NGBoost-based prediction of carbonation in Alkali-activated slag concrete enhanced by metaheuristic algorithms
Concrete carbonation represents a critical deterioration mechanism that significantly compromises the long-term structural integrity of reinforced concrete systems. Traditional methods for assessing carbonation depth (CD) in Alkali-activated slag concrete (AASC) are labor-intensive, costly, and dema...
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| Published in: | Construction & building materials Vol. 500; p. 144257 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
21.11.2025
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| ISSN: | 0950-0618 |
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| Abstract | Concrete carbonation represents a critical deterioration mechanism that significantly compromises the long-term structural integrity of reinforced concrete systems. Traditional methods for assessing carbonation depth (CD) in Alkali-activated slag concrete (AASC) are labor-intensive, costly, and demand specialized expertise. To address the current gap in applying machine learning (ML) techniques for accurate CD prediction in AASC, this study developed ML frameworks for carbonation forecasting by integrating natural gradient boosting (NGBoost) with three bio-inspired optimization techniques: beluga whale optimization (BWO), remora optimization (ROA), and crayfish optimization (COA). The models were trained on a dataset of 136 data points with 9 input variables. The study employed a comprehensive evaluation framework incorporating four key metrics (Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE)) and Taylor diagram analysis to assess comparative model performance. The results showed that the COA-NGBoost algorithm achieved superior performance with R2 of 0.946, MAPE of 0.145, MAE of 1.451 mm, and RMSE of 2.099 mm. Shapley additive explanations (SHAP) analysis identified environmental conditions, specifically CO2 concentration and exposure time, as key factors influencing CD. Furthermore, an interactive software tool incorporating the COA-NGBoost framework was developed to facilitate probabilistic CD prediction and optimize AASC mixture proportioning. This investigation demonstrates the successful implementation of the proposed framework in parametric analysis of CD factors within AASC systems, providing valuable insights for durability-based design strategies.
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•A novel natural gradient boosting (NGBoost) model accurately predicts carbonation depth (CD) in alkali-activated slag concrete (AASC).•Hyperparameter optimization using three novel bio-inspired algorithms, significantly improves NGBoost model performance.•The optimized COA-NGBoost model demonstrates exceptional accuracy (R2 = 0.946) and low error metrics.•SHAP analysis reveals CO2 concentration and exposure time as primary drivers of AASC carbonation.•A GUI enables easy prediction of AASC-CD progression, facilitating practical implementation in infrastructure management. |
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| AbstractList | Concrete carbonation represents a critical deterioration mechanism that significantly compromises the long-term structural integrity of reinforced concrete systems. Traditional methods for assessing carbonation depth (CD) in Alkali-activated slag concrete (AASC) are labor-intensive, costly, and demand specialized expertise. To address the current gap in applying machine learning (ML) techniques for accurate CD prediction in AASC, this study developed ML frameworks for carbonation forecasting by integrating natural gradient boosting (NGBoost) with three bio-inspired optimization techniques: beluga whale optimization (BWO), remora optimization (ROA), and crayfish optimization (COA). The models were trained on a dataset of 136 data points with 9 input variables. The study employed a comprehensive evaluation framework incorporating four key metrics (Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE)) and Taylor diagram analysis to assess comparative model performance. The results showed that the COA-NGBoost algorithm achieved superior performance with R2 of 0.946, MAPE of 0.145, MAE of 1.451 mm, and RMSE of 2.099 mm. Shapley additive explanations (SHAP) analysis identified environmental conditions, specifically CO2 concentration and exposure time, as key factors influencing CD. Furthermore, an interactive software tool incorporating the COA-NGBoost framework was developed to facilitate probabilistic CD prediction and optimize AASC mixture proportioning. This investigation demonstrates the successful implementation of the proposed framework in parametric analysis of CD factors within AASC systems, providing valuable insights for durability-based design strategies.
[Display omitted]
•A novel natural gradient boosting (NGBoost) model accurately predicts carbonation depth (CD) in alkali-activated slag concrete (AASC).•Hyperparameter optimization using three novel bio-inspired algorithms, significantly improves NGBoost model performance.•The optimized COA-NGBoost model demonstrates exceptional accuracy (R2 = 0.946) and low error metrics.•SHAP analysis reveals CO2 concentration and exposure time as primary drivers of AASC carbonation.•A GUI enables easy prediction of AASC-CD progression, facilitating practical implementation in infrastructure management. |
| ArticleNumber | 144257 |
| Author | Fu, Qianwang Zheng, Wenbo Chen, Yun |
| Author_xml | – sequence: 1 givenname: Yun surname: Chen fullname: Chen, Yun organization: School of Civil Engineering and Architecture, Hainan University, Haikou, Hainan 570228, China – sequence: 2 givenname: Qianwang orcidid: 0000-0002-1124-4735 surname: Fu fullname: Fu, Qianwang email: fqw324@hainanu.edu.cn organization: School of Civil Engineering and Architecture, Hainan University, Haikou, Hainan 570228, China – sequence: 3 givenname: Wenbo surname: Zheng fullname: Zheng, Wenbo organization: School of Civil Engineering and Architecture, Hainan University, Haikou, Hainan 570228, China |
