SpringerLink
Log in

Mechanical Properties Optimization and Simulation of Soil–Saw Dust Ash Blend Using Extreme Vertex Design (EVD) Method

  • Original Research Paper
  • Published:
International Journal of Pavement Research and Technology Aims and scope Submit manuscript

Abstract

This experimental investigation involves adaptation of constrained simplex mixture design optimization technique for modeling of the mechanical behavior of soil–saw dust ash (SDA) which is a derivative from industrial byproducts to provide efficient waste management practices. The soil enhancement protocols deployed were to improve its engineering properties for pavement foundation purposes. The statistical analyses engaged in this research study were achieved using Design Expert software for the formulation and imposition of the components constraints, derivation of the mixture experimental runs and design proportions of ingredients. The experimental responses obtained from the laboratory works showed a maximum unconfined compressive strength (UCS) and California bearing ratio (CBR) results of 248 kN/m2 and 35% with mixture fraction of 0.875:0.125 for soil and SDA two component mixture respectively. For the investigation and development of the Extreme Vertex Design (EVD)-model, information from experimental exercises was used. The procedures included statistical assessment, ANOVA, diagnostic tests, and influence statistics, as well as numerical optimization utilizing the desirability function to examine the datasets. Desirability score of 1.0 was derived at a mix ratio of 0.8125: 0.1875 for the two components of soil–SDA to produce maximized CBR and UCS response of 35.053% and 257.152 kN/m2 respectively. Furthermore, the adequacy of the model generated was assessed by statistical validation and simulation exercises using student’s t-test and F-test with P(T < = t) one-tail of 0.490 and 0.499 for F-test and P(F< = f) two-tail of 0.960 and 0.977 for t-test calculated for CBR and UCS responses, respectively. The calculated statistical results with P-value > 0.05 signified that there is no significant difference between the actual and EVD-model simulated results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data Availability

All data generated or analyzed during this study are included in this published article.

References

  1. Netterberg, F. (2014). Review of specification for the use of laterite in road pavements. Council for Scientific and Industrial Research, 10, 1–60.

    Google Scholar 

  2. Attah, I. C., Okafor, F. O., & Ugwu, O. O. (2021). Optimization of California bearing ratio of tropical black clay soil treated with cement kiln dust and metakaolin blend. International Journal of Pavement Research and Technology, 14(6), 655–667. https://doi.org/10.1007/s42947-020-0003-6.

    Article  Google Scholar 

  3. Gidigasu, M. D. (1976). Laterite soil engineering: Pedogenesis and engineering principles. Amsterdam Elsevier Scientific.

    Google Scholar 

  4. Adeyemi, G. O., Afolagboye, L. O., & Chukwuemeka, C. A. (2015). Geotechnical properties of noncrystalline coastal plain sand derived lateritic soils from Ogua, Niger Delta, Nigeria. African Journal of Science, Technology, Innovation and Development, 7(4), 230–235.

    Google Scholar 

  5. Toll, D. G. (2012). Tropical soils. In J. Burland, T. Chapman, H. Skinner, & M. Brown (Eds.), ICE manual of geotechnical engineering (Vol. I, pp. 341–361). Thomas Telford Ltd.

    Google Scholar 

  6. Nicholson, P. G., & Kasyap, V. (2003). Fly ash stabilization of tropical Hawaiian soils. In K. D. Sharp (Ed.), Fly ash for soil improvement. ASCE Geotechnical special publication no. 36 (pp. 15–29). ASCE.

    Google Scholar 

  7. Lee, I. K., & Coop, M. R. (2005). The intrinsic behavior of a decomposed granite soil. Géotechnique, 45(1), 117–130.

    Google Scholar 

  8. Nicholson, P. G. (2015). Soil improvement and ground modification methods soil improvement modification. Waltham: Butterworth Heinemann.

    Google Scholar 

  9. Millogo, Y., Hajjaji, M., Ouedraogo, R., & Gomina, M. (2008). Cement-lateritic gravels mixtures: Microstructure and strength characteristics. Construction and Building Materials, 22(10), 2078–2086.

