Department of Civil Engineering, Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Department of Materials Engineering and Construction Processes, Faculty of Civil Engineering, Wroclaw University of Science and Technology: Politechnika Wroclawska 27Karkala Taluk,
Department of Materials Engineering and Construction Processes, Faculty of Civil Engineering, Wroclaw University of Science and Technology: Politechnika Wroclawska 27Wrocław, Poland
This work is licensed under the Creative Commons Attribution 4.0 International License.
S. Suanmali and V. Ammarapala, “Maintenance Budget Planning: A Case Study for Rigid Pavement Maintenance System in Thailand,” 2010, doi: 10.1109/icsssm.2010.5530086.SuanmaliS.AmmarapalaV.“Maintenance Budget Planning: A Case Study for Rigid Pavement Maintenance System in Thailand,”201010.1109/icsssm.2010.5530086Open DOISearch in Google Scholar
E. Ghani, A. G. Goswami, and W. R. Kerr, “Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing,” The Economic Journal, 2015, doi: 10.1111/ecoj.12207.GhaniE.GoswamiA. G.KerrW. R.“Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing,”The Economic Journal201510.1111/ecoj.12207Open DOISearch in Google Scholar
A. Das, E. Ghani, A. G. Goswami, W. Kerr, and R. Nanda, “Infrastructure and Finance: Evidence from India’s GQ Highway Network,” SSRN Electronic Journal, 2019, doi: 10.2139/ssrn.3399447.DasA.GhaniE.GoswamiA. G.KerrW.NandaR.“Infrastructure and Finance: Evidence from India’s GQ Highway Network,”SSRN Electronic Journal201910.2139/ssrn.3399447Open DOISearch in Google Scholar
R. Kumar and S. Sharma, “Perpetual Flexible Pavement vs. Rigid Pavement: An Economic and Environmental Cost Comparison,” IOP Conf Ser Earth Environ Sci, 2022, doi: 10.1088/1755-1315/1084/1/012053.KumarR.SharmaS.“Perpetual Flexible Pavement vs. Rigid Pavement: An Economic and Environmental Cost Comparison,”IOP Conf Ser Earth Environ Sci202210.1088/1755-1315/1084/1/012053Open DOISearch in Google Scholar
K. Sharma and M. K. Trivedi, “Statistical Analysis of Delay-Causing Factors in Indian Highway Construction Projects Under Hybrid Annuity Model,” Transportation Research Record Journal of the Transportation Research Board, 2023, doi: 10.1177/03611981231161594.SharmaK.TrivediM. K.“Statistical Analysis of Delay-Causing Factors in Indian Highway Construction Projects Under Hybrid Annuity Model,”Transportation Research Record Journal of the Transportation Research Board202310.1177/03611981231161594Open DOISearch in Google Scholar
“Year End Review-2022 : Ministry of Road Transport and Highways.”“Year End Review-2022 : Ministry of Road Transport and Highways.”Search in Google Scholar
P. Solanki and S. Sharma, “Performance Rating of National Highways Based on Road User Perspective – A Case Study of Kalka–Shimla Highway,” IOP Conf Ser Earth Environ Sci, 2022, doi: 10.1088/1755-1315/1084/1/012055.SolankiP.SharmaS.“Performance Rating of National Highways Based on Road User Perspective – A Case Study of Kalka–Shimla Highway,”IOP Conf Ser Earth Environ Sci202210.1088/1755-1315/1084/1/012055Open DOISearch in Google Scholar
C. Aditya, D. Irawan, and S. Silviana, “Implementation of Marble Waste as Aggregate Material Rigid Pavement,” Eureka Physics and Engineering, 2021, doi: 10.21303/2461-4262.2021.001932.AdityaC.IrawanD.SilvianaS.“Implementation of Marble Waste as Aggregate Material Rigid Pavement,”Eureka Physics and Engineering202110.21303/2461-4262.2021.001932Open DOISearch in Google Scholar
T. Phoo-ngernkham, P. Chindaprasirt, V. Sata, S. Hanjitsuwan, and S. Hatanaka, “The effect of adding nano-SiO2 and nano-Al2O3 on properties of high calcium fly ash geopolymer cured,” 2014, doi: 10.1016/J.MATDES.2013.09.049.Phoo-ngernkhamT.ChindaprasirtP.SataV.HanjitsuwanS.HatanakaS.“The effect of adding nano-SiO2 and nano-Al2O3 on properties of high calcium fly ash geopolymer cured,”201410.1016/J.MATDES.2013.09.049Open DOISearch in Google Scholar
