Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity

References

• Aci-522r, 2010. Report on pervious concrete. Farmington Hills, MI: ACI Committee.
   Google Scholar
• Adato, J. and Delatte, N.J., 2013. Using ultrasonic pulse velocity (upv) to predict properties and performance of pervious concrete. Ohio: Cleveland State University.
   Google Scholar
• Adewumi, A.A., et al., 2016. Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach. Applied Soft Computing, 42, 342–350. Available from: https://www.sciencedirect.com/science/article/pii/S1568494616300527.
   Web of Science ®Google Scholar
• Ahmad, A., et al., 2022. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Studies in Construction Materials, 16, e00840. Available from: https://www.sciencedirect.com/science/article/pii/S2214509521003557.
   Google Scholar
• Ahmad, S.A., et al., 2023. Mathematical modeling techniques to predict the compressive strength of pervious concrete modified with waste glass powders. Asian Journal of Civil Engineering, doi:10.1007/s42107-023-00811-1.
   Google Scholar
• Ahmed, H.U., et al., 2022. Mathematical modeling techniques to predict the compressive strength of high-strength concrete incorporated metakaolin with multiple mix proportions. Cleaner Materials, 5, 100132. Available from: https://www.sciencedirect.com/science/article/pii/S2772397622000922.
   Google Scholar
• Ahmmad, R., et al., 2016. Performance evaluation of palm oil clinker as coarse aggregate in high strength lightweight concrete. Journal of Cleaner Production, 112, 566–574. Available from: https://www.sciencedirect.com/science/article/pii/S0959652615011336.
   Web of Science ®Google Scholar
• Alemu, A.S., et al., 2021. Practical considerations of porosity, strength, and acoustic absorption of structural pervious concrete. Case Studies in Construction Materials, 15, e00764. Available from: https://www.sciencedirect.com/science/article/pii/S2214509521002795.
   Google Scholar
• Almasaeid, H.H. and Salman, D.G., 2022. Application of artificial neural network to predict the properties of permeable concrete. Civil Engineering and Architecture, 10 (6), 2305–2305.
   Google Scholar
• Amari, S. and Wu, S., 1999. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12 (6), 783–789. Available from: https://www.sciencedirect.com/science/article/pii/S0893608099000325.
   PubMed Web of Science ®Google Scholar
• Amini, K., Wang, X., and Delatte, N., 2018. Statistical modeling of hydraulic and mechanical properties of pervious concrete using nondestructive tests. Journal of Materials in Civil Engineering, 30 (6), 04018077. [Accessed 20 June 2023]. doi:10.1061/(ASCE)MT.1943-5533.0002268.
   Web of Science ®Google Scholar
• An, W. and Liang, M., 2013. Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises. Neurocomputing, 110, 101–110. Available from: https://www.sciencedirect.com/science/article/pii/S0925231213000106.
   Web of Science ®Google Scholar
• Anburuvel, A. and Niruban Subramaniam, D., 2022. Investigation of the effects of compaction on compressive strength and porosity characteristics of pervious concrete. Transportation Research Record, 2676 (9), 513–525. [Accessed 2 January 2023]. doi:10.1177/03611981221087236.
   Web of Science ®Google Scholar
• Anburuvel, A. and Subramaniam, D.N., 2022a. Influence of aggregate gradation and compaction on compressive strength and porosity characteristics of pervious concrete. International Journal of Pavement Engineering, 1–14. doi:10.1080/10298436.2022.2055022.
   Web of Science ®Google Scholar
• Anburuvel, A. and Subramaniam, D.N., 2022b. A novel multi-variable model for the estimation of compressive strength of pervious concrete. International Journal of Pavement Research and Technology. Available from, doi:10.1007/s42947-022-00266-8.
   Google Scholar
• Anburuvel, A. and Subramaniam, D.N., 2023. Influence of aggregate gradation and compaction on compressive strength and porosity characteristics of pervious concrete. International Journal of Pavement Engineering, 24 (2), 2055022. doi:10.1080/10298436.2022.2055022.
   Web of Science ®Google Scholar
• Armaghani, D.J. and Asteris, P.G., 2021. A comparative study of ann and anfis models for the prediction of cement-based mortar materials compressive strength. Neural Computing and Applications, 33 (9), 4501–4532. doi:10.1007/s00521-020-05244-4.