    Google Scholar 

  10. Eberemu, A. O. (2011). Consolidation properties of compacted lateritic soil treated with rice husk ash. Geomaterials, 01(03), 70–78.

    Google Scholar 

  11. Joel, M., & Agbede, I. O. (2011). Mechanical-cement stabilization of laterite for use as flexible pavement material. Journal of Materials in Civil Engineering, 23(2), 146–152.

    Google Scholar 

  12. Reddy, N. G., Tahasildar, J., & Rao, B. H. (2015). Evaluating the influence of additives on swelling characteristics of expansive soils. International Journal of Geosynthetics and Ground Engineering, 1(1), 7.

    Google Scholar 

  13. Osinubi, K. J. (2008). Permeability of lime treated lateritic soil. Journal of Transportation Engineering, 124(5), 465–469.

    Google Scholar 

  14. Ezeokpube, G. C., Ahaneku, I. E., Alaneme, G. U., Attah, I. C., Etim, R. K., Olaiya, B. C., & Udousoro, I. M. (2022). Assessment of mechanical properties of soil-lime-crude oil-contaminated soil blend using regression model for sustainable pavement foundation construction. Advances in Materials Science and Engineering, 2022, 7207842. https://doi.org/10.1155/2022/7207842.

    Article  Google Scholar 

  15. Li, Y., Tan, Y. Z., Jiang, J., & **a, C. F. (2012). Experimental study on the compressibility of cement improved laterite soil. Applied Mechanics and Materials, 170–173, 371–374.

    Google Scholar 

  16. Oyediran, I. A., & Ayeni, O. O. (2020). Comparative effect of microbial induced calcite precipitate, cement and rice husk ash on the geotechnical properties of soils. SN Applied Sciences, 2(7), 1157.

    Google Scholar 

  17. Onyelowe, K. C., Van, D. B., Ubachukwu, O., Ezugwu, C., Salahudeen, B., Van, M. N., Ikeagwuani, C., Amhadi, T., Sosa, F., Wu, W., Duc, T. T., Eberemu, A., Duc, T. P., Barah, O., Ikpa, C., Orji, F., Alaneme, G., Amanamba, E., Ugwuanyi, H., … Ugorji, B. (2019). Recycling and reuse of solid wastes; a hub for ecofriendly, ecoefficient and sustainable soil, concrete, wastewater and pavement reengineering. International Journal of Low-Carbon Technologies, 14, 440–451. https://doi.org/10.1093/ijlct/ctz028.

    Article  Google Scholar 

  18. Mengue, E., Mroueh, H., Lancelot, L., & MedjoEko, R. (2017). Physicochemical and consolidation properties of compacted lateritic soil treated with cement. Soils and Foundations, 57(1), 60–79.

    Google Scholar 

  19. Amadi, A. A. (2010). Pozzolanic influence of fly ash in mobilizing the compressive strength of lateritic soil. AU Journal of Technology, 14(2), 139–146.

    Google Scholar 

  20. Gana, A. J., & Tabat, J. B. (2017). Stabilization of clay soil with cement and sawdust ash. International Journal of Engineering and Emerging Scientific Discovery, 2(3). ISSN: 2536-7250 (Print): 2536–7269 (Online).

  21. Eberemu, A. O. (2013). Evaluation of bagasse ash treated lateritic soil as a potential barrier material in waste containment application. Acta Geotechnica, 8(4), 407–421.

    Google Scholar 

  22. Athulya, G. K., Dutta, S., & Mandal, J. N. (2017). Performance evaluation of stabilized soil–slag mixes as highway construction material. International Journal of Geotechnical Engineering, 11(1), 51–61.

    Google Scholar 

  23. Onyelowe, K. C., Fazal, E. J., Michael, E. O., Ifeanyichukwu, C. O., Alaneme, G. U., & Chidozie, I. (2021). Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction. Jurnal Kejuruteraan, 33(4), 845–852. https://doi.org/10.17576/jkukm-2021-33(4)-07.

    Article  Google Scholar 

  24. Alhassan, M., & Mustapha, A. (2007). Effect of rice husk ash on cement stabilized laterite. Leonardo Electronic Journal of Practices and Technologies, 11(11), 47–58.