H. Assaedi, F. U. A. Shaikh, and I. M. Low, “Effect of nano-clay on mechanical and thermal properties of geopolymer,” Journal of Asian Ceramic Societies, 2016, doi: 10.1016/J.JASCER.2015.10.004.AssaediH.ShaikhF. U. A.LowI. M.“Effect of nano-clay on mechanical and thermal properties of geopolymer,”Journal of Asian Ceramic Societies201610.1016/J.JASCER.2015.10.004Open DOISearch in Google Scholar
M. A. Uddin, M. T. Bashir, A. M. Khan, F. Alsharari, F. Farid, and R. Alrowais, “Effect of Silica Fume on Compressive Strength and Water Absorption of the Portland Cement–Silica Fume Blended Mortar,” Arab J Sci Eng, vol. 49, no. 4, pp. 4803–4811, Apr. 2024, doi: 10.1007/s13369-023-08204-x.UddinM. A.BashirM. T.KhanA. M.AlsharariF.FaridF.AlrowaisR.“Effect of Silica Fume on Compressive Strength and Water Absorption of the Portland Cement–Silica Fume Blended Mortar,”Arab J Sci Eng49448034811Apr.202410.1007/s13369-023-08204-xOpen DOISearch in Google Scholar
F. Matalkah, A. Ababneh, and R. Aqel, “Effects of nanomaterials on mechanical properties, durability characteristics and microstructural features of alkali-activated binders: A comprehensive review,” Constr Build Mater, vol. 336, p. 127545, Jun. 2022, doi: 10.1016/j.conbuildmat.2022.127545.MatalkahF.AbabnehA.AqelR.“Effects of nanomaterials on mechanical properties, durability characteristics and microstructural features of alkali-activated binders: A comprehensive review,”Constr Build Mater336127545Jun.202210.1016/j.conbuildmat.2022.127545Open DOISearch in Google Scholar
M. Rezaei Shahmirzadi, A. Gholampour, A. Kashani, and T. D. Ngo, “Geopolymer mortars for use in construction 3D printing: Effect of LSS, graphene oxide and nanoclay at different environmental conditions,” Constr Build Mater, vol. 409, p. 133967, Dec. 2023, doi: 10.1016/j.conbuildmat.2023.133967.Rezaei ShahmirzadiM.GholampourA.KashaniA.NgoT. D.“Geopolymer mortars for use in construction 3D printing: Effect of LSS, graphene oxide and nanoclay at different environmental conditions,”Constr Build Mater409133967Dec.202310.1016/j.conbuildmat.2023.133967Open DOISearch in Google Scholar
M. T. Bashir, S. Muhammad, M. J. Butt, M. Alzara, and M. S. El-kady, “Aspect ratio effect of multi-walled carbon nanotubes and carbon fibers on high-performance cement mortar matrices,” Innovative Infrastructure Solutions, vol. 5, no. 2, Aug. 2020, doi: 10.1007/s41062-020-00290-2.BashirM. T.MuhammadS.ButtM. J.AlzaraM.El-kadyM. S.“Aspect ratio effect of multi-walled carbon nanotubes and carbon fibers on high-performance cement mortar matrices,”Innovative Infrastructure Solutions52Aug.202010.1007/s41062-020-00290-2Open DOISearch in Google Scholar
“MoRTH Annual Report for the Year 2022–23.”“MoRTH Annual Report for the Year 2022–23.”Search in Google Scholar
T. T. Nguyen and K. Dinh, “An artificial intelligence approach for concrete hardened property estimation,” Journal of Science and Technology in Civil Engineering (STCE) - NUCE, vol. 14, no. 2, pp. 40–52, Apr. 2020, doi: 10.31814/stce.nuce2020-14(2)-04.NguyenT. T.DinhK.“An artificial intelligence approach for concrete hardened property estimation,”Journal of Science and Technology in Civil Engineering (STCE) - NUCE1424052Apr.202010.31814/stce.nuce2020-14(2)-04Open DOISearch in Google Scholar