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| Cites_doi | 10.1016/j.conbuildmat.2019.117336 10.1016/j.conbuildmat.2024.138351 10.1016/j.conbuildmat.2012.01.017 10.1016/j.cemconres.2017.02.009 10.1016/j.jcou.2020.101185 10.1016/j.jclepro.2024.141172 10.1016/j.knosys.2022.110192 10.1617/s11527-012-9842-1 10.1016/j.knosys.2022.109215 10.1007/s10462-023-10567-4 10.1016/j.conbuildmat.2005.01.052 10.1016/j.jclepro.2020.123697 10.1016/j.conbuildmat.2021.124389 10.1617/s11527-014-0289-4 10.1002/suco.202300173 10.1016/j.autcon.2024.105943 10.1016/j.jrmge.2021.06.012 10.1016/j.asoc.2024.111661 10.1016/j.conbuildmat.2019.117455 10.1061/(ASCE)ST.1943-541X.0003401 10.1088/1757-899X/322/2/022048 10.1016/S0008-8846(01)00574-9 10.1016/j.asoc.2020.106552 10.1016/j.eswa.2021.115665 10.1016/j.conbuildmat.2022.126359 10.1016/j.engfailanal.2022.106786 10.1016/j.aei.2020.101201 10.1016/j.asoc.2022.109641 10.1038/s43017-020-0093-3 10.1007/s11709-024-1039-5 10.1016/j.clay.2013.02.020 10.1016/j.ceramint.2017.12.226 10.1103/PhysRevE.49.4677 10.1016/j.conbuildmat.2023.131781 10.1002/suco.202200269 10.1016/j.jclepro.2024.142746 10.1007/s11709-024-1041-y 10.1016/j.asoc.2021.108182 10.1016/j.conbuildmat.2024.136176 10.1016/j.conbuildmat.2015.03.036 10.1016/j.conbuildmat.2023.133412 10.28991/CEJ-2023-09-11-020 10.1016/j.cemconcomp.2018.01.013 10.21809/rilemtechlett.2022.157 10.1016/j.conbuildmat.2021.122496 10.1016/j.cemconcomp.2020.103863 10.1016/S0008-8846(00)00227-1 10.1007/s00521-017-3052-2 10.1016/j.autcon.2024.105516 10.1016/j.knosys.2023.110554 10.1016/j.cemconcomp.2013.12.001 10.1016/j.conbuildmat.2020.121050 10.1016/j.cma.2020.113609 10.1016/j.conbuildmat.2020.118581 10.1016/j.conbuildmat.2019.03.267 10.1007/s11831-021-09644-0 10.1016/j.conbuildmat.2022.128483 |
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| Keywords | Beluga Whale Optimization algorithm Natural gradient boosting Alkali-activated slag concrete Remora Optimization Algorithm Carbonation depth Crayfish Optimization Algorithm |
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| References | Shahmansouri, Yazdani, Ghanbari (bib36) 2021; 279 Singh, Middendorf (bib32) 2020; 237 Shi, Yao, Wang (bib13) 2021; 304 T. Duan, A. Avati, D. Ding, et al, NGBoost: natural gradient boosting for probabilistic prediction, arXiv Prints, (2019), arXiv:1910.03225. Le, Nguyen, Sang-To (bib40) 2024; 18 Yıldız, Kumar, Panagant (bib59) 2023; 271 Xiao, Li, Guan (bib16) 2018; 322 Sun, Lee (bib57) 2024; 426 Al Mashhadani, Wong, Kong (bib3) 2023; 9 Shahmansouri, Bengar, Ghanbari (bib34) 2020; 31 Han, Lv, Lin (bib66) 2023; 392 Zhang, Zhang, Bao (bib27) 2023; 65 Xi, Zhang, Li (bib75) 2024; 18 Liang, Lu, Ma (bib1) 2020; 39 Gluth, Grengg, Ukrainczyk (bib6) 2022; 7 Mei, Sun, Li (bib47) 2022; 142 Huo, Wang, Huang (bib28) 2023; 76 Luo, Li, Wang (bib68) 2024; 442 Cao, Su, Antwi-Afari (bib69) 2024; 465 Jiang, Lin, Cai (bib11) 2000; 30 Felix, Carrazedo, Possan (bib21) 2021; 266 Biswas, Li, Zhang (bib25) 2022; 346 Jiang (bib50) 2019 Aye, Wansaseub, Kumar (bib58) 2023; 137 Habert, Miller, John (bib5) 2020; 1 Provis (bib4) 2018; 114 Singh, Ishwarya, Gupta (bib33) 2015; 85 Law, Adam, Molyneaux (bib48) 2012; 45 Chen, Feng, Wang (bib42) 2022; 148 Khunthongkeaw, Tangtermsirikul, Leelawat (bib10) 2006; 20 Kumar, Yildiz, Mehta (bib60) 2023; 261 2007, EN 14630,European Committee for StandardizationBrussels. Products and systems for the protection and repair of concrete structures-test methods-determination of carbonation depth in hardened concrete by the phenolphthalein method, Brussels: European Committee for Standardization, 2007. Moghaddas, Nekoei, Golafshani (bib2) 2022; 130 Chen, Wu, Xia (bib30) 2021; 279 Liu (bib51) 2019 Chen, Liu, Shen (bib71) 2025; 170 Behnood, Golafshani (bib18) 2022; 29 Bernal, Ke, Criado (bib29) 2017; 36 Shahmansouri, Nematzadeh, Behnood (bib37) 2021; 36 Bernal, Gutiérrez, Provis (bib72) 2012; 33 Zhang, Xiao (bib12) 2018; 88 Lu (bib53) 2019 Luo, Li, Lin (bib67) 2023; 406 Carević, Ignjatović, Dragaš (bib9) 2019; 213 Silva, Neves, De Brito (bib17) 2014; 50 Zhong, Li, Meng (bib63) 2022; 251 Mantegna (bib64) 1994; 49 Kellouche, Boukhatem, Ghrici (bib22) 2019; 31 Aguayo, Torres, Thombare (bib15) 2020 Shahmansouri, Bengar, Ghanbari (bib35) 2020; 5 Bernal, Provis, Mejía de Gutiérrez (bib49) 2015; 48 Zhang, Zhou, Zhou (bib14) 2013; 79 Gomaa, Han, Gawady (bib39) 2021; 115 Sáez del Bosque, Van den Heede, Belie (bib7) 2020; 234 Lahoti, Wong, Yang (bib31) 2018; 44 Chen, Lin, Sagoe-Crentsil (bib23) 2022; 321 Neto, Cascudo (bib76) 2025; 111 Deka (bib41) 2019 Tambara, Jr, Hirsch, Dehn (bib73) 2024; 449 Zhu, Chu, Wang (bib45) 2021; 13 Xu (bib52) 2019 Gardoni, Der Kiureghian, Mosalam (bib43) 2002; 128 Tambara, Jr, Hirsch, Dehn (bib55) 2024; 449 Nguyen, Nguyen, Le (bib38) 2020; 247 Hosseinzadeh, Dehestani, Hosseinzadeh (bib56) 2023; 76 Jia, Peng, Lang (bib62) 2021; 185 Wu, Feng, Liu (bib70) 2024; 165 Tran, Mai, To (bib26) 2023; 24 Jia, Rao, Wen (bib65) 2023; 56 Abuodeh, Abdalla, Hawileh (bib20) 2020; 95 Chakraborty, Elhegazy, Elzarka (bib46) 2020; 46 Bakhareva, Sanjayana, Chengb (bib74) 2001; 31 Golafshani, Behnood, Kim (bib24) 2024; 159 Pradhan, Mishra, Biswal (bib54) 2024; 25 Abualigah, Diabat, Mirjalili (bib61) 2021; 376 Fatemi, Ganjali, Semiromi (bib77) 2025; 23 Mahjoubi, Meng, Bao (bib19) 2022; 115 Liang (10.1016/j.conbuildmat.2025.144257_bib1) 2020; 39 Xi (10.1016/j.conbuildmat.2025.144257_bib75) 2024; 18 Abuodeh (10.1016/j.conbuildmat.2025.144257_bib20) 2020; 95 10.1016/j.conbuildmat.2025.144257_bib8 Aguayo (10.1016/j.conbuildmat.2025.144257_bib15) 2020 Xiao (10.1016/j.conbuildmat.2025.144257_bib16) 2018; 322 Shahmansouri (10.1016/j.conbuildmat.2025.144257_bib35) 2020; 5 Zhu (10.1016/j.conbuildmat.2025.144257_bib45) 2021; 13 Silva (10.1016/j.conbuildmat.2025.144257_bib17) 2014; 50 Tran (10.1016/j.conbuildmat.2025.144257_bib26) 