    Google Scholar 

  25. Abdu, A. A., Musa, K. A., & Arafat, Y. S. (2017). Compaction behaviour of lateritic soils stabilized with blends of groundnut shell ash and metakaolin. Journal of Environment and Earth Science, 7(10), 28–39.

    Google Scholar 

  26. Edeh, J. E., Joel, M., & Ogbodo, V. O. (2020). Effects of oil palm fibre ash on cement stabilised lateritic soil used for highway construction. International Journal of Pavement Engineering, 23, 1–7.

    Google Scholar 

  27. Etim, R. K., Attah, I. C., & Yohanna, P. (2020). Experimental study on potential of oyster shell ash in structural strength improvement of lateritic soil for road construction. International Journal of Pavement Research Technology, Chinese Society of Pavement Engineering, 13(4), 341–351. https://doi.org/10.1007/s42947-020-0290-y.

    Article  Google Scholar 

  28. Attah, I. C., Etim, R. K., Yohanna, P., & Usanga, I. N. (2021). Understanding the effect of compaction energies on the strength indices and durability of oyster shell ash-lateritic soil mixtures for use in road works. Engineering and Applied Science Research, 48(2), 151–160. https://doi.org/10.14456/easr.2021.17.

    Article  Google Scholar 

  29. Edeh, J. E., Agbede, I. O., & Tyoyila, A. (2014). Evaluation of sawdust ash-stabilized lateritic soil as highway pavement material. Journal of Materials in Civil Engineering. https://doi.org/10.1061/(ASCE)18MT.1943-5533.0000795.

    Article  Google Scholar 

  30. Elinwa, A. U., & Mahmood, Y. A. (2002). “Ash from timber waste as cement replacement material. Cement and Concrete Composites, 24, 219–222.

    Google Scholar 

  31. Jian-Tong, D., Pei-Yu, Y., Shu-Lin, L., & **-Quan, Z. (1999). Extreme vertices design of concrete with combined mineral admixtures. Cement and Concrete Research, 29(6), 957–960. https://doi.org/10.1016/S0008-8846(99)00069-1.

    Article  Google Scholar 

  32. Onyelowe, K. C., Alaneme, G. U., Van Bui, D., Van, M. N., Ezugwu, C., Amhadi, T., Sosa, F., Orji, F., & Ugorji, B. (2019). Generalized review on EVD and constraints simplex method of materials properties optimization for civil engineering. Civil Engineering Journal, 5(3), 729–749. https://doi.org/10.28991/cej-2019-03091283.

    Article  Google Scholar 

  33. McLean, R. A., & Anderson, V. L. (2012). Extreme vertices design of mixture. University of Tennessee.

    Google Scholar 

  34. Wangkananon, W., Phuaksaman, C., Koobkokkruad, T., & Natakankitkul, S. (2018). An extreme vertices mixture design approach to optimization of tyrosinase inhibition effects. Engineering Journal, 22(1), 175–185. https://doi.org/10.4186/ej.2018.22.1.175.

    Article  Google Scholar 

  35. Cornell, J. A. (2011). Experiments with mixtures: Designs, models and the analysis of mixture data (3rd ed.). New York, NY, USA: John Wiley and Sons.

    Google Scholar 

  36. Scheffé, H. (1963). The simplex-centroid design for experiments with mixtures. Journal of the Royal Statistical Society Series B, 25, 235–263.

    MathSciNet  Google Scholar 

  37. Zheng, S. S., Wang, X. F., Lou, H. J., & Li, Z. Q. (2012). Optimization design for mix proportioning of high strength and high performance concrete. Advanced Materials Research, 368–373(2012), 432–435.

    Google Scholar 

  38. Onyelowe, K. C., Alaneme, G., Igboayaka, C., Orji, F., Ugwuanyi, H., Van Bui, D., & Van Nguyen, M. (2019). Scheffe optimization of swelling California bearing ratio, compressive strength, and durability potentials of quarry dust stabilized soft clay soil. Materials Science for Energy Technologies, 2(1), 67–77. https://doi.org/10.1016/j.mset.2018.10.005.