F. Jamali, S. R. Mousavi, A. B. Peyma, and Y. Moodi, “Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods,” Journal of Reinforced Plastics and Composites, vol. 41, no. 17–18, pp. 679–704, Sep. 2022, doi: 10.1177/07316844211068116.JamaliF.MousaviS. R.PeymaA. B.MoodiY.“Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods,”Journal of Reinforced Plastics and Composites4117–18679704Sep.202210.1177/07316844211068116Open DOISearch in Google Scholar
T. Li, J. Xiao, and A. Singh, “Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network,” Advances in Structural Engineering, vol. 25, no. 1, pp. 133–145, Jan. 2022, doi: 10.1177/13694332211046348.LiT.XiaoJ.SinghA.“Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network,”Advances in Structural Engineering251133145Jan.202210.1177/13694332211046348Open DOISearch in Google Scholar
R. Haddad and N. Qarqaz, “Predicting NSMR–concrete bond strength using artificial neural networks: A comparative-analysis study,” Structural Concrete, vol. 24, no. 5, pp. 6421–6435, Oct. 2023, doi: 10.1002/suco.202200630.HaddadR.QarqazN.“Predicting NSMR–concrete bond strength using artificial neural networks: A comparative-analysis study,”Structural Concrete24564216435Oct.202310.1002/suco.202200630Open DOISearch in Google Scholar
D. Van Dao, S. H. Trinh, H.-B. Ly, and B. T. Pham, “Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches,” Applied Sciences, vol. 9, no. 6, p. 1113, Mar. 2019, doi: 10.3390/app9061113.Van DaoD.TrinhS. H.LyH.-B.PhamB. T.“Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches,”Applied Sciences961113Mar.201910.3390/app9061113Open DOISearch in Google Scholar
Y. R. Wang, L. T. Q. Ngo, Y. F. Shih, Y. L. Lu, and Y. M. Chen, “Adapting ANNs in SONREB Test to Estimate Concrete Compressive Strength,” Key Eng Mater, vol. 792, pp. 166–169, Dec. 2018, doi: 10.4028/www.scientific.net/KEM.792.166.WangY. R.NgoL. T. Q.ShihY. F.LuY. L.ChenY. M.“Adapting ANNs in SONREB Test to Estimate Concrete Compressive Strength,”Key Eng Mater792166169Dec.201810.4028/www.scientific.net/KEM.792.166Open DOISearch in Google Scholar
Y. Wu and Y. Zhou, “Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques,” Environmental Science and Pollution Research, vol. 29, no. 59, pp. 89198–89209, Dec. 2022, doi: 10.1007/s11356-022-22048-2.WuY.ZhouY.“Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques,”Environmental Science and Pollution Research29598919889209Dec.202210.1007/s11356-022-22048-2Open DOISearch in Google Scholar
S. Ray, M. M. Rahman, M. Haque, M. W. Hasan, and M. M. Alam, “Performance evaluation of SVR and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber,” Journal of King Saud University - Engineering Sciences, vol. 35, no. 2, pp. 92–100, Feb. 2023, doi: 10.1016/j.jksues.2021.02.009.RayS.RahmanM. M.HaqueM.HasanM. W.AlamM. M.“Performance evaluation of SVR and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber,”Journal of King Saud University - Engineering Sciences35292100Feb.202310.1016/j.jksues.2021.02.009Open DOISearch in Google Scholar
H. A. Shah et al., “Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin,” Materials, vol. 15, no. 15, p. 5435, Aug. 2022, doi: 10.3390/ma15155435.ShahH. A.“Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin,”Materials15155435Aug.202210.3390/ma15155435Open DOISearch in Google Scholar
N. Moradi, M. H. Tavana, M. R. Habibi, M. Amiri, M. J. Moradi, and V. Farhangi, “Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach,” Materials, vol. 15, no. 15, p. 5336, Aug. 2022, doi: 10.3390/ma15155336.MoradiN.TavanaM. H.HabibiM. R.AmiriM.MoradiM. J.FarhangiV.“Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach,”Materials15155336Aug.202210.3390/ma15155336Open DOISearch in Google Scholar
J. Huang, M. Zhou, H. Yuan, M. M. S. Sabri, and X. Li, “Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method,” Materials, vol. 15, no. 10, p. 3500, May 2022, doi: 10.3390/ma15103500.HuangJ.ZhouM.YuanH.SabriM. M. S.LiX.“Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method,”Materials15103500May202210.3390/ma15103500Open DOISearch in Google Scholar