2023; 24 Luo (10.1016/j.conbuildmat.2025.144257_bib68) 2024; 442 Al Mashhadani (10.1016/j.conbuildmat.2025.144257_bib3) 2023; 9 Moghaddas (10.1016/j.conbuildmat.2025.144257_bib2) 2022; 130 Mahjoubi (10.1016/j.conbuildmat.2025.144257_bib19) 2022; 115 Nguyen (10.1016/j.conbuildmat.2025.144257_bib38) 2020; 247 Kumar (10.1016/j.conbuildmat.2025.144257_bib60) 2023; 261 Deka (10.1016/j.conbuildmat.2025.144257_bib41) 2019 Gomaa (10.1016/j.conbuildmat.2025.144257_bib39) 2021; 115 Mantegna (10.1016/j.conbuildmat.2025.144257_bib64) 1994; 49 Behnood (10.1016/j.conbuildmat.2025.144257_bib18) 2022; 29 Singh (10.1016/j.conbuildmat.2025.144257_bib32) 2020; 237 Jia (10.1016/j.conbuildmat.2025.144257_bib62) 2021; 185 Law (10.1016/j.conbuildmat.2025.144257_bib48) 2012; 45 Provis (10.1016/j.conbuildmat.2025.144257_bib4) 2018; 114 Felix (10.1016/j.conbuildmat.2025.144257_bib21) 2021; 266 Xu (10.1016/j.conbuildmat.2025.144257_bib52) 2019 Wu (10.1016/j.conbuildmat.2025.144257_bib70) 2024; 165 Shahmansouri (10.1016/j.conbuildmat.2025.144257_bib34) 2020; 31 Fatemi (10.1016/j.conbuildmat.2025.144257_bib77) 2025; 23 Chakraborty (10.1016/j.conbuildmat.2025.144257_bib46) 2020; 46 Zhang (10.1016/j.conbuildmat.2025.144257_bib12) 2018; 88 10.1016/j.conbuildmat.2025.144257_bib44 Habert (10.1016/j.conbuildmat.2025.144257_bib5) 2020; 1 Khunthongkeaw (10.1016/j.conbuildmat.2025.144257_bib10) 2006; 20 Jia (10.1016/j.conbuildmat.2025.144257_bib65) 2023; 56 Luo (10.1016/j.conbuildmat.2025.144257_bib67) 2023; 406 Bernal (10.1016/j.conbuildmat.2025.144257_bib29) 2017; 36 Singh (10.1016/j.conbuildmat.2025.144257_bib33) 2015; 85 Lu (10.1016/j.conbuildmat.2025.144257_bib53) 2019 Shahmansouri (10.1016/j.conbuildmat.2025.144257_bib37) 2021; 36 Biswas (10.1016/j.conbuildmat.2025.144257_bib25) 2022; 346 Shi (10.1016/j.conbuildmat.2025.144257_bib13) 2021; 304 Bernal (10.1016/j.conbuildmat.2025.144257_bib72) 2012; 33 Liu (10.1016/j.conbuildmat.2025.144257_bib51) 2019 Chen (10.1016/j.conbuildmat.2025.144257_bib71) 2025; 170 Zhang (10.1016/j.conbuildmat.2025.144257_bib27) 2023; 65 Shahmansouri (10.1016/j.conbuildmat.2025.144257_bib36) 2021; 279 Sáez del Bosque (10.1016/j.conbuildmat.2025.144257_bib7) 2020; 234 Chen (10.1016/j.conbuildmat.2025.144257_bib30) 2021; 279 Jiang (10.1016/j.conbuildmat.2025.144257_bib50) 2019 Sun (10.1016/j.conbuildmat.2025.144257_bib57) 2024; 426 Cao (10.1016/j.conbuildmat.2025.144257_bib69) 2024; 465 Jiang (10.1016/j.conbuildmat.2025.144257_bib11) 2000; 30 Bakhareva (10.1016/j.conbuildmat.2025.144257_bib74) 2001; 31 Mei (10.1016/j.conbuildmat.2025.144257_bib47) 2022; 142 Han (10.1016/j.conbuildmat.2025.144257_bib66) 2023; 392 Zhong (10.1016/j.conbuildmat.2025.144257_bib63) 2022; 251 Chen (10.1016/j.conbuildmat.2025.144257_bib23) 2022; 321 Abualigah (10.1016/j.conbuildmat.2025.144257_bib61) 2021; 376 Le (10.1016/j.conbuildmat.2025.144257_bib40) 2024; 18 Kellouche (10.1016/j.conbuildmat.2025.144257_bib22) 2019; 31 Aye (10.1016/j.conbuildmat.2025.144257_bib58) 2023; 137 Tambara (10.1016/j.conbuildmat.2025.144257_bib73) 2024; 449 Chen (10.1016/j.conbuildmat.2025.144257_bib42) 2022; 148 Golafshani (10.1016/j.conbuildmat.2025.144257_bib24) 2024; 159 Neto (10.1016/j.conbuildmat.2025.144257_bib76) 2025; 111 Gardoni (10.1016/j.conbuildmat.2025.144257_bib43) 2002; 128 Bernal (10.1016/j.conbuildmat.2025.144257_bib49) 2015; 48 Carević (10.1016/j.conbuildmat.2025.144257_bib9) 2019; 213 Pradhan (10.1016/j.conbuildmat.2025.144257_bib54) 2024; 25 Tambara (10.1016/j.conbuildmat.2025.144257_bib55) 2024; 449 Zhang (10.1016/j.conbuildmat.2025.144257_bib14) 2013; 79 Yıldız (10.1016/j.conbuildmat.2025.144257_bib59) 2023; 271 Gluth (10.1016/j.conbuildmat.2025.144257_bib6) 2022; 7 Hosseinzadeh (10.1016/j.conbuildmat.2025.144257_bib56) 2023; 76 Lahoti (10.1016/j.conbuildmat.2025.144257_bib31) 2018; 44 Huo (10.1016/j.conbuildmat.2025.144257_bib28) 2023; 76 |
| References_xml | – volume: 142 year: 2022 ident: bib47 article-title: Probabilistic prediction model of steel to concrete bond failure under high temperature by machine learning publication-title: Eng. Fail. Anal. – volume: 442 year: 2024 ident: bib68 article-title: Machine learning based modeling for predicting the compressive strength of solid waste material-incorporated magnesium phosphate cement publication-title: J. Clean. Prod. – volume: 45 start-page: 1425 year: 2012 end-page: 1437 ident: bib48 article-title: Durability assessment of alkali activated slag (AAS) concrete publication-title: Mater. Struct. – volume: 88 start-page: 86 year: 2018 end-page: 99 ident: bib12 article-title: Prediction model of carbonation depth for recycled aggregate concrete publication-title: Cem. Concr. Compos – volume: 95 year: 2020 ident: bib20 article-title: Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques publication-title: Appl. Soft. Comput. – volume: 234 year: 2020 ident: bib7 article-title: Carbonation of concrete with construction and demolition waste based recycled aggregates and cement with recycled content publication-title: Constr. Build. Mater. – volume: 46 year: 2020 ident: bib46 article-title: A novel construction cost prediction model using hybrid natural and light gradient boosting publication-title: Adv. Eng. Inf. – volume: 18 start-page: 30 year: 2024 end-page: 50 ident: bib75 article-title: A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete publication-title: Front. Struct. Civ. Eng. – volume: 50 start-page: 73 year: 2014 end-page: 81 ident: bib17 article-title: Statistical modelling of carbonation in reinforced concrete publication-title: Cem. Concr. Compos – volume: 426 year: 2024 ident: bib57 article-title: An interpretable probabilistic machine learning model for forecasting compressive strength of oil palm shell-based lightweight aggregate concrete containing fly ash or silica fume publication-title: Constr. Build. Mater. – volume: 449 year: 2024 ident: bib73 article-title: Carbonation resistance of alkali-activated