    Article  Google Scholar 

  39. Scheffé, H. (1958). Experiments with mixtures. Journal of the Royal Statistical Society Series B, 20, 344–360.

    MathSciNet  Google Scholar 

  40. Gullu, H., & Fedakar, H. I. (2017). Response surface methodology for optimization of stabilizer dosage rates of marginal sand stabilizer with sludge ash and fiber based on UCS performances. KSCE Journal of Civil Engineering, 21(5), 1717–1727. https://doi.org/10.1007/s12205-016-0724-x.

    Article  Google Scholar 

  41. Alaneme, G. U., & Mbadike, E. M. (2019). Optimization of flexural strength of palm nut fibre concrete using Scheffe’s theory. Materials Science for Energy Technologies, 2, 272–287. https://doi.org/10.1016/j.mset.2019.01.006.

    Article  Google Scholar 

  42. Alaneme, G. U., Attah, I. C., Etim, R. K., & Dimonyeka, M. U. (2021). Mechanical properties optimization of soil–cement kiln dust mixture using extreme vertex design. International Journal of Pavement Research Technology. https://doi.org/10.1007/s42947-021-00048-8.

    Article  Google Scholar 

  43. Sabarish, K. V., Akish, R. M., & Paul, P. (2019). (2019) Optimizing the concrete materials by Taguchi optimization method. IOP Conference Series: Materials Science and Engineering, 574, 012002. https://doi.org/10.1088/1757-899X/574/1/012002.

    Article  Google Scholar 

  44. American Standard for Testing Material. (1992). Annual book of standards (Vol. 4.08). Philadelphia: American Society for Testing and Materials.

    Google Scholar 

  45. ASTM specification C618–78 Specification for fly ash and raw or calcined natural pozzolan for use as a mineral admixture in Portland cement concrete.

  46. BS 8615–1:2019. Specification for pozzolanic materials for use with Portland cement, natural pozzolana and natural calcined pozzolana. 389 Chiswick High Road, London W4 4AL UK.

  47. BS 1377-2:2022. Methods of test for soils for civil engineering purposes - Classification tests and determination of geotechnical properties. 389 Chiswick High Road, London W4 4AL UK.

  48. British Standard (BS) 1924. (1990). Method of testing for stabilized soils. British Standard Institution.

    Google Scholar 

  49. Onyelowe, K. C., Alaneme, G. U., Onyia, M. E., Van Bui, D., Diomonyeka, M. U., Nnadi, E., Ogbonna, C., Odum, L. O., Aju, D. E., Abel, C., Udousoro, I. M., & Onukwugha, E. (2021). Comparative modeling of strength properties of hydrated-lime activated rice-husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy logic (FL). Jurnal Kejuruteraan, 33(2), 365–384. https://doi.org/10.17576/jkukm-2021-33(2)-20.

    Article  Google Scholar 

  50. Onyelowe, K. C. (2017). Mathematical advances in soil bearing capacity. Electronic Journal of Geotechnical Engineering, 22(12), 4735–4743.

    Google Scholar 

  51. Attah, I. C., Okafor, F. O., & Ugwu, O. O. (2022). Durability performance of expansive soil ameliorated with binary blend of additives for infrastructure delivery. Innovative Infrastructure Solutions, 7, 234. https://doi.org/10.1007/s41062-022-00834-.

    Article  Google Scholar 

  52. Butt, W. A., Gupta, K., & Jha, J. N. (2016). Strength behavior of clayey soil stabilized with saw dust ash. Geo-Engineering, 7, 18. https://doi.org/10.1186/s40703-016-0032-9.

    Article  Google Scholar 

  53. Design Expert 11. (2018). Design of experiment software. Minneapolis, USA: Stat-Ease Inc.

    Google Scholar 

  54. Minitab 18. (2018). Minitab statistical software. Pennsylvania, USA: Minitab Inc.

    Google Scholar 

  55. Aju, D. E., Onyelowe, K. C., & Alaneme, G. U. (2021). Constrained vertex optimization and simulation of the unconfined compressive strength of geotextile reinforced soil for flexible pavement foundation construction. Cleaner Engineering and Technology. https://doi.org/10.1016/j.clet.2021.100287.