A. M. R. Bulbul et al., “In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches,” Materials, vol. 15, no. 21, p. 7764, Nov. 2022, doi: 10.3390/ma15217764.BulbulA. M. R.“In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches,”Materials15217764Nov.202210.3390/ma15217764Open DOISearch in Google Scholar
S. Marathe and A. P. Rodrigues, “Intelligent Models for Prediction of Compressive Strength of Geopolymer Pervious Concrete Hybridized with Agro-Industrial and Construction-Demolition Wastes,” Studia Geotechnica et Mechanica, 2024, doi: 10.2478/sgem-2024-0020.MaratheS.RodriguesA. P.“Intelligent Models for Prediction of Compressive Strength of Geopolymer Pervious Concrete Hybridized with Agro-Industrial and Construction-Demolition Wastes,”Studia Geotechnica et Mechanica202410.2478/sgem-2024-0020Open DOISearch in Google Scholar
A. Sheshadri, S. Marathe, M. Bettadapura Manjunath, A. Jayasimhan, and Ł. Sadowski, “Effective Utilization of Foundry Waste as Aggregates in Developing Eco-Friendly Alkali-Activated and Conventional Concretes for Sustainable Pavement Infrastructure,” Practice Periodical on Structural Design and Construction, vol. 29, no. 3, Aug. 2024, doi: 10.1061/PPSCFX.SCENG-1501.SheshadriA.MaratheS.Bettadapura ManjunathM.JayasimhanA.SadowskiŁ.“Effective Utilization of Foundry Waste as Aggregates in Developing Eco-Friendly Alkali-Activated and Conventional Concretes for Sustainable Pavement Infrastructure,”Practice Periodical on Structural Design and Construction293Aug.202410.1061/PPSCFX.SCENG-1501Open DOISearch in Google Scholar
B. S. E. N. 15167-1:2006, Ground granulated blast furnace slagfor use in concrete, mortar and grout, vol. 3. The European Standard EN, 2006.B. S. E. N. 15167-1:2006Ground granulated blast furnace slagfor use in concrete, mortar and grout3The European Standard EN2006Search in Google Scholar
I. 12089-1987, Specification for granulated slag for the manufacture of Portland slag cement. Bureau of Indian Standards, New Delhi, 1987.I. 12089-1987Specification for granulated slag for the manufacture of Portland slag cementBureau of Indian StandardsNew Delhi1987Search in Google Scholar
N. Delhi. Bureau of Indian Standards, “IS:383 (2016) Coarse and Fine Aggregate for Concrete — Specification. 1–21.,” 1970.N. Delhi. Bureau of Indian Standards“IS:383 (2016) Coarse and Fine Aggregate for Concrete — Specification. 1–21.,”1970Search in Google Scholar
IS:14212, “Indian Standard Sodium and Potassium Silicates - Methods of Test,” 1995, Bureau of Indian Standards, New Delhi.IS:14212“Indian Standard Sodium and Potassium Silicates - Methods of Test,”1995Bureau of Indian StandardsNew DelhiSearch in Google Scholar
S. Marathe, I. R. Mithanthaya, B. M. Mithun, S. Shetty, and A. P. K, “Performance of slag-fly ash based alkali activated concrete for paver applications utilizing powdered waste glass as a binding ingredient,” International Journal of Pavement Research and Technology, 2020, doi: 10.1007/s42947-020-0173-2.MaratheS.MithanthayaI. R.MithunB. M.ShettyS.KA. P.“Performance of slag-fly ash based alkali activated concrete for paver applications utilizing powdered waste glass as a binding ingredient,”International Journal of Pavement Research and Technology202010.1007/s42947-020-0173-2Open DOISearch in Google Scholar
B. M. Mithun, “Performance of alkali activated slag concrete mixes incorporating copper slag as fine aggregate,” 2017.MithunB. M.“Performance of alkali activated slag concrete mixes incorporating copper slag as fine aggregate,”2017Search in Google Scholar