   Web of Science ®Google Scholar
• Asteris, P.G., et al., 2020. A novel heuristic algorithm for the modeling and risk assessment of the covid-19 pandemic phenomenon. Computer Modeling in Engineering & Sciences, 125 (2), 815–828.
   Web of Science ®Google Scholar
• Astm-C597, 2010. Standard test method for pulse velocity through concrete. West Conshohocken, PA: ASTM International.
   Google Scholar
• Astm-D7063/D7063m, 2018. Standard test method for effective porosity and effective air voids of compacted asphalt mixture samples. West Conshohocken, PA: ASTM International.
   Google Scholar
• Badarna, M. and Shimshoni, I., 2019. Selective sampling for trees and forests. Neurocomputing, 358, 93–108. Available from: https://www.sciencedirect.com/science/article/pii/S0925231219306617.
   Web of Science ®Google Scholar
• Bs-1377, 1990. Methods of test for soils for civil engineering purposes. London: British Standards Institution (BSI).
   Google Scholar
• Bs-En-1097, 2020. Tests for mechanical and physical properties of aggregates. London: British Standards Institution (BSI).
   Google Scholar
• Bs-En-933, 2017. Tests for geometrical properties of aggregates. London: British Standards Institution (BSI).
   Google Scholar
• Calabrese, L., et al., 2015. Identification of damage evolution during scc on 17-4 ph stainless steel by combining electrochemical noise and acoustic emission techniques. Corrosion Science, 98, 573–584. Available from: https://www.sciencedirect.com/science/article/pii/S0010938X15002644.
   Web of Science ®Google Scholar
• Chandrappa, A.K. and Biligiri, K.P., 2016. Pervious concrete as a sustainable pavement material – research findings and future prospects: a state-of-the-art review. Construction and Building Materials, 111, 262–274. Available from: https://www.sciencedirect.com/science/article/pii/S0950061816301131.
   Web of Science ®Google Scholar
• Delatte, N., Mrkajic, A., and Miller, D.I., 2009. Field and laboratory evaluation of pervious concrete pavements. Transportation Research Record, 2113 (1), 132–139. [Accessed 05 November 2023]. doi:10.3141/2113-16.
   Google Scholar
• Elgeldawi, E., et al., 2021. Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis. Informatics, 8 (4), 79.
   Google Scholar
• Elith, J., Leathwick, J.R., and Hastie, T., 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77 (4), 802–813.
   PubMed Web of Science ®Google Scholar
• Farahani, J.N., et al., 2017. Engineering properties of lightweight aggregate concrete containing binary and ternary blended cement. Journal of Cleaner Production, 149, 976–988. Available from: https://www.sciencedirect.com/science/article/pii/S0959652617302913.
   Web of Science ®Google Scholar
• Feng, D.-C., et al., 2020. Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach. Construction and Building Materials, 230, 117000. Available from: https://www.sciencedirect.com/science/article/pii/S0950061819324420.
   Web of Science ®Google Scholar
• Filho, S.T.M., et al., 2020. Characterization of pervious concrete focusing on non-destructive testing. Revista IBRACON de Estruturas e Materiais, 13 (3), 483–500. [Accessed 04 February 2021].
   Google Scholar
• Gao, W., et al., 2019. Development of a novel soft-computing framework for the simulation aims: a case study. Engineering with Computers, 35 (1), 315–322. doi:10.1007/s00366-018-0601-y.
   Web of Science ®Google Scholar
• Guillen, M.D., Aparicio, J., and Esteve, M., 2023. Gradient tree boosting and the estimation of production frontiers. Expert Systems with Applications, 214, 119134. Available from: https://www.sciencedirect.com/science/article/pii/S0957417422021522.
   Web of Science ®Google Scholar
• Hu, L., et al., 2016. Centrifuge-less dispersive liquid-liquid microextraction base on the solidification of switchable solvent for rapid on-site extraction of four pyrethroid insecticides in water samples. Journal of Chromatography A, 1472, 1–9. Available from: https://www.sciencedirect.com/science/article/pii/S0021967316313450.
   PubMed Web of Science ®Google Scholar
• Huang, J., et al., 2020. Predicting the permeability of pervious concrete based on the beetle antennae search algorithm and random forest model. Advances in Civil Engineering, 2020, 8863181. doi:10.1155/2020/8863181.
   Web of Science ®Google Scholar
• Huang, J., Zhang, J., and Gao, Y., 2022. Evaluating the clogging behavior of pervious concrete (pc) using the machine learning techniques. Computer Modeling in Engineering & Sciences, 130 (2), 805–821.