GGBFS/calcined clay concrete under natural and accelerated conditions publication-title: Constr. Build. Mater. – year: 2019 ident: bib52 article-title: Study on carbonation resistance and mechanism of alkali slag concrete – volume: 65 year: 2023 ident: bib27 article-title: A framework for predicting the carbonation depth of concrete incorporating fly ash based on a least squares support vector machine and metaheuristic algorithms publication-title: J. Build. Eng. – volume: 36 start-page: 1 year: 2017 end-page: 10 ident: bib29 article-title: Factors controlling carbonation resistance of alkali-activated materials publication-title: ACI – volume: 25 start-page: 2839 year: 2024 end-page: 2854 ident: bib54 article-title: Effects of rice husk ash on strength and durability performance of slag-based alkali-activated concrete publication-title: Struct. Concr. – start-page: 365 year: 2020 end-page: 371 ident: bib15 article-title: Evaluating carbonation-induced corrosion in high-volume publication-title: SCM mixtures through the square root model – volume: 170 year: 2025 ident: bib71 article-title: Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization publication-title: Autom. Constr. – volume: 44 start-page: 5726 year: 2018 end-page: 5734 ident: bib31 article-title: Effects of Si/Al molar ratio on strength endurance and volume stability of metakaolin geopolymers subject to elevated temperature publication-title: Ceram. Int. – volume: 20 start-page: 744 year: 2006 end-page: 753 ident: bib10 article-title: A study on carbonation depth prediction for fly ash concrete publication-title: Constr. Build. Mater. – volume: 279 year: 2021 ident: bib36 article-title: Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite publication-title: J. Clean. Prod. – volume: 137 start-page: 2111 year: 2023 end-page: 2128 ident: bib58 article-title: Airfoil shape optimisation using a multi-fidelity surrogate-assisted metaheuristic with a new multi-objective infill sampling technique publication-title: Cmes. Comp. Model. Eng. – volume: 31 start-page: 969 year: 2019 end-page: 988 ident: bib22 article-title: Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network publication-title: Neural Comput. Appl. – volume: 465 year: 2024 ident: bib69 article-title: Enhancing mix proportion design of low carbon concrete for shield segment using a combination of bayesian optimization-NGBoost and NSGA-III algorithm publication-title: J. Clean. Prod. – volume: 39 year: 2020 ident: bib1 article-title: Carbonation behavior of recycled concrete with CO publication-title: J. CO – year: 2019 ident: bib51 article-title: Study of the low temperature hardening and durability performance of recycled fine aggregate based alkali-activated slag concrete – volume: 304 year: 2021 ident: bib13 article-title: A modified numerical model for predicting carbonation depth of concrete with stress damage publication-title: Constr. Build. Mater. – volume: 24 start-page: 2145 year: 2023 end-page: 2169 ident: bib26 article-title: Machine learning approach in investigating carbonation depth of concrete containing fly ash publication-title: Struct. Concr. – volume: 114 start-page: 40 year: 2018 end-page: 48 ident: bib4 article-title: Alkali-activated materials publication-title: Cem. Concr. Res. – volume: 13 start-page: 1231 year: 2021 end-page: 1245 ident: bib45 article-title: Prediction of rockhead using a hybrid N-XGBoost machine learning framework publication-title: J. Rock. Mech. Geotech. Eng. – volume: 346 year: 2022 ident: bib25 article-title: Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete publication-title: Constr. Build. Mater. – volume: 76 year: 2023 ident: bib28 article-title: Predicting carbonation depth of concrete using a hybrid ensemble model publication-title: J. Build. Eng. – volume: 271 year: 2023 ident: bib59 article-title: A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems publication-title: Knowl. Based Syst. – volume: 148 start-page: 04022096 year: 2022 ident: bib42 article-title: Probabilistic machine learning methods for performance prediction of structure and infrastructures through natural gradient boosting publication-title: J. Struct. Eng. – volume: 76 year: 2023 ident: bib56 article-title: Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms publication-title: J. Build. Eng. – volume: 36 year: 2021 ident: bib37 article-title: Mechanical properties of GGBFS-based geopolymer concrete incorporating natural zeolite and silica fume with an optimum design using response surface method publication-title: J. Build. Eng. – reference: 2007, EN 14630,European Committee for StandardizationBrussels. Products and systems for the protection and repair of concrete structures-test methods-determination of carbonation depth in hardened concrete by the phenolphthalein method, Brussels: European Committee for Standardization, 2007. – volume: 261 year: 2023 ident: bib60 article-title: Chaotic marine predators algorithm for global optimization of real-world engineering problems publication-title: Knowl. Based Syst. – volume: 33 start-page: 99 year: 2012 end-page: 108 ident: bib72 article-title: Engineering and durability properties of concretes based on alkali-activated granulated blast furnace slag/metakaolin blends publication-title: Constr. Build. Mater. – volume: 29 start-page: 1941 year: 2022 end-page: 1964 ident: bib18 article-title: Artificial intelligence to model the performance of concrete mixtures and elements: a review publication-title: Arch. Comput. Method. E. – volume: 185 year: 2021 ident: bib62 article-title: Remora optimization algorithm publication-title: Expert. Syst. Appl. – volume: 392 year: 2023 ident: bib66 article-title: Exploring interpretable ensemble learning to predict mechanical strength and thermal conductivity of aerogel incorporated concrete publication-title: Constr. Build. Mater. – volume: 449 year: 2024 ident: bib55 article-title: Carbonation resistance of alkali-activated