IS-1199(Part2), “Fresh Concrete—Methods of Sampling, Testing and Analysis; Part 2 Determination of Consistency of Fresh Concrete,” 2018, Bureau of Indian Standards, New Delhi.IS-1199(Part2)“Fresh Concrete—Methods of Sampling, Testing and Analysis; Part 2 Determination of Consistency of Fresh Concrete,”2018Bureau of Indian StandardsNew DelhiSearch in Google Scholar
B. of Indian Standards, “IS 5816 (1999): Method of Test Splitting Tensile Strength of Concrete”.B. of Indian Standards“IS 5816 (1999): Method of Test Splitting Tensile Strength of Concrete”Search in Google Scholar
N. Anil Kumar Reddy and K. Ramujee, “Comparative study on mechanical properties of fly ash & GGBFS based geopolymer concrete and OPC concrete using nano-alumina,” Mater Today Proc, vol. 60, pp. 399–404, 2022, doi: 10.1016/j.matpr.2022.01.260.Anil Kumar ReddyN.RamujeeK.“Comparative study on mechanical properties of fly ash & GGBFS based geopolymer concrete and OPC concrete using nano-alumina,”Mater Today Proc60399404202210.1016/j.matpr.2022.01.260Open DOISearch in Google Scholar
E. Mohseni, M. J. Kazemi, M. Koushkbaghi, B. Zehtab, and B. Behforouz, “Evaluation of mechanical and durability properties of fiber-reinforced lightweight geopolymer composites based on rice husk ash and nano-alumina,” Constr Build Mater, vol. 209, pp. 532–540, Jun. 2019, doi: 10.1016/j.conbuildmat.2019.03.067.MohseniE.KazemiM. J.KoushkbaghiM.ZehtabB.BehforouzB.“Evaluation of mechanical and durability properties of fiber-reinforced lightweight geopolymer composites based on rice husk ash and nano-alumina,”Constr Build Mater209532540Jun.201910.1016/j.conbuildmat.2019.03.067Open DOISearch in Google Scholar
S. Shahbazpanahi, M. K. Tajara, R. H. Faraj, and A. Mosavi, “Studying the C–H Crystals and Mechanical Properties of Sustainable Concrete Containing Recycled Coarse Aggregate with Used Nano-Silica,” Crystals (Basel), vol. 11, no. 2, p. 122, Jan. 2021, doi: 10.3390/cryst11020122.ShahbazpanahiS.TajaraM. K.FarajR. H.MosaviA.“Studying the C–H Crystals and Mechanical Properties of Sustainable Concrete Containing Recycled Coarse Aggregate with Used Nano-Silica,”Crystals (Basel)112122Jan.202110.3390/cryst11020122Open DOISearch in Google Scholar
K. P. Bautista-Gutierrez, A. L. Herrera-May, J. M. Santamaría-López, A. Honorato-Moreno, and S. A. Zamora-Castro, “Recent Progress in Nanomaterials for Modern Concrete Infrastructure: Advantages and Challenges,” Materials, vol. 12, no. 21, p. 3548, Oct. 2019, doi: 10.3390/ma12213548.Bautista-GutierrezK. P.Herrera-MayA. L.Santamaría-LópezJ. M.Honorato-MorenoA.Zamora-CastroS. A.“Recent Progress in Nanomaterials for Modern Concrete Infrastructure: Advantages and Challenges,”Materials12213548Oct.201910.3390/ma12213548Open DOISearch in Google Scholar
M. A. Faris et al., “Properties of Hooked Steel Fibers Reinforced Alkali Activated Material Concrete,” MATEC Web of Conferences, vol. 78, p. 01068, Oct. 2016, doi: 10.1051/matecconf/20167801068.FarisM. A.“Properties of Hooked Steel Fibers Reinforced Alkali Activated Material Concrete,”MATEC Web of Conferences7801068Oct.201610.1051/matecconf/20167801068Open DOISearch in Google Scholar
H. A. Abdel-Gawwad, M. S. Mohammed, and T. Alomayri, “Single and dual effects of magnesia and alumina nano-particles on strength and drying shrinkage of alkali activated slag,” Constr Build Mater, vol. 228, p. 116827, Dec. 2019, doi: 10.1016/j.conbuildmat.2019.116827.Abdel-GawwadH. A.MohammedM. S.AlomayriT.“Single and dual effects of magnesia and alumina nano-particles on strength and drying shrinkage of alkali activated slag,”Constr Build Mater228116827Dec.201910.1016/j.conbuildmat.2019.116827Open DOISearch in Google Scholar
