Medical Fields Engineering Fields Business Administration Information Technology

4. INTRODUCTION Flow Chart of MCDA 4

5. INTRODUCTION Methods of MCDA There are different method for Multi-criteria decision analysis and here are the commonly used MCDM methods: 5 Analytical Hierarchical Process (AHP) Technique for order of preferences by similarity to ideal solution (TOPSIS) Evaluation based on distance from average solution (EDAS) Elimination and Choice Translating Reality (ELECTRE) Vlekriterijumsko Kompromiseno Rangiranje (VIKOR)

6. LITERATURE REVIEW Zafar, et al., (2022) Integrating technical-environmental-economical perspectives for optimizing rubber content in concrete by multi-criteria analysis In technical perspective the performance of rubber crumb incorporated concrete was better. The CO2 emissions and Cost were less for powder rubber incorporated concrete Raw material conservation is better for rubber chip incorporated concrete Finally, all perspectives were integrated, and a framework having 13 criteria and their weightage was evaluated by Entropy method. As a result, 5% rubber chip concrete showed top ranking. 6

7. LITERATURE REVIEW Li, et al., (2023) A novel multi-criteria comprehensive evaluation model of Fly ash-based Geopolymer concrete When considering the 28-day compressive strength of FA-GPC A1, A2, A3 and A5 belong to the fourth gray (Excellent) category with good performance. When considering environmental impact A1, A2, A3 showed good performance. When considering sulphate erosion resistance A2 and A5 showed good performance. A3 > A1 > A2 > A5, overall A3 showed the best performance. (GCA) 7

8. LITERATURE REVIEW 8

9. LITERATURE REVIEW Tosic , et al., (2015) Multicriteria optimization of natural and recycled aggregate concrete for structural use For criteria ranking “equal importance” the alternative RAC-50 is the compromise solution. In case of “environmental advantage” criteria ranking the compromise solution consists of two alternative (RAC 50 and RAC 100) Under the “economical advantage” criteria ranking the compromise solutions are again two alternatives (RAC 50 and NAC 50). (VIKOR method) 9

10. LITERATURE REVIEW Tosic , et al., (2015) Multicriteria optimization of natural and recycled aggregate concrete for structural use 10

11. LITERATURE REVIEW 11 Wang, et al., (2023) A multiple criteria decision-making for water allocation of environment flows considering the value trade-offs-A case study of River in China Phan, et al., (2023) Multi-criteria decision analysis to select the most appropriate risk mitigation strategy to reduce aflatoxin in take in Mekong Delta, Vietnam Stirbanovic, et al., (2019) Application of MCDM technique for selection of fuel in power plant Bhojane, et al., (2023) Application of MCDM methods for flotation machine selection Lin, et al., (2023) Safety assessment of excavation system via TOPSIS-based MCDM modelling in fuzzy environment

12. PROBLEM STATEMENT 12 Multi-criteria decision analysis (MCDA) being a part of the curriculum across various department such as Architecture Engineering, Environmental Engineering and Mathematics etc. We observe need of the application of MCDA in Civil Engineering. This discrepancy raises concerns about the preparedness of Civil Engineering graduates to navigate complex decision making scenarios effectively. This study proposes the development of user-friendly computer based tools in which a decision support system is developed to select the best alternative using different criteria based on the preferences of users. …………………….

13. RESEARCH OBJECTIVES The main objective of this proposed research work is to develop user-friendly computer-based tools of MCDA to help the decision maker to choose the best option among given based on different criteria. Following are the specific objectives of this research work: Development of user friendly Excel-based tools of MCDA (AHP and TOPSIS) Application of MCDA in field of Civil Engineering 13

14. RESEARCH METHODOLOGY 14 1 Understanding of MCDM 2 Literature Review 3 Development of Excel Based Tools of MCDM 4 Application of MCDM to different examples 5 Thesis write-up

15. 15 EXCEL-BASED TOOLS

16. APPLICATION OF MCDA TO DIFFERENT EXAMPLES C-35 = Concrete having 0% RCA and target compressive strength of 35 MPa 25R-35 = Concrete having 25% RCA and target compressive strength of 35 MPa 50R-35 = Concrete having 50% RCA and target compressive strength of 35 MPa 75R-35 = Concrete having 75% RCA and target compressive strength of 35 MPa 100R-35 = Concrete having 100% RCA and target compressive strength of 35 MPa 16 Example #1 with AHP (Rashid et al. 2020) Multi-Criteria Optimization of recycled aggregate concrete mixes