    Article  Google Scholar 

  56. Alaneme, G. U., Onyelowe, K. C., Onyia, M. E., Van Bui, D., Mbadike, E. M., Ezugwu, C. N., Dimonyeka, M. U., Attah, I. C., Ogbonna, C., Abel, C., Ikpa, C. C., & Udousoro, I. M. (2020). Modelling volume change properties of hydrated-lime activated rice husk ash modified soft soil for construction purposes by artificial neural network. Umudike Journal of Engineering and Technology, 6(1), 88–110. https://doi.org/10.33922/j.ujet_v6i1_9.

    Article  Google Scholar 

  57. Owoyemi, O. O. (2021). Effect of sawdust on the geotechnical properties of a lateritic soil. Journal of Mining and Geology, 57(1), 127–139.

    Google Scholar 

  58. Damiri, S., Pouretedal, H. R., & Bakhshi, O. (2016). An extreme vertices mixture design approach to the optimization of methylal production process using p-toluenesulfonic acid as catalyst. Chemical Engineering Research Design, 112, 155–162.

    Google Scholar 

  59. Alaneme, G. U., Attah, I. C., Mbadike, E. M., Dimonyeka, M. U., Usanga, I. N., & Nwankwo, H. F. (2022). Mechanical strength optimization and simulation of cement kiln dust concrete using extreme vertex design method. Nanotechnology for Environmental Engineering, 7, 467–490. https://doi.org/10.1007/s41204-021-00175-4.

    Article  Google Scholar 

  60. Wigena, A. H., Sumertajaya, I. M., & Syafitri, U. (2018). Constrained experimental regions on ground granulated blast furnace slag concrete. Applied Mathematical Sciences, 12(26), 1251–1258. https://doi.org/10.12988/ams.2018.89128.

    Article  Google Scholar 

  61. Arina, F., Wigena, A. H., Sumertajaya, I. M., & Syafitri, U. (2018). Split plot mixture process variable experiment on steel slag concrete. IOP Conference Series: Materials Science and Engineering, 187, 012049. https://doi.org/10.1088/1755-1315/187/1/012049.

    Article  Google Scholar 

  62. Ogunwusi, A. A. (2014). Wood waste generation in the forest industry in Nigeria and prospects for its industrial utilization. Civil and Environmental Research, 6(9), 62–69.

    Google Scholar 

  63. Alaneme, G. U., Onyelowe, K. C., Onyia, M. E., Van Bui, D., Mbadike, E. M., Dimonyeka, M. U., Attah, I. C., Ogbonna, C., Iro, U. I., Kumari, S., Firoozi, A. A., & Oyagbola, I. (2020). Modelling of the swelling potential of soil treated with quicklime-activated rice husk ash using fuzzy logic. Umudike Journal of Engineering and Technology, 6(1), 1–22.

    Google Scholar 

  64. Amhadi, T. S., & Assaf, G. J. (2018). Overview of soil stabilization methods in road construction. GeoMEast, 21–33, 2019. https://doi.org/10.1007/978-3-030-01911-2_3.

    Article  Google Scholar 

  65. Alaneme, G. U., Mbadike, E. M., Attah, I. C., & Udousoro, I. M. (2022). Mechanical behaviour optimization of saw dust ash and quarry dust concrete using adaptive neuro-fuzzy inference system. Innovative Infrastructure Solutions, 7, 122. https://doi.org/10.1007/s41062-021-00713-8.

    Article  Google Scholar 

  66. Trivedi, J. S., Nair, S., & Iyyunni, C. (2013). Optimum utilization of fly ash for stabilization of sub-grade soil using genetic algorithm. Procedia Engineering, 51, 250–258.

    Google Scholar 

  67. Le, H., Nguyen, T., Nguyen, D., & Prakash, I. (2020). Prediction of soil unconfined compressive strength using artificial neural network model. Vietnam Journal of Earth Science, 42, 255–264. https://doi.org/10.5625/0866-7187/42/3/15342.