A. Hakamy, F. U. A. Shaikh, and I. M. Low, “Characteristics of nanoclay and calcined nanoclay-cement nanocomposites,” Composites Part B-engineering, 2015, doi: 10.1016/J.COMPOSITESB.2015.03.074.HakamyA.ShaikhF. U. A.LowI. M.“Characteristics of nanoclay and calcined nanoclay-cement nanocomposites,”Composites Part B-engineering201510.1016/J.COMPOSITESB.2015.03.074Open DOISearch in Google Scholar
J. J. Ekaputri, C. Fujiyama, N. Chijiwa, T. D. Ho, and H. T. Nguyen, “Improving Geopolymer Characteristics with Addition of Poly-Vinyl Alcohol (PVA) Fibers,” Civil Engineering Dimension, vol. 23, no. 1, pp. 28–34, Apr. 2021, doi: 10.9744/ced.23.1.28-34.EkaputriJ. J.FujiyamaC.ChijiwaN.HoT. D.NguyenH. T.“Improving Geopolymer Characteristics with Addition of Poly-Vinyl Alcohol (PVA) Fibers,”Civil Engineering Dimension2312834Apr.202110.9744/ced.23.1.28-34Open DOISearch in Google Scholar
A. Ashrafian, M. J. Taheri Amiri, M. Rezaie-Balf, T. Ozbakkaloglu, and O. Lotfi-Omran, “Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods,” Constr Build Mater, vol. 190, pp. 479–494, Nov. 2018, doi: 10.1016/j.conbuildmat.2018.09.047.AshrafianA.Taheri AmiriM. J.Rezaie-BalfM.OzbakkalogluT.Lotfi-OmranO.“Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods,”Constr Build Mater190479494Nov.201810.1016/j.conbuildmat.2018.09.047Open DOISearch in Google Scholar
M. F.M. Zain and S. M. Abd, “Multiple Regression Model for Compressive Strength Prediction of High Performance Concrete,” Journal of Applied Sciences, vol. 9, no. 1, pp. 155–160, Dec. 2008, doi: 10.3923/jas.2009.155.160.ZainM. F.M.AbdS. M.“Multiple Regression Model for Compressive Strength Prediction of High Performance Concrete,”Journal of Applied Sciences91155160Dec.200810.3923/jas.2009.155.160Open DOISearch in Google Scholar
G.-H. Kim, J.-M. Shin, S. Kim, and Y. Shin, “Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine,” Journal of Building Construction and Planning Research, vol. 01, no. 01, pp. 1–7, 2013, doi: 10.4236/jbcpr.2013.11001.KimG.-H.ShinJ.-M.KimS.ShinY.“Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine,”Journal of Building Construction and Planning Research010117201310.4236/jbcpr.2013.11001Open DOISearch in Google Scholar
A. F. Hayes, “Statistical Methods for Communication Science.”HayesA. F.“Statistical Methods for Communication Science.”Search in Google Scholar
S. V. Patil, K. Balakrishna Rao, and G. Nayak, “Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network,” Journal of Engineering, Design and Technology, vol. 21, no. 6, pp. 1690–1709, Nov. 2023, doi: 10.1108/JEDT-07-2021-0373.PatilS. V.Balakrishna RaoK.NayakG.“Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network,”Journal of Engineering, Design and Technology21616901709Nov.202310.1108/JEDT-07-2021-0373Open DOISearch in Google Scholar
H. Almuallim, S. Kaneda, and Y. Akiba, “3 DEVELOPMENT AND APPLICATIONS OF DECISION TREES,” 2002.AlmuallimH.KanedaS.AkibaY.“3 DEVELOPMENT AND APPLICATIONS OF DECISION TREES,”2002Search in Google Scholar
B. A. Young, A. Hall, L. Pilon, P. Gupta, and G. Sant, “Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods,” Cem Concr Res, vol. 115, pp. 379–388, Jan. 2019, doi: 10.1016/j.cemconres.2018.09.006.YoungB. A.HallA.PilonL.GuptaP.SantG.“Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods,”Cem Concr Res115379388Jan.201910.1016/j.cemconres.2018.09.006Open DOISearch in Google Scholar
T. Hastie, R. Tibshirani, and J. Friedman, “Springer Series in Statistics The Elements of Statistical Learning Data Mining, Inference, and Prediction.”HastieT.TibshiraniR.FriedmanJ.“Springer Series in Statistics The Elements of Statistical Learning Data Mining, Inference, and Prediction.”Search in Google Scholar