17. 17 Example #1 with AHP (Rashid et al. 2020) Data Collected Criteria C-35 25R-35 50R-35 75R-35 100R-35 Criteria Type Cost 5900 5800 5700 5620 5530 Non-Beneficial Strength 35.5 33.6 31 30.7 29.7 Beneficial Vol Raw Mat. 100 88 76 66 54 Non-Beneficial CO2 250 248 246 244 242 Non-Beneficial Normalized Decision Matrix Criteria C-35 25R-35 50R-35 75R-35 100R-35 cost 0.59 0.70 0.81 0.90 1.00 strength 1.00 0.89 0.74 0.72 0.66 Vol Raw Mat. 0.60 0.70 0.81 0.89 1.00 CO2 0.60 0.70 0.80 0.90 1.00

18. 18 Example #1 with AHP (Rashid et al. 2020) Weighted Normalized Decision Matrix Final Decision Matrix Criteria Weightage C-35 25R-35 50R-35 75R-35 100R-35 cost 0.08 0.05 0.06 0.06 0.07 0.08 strength 0.13 0.13 0.11 0.09 0.09 0.08 Vol Raw Mat. 0.49 0.29 0.34 0.40 0.44 0.49 CO2 0.31 0.18 0.21 0.24 0.27 0.31 Criteria C-35 25R-35 50R-35 75R-35 100R-35 cost 0.05 0.06 0.06 0.07 0.08 strength 0.13 0.11 0.09 0.09 0.08 Vol Raw Mat. 0.29 0.34 0.40 0.44 0.49 CO2 0.18 0.21 0.24 0.27 0.31 Score 0.65 0.72 0.80 0.87 0.96 Rank 5th 4th 3rd 2nd 1st Final 100R-35

19. Example #2 with AHP (Zafar et al. 2022) 19 Integrating technical-environmental-economical perspectives for optimizing rubber content in concrete by multi-criteria analysis CC Cement Concrete RP-5 Concrete mixture with rubber powder replacing 5% of the cement RP-10 Concrete mixture with rubber powder replacing 10% of the cement RP-15 Concrete mixture with rubber powder replacing 15% of the cement RP-20 Concrete mixture with rubber powder replacing 20% of the cement RS-5 Concrete mixture with rubber crumbs replacing 5% of the Sand RS-10 Concrete mixture with rubber crumbs replacing 10% of the Sand RS-15 Concrete mixture with rubber crumbs replacing 15% of the Sand RS-20 Concrete mixture with rubber crumbs replacing 20% of the Sand RG-5 Concrete mixture with rubber chips replacing 5% of the Gravels RG=10 Concrete mixture with rubber chips replacing 10% of the Gravels RG-15 Concrete mixture with rubber chips replacing 15% of the Gravels RG-20 Concrete mixture with rubber chips replacing 20% of the Gravels

20. 20 Example #2 with AHP (Zafar et al. 2022) Data Collected

21. 21 Example #2 with AHP (Zafar et al. 2022) Normalized Decision Matrix Weighted Normalized Decision Matrix Criteria CC RP-5 RP-10 RP-15 RP-20 RS-5 RS-10 RS-15 RS-20 RG-5 RG=10 RG-15 RG-20 Workability 0.26 0.26 0.27 0.27 0.29 0.28 0.28 0.29 0.32 0.26 0.26 0.27 0.28 Strength 0.36 0.29 0.25 0.22 0.21 0.32 0.29 0.26 0.24 0.32 0.29 0.26 0.25 Durability 0.31 0.27 0.25 0.25 0.24 0.29 0.28 0.28 0.27 0.29 0.28 0.28 0.27 Environmental 0.29 0.28 0.27 0.26 0.25 0.29 0.28 0.28 0.28 0.29 0.28 0.28 0.28 Criteria Weightage CC RP-5 RP-10 RP-15 RP-20 RS-5 RS-10 RS-15 RS-20 RG-5 RG=10 RG-15 RG-20 Workability 0.15 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.04 0.04 Strength 0.23 0.08 0.07 0.06 0.05 0.05 0.07 0.07 0.06 0.06 0.07 0.07 0.06 0.06 Durability 0.38 0.12 0.11 0.10 0.10 0.09 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10 Environmental 0.23 0.07 0.06 0.06 0.06 0.06 0.07 0.07 0.06 0.06 0.07 0.06 0.06 0.06