    Article  Google Scholar 

  68. Obeta, I. N., Ikeagwuani, C. C., Attama, C. M., & Okafor, J. (2019). Stability and durability of sawdust ash-lime stabilised black cotton soil. Nigerian Journal of Technology, 38(1), 75. https://doi.org/10.4314/njt.v38i1.10

    Article  Google Scholar 

  69. Onyelowe, K. C., Jalal, F. E., Onyia, M. E., Onuoha, I. C., & Alaneme, G. U. (2021). Application of gene expression programming to evaluate strength characteristics of hydrated-lime-activated rice husk ash-treated expansive soil. Applied Computational Intelligence and Soft Computing. https://doi.org/10.1155/2021/6686347

    Article  Google Scholar 

  70. Eriksson, L. (2008). Design of experiments: Principles and applications. Malmo, Sweden: MKS Umetrics AB.

    Google Scholar 

  71. Snee, R. D. (1979). Experiment design for mixture systems with multicomponent constraints. Communication in Statistics-Theory and Methods, 17, 149–159.

    Google Scholar 

  72. Kumar, R. G., & Sanghvi, I. (2015). Optimization techniques: An overview for formulation development. Asian Journal of Pharmaceutical Research, 5, 217–221.

    Google Scholar 

  73. Stahle, L., & Wold, S. (1989). Analysis of variance (ANOVA). Chemometrics and Intelligent Laboratory Systems, 6, 259–272. https://doi.org/10.1155/2018/9308580

    Article  Google Scholar 

  74. Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimal experimental design with SAS. Oxford University Press.

    Google Scholar 

  75. George, U. A., & Elvis, M. (2019). Modelling of the mechanical properties of concrete with cement ratio partially replaced by aluminium waste and sawdust ash using artificial neural network. SN Applied Sciences, 1, 1514. https://doi.org/10.1007/s42452-019-1504-2

    Article  Google Scholar 

  76. Smith, W. F. (2005). Experimental design for formulation. The American Statistical Association and the Society for Industrial and Applied Mathematics.

    Google Scholar 

  77. Marquardt, D. W., & Snee, R. D. (1974). Test statistics for mixture models. Technometrics, 14, 533–537.

    Google Scholar 

  78. Shobha, R., Hiremath, R., & Vanaja K. (2016). Optimization techniques in pharmaceutical formulation and processing. Textbook of Industrial Pharmacy. Drug Delivery Systems, Cosmetic and Herbal Drug Technology, 158–168.

  79. Borkowski, J. J. (2003). Using genetic algorithm to generate small exact response surface designs. Journal of Probability and Statistical Science, 1, 65–88.

    Google Scholar 

  80. Iro, U. I., Alaneme, G. U., Milad, A., Olaiya, B. C., Otu, O. N., Isu, E. U., & Amuzie, M. N. (2022). Optimization and simulation of saw dust ash concrete using extreme vertex design method. Advances in Materials Science and Engineering, 2022, 5082139. https://doi.org/10.1155/2022/5082139

    Article  Google Scholar 

  81. Snee, R. D., & Marquardt, D. W. (1974). Extreme vertices designs for linear mixture models. Technometrics, 16(3), 399–408.

    Google Scholar 

  82. Ozol-Godfrey, A., Anderson-Cook, C. M., & Montgomery, D. C. (2005). Fraction of design space plots for examining model robustness. Journal of Quality Technology, 37, 223–235.

    Google Scholar 

  83. Box, G. E. P., & Draper, N. R. (1959). A basis for the selection of a response surface design. Journal of American Statistical Association, 54, 622–654.

    MathSciNet  Google Scholar 

  84. Cox, D. R. (1971). A note on polynomial response functions for mixtures. Biometrika, 58, 155–159. https://doi.org/10.1093/biomet/58.1.155

    Article  Google Scholar 

  85. Attah, I. C., Etim, R. K., Alaneme, G. U., & Ekpo, D. U. (2022). Scheffe’s approach for single additive optimization in selected soils amelioration studies for cleaner environment and sustainable subgrade materials. Cleaner Materials, 5, 100126. https://doi.org/10.1016/j.clema.2022.100126. ISSN 2772-3976.