   Web of Science ®Google Scholar
• Huda, M.N., et al., 2018. Performance of high strength lightweight concrete using palm wastes. IIUM Engineering Journal, 19 (2), 30–42. Available from: https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/919 [Accessed 23 February 2022].
   Google Scholar
• Ibrahim, A., et al., 2014. Experimental study on portland cement pervious concrete mechanical and hydrological properties. Construction and Building Materials, 50, 524–529. Available from: https://www.sciencedirect.com/science/article/pii/S0950061813008623.
   Web of Science ®Google Scholar
• Le, B.-A., et al., 2022. Predicting the compressive strength and the effective porosity of pervious concrete using machine learning methods. KSCE Journal of Civil Engineering, 26 (11), 4664–4679. doi:10.1007/s12205-022-1918-z.
   Web of Science ®Google Scholar
• Le, B.-A., Vu, V.-H., Seo, S.-Y., Tran, B.-V., Nguyen-Sy, T., Le, M.-C., and Vu, T.-S., 2022. Predicting the compressive strength and the effective porosity of pervious concrete using machine learning methods. KSCE Journal of Civil Engineering, 26 (11), 4664–4679. doi:10.1007/s12205-022-1918-z.
   Web of Science ®Google Scholar
• Li, J., et al., 2017. Preparation and performance evaluation of an innovative pervious concrete pavement. Construction and Building Materials, 138, 479–485. Available from: https://www.sciencedirect.com/science/article/pii/S0950061817301666.
   Web of Science ®Google Scholar
• Li, L.G., et al., 2021. Roles of mortar volume in porosity, permeability and strength of pervious concrete. Journal of Infrastructure Preservation and Resilience, 2 (1), 19. doi:10.1186/s43065-021-00033-2.
   Google Scholar
• Lin, P., et al., 2022. Machine learning-enabled estimation of crosswind load effect on tall buildings. Journal of Wind Engineering and Industrial Aerodynamics, 220, 104860. Available from: https://www.sciencedirect.com/science/article/pii/S0167610521003342.
   Web of Science ®Google Scholar
• Lu, X., et al., 2021. Equivalent method for obtaining concrete age on the basis of electrical resistivity. Scientific Reports, 11 (1), 21720. doi:10.1038/s41598-021-00447-8.
   PubMed Web of Science ®Google Scholar
• Lundberg, S.M. and Lee, S.-I., 2017. A unified approach to interpreting model predictionsed. Advances in Neural Information Processing Systems. doi:10.48550/arXiv.1705.07874.
   Google Scholar
• Makariou, D., Barrieu, P., and Chen, Y., 2021. A random forest based approach for predicting spreads in the primary catastrophe bond market. Insurance: Mathematics and Economics, 101, 140–162. Available from: https://www.sciencedirect.com/science/article/pii/S0167668721001153.
   Web of Science ®Google Scholar
• Malami, S.I., et al., 2022. Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste. Modeling Earth Systems and Environment, 8 (2), 1933–1947. doi:10.1007/s40808-021-01195-4.
   Web of Science ®Google Scholar
• Mandal, T., Tinjum, J.M., and Edil, T.B., 2016. Non-destructive testing of cementitiously stabilized materials using ultrasonic pulse velocity test. Transportation Geotechnics, 6, 97–107. Available from: https://www.sciencedirect.com/science/article/pii/S2214391215000240.
   Web of Science ®Google Scholar
• Marani, A. and Nehdi, M.L., 2020. Machine learning prediction of compressive strength for phase change materials integrated cementitious composites. Construction and Building Materials, 265, 120286. Available from: https://www.sciencedirect.com/science/article/pii/S0950061820322911.
   Web of Science ®Google Scholar
• Martín-Del-Rio, J.J., Canivell, J., and Falcón, R.M., 2020. The use of non-destructive testing to evaluate the compressive strength of a lime-stabilised rammed-earth wall: Rebound index and ultrasonic pulse velocity. Construction and Building Materials, 242, 118060. Available from: https://www.sciencedirect.com/science/article/pii/S0950061820300659.
   Web of Science ®Google Scholar
• Montes, F., Valavala, S. and Haselbach, L.M., 2005. A new test method for porosity measurements of portland cement pervious concrete. Journal of ASTM International, 2 (1), JAI12931. Available from: http://www.astm.org/cgi-bin/doiLink.cgi?JAI12931.