22. 22 Example #2 with AHP (Zafar et al. 2022) Final Decision Matrix Criteria CC RP-5 RP-10 RP-15 RP-20 RS-5 RS-10 RS-15 RS-20 RG-5 RG=10 RG-15 RG-20 Workability 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.04 0.04 Strength 0.08 0.07 0.06 0.05 0.05 0.07 0.07 0.06 0.06 0.07 0.07 0.06 0.06 Durability 0.12 0.11 0.10 0.10 0.09 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10 Environmental 0.07 0.06 0.06 0.06 0.06 0.07 0.07 0.06 0.06 0.07 0.06 0.06 0.06 Score 0.31 0.28 0.26 0.25 0.24 0.30 0.28 0.28 0.28 0.29 0.28 0.27 0.27 Rank 1st 8th 11th 12th 13th 2nd 4th 6th 7th 3rd 5th 9th 10th Final CC

23. Example #3 with TOPSIS (Bhojane et al. 2019) This paper presents a thermodynamics-assisted Multi-Criteria Decision Making (MCDM) technique for selecting the optimal fuel alternative in a thermal power station. There were total 33 samples of fuel used is this study and best fuel sample is selected based on thermodynamics parameters using TOPSIS method. 23 Application of MCDM technique for selection of fuel in power plant

24. 24 Example #3 with TOPSIS (Bhojane et al. 2019) Data Collected Weights 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Alternatives C H N O S Mass of water form. HHV Quality Energy of fuel Flame Temp. Alternative 1 35.00 2.00 0.92 6.15 0.44 2.57 13.66 10.87 148.52 2355.71 Alternative 2 57.92 5.52 0.98 34.93 0.65 2.52 30.64 6.70 205.35 2052.34 Alternative 3 60.82 5.43 0.60 29.70 3.45 2.41 32.44 7.35 238.58 2083.69 Alternative 4 62.29 5.34 3.44 28.01 0.92 3.10 33.11 7.13 236.06 2002.15 Alternative 5 62.70 5.48 0.54 27.26 3.97 2.63 33.59 7.57 254.11 2065.04 Alternative 6 62.77 5.68 1.49 28.95 1.11 2.09 33.59 7.06 237.16 2129.52 Alternative 7 62.90 5.42 1.39 26.47 3.82 2.99 33.71 7.59 255.90 2022.17 Alternative 8 62.93 5.41 0.73 25.42 5.51 3.18 33.89 7.86 266.41 2001.53 Alternative 9 62.95 5.42 0.65 29.68 1.30 3.30 33.15 7.15 237.04 1971.79 Alternative 10 63.53 5.12 1.31 25.43 4.61 2.39 24.72 10.69 264.32 2098.21 Alternative 11 63.85 5.08 1.70 25.07 4.30 2.95 33.79 7.84 264.90 2028.59 Alternative 12 64.54 4.85 0.92 24.56 5.13 1.92 33.79 8.07 272.66 2157.45 Alternative 13 64.69 5.09 0.88 21.79 7.55 2.07 34.68 8.39 291.08 2151.18 Alternative 14 64.81 5.42 0.44 24.08 5.25 3.10 34.79 7.93 275.80 2016.40 Alternative 15 64.84 4.70 2.05 22.58 5.83 3.25 34.03 8.27 281.44 1994.65 Alternative 16 64.86 5.13 2.41 23.91 3.69 3.11 34.42 7.81 268.66 2012.67 Alternative 17 65.01 5.55 1.93 24.48 3.03 2.92 34.97 7.58 265.18 2039.95 Alternative 18 65.58 5.49 0.60 22.16 6.17 1.83 35.49 8.10 287.59 2184.72 Alternative 19 65.98 5.20 2.53 23.15 3.14 3.11 35.03 7.77 272.32 2015.78 Alternative 20 66.19 4.70 1.01 28.08 0.02 1.70 33.49 7.38 247.14 2170.73 Alternative 21 66.20 5.05 2.62 17.03 9.10 2.26 36.00 8.75 315.02 2142.78 Alternative 22 66.96 5.15 2.08 22.94 2.87 1.67 35.33 7.80 275.47 2198.18 Alternative 23 67.05 4.65 1.22 23.71 3.37 1.93 34.50 8.01 276.20 2156.63 Alternative 24 67.19 5.04 0.99 26.55 0.23 1.55 34.60 7.39 255.82 2199.81 Alternative 25 67.25 4.76 2.06 18.90 7.03 2.30 35.59 8.57 305.12 2127.81 Alternative 26 67.53 4.94 2.40 20.50 4.63 3.25 35.66 8.17 291.40 2005.36 Alternative 27 68.57 5.43 1.33 23.74 0.93 1.88 36.13 7.51 271.23 2172.21 Alternative 28 68.74 5.92 1.24 21.85 2.25 2.47 37.24 7.59 282.72 2109.20 Alternative 29 68.83 5.14 1.09 24.79 0.15 1.86 35.61 7.47 266.06 2167.90 Alternative 30 69.73 4.66 1.71 19.69 4.21 2.17 36.15 8.30 299.97 2141.50 Alternative 31 70.37 5.12 1.04 22.86 0.62 1.64 36.45 7.65 278.81 2201.67 Alternative 32 73.48 4.90 2.00 17.53 2.09 1.80 38.15 8.13 310.04 2197.44 Alternative 33 70.91 4.84 2.44 17.52 4.29 1.46 37.20 8.33 310.04 2239.86