    Article  Google Scholar 

  86. Sahni, N. S., Piepel, G. F., & Naes, T. (2009). Product and process improvement using mixture-process variable methods and robust optimization techniques. Journal of Quality Technology, 41, 181–197.

    Google Scholar 

  87. Fedorov, V. V. (1972). Theory of optimal experiments. New York, NY, USA: Academic press.

    Google Scholar 

  88. Bolton, S. (1997). Optimization techniques in pharmaceutical statistics. Practical and clinical Applications (3rd ed.). New York: Marcel Dekker.

    Google Scholar 

  89. Attah, I. C., Etim, R. K., Alaneme, G. U., & Bassey, O. B. (2020). Optimization of mechanical properties of rice husk ash concrete using Scheffe’s theory. SN Applied Science. https://doi.org/10.1007/s42452-020-2727-y

    Article  Google Scholar 

  90. Coetzer, R., & Haines, L. M. (2017). The construction of D- and I-optimal designs for mixture experiments with linear constraints on the components. Chemometrics and Intelligent Laboratory Systems, 171, 112–124.

    Google Scholar 

  91. Alaneme, G. U., Mbadike, E. M., Iro, U. I., Udousoro, I. M., & Ifejimalu, W. C. (2021). Adaptive neuro-fuzzy inference system prediction model for the mechanical behaviour of rice husk ash and periwinkle shell concrete blend for sustainable construction. Asian Journal of Civil Engineering, 2021(22), 959–974. https://doi.org/10.1007/s42107-021-00357-0

    Article  Google Scholar 

  92. Esbensen, K. H., Guyot, D., Westad, F., & Houmoller, L. P. (2002). Multivariate data analysis, in practice: An introduction to multivariate data analysis and experimental design. Aalborg University.

    Google Scholar 

  93. Mitchell, T. J. (1974). An algorithm for the construction of D-optimal experimental designs. Technometrics, 16, 203–210.

    MathSciNet  Google Scholar 

  94. Alaneme, G. U., Dimonyeka, M. U., Ezeokpube, G. C., Uzoma, I. I., & Udousoro, I. M. (2021). Failure assessment of dysfunctional flexible pavement drainage facility using fuzzy analytical hierarchical process. Innovative Infrastructure Solutions. https://doi.org/10.1007/s41062-021-00487-z

    Article  Google Scholar 

  95. Ujong, J. A., Mbadike, E. M., & Alaneme, G. U. (2022). Prediction of cost and duration of building construction using artificial neural network. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-022-00474-4

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, P.M.B. 7267, Umuahia, 440109, Abia State, Nigeria

    George Uwadiegwu Alaneme, Uzoma Ibe Iro, Uzochukwu Prince Chibuisi & Joshua Agada

  2. Department of Civil Engineering, Kampala International University, Kansanga, Uganda

    George Uwadiegwu Alaneme & Bamidele Charles Olaiya

  3. Department of Civil Engineering, University of Nizwa, Birkat-Al-Mouz, 616, Nizwa, Sultanate of Oman

    Abdalrhman Milad

  4. Department of Civil Engineering, University of Cross River State, Calabar, Nigeria

    Obeten Nicholas Otu

Authors
  1. George Uwadiegwu Alaneme
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uzoma Ibe Iro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdalrhman Milad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bamidele Charles Olaiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Obeten Nicholas Otu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Uzochukwu Prince Chibuisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joshua Agada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Uwadiegwu Alaneme.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Consent to Publish and Ethical Approval

We hereby declare interests to submit the above manuscripts for your consideration for possible publication. By this, we affirm that this paper is only being considered for publication in this journal alone and hasn’t been submitted to any other journal. We also affirm that the content of this work is original and has followed the journal template. Hence, it is before you for consideration.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alaneme, G.U., Iro, U.I., Milad, A. et al. Mechanical Properties Optimization and Simulation of Soil–Saw Dust Ash Blend Using Extreme Vertex Design (EVD) Method. Int. J. Pavement Res. Technol. 17, 827–853 (2024). https://doi.org/10.1007/s42947-023-00272-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42947-023-00272-4

Keywords

Search

Navigation