M. Timur Cihan, “Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods,” Advances in Civil Engineering, vol. 2019, no. 1, Jan. 2019, doi: 10.1155/2019/3069046.Timur CihanM.“Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods,”Advances in Civil Engineering20191Jan.201910.1155/2019/3069046Open DOISearch in Google Scholar
G. Biau and E. Scornet, “A random forest guided tour,” TEST, vol. 25, no. 2, pp. 197–227, Jun. 2016, doi: 10.1007/s11749-016-0481-7.BiauG.ScornetE.“A random forest guided tour,”TEST252197227Jun.201610.1007/s11749-016-0481-7Open DOISearch in Google Scholar
L. Breiman, “Random Forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.BreimanL.“Random Forests,”Mach Learn451532200110.1023/A:1010933404324Open DOISearch in Google Scholar
M. A. DeRousseau, E. Laftchiev, J. R. Kasprzyk, B. Rajagopalan, and W. V. Srubar, “A comparison of machine learning methods for predicting the compressive strength of field-placed concrete,” Constr Build Mater, vol. 228, p. 116661, Dec. 2019, doi: 10.1016/j.conbuildmat.2019.08.042.DeRousseauM. A.LaftchievE.KasprzykJ. R.RajagopalanB.SrubarW. V.“A comparison of machine learning methods for predicting the compressive strength of field-placed concrete,”Constr Build Mater228116661Dec.201910.1016/j.conbuildmat.2019.08.042Open DOISearch in Google Scholar
F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825–2830, 2011, [Online]. Available: http://jmlr.org/papers/v12/pedregosa11a.htmlPedregosaF.“Scikit-learn: Machine Learning in Python,”Journal of Machine Learning Research1285282528302011[Online]. Available: http://jmlr.org/papers/v12/pedregosa11a.htmlSearch in Google Scholar
H. Nguyen, T. Vu, T. P. Vo, and H.-T. Thai, “Efficient machine learning models for prediction of concrete strengths,” Constr Build Mater, vol. 266, p. 120950, Jan. 2021, doi: 10.1016/j.conbuildmat.2020.120950.NguyenH.VuT.VoT. P.ThaiH.-T.“Efficient machine learning models for prediction of concrete strengths,”Constr Build Mater266120950Jan.202110.1016/j.conbuildmat.2020.120950Open DOISearch in Google Scholar
R. Cook, J. Lapeyre, H. Ma, and A. Kumar, “Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models,” Journal of Materials in Civil Engineering, vol. 31, no. 11, Nov. 2019, doi: 10.1061/(ASCE)MT.1943-5533.0002902.CookR.LapeyreJ.MaH.KumarA.“Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models,”Journal of Materials in Civil Engineering3111Nov.201910.1061/(ASCE)MT.1943-5533.0002902Open DOISearch in Google Scholar
K. Yan and C. Shi, “Prediction of elastic modulus of normal and high strength concrete by support vector machine,” Constr Build Mater, vol. 24, no. 8, pp. 1479–1485, Aug. 2010, doi: 10.1016/j.conbuildmat.2010.01.006.YanK.ShiC.“Prediction of elastic modulus of normal and high strength concrete by support vector machine,”Constr Build Mater24814791485Aug.201010.1016/j.conbuildmat.2010.01.006Open DOISearch in Google Scholar
V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY: Springer New York, 2000. doi: 10.1007/978-1-4757-3264-1.VapnikV. N.The Nature of Statistical Learning TheoryNew York, NYSpringer New York200010.1007/978-1-4757-3264-1Open DOISearch in Google Scholar
C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Trans Intell Syst Technol, vol. 2, Aug. 2007.ChangC.-C.LinC.-J.“LIBSVM: A library for support vector machines,”ACM Trans Intell Syst Technol2Aug.2007Search in Google Scholar
F. Nelli, “Machine Learning with scikit-learn,” in Python Data Analytics, Berkeley, CA: Apress, 2018, pp. 313–347. doi: 10.1007/978-1-4842-3913-1_8.NelliF.“Machine Learning with scikit-learn,”inPython Data AnalyticsBerkeley, CAApress201831334710.1007/978-1-4842-3913-1_8Open DOISearch in Google Scholar