25. 25 Example #3 with TOPSIS (Bhojane et al. 2019) Normalized Decision Matrix Weights 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Alternatives C H N O S Mass of water form. HHV Quality Energy of fuel Flame Temp. Alternative 1 0.09 0.07 0.10 0.04 0.02 0.18 0.07 0.24 0.10 0.19 Alternative 2 0.16 0.19 0.10 0.25 0.03 0.18 0.16 0.15 0.13 0.17 Alternative 3 0.16 0.18 0.06 0.21 0.15 0.17 0.17 0.16 0.15 0.17 Alternative 4 0.17 0.18 0.36 0.20 0.04 0.22 0.17 0.15 0.15 0.17 Alternative 5 0.17 0.19 0.06 0.20 0.17 0.19 0.17 0.16 0.16 0.17 Alternative 6 0.17 0.19 0.16 0.21 0.05 0.15 0.17 0.15 0.15 0.18 Alternative 7 0.17 0.18 0.15 0.19 0.16 0.21 0.17 0.17 0.17 0.17 Alternative 8 0.17 0.18 0.08 0.18 0.23 0.22 0.17 0.17 0.17 0.17 Alternative 9 0.17 0.18 0.07 0.21 0.06 0.23 0.17 0.16 0.15 0.16 Alternative 10 0.17 0.17 0.14 0.18 0.20 0.17 0.13 0.23 0.17 0.17 Alternative 11 0.17 0.17 0.18 0.18 0.18 0.21 0.17 0.17 0.17 0.17 Alternative 12 0.17 0.16 0.10 0.18 0.22 0.14 0.17 0.18 0.18 0.18 Alternative 13 0.17 0.17 0.09 0.16 0.32 0.15 0.18 0.18 0.19 0.18 Alternative 14 0.17 0.18 0.05 0.17 0.22 0.22 0.18 0.17 0.18 0.17 Alternative 15 0.17 0.16 0.22 0.16 0.25 0.23 0.17 0.18 0.18 0.16 Alternative 16 0.17 0.17 0.25 0.17 0.16 0.22 0.18 0.17 0.17 0.17 Alternative 17 0.17 0.19 0.20 0.18 0.13 0.21 0.18 0.16 0.17 0.17 Alternative 18 0.18 0.19 0.06 0.16 0.26 0.13 0.18 0.18 0.19 0.18 Alternative 19 0.18 0.18 0.27 0.17 0.13 0.22 0.18 0.17 0.18 0.17 Alternative 20 0.18 0.16 0.11 0.20 0.00 0.12 0.17 0.16 0.16 0.18 Alternative 21 0.18 0.17 0.28 0.12 0.39 0.16 0.18 0.19 0.20 0.18 Alternative 22 0.18 0.18 0.22 0.17 0.12 0.12 0.18 0.17 0.18 0.18 Alternative 23 0.18 0.16 0.13 0.17 0.14 0.14 0.18 0.17 0.18 0.18 Alternative 24 0.18 0.17 0.10 0.19 0.01 0.11 0.18 0.16 0.17 0.18 Alternative 25 0.18 0.16 0.22 0.14 0.30 0.16 0.18 0.19 0.20 0.18 Alternative 26 0.18 0.17 0.25 0.15 0.20 0.23 0.18 0.18 0.19 0.17 Alternative 27 0.18 0.18 0.14 0.17 0.04 0.13 0.18 0.16 0.18 0.18 Alternative 28 0.18 0.20 0.13 0.16 0.10 0.17 0.19 0.17 0.18 0.17 Alternative 29 0.18 0.17 0.12 0.18 0.01 0.13 0.18 0.16 0.17 0.18 Alternative 30 0.19 0.16 0.18 0.14 0.18 0.15 0.18 0.18 0.19 0.18 Alternative 31 0.19 0.17 0.11 0.16 0.03 0.12 0.19 0.17 0.18 0.18 Alternative 32 0.20 0.17 0.24 0.10 0.09 0.13 0.19 0.18 0.20 0.18 Alternative 33 0.19 0.16 0.26 0.58 0.18 0.10 0.19 0.18 0.20 0.18