   Google Scholar
• Ndagi, A., et al., 2019. Non-destructive assessment of concrete deterioration by ultrasonic pulse velocity: a review. IOP Conference Series: Earth and Environmental Science, 357 (1), 012015. doi:10.1088/1755-1315/357/1/012015.
   Google Scholar
• Pradhan, S.K. and Behera, N., 2022. Performance assessment of pervious concrete road on strength and permeability by using silica fume. Materials Today: Proceedings, 60, 559–568. Available from: https://www.sciencedirect.com/science/article/pii/S2214785322005831.
   Google Scholar
• Quan Tran, V., Quoc Dang, V., and Si Ho, L., 2022. Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach. Construction and Building Materials, 323, 126578. Available from: https://www.sciencedirect.com/science/article/pii/S0950061822002707.
   Web of Science ®Google Scholar
• Rao, S.K., Sravana, P., and Rao, T.C., 2016. Experimental studies in ultrasonic pulse velocity of roller compacted concrete pavement containing fly ash and m-sand. International Journal of Pavement Research and Technology, 9 (4), 289–301. Available from: https://www.sciencedirect.com/science/article/pii/S1996681416300049.
   Google Scholar
• Ridengaoqier, E., et al., 2021. Experimental study on the porosity evaluation of pervious concrete by using ultrasonic wave testing on surfaces. Construction and Building Materials, 300, 123959. Available from: https://www.sciencedirect.com/science/article/pii/S0950061821017190.
   Web of Science ®Google Scholar
• Saini, I., Singh, D., and Khosla, A., 2013. Qrs detection using k-nearest neighbor algorithm (knn) and evaluation on standard ecg databases. Journal of Advanced Research, 4 (4), 331–344. Available from: https://www.sciencedirect.com/science/article/pii/S209012321200046X.
   PubMedGoogle Scholar
• Sathiparan, N., et al., 2023a. Prediction of characteristics of cement stabilized earth blocks using non-destructive testing: Ultrasonic pulse velocity and electrical resistivity. Materialia, 29, 101794. Available from: https://www.sciencedirect.com/science/article/pii/S2589152923001217.
   Google Scholar
• Sathiparan, N., 2023. Performance of sustainable cement mortar containing different types of masonry construction and demolition wastes. Clean Technologies and Environmental Policy, doi:10.1007/s10098-023-02681-2.
   Web of Science ®Google Scholar
• Sathiparan, N. and Jeyananthan, P., 2023. Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parameters. Materials Today Communications, 35, 106282. Available from: https://www.sciencedirect.com/science/article/pii/S235249282300973X.
   Web of Science ®Google Scholar
• Sathiparan, N., Jeyananthan, P., and Subramaniam, D.N., 2023b. Effect of aggregate size, aggregate to cement ratio and compaction energy on ultrasonic pulse velocity of pervious concrete: Prediction by an analytical model and machine learning techniques. Asian Journal of Civil Engineering, doi:10.1007/s42107-023-00790-3.
   Google Scholar
• Sathiparan, N., Jeyananthan, P., and Subramaniam, D.N., 2023c. Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach. International Journal of Pavement Engineering, 24 (2), 2287146. doi:10.1080/10298436.2023.2287146.
   Google Scholar
• Shafigh, P., et al., 2016. Engineering properties of lightweight aggregate concrete containing limestone powder and high volume fly ash. Journal of Cleaner Production, 135, 148–157. Available from: https://www.sciencedirect.com/science/article/pii/S0959652616307594.
   Web of Science ®Google Scholar
• Shah, S.F.A., et al., 2022. Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning. Construction and Building Materials, 360, 129534. Available from: https://www.sciencedirect.com/science/article/pii/S0950061822031907.
   Web of Science ®Google Scholar
• Shirgir, B., Mamdoohi, A.R., and Hassani, A., 2016. Prediction of pervious concrete permeability and compressive strength using artificial neural networks. International Journal of Transportation Engineering, 2 (4), 307–316. Available from: https://www.icevirtuallibrary.com/doi/abs/10.1680macr.15.00076.
   Google Scholar
• Singh, N. and Singh, S.P., 2018. Evaluating the performance of self compacting concretes made with recycled coarse and fine aggregates using non destructive testing techniques. Construction and Building Materials, 181, 73–84. Available from: https://www.sciencedirect.com/science/article/pii/S0950061818314296.
