[1] [1] LIU C, MA Q, LI L L, et al. Artificial intelligence metamaterials［J］. Acta Opt Sin, 2021, 41(8): 0823004.

[2] [2] TAFFESE W Z, SISTONEN E. Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions［J］. Autom Constr, 2017, 77: 1-14.

[5] [5] GUO S H, AGARWAL M, COOPER C, et al. Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm［J］. J Manuf Syst, 2022, 62: 145-163.

[9] [9] NUNEZ I, MARANI A, FLAH M, et al. Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review［J］. Constr Build Mater, 2021, 310: 125279.

[10] [10] LI J Q, WU Z M, SHI C J, et al. Durability of ultra-high performance concrete-A review［J］. Constr Build Mater, 2020, 255: 119296.

[12] [12] ZHAO N, ZHANG A L. The Prediction of Frost-Resisting Durability of Concrete Based on Artificial Neutral Network［C］//Proceedings of International Conference on Engineering and Business Management (EBM2011). Scientific Research Publishing, 2011: 1501-1504.

[16] [16] MALAMI S I, ANWAR F H, ABDULRAHMAN S, et al. Implementation of hybrid neuro-fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: a soft computing technique［J］. Results Eng, 2021, 10: 100228.

[17] [17] CHEN B W, JI W, RHO S. Divide-and-conquer signal processing, feature extraction, and machine learning for big data［J］. Neurocomputing, 2016, 174: 383.

[18] [18] CAI R, HAN T H, LIAO W Y, et al. Prediction of surface chloride concentration of marine concrete using ensemble machine learning［J］. Cem Concr Res, 2020, 136: 106164.

[19] [19] CHEN Z Y, LIN J L, SAGOE-CRENTSIL K, et al. Development of hybrid machine learning-based carbonation models with weighting function［J］. Constr Build Mater, 2022, 321: 126359.

[23] [23] AZIM I, YANG J, JAVED M F, et al. Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming［J］. Structures, 2020, 25: 212-228.

[24] [24] GANDOMI A H, ROKE D A. Assessment of artificial neural network and genetic programming as predictive tools［J］. Adv Eng Softw, 2015, 88: 63-72.

[25] [25] GANDOMI A H, BABANAJAD S K, ALAVI A H, et al. Novel approach to strength modeling of concrete under triaxial compression［J］. J Mater Civ Eng, 2012, 24(9): 1132-1143.

[26] [26] KIEWICZ J. The applications of ANNs in certain materials-analysis problems［J］. J Mater Process Technol, 2000, 106(1-3): 74-79.

[30] [30] HARIRI-ARDEBILI M A, POURKAMALI-ANARAKI F. Support vector machine based reliability analysis of concrete dams［J］. Soil Dyn Earthq Eng, 2018, 104: 276-295.

[31] [31] EL ASRI Y, BENAICHA M, ZAHER M, et al. Prediction of plastic viscosity and yield stress of self-compacting concrete using machine learning technics［J］. Mater Today Proc, 2022, 59: A7-A13.

[32] [32] AHMAD A, FAROOQ F, NIEWIADOMSKI P, et al. Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm［J］. Materials (Basel), 2021, 14(4): 794.

[35] [35] IQBAL M F, JAVED M F, RAUF M, et al. Sustainable utilization of foundry waste: forecasting mechanical properties of foundry sand based concrete using multi-expression programming［J］. Sci Total Environ, 2021, 780: 146524.

[36] [36] IQBAL M F, LIU Q F, AZIM I, et al. Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming［J］. J Hazard Mater, 2