26. 26 Example #3 with TOPSIS (Bhojane et al. 2019) Weighted Normalized Decision Matrix Weights 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Alternatives C H N O S Mass of water form. HHV Quality Energy of fuel Flame Temp. Alternative 1 0.009 0.007 0.010 0.004 0.002 0.018 0.007 0.024 0.010 0.019 Alternative 2 0.016 0.019 0.010 0.025 0.003 0.018 0.016 0.015 0.013 0.017 Alternative 3 0.016 0.018 0.006 0.021 0.015 0.017 0.017 0.016 0.015 0.017 Alternative 4 0.017 0.018 0.036 0.020 0.004 0.022 0.017 0.015 0.015 0.017 Alternative 5 0.017 0.019 0.006 0.020 0.017 0.019 0.017 0.016 0.016 0.017 Alternative 6 0.017 0.019 0.016 0.021 0.005 0.015 0.017 0.015 0.015 0.018 Alternative 7 0.017 0.018 0.015 0.019 0.016 0.021 0.017 0.017 0.017 0.017 Alternative 8 0.017 0.018 0.008 0.018 0.023 0.022 0.017 0.017 0.017 0.017 Alternative 9 0.017 0.018 0.007 0.021 0.006 0.023 0.017 0.016 0.015 0.016 Alternative 10 0.017 0.017 0.014 0.018 0.020 0.017 0.013 0.023 0.017 0.017 Alternative 11 0.017 0.017 0.018 0.018 0.018 0.021 0.017 0.017 0.017 0.017 Alternative 12 0.017 0.016 0.010 0.018 0.022 0.014 0.017 0.018 0.018 0.018 Alternative 13 0.017 0.017 0.009 0.016 0.032 0.015 0.018 0.018 0.019 0.018 Alternative 14 0.017 0.018 0.005 0.017 0.022 0.022 0.018 0.017 0.018 0.017 Alternative 15 0.017 0.016 0.022 0.016 0.025 0.023 0.017 0.018 0.018 0.016 Alternative 16 0.017 0.017 0.025 0.017 0.016 0.022 0.018 0.017 0.017 0.017 Alternative 17 0.017 0.019 0.020 0.018 0.013 0.021 0.018 0.016 0.017 0.017 Alternative 18 0.018 0.019 0.006 0.016 0.026 0.013 0.018 0.018 0.019 0.018 Alternative 19 0.018 0.018 0.027 0.017 0.013 0.022 0.018 0.017 0.018 0.017 Alternative 20 0.018 0.016 0.011 0.020 0.000 0.012 0.017 0.016 0.016 0.018 Alternative 21 0.018 0.017 0.028 0.012 0.039 0.016 0.018 0.019 0.020 0.018 Alternative 22 0.018 0.018 0.022 0.017 0.012 0.012 0.018 0.017 0.018 0.018 Alternative 23 0.018 0.016 0.013 0.017 0.014 0.014 0.018 0.017 0.018 0.018 Alternative 24 0.018 0.017 0.010 0.019 0.001 0.011 0.018 0.016 0.017 0.018 Alternative 25 0.018 0.016 0.022 0.014 0.030 0.016 0.018 0.019 0.020 0.018 Alternative 26 0.018 0.017 0.025 0.015 0.020 0.023 0.018 0.018 0.019 0.017 Alternative 27 0.018 0.018 0.014 0.017 0.004 0.013 0.018 0.016 0.018 0.018 Alternative 28 0.018 0.020 0.013 0.016 0.010 0.017 0.019 0.017 0.018 0.017 Alternative 29 0.018 0.017 0.012 0.018 0.001 0.013 0.018 0.016 0.017 0.018 Alternative 30 0.019 0.016 0.018 0.014 0.018 0.015 0.018 0.018 0.019 0.018 Alternative 31 0.019 0.017 0.011 0.016 0.003 0.012 0.019 0.017 0.018 0.018 Alternative 32 0.020 0.017 0.024 0.010 0.009 0.013 0.019 0.018 0.020 0.018 Alternative 33 0.019 0.016 0.026 0.058 0.018 0.010 0.019 0.018 0.020 0.018