D. P. Solomatine and D. L. Shrestha, “AdaBoost.RT: a boosting algorithm for regression problems,” in 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), IEEE, pp. 1163–1168. doi: 10.1109/IJCNN.2004.1380102.SolomatineD. P.ShresthaD. L.“AdaBoost.RT: a boosting algorithm for regression problems,”2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)IEEE1163116810.1109/IJCNN.2004.1380102Open DOISearch in Google Scholar
Y. Freund and R. E. Schapire, “A Short Introduction to Boosting,” 1999. [Online]. Available: https://api.semanticscholar.org/CorpusID:9621074FreundY.SchapireR. E.“A Short Introduction to Boosting,”1999[Online]. Available: https://api.semanticscholar.org/CorpusID:9621074Search in Google Scholar
Y. Freund and R. E. Schapire, “Experiments with a New Boosting Algorithm,” in International Conference on Machine Learning, 1996. [Online]. Available: https://api.semanticscholar.org/CorpusID:1836349FreundY.SchapireR. E.“Experiments with a New Boosting Algorithm,”inInternational Conference on Machine Learning1996[Online]. Available: https://api.semanticscholar.org/CorpusID:1836349Search in Google Scholar
R. E. Schapire, “Explaining AdaBoost,” in Empirical Inference, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 37–52. doi: 10.1007/978-3-642-41136-6_5.SchapireR. E.“Explaining AdaBoost,”inEmpirical InferenceBerlin, HeidelbergSpringer Berlin Heidelberg2013375210.1007/978-3-642-41136-6_5Open DOISearch in Google Scholar
J. H. Friedman, “Greedy function approximation: A gradient boosting machine.,” The Annals of Statistics, vol. 29, no. 5, Oct. 2001, doi: 10.1214/aos/1013203451.FriedmanJ. H.“Greedy function approximation: A gradient boosting machine.,”The Annals of Statistics295Oct.200110.1214/aos/1013203451Open DOISearch in Google Scholar
R. Zemel and T. Pitassi, “A Gradient-Based Boosting Algorithm for Regression Problems,” Aug. 2001.ZemelR.PitassiT.“A Gradient-Based Boosting Algorithm for Regression Problems,”Aug.2001Search in Google Scholar
J. H. Friedman, “Stochastic gradient boosting,” Comput Stat Data Anal, vol. 38, no. 4, pp. 367–378, Feb. 2002, doi: 10.1016/S0167-9473(01)00065-2.FriedmanJ. H.“Stochastic gradient boosting,”Comput Stat Data Anal384367378Feb.200210.1016/S0167-9473(01)00065-2Open DOISearch in Google Scholar
D. N. Lawley, “Tests of Significance for the Latent Roots of Covariance and Correlation Matrices,” Biometrika, vol. 43, no. 1/2, p. 128, Jun. 1956, doi: 10.2307/2333586.LawleyD. N.“Tests of Significance for the Latent Roots of Covariance and Correlation Matrices,”Biometrika431/2128Jun.195610.2307/2333586Open DOISearch in Google Scholar
J. H. Steiger, “Tests for comparing elements of a correlation matrix.,” Psychol Bull, vol. 87, no. 2, pp. 245–251, Mar. 1980, doi: 10.1037/0033-2909.87.2.245.SteigerJ. H.“Tests for comparing elements of a correlation matrix.,”Psychol Bull872245251Mar.198010.1037/0033-2909.87.2.245Open DOISearch in Google Scholar
Q. Ma, J. Li, M. Aamer, and G. Huang, “Effect of Chinese Milk Vetch (Astragalus sinicus L.) and Rice Straw Incorporated in Paddy Soil on Greenhouse Gas Emission and Soil Properties,” Agronomy, vol. 10, no. 5, p. 717, May 2020, doi: 10.3390/agronomy10050717.MaQ.LiJ.AamerM.HuangG.“Effect of Chinese Milk Vetch (Astragalus sinicus L.) and Rice Straw Incorporated in Paddy Soil on Greenhouse Gas Emission and Soil Properties,”Agronomy105717May202010.3390/agronomy10050717Open DOISearch in Google Scholar
R. Alyousef et al., “Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures,” Case Studies in Construction Materials, vol. 19, Dec. 2023, doi: 10.1016/j.cscm.2023.e02418.AlyousefR.“Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures,”Case Studies in Construction Materials19Dec.202310.1016/j.cscm.2023.e02418Open DOISearch in Google Scholar