GGBFS/ calcined clay concrete under natural and accelerated conditions publication-title: Constr. Build. Mater. – volume: 23 year: 2025 ident: bib77 article-title: Thermal and carbonation resistance of tunnel concrete: Experimental evaluation and hybrid ANN-GPR modeling under fire-CO₂ exposure publication-title: Case. Stud. Constr. Mat. – volume: 251 year: 2022 ident: bib63 article-title: Beluga whale optimization: a novel nature-inspired metaheuristic algorithm publication-title: Knowl. Based Syst. – volume: 165 year: 2024 ident: bib70 article-title: Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning publication-title: Autom. Constr. – volume: 1 start-page: 559 year: 2020 end-page: 573 ident: bib5 article-title: Environmental impacts and decarbonization strategies in the cement and concrete industries publication-title: Nat. Rev. Earth. Env. – volume: 237 year: 2020 ident: bib32 article-title: Geopolymers as an alternative to Portland cement: an overview publication-title: Constr. Build. Mater. – volume: 31 start-page: 1277 year: 2001 end-page: 1283 ident: bib74 article-title: Resistance of alkali-activated slag concrete to carbonation publication-title: Cem. Concr. Res. – volume: 31 year: 2020 ident: bib34 article-title: Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method publication-title: J. Build. Eng. – year: 2019 ident: bib50 article-title: Study on the properties of concrete mixed with rice husk ash – volume: 279 year: 2021 ident: bib30 article-title: Geopolymer concrete durability subjected to aggressive environments-a review of influence factors and comparison with ordinary Portland cement publication-title: Constr. Build. Mater. – volume: 85 start-page: 78 year: 2015 end-page: 90 ident: bib33 article-title: Geopolymer concrete: a review of some recent developments publication-title: Constr. Build. Mater. – volume: 56 start-page: 1919 year: 2023 end-page: 1979 ident: bib65 article-title: Crayfish optimization algorithm publication-title: Artif. Intell. Rev. – volume: 406 year: 2023 ident: bib67 article-title: Research on predicting compressive strength of magnesium silicate hydrate cement based on machine learning publication-title: Constr. Build. Mater. – volume: 111 year: 2025 ident: bib76 article-title: Prediction of natural carbonation depths in concretes with ensemble metamodel based on artificial neural networks from time series analysis with 20 years of exposure publication-title: J. Build. Eng. – volume: 247 year: 2020 ident: bib38 article-title: Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches publication-title: Constr. Build. Mater. – volume: 79 start-page: 36 year: 2013 end-page: 40 ident: bib14 article-title: Studies on forecasting of carbonation depth of slag high performance concrete considering gas permeability publication-title: Appl. Clay. Sci. – volume: 159 year: 2024 ident: bib24 article-title: Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete publication-title: Appl. Soft. Comput. – volume: 18 start-page: 1028 year: 2024 end-page: 1049 ident: bib40 article-title: Machine learning based models for predicting compressive strength of geopolymer concrete publication-title: Front. Struct. Civ. Eng. – volume: 376 year: 2021 ident: bib61 article-title: The arithmetic optimization algorithm publication-title: Comput. Method. Appl. M. – volume: 49 start-page: 4677 year: 1994 end-page: 4683 ident: bib64 article-title: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes publication-title: Phys. Rev. E. – volume: 48 start-page: 653 year: 2015 end-page: 669 ident: bib49 article-title: Accelerated carbonation testing of alkali-activated slag/metakaolin blended concretes: effect of exposure conditions publication-title: Mater. Struct. – volume: 128 start-page: 1024 year: 2002 end-page: 1038 ident: bib43 article-title: Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations publication-title: J. Eng. Mech. – reference: T. Duan, A. Avati, D. Ding, et al, NGBoost: natural gradient boosting for probabilistic prediction, arXiv Prints, (2019), arXiv:1910.03225. – volume: 7 start-page: 58 year: 2022 end-page: 67 ident: bib6 article-title: Acid resistance of alkali-activated materials: recent advances and research needs publication-title: Rilem. Tech. Lett. – volume: 322 year: 2018 ident: bib16 article-title: Prediction Model for carbonation depth of concrete subjected to freezing-thawing cycles publication-title: IOP Conference Series Materials Science Engineering – volume: 321 year: 2022 ident: bib23 article-title: Development of hybrid machine learning-based carbonation models with weighting function publication-title: Constr. Build. Mater. – volume: 5 start-page: 92 year: 2020 end-page: 117 ident: bib35 article-title: Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming publication-title: J. Concr. Struct. M. – volume: 213 start-page: 194 year: 2019 end-page: 208 ident: bib9 article-title: Model for practical carbonation depth prediction for high volume fly ash concrete and recycled aggregate concrete publication-title: Constr. Build. Mater. – volume: 115 year: 2022 ident: bib19 article-title: Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC) publication-title: Appl. Soft. Comput. – year: 2019 ident: bib41 article-title: A primer on machine learning applications in civil engineering – volume: 9 start-page: 2927 year: 2023 end-page: 2957 ident: bib3 article-title: An evaluative review of recycled waste material utilization in high-performance concrete publication-title: Civ. Eng. J. – volume: 115 year: 2021 ident: bib39 article-title: Machine learning to predict properties of fresh and hardened alkali-activated concrete publication-title: Cem. Concr. Comp. – volume: 266 year: 2021 ident: bib21 article-title: Carbonation model for fly ash concrete based on artificial neural network: development and parametric analysis publication-title: Constr. Build. Mater. – volume: 130 year: 2022 ident: bib2 article-title: Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete publication-title: Appl. Soft. Comput. – volume: 30 start-page: 699 year: 2000 end-page: 702 ident: bib11 article-title: A model for predicting carbonation of high-volume fly ash concrete publication-title: Cem. Concr. Res. – year: 2019 ident: bib53 article-title: Research on steel corrosion mechanism of alkali-activated slag concrete in carbonation environments – volume: 234 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib7 article-title: Carbonation of concrete with construction and demolition waste based recycled aggregates and cement with recycled content publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2019.117336 – volume: 449 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib55 article-title: Carbonation resistance of alkali-activated GGBFS/ calcined clay concrete under natural and accelerated conditions publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2024.138351 – volume: 33 start-page: 99 year: 2012 ident: 10.1016/j.conbuildmat.2025.144257_bib72 article-title: Engineering and durability properties of concretes based on alkali-activated granulated blast furnace slag/metakaolin blends publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2012.01.017 – volume: 114 start-page: 40 year: 2018 ident: 10.1016/j.conbuildmat.2025.144257_bib4 article-title: Alkali-activated materials publication-title: Cem. Concr. Res. doi: 10.1016/j.cemconres.2017.02.009 – ident: 10.1016/j.conbuildmat.2025.144257_bib8 – volume: 39 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib1 article-title: Carbonation behavior of recycled concrete with CO2-curing recycled aggregate under various environments publication-title: J. CO2. Util. doi: 10.1016/j.jcou.2020.101185 – volume: 442 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib68 article-title: Machine learning based modeling for predicting the compressive strength of solid waste material-incorporated magnesium phosphate cement publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2024.141172 – volume: 261 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib60 article-title: Chaotic marine predators algorithm for global optimization of real-world engineering problems publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2022.110192 – volume: 45 start-page: 1425 year: 2012 ident: 10.1016/j.conbuildmat.2025.144257_bib48 article-title: Durability assessment of alkali activated slag (AAS) concrete publication-title: Mater. Struct. doi: 10.1617/s11527-012-9842-1 – volume: 36 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib37 article-title: Mechanical properties of GGBFS-based geopolymer concrete incorporating natural zeolite and silica fume with an optimum design using response surface method publication-title: J. Build. Eng. – volume: 251 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib63 article-title: Beluga whale optimization: a novel nature-inspired metaheuristic algorithm publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2022.109215 – volume: 56 start-page: 1919 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib65 article-title: Crayfish optimization algorithm publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-023-10567-4 – volume: 20 start-page: 744 issue: 9 year: 2006 ident: 10.1016/j.conbuildmat.2025.144257_bib10 article-title: A study on carbonation depth prediction for fly ash concrete publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2005.01.052 – volume: 279 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib36 article-title: Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.123697 – volume: 304 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib13 article-title: A modified numerical model for predicting carbonation depth of concrete with stress damage publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2021.124389 – volume: 48 start-page: 653 year: 2015 ident: 10.1016/j.conbuildmat.2025.144257_bib49 article-title: Accelerated carbonation testing of alkali-activated slag/metakaolin blended concretes: effect of exposure conditions publication-title: Mater. Struct. doi: 10.1617/s11527-014-0289-4 – volume: 25 start-page: 2839 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib54 article-title: Effects of rice husk ash on strength and durability performance of slag-based alkali-activated concrete publication-title: Struct. Concr. doi: 10.1002/suco.202300173 – volume: 170 year: 2025 ident: 10.1016/j.conbuildmat.2025.144257_bib71 article-title: Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization publication-title: Autom. Constr. doi: 10.1016/j.autcon.2024.105943 – volume: 13 start-page: 1231 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib45 article-title: Prediction of rockhead using a hybrid N-XGBoost machine learning framework publication-title: J. Rock. Mech. Geotech. Eng. doi: 10.1016/j.jrmge.2021.06.012 – volume: 159 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib24 article-title: Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2024.111661 – volume: 237 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib32 article-title: Geopolymers as an alternative to Portland cement: an overview publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2019.117455 – volume: 148 start-page: 04022096 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib42 article-title: Probabilistic machine learning methods for performance prediction of structure and infrastructures through natural gradient boosting publication-title: J. Struct. Eng. doi: 10.1061/(ASCE)ST.1943-541X.0003401 – volume: 322 year: 2018 ident: 10.1016/j.conbuildmat.2025.144257_bib16 article-title: Prediction Model for carbonation depth of concrete subjected to freezing-thawing cycles publication-title: IOP Conference Series Materials Science Engineering doi: 10.1088/1757-899X/322/2/022048 – volume: 31 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib34 article-title: Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method publication-title: J. Build. Eng. – volume: 76 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib28 article-title: Predicting carbonation depth of concrete using a hybrid ensemble model publication-title: J. Build. Eng. – year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib52 – volume: 23 year: 2025 ident: 10.1016/j.conbuildmat.2025.144257_bib77 article-title: Thermal and carbonation resistance of tunnel concrete: Experimental evaluation and hybrid ANN-GPR modeling under fire-CO₂ exposure publication-title: Case. Stud. Constr. Mat. – volume: 36 start-page: 1 year: 2017 ident: 10.1016/j.conbuildmat.2025.144257_bib29 article-title: Factors controlling carbonation resistance of alkali-activated materials publication-title: ACI – volume: 31 start-page: 1277 year: 2001 ident: 10.1016/j.conbuildmat.2025.144257_bib74 article-title: Resistance of alkali-activated slag concrete to carbonation publication-title: Cem. Concr. Res. doi: 10.1016/S0008-8846(01)00574-9 – volume: 95 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib20 article-title: Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2020.106552 – volume: 185 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib62 article-title: Remora optimization algorithm publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2021.115665 – volume: 321 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib23 article-title: Development of hybrid machine learning-based carbonation models with weighting function publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2022.126359 – start-page: 365 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib15 article-title: Evaluating carbonation-induced corrosion in high-volume – volume: 449 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib73 article-title: Carbonation resistance of alkali-activated GGBFS/calcined clay concrete under natural and accelerated conditions publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2024.138351 – volume: 128 start-page: 1024 year: 2002 ident: 10.1016/j.conbuildmat.2025.144257_bib43 article-title: Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations publication-title: J. Eng. Mech. – volume: 76 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib56 article-title: Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms publication-title: J. Build. Eng. – volume: 142 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib47 article-title: Probabilistic prediction model of steel to concrete bond failure under high temperature by machine learning publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2022.106786 – volume: 46 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib46 article-title: A novel construction cost prediction model using hybrid natural and light gradient boosting publication-title: Adv. Eng. Inf. doi: 10.1016/j.aei.2020.101201 – volume: 130 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib2 article-title: Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2022.109641 – volume: 1 start-page: 559 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib5 article-title: Environmental impacts and decarbonization strategies in the cement and concrete industries publication-title: Nat. Rev. Earth. Env. doi: 10.1038/s43017-020-0093-3 – volume: 18 start-page: 1028 issue: 7 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib40 article-title: Machine learning based models for predicting compressive strength of geopolymer concrete publication-title: Front. Struct. Civ. Eng. doi: 10.1007/s11709-024-1039-5 – volume: 79 start-page: 36 year: 2013 ident: 10.1016/j.conbuildmat.2025.144257_bib14 article-title: Studies on forecasting of carbonation depth of slag high performance concrete considering gas permeability publication-title: Appl. Clay. Sci. doi: 10.1016/j.clay.2013.02.020 – volume: 44 start-page: 5726 issue: 5 year: 2018 ident: 10.1016/j.conbuildmat.2025.144257_bib31 article-title: Effects of Si/Al molar ratio on strength endurance and volume stability of metakaolin geopolymers subject to elevated temperature publication-title: Ceram. Int. doi: 10.1016/j.ceramint.2017.12.226 – ident: 10.1016/j.conbuildmat.2025.144257_bib44 – volume: 49 start-page: 4677 issue: 5 year: 1994 ident: 10.1016/j.conbuildmat.2025.144257_bib64 article-title: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes publication-title: Phys. Rev. E. doi: 10.1103/PhysRevE.49.4677 – volume: 392 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib66 article-title: Exploring interpretable ensemble learning to predict mechanical strength and thermal conductivity of aerogel incorporated concrete publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2023.131781 – volume: 24 start-page: 2145 issue: 2 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib26 article-title: Machine learning approach in investigating carbonation depth of concrete containing fly ash publication-title: Struct. Concr. doi: 10.1002/suco.202200269 – volume: 5 start-page: 92 issue: 1 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib35 article-title: Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming publication-title: J. Concr. Struct. M. – volume: 465 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib69 article-title: Enhancing mix proportion design of low carbon concrete for shield segment using a combination of bayesian optimization-NGBoost and NSGA-III algorithm publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2024.142746 – volume: 18 start-page: 30 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib75 article-title: A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete publication-title: Front. Struct. Civ. Eng. doi: 10.1007/s11709-024-1041-y – volume: 137 start-page: 2111 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib58 article-title: Airfoil shape optimisation using a multi-fidelity surrogate-assisted metaheuristic with a new multi-objective infill sampling technique publication-title: Cmes. Comp. Model. Eng. – volume: 115 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib19 article-title: Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC) publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2021.108182 – volume: 426 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib57 article-title: An interpretable probabilistic machine learning model for forecasting compressive strength of oil palm shell-based lightweight aggregate concrete containing fly ash or silica fume publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2024.136176 – volume: 85 start-page: 78 year: 2015 ident: 10.1016/j.conbuildmat.2025.144257_bib33 article-title: Geopolymer concrete: a review of some recent developments publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2015.03.036 – year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib51 – volume: 406 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib67 article-title: Research on predicting compressive strength of magnesium silicate hydrate cement based on machine learning publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2023.133412 – volume: 9 start-page: 2927 issue: 11 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib3 article-title: An evaluative review of recycled waste material utilization in high-performance concrete publication-title: Civ. Eng. J. doi: 10.28991/CEJ-2023-09-11-020 – volume: 88 start-page: 86 year: 2018 ident: 10.1016/j.conbuildmat.2025.144257_bib12 article-title: Prediction model of carbonation depth for recycled aggregate concrete publication-title: Cem. Concr. Compos doi: 10.1016/j.cemconcomp.2018.01.013 – volume: 7 start-page: 58 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib6 article-title: Acid resistance of alkali-activated materials: recent advances and research needs publication-title: Rilem. Tech. Lett. doi: 10.21809/rilemtechlett.2022.157 – volume: 279 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib30 article-title: Geopolymer concrete durability subjected to aggressive environments-a review of influence factors and comparison with ordinary Portland cement publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2021.122496 – year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib41 – volume: 111 year: 2025 ident: 10.1016/j.conbuildmat.2025.144257_bib76 article-title: Prediction of natural carbonation depths in concretes with ensemble metamodel based on artificial neural networks from time series analysis with 20 years of exposure publication-title: J. Build. Eng. – volume: 115 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib39 article-title: Machine learning to predict properties of fresh and hardened alkali-activated concrete publication-title: Cem. Concr. Comp. doi: 10.1016/j.cemconcomp.2020.103863 – volume: 30 start-page: 699 issue: 5 year: 2000 ident: 10.1016/j.conbuildmat.2025.144257_bib11 article-title: A model for predicting carbonation of high-volume fly ash concrete publication-title: Cem. Concr. Res. doi: 10.1016/S0008-8846(00)00227-1 – volume: 31 start-page: 969 year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib22 article-title: Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-3052-2 – volume: 165 year: 2024 ident: 10.1016/j.conbuildmat.2025.144257_bib70 article-title: Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning publication-title: Autom. Constr. doi: 10.1016/j.autcon.2024.105516 – volume: 65 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib27 article-title: A framework for predicting the carbonation depth of concrete incorporating fly ash based on a least squares support vector machine and metaheuristic algorithms publication-title: J. Build. Eng. – year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib53 – volume: 271 year: 2023 ident: 10.1016/j.conbuildmat.2025.144257_bib59 article-title: A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2023.110554 – volume: 50 start-page: 73 year: 2014 ident: 10.1016/j.conbuildmat.2025.144257_bib17 article-title: Statistical modelling of carbonation in reinforced concrete publication-title: Cem. Concr. Compos doi: 10.1016/j.cemconcomp.2013.12.001 – volume: 266 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib21 article-title: Carbonation model for fly ash concrete based on artificial neural network: development and parametric analysis publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2020.121050 – year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib50 – volume: 376 year: 2021 ident: 10.1016/j.conbuildmat.2025.144257_bib61 article-title: The arithmetic optimization algorithm publication-title: Comput. Method. Appl. M. doi: 10.1016/j.cma.2020.113609 – volume: 247 year: 2020 ident: 10.1016/j.conbuildmat.2025.144257_bib38 article-title: Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2020.118581 – volume: 213 start-page: 194 year: 2019 ident: 10.1016/j.conbuildmat.2025.144257_bib9 article-title: Model for practical carbonation depth prediction for high volume fly ash concrete and recycled aggregate concrete publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2019.03.267 – volume: 29 start-page: 1941 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib18 article-title: Artificial intelligence to model the performance of concrete mixtures and elements: a review publication-title: Arch. Comput. Method. E. doi: 10.1007/s11831-021-09644-0 – volume: 346 year: 2022 ident: 10.1016/j.conbuildmat.2025.144257_bib25 article-title: Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2022.128483 |
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| SubjectTerms | Alkali-activated slag concrete Beluga Whale Optimization algorithm Carbonation depth Crayfish Optimization Algorithm Natural gradient boosting Remora Optimization Algorithm |
| Title | NGBoost-based prediction of carbonation in Alkali-activated slag concrete enhanced by metaheuristic algorithms |
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