27. 27 Example #3 with TOPSIS (Bhojane et al. 2019) Final Decision Matrix Weights 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Si+ Si- Pi Rank Alternatives C H N O S Mass of water form. HHV Quality Energy of fuel Flame Temp. Alternative 1 0.009 0.007 0.010 0.004 0.002 0.018 0.007 0.024 0.010 0.019 0.074 0.012 0.140 33th Alternative 2 0.016 0.019 0.010 0.025 0.003 0.018 0.016 0.015 0.013 0.017 0.057 0.028 0.328 26th Alternative 3 0.016 0.018 0.006 0.021 0.015 0.017 0.017 0.016 0.015 0.017 0.054 0.029 0.350 23th Alternative 4 0.017 0.018 0.036 0.020 0.004 0.022 0.017 0.015 0.015 0.017 0.054 0.040 0.427 6th Alternative 5 0.017 0.019 0.006 0.020 0.017 0.019 0.017 0.016 0.016 0.017 0.055 0.030 0.352 22th Alternative 6 0.017 0.019 0.016 0.021 0.005 0.015 0.017 0.015 0.015 0.018 0.055 0.029 0.343 24th Alternative 7 0.017 0.018 0.015 0.019 0.016 0.021 0.017 0.017 0.017 0.017 0.052 0.031 0.371 18th Alternative 8 0.017 0.018 0.008 0.018 0.023 0.022 0.017 0.017 0.017 0.017 0.053 0.033 0.385 15th Alternative 9 0.017 0.018 0.007 0.021 0.006 0.023 0.017 0.016 0.015 0.016 0.060 0.025 0.299 32th Alternative 10 0.017 0.017 0.014 0.018 0.020 0.017 0.013 0.023 0.017 0.017 0.050 0.032 0.390 14th Alternative 11 0.017 0.017 0.018 0.018 0.018 0.021 0.017 0.017 0.017 0.017 0.050 0.032 0.391 13th Alternative 12 0.017 0.016 0.010 0.018 0.022 0.014 0.017 0.018 0.018 0.018 0.052 0.033 0.392 12th Alternative 13 0.017 0.017 0.009 0.016 0.032 0.015 0.018 0.018 0.019 0.018 0.051 0.040 0.442 4th Alternative 14 0.017 0.018 0.005 0.017 0.022 0.022 0.018 0.017 0.018 0.017 0.056 0.032 0.369 19th Alternative 15 0.017 0.016 0.022 0.016 0.025 0.023 0.017 0.018 0.018 0.016 0.049 0.037 0.434 5th Alternative 16 0.017 0.017 0.025 0.017 0.016 0.022 0.018 0.017 0.017 0.017 0.050 0.035 0.410 8th Alternative 17 0.017 0.019 0.020 0.018 0.013 0.021 0.018 0.016 0.017 0.017 0.052 0.031 0.376 17th Alternative 18 0.018 0.019 0.006 0.016 0.026 0.013 0.018 0.018 0.019 0.018 0.053 0.037 0.408 9th Alternative 19 0.018 0.018 0.027 0.017 0.013 0.022 0.018 0.017 0.018 0.017 0.051 0.035 0.403 10th Alternative 20 0.018 0.016 0.011 0.020 0.000 0.012 0.017 0.016 0.016 0.018 0.061 0.027 0.306 30th Alternative 21 0.018 0.017 0.028 0.012 0.039 0.016 0.018 0.019 0.020 0.018 0.047 0.051 0.520 2nd Alternative 22 0.018 0.018 0.022 0.017 0.012 0.012 0.018 0.017 0.018 0.018 0.052 0.033 0.392 11th Alternative 23 0.018 0.016 0.013 0.017 0.014 0.014 0.018 0.017 0.018 0.018 0.054 0.030 0.355 21th Alternative 24 0.018 0.017 0.010 0.019 0.001 0.011 0.018 0.016 0.017 0.018 0.061 0.027 0.312 28th Alternative 25 0.018 0.016 0.022 0.014 0.030 0.016 0.018 0.019 0.020 0.018 0.048 0.042 0.463 3rd Alternative 26 0.018 0.017 0.025 0.015 0.020 0.023 0.018 0.018 0.019 0.017 0.050 0.036 0.419 7th Alternative 27 0.018 0.018 0.014 0.017 0.004 0.013 0.018 0.016 0.018 0.018 0.059 0.028 0.323 27th Alternative 28 0.018 0.020 0.013 0.016 0.010 0.017 0.019 0.017 0.018 0.017 0.057 0.028 0.332 25th Alternative 29 0.018 0.017 0.012 0.018 0.001 0.013 0.018 0.016 0.017 0.018 0.061 0.027 0.305 31th Alternative 30 0.019 0.016 0.018 0.014 0.018 0.015 0.018 0.018 0.019 0.018 0.052 0.033 0.384 16th Alternative 31 0.019 0.017 0.011 0.016 0.003 0.012 0.019 0.017 0.018 0.018 0.061 0.027 0.309 29th Alternative 32 0.020 0.017 0.024 0.010 0.009 0.013 0.019 0.018 0.020 0.018 0.058 0.033 0.363 20th Alternative 33 0.019 0.016 0.026 0.058 0.018 0.010 0.019 0.018 0.020 0.018 0.024 0.065 0.731 1st