   Web of Science ®Google Scholar
• Song, H., et al., 2021. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Construction and Building Materials, 308, 125021. Available from: https://www.sciencedirect.com/science/article/pii/S0950061821027677.
   Web of Science ®Google Scholar
• Subramaniam, D.N., Hareindirasarma, S., and Janarthanan, B., 2022. An alternative approach to optimize aggregate-to-cement ratio and compaction in pervious concrete. Arabian Journal for Science and Engineering, 47 (10), 13063–13071. doi:10.1007/s13369-022-06737-1.
   Web of Science ®Google Scholar
• Subramaniam, D.N., Jeyananthan, P., and Sathiparan, N., 2023. Soft computing techniques to predict the electrical resistivity of pervious concrete. Asian Journal of Civil Engineering. Available from, doi:10.1007/s42107-023-00806-y.
   Google Scholar
• Subramaniam, D.N. and Sathiparan, N., 2022. Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: Mechanical characteristics and sustainability analysis. International Journal of Pavement Engineering, 1–18. doi:10.1080/10298436.2022.2075867.
   Web of Science ®Google Scholar
• Sudhir Kumar, B., Srikanth, K., and Eeshwar, T., 2024. Implementation of soft computing techniques in forecasting compressive strength and permeability of pervious concrete blended with ground granulated blast-furnace slag. Journal of Soft Computing in Civil Engineering, 8 (2), 19–45. Available from: https://www.jsoftcivil.com/article_172918.html.
   Google Scholar
• Sun, J., et al., 2019. Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression. Construction and Building Materials, 207, 440–449. Available from: https://www.sciencedirect.com/science/article/pii/S095006181930412X.
   Web of Science ®Google Scholar
• Wang, X., et al., 2016. Monitoring of setting time of self-consolidating concrete using ultrasonic wave propagation method and other tools. Magazine of Concrete Research, 68 (3), 151–162. Available from: https://www.icevirtuallibrary.com/doi/abs/10.1680macr.15.00076.
   Web of Science ®Google Scholar
• Wei, X., Xiao, L., and Li, Z., 2012. Prediction of standard compressive strength of cement by the electrical resistivity measurement. Construction and Building Materials, 31, 341–346. Available from: https://www.sciencedirect.com/science/article/pii/S0950061812000074.
   Web of Science ®Google Scholar
• Wijekoon, S.H., et al., 2023. A mathematical model to predict the porosity and compressive strength of pervious concrete based on the aggregate size, aggregate-to-cement ratio and compaction effort. Asian Journal of Civil Engineering, doi:10.1007/s42107-023-00757-4.
   Google Scholar
• Xia, Y., 2020. Chapter eleven - correlation and association analyses in microbiome study integrating multiomics in health and disease. In: J. Sun, ed. Progress in molecular biology and translational science. Massachusetts: Academic Press, 309–491.
   Google Scholar
• Xu, G., et al., 2018. Investigation on the properties of porous concrete as road base material. Construction and Building Materials, 158, 141–148. Available from: https://www.sciencedirect.com/science/article/pii/S095006181731975X.
   Web of Science ®Google Scholar
• Zaetang, Y., et al., 2015. Use of coal ash as geopolymer binder and coarse aggregate in pervious concrete. Construction and Building Materials, 96, 289–295. Available from: https://www.sciencedirect.com/science/article/pii/S0950061815302701.
   Web of Science ®Google Scholar
• Zhang, J., et al., 2022a. Machine learning prediction models for compressive strength of calcined sludge-cement composites. Construction and Building Materials, 346, 128442. Available from: https://www.sciencedirect.com/science/article/pii/S095006182202102X.
   Web of Science ®Google Scholar
• Zhang, L.V., Marani, A., and Nehdi, M.L., 2022b. Chemistry-informed machine learning prediction of compressive strength for alkali-activated materials. Construction and Building Materials, 316, 126103. Available from: https://www.sciencedirect.com/science/article/pii/S0950061821038356.
   Web of Science ®Google Scholar
• Ziamiavaghi, B. and Toufigh, V., 2023. Fracture toughness evaluation of ground granulated blast furnace slag concrete using experimental study and machine learning techniques. Engineering Fracture Mechanics, 291, 109577. Available from: https://www.sciencedirect.com/science/article/pii/S0013794423005350.
   Web of Science ®Google Scholar