28. REFERENCES Rawaz Kurda, Jorge de Brito, José D. Silvestre, A multi-criteria decision method for concrete optimization, Environmental Impact Assessment Review 74 (2019) 73-85. Idrees Zafar, Khuram Rashid, Samia Tariq, Asif Ali, Minkwan Ju, Integrating technical-environmental-economical perspectives for optimizing rubber content in concrete by multi-criteria analysis, Construction and Building Materials 319 (2022) 125820. Yanlin Li, Xiaoshuang Shi, Ying Feng, Yanpeng Su , Yuhao Zhang, Yunhui Pu, Qingyuan Wang, A novel multi-criteria comprehensive evaluation model of fly ash-based geopolymer concrete, Construction and Building Materials 396 (2023) 132253. Nikola Tosic , Snezana Marinkovic , Tina Dasic , Milas Stanic , Multicriteria optimization of natural and recycled aggregate concrete for structural use, Jounel of Cleaner Production 87 (2015) 766-776. Nathanael Risson dos Santos, Elcio Cassimiro Alves, Moacir Kripka, Towards sustainable reinforced concrete beams: multi-objective optimization for cost, CO2 emission, and crack prevention, Asian Journal of Civil Engineering (2023). https://doi.org/10.1007/s42107-023-00795-y 28

29. Thank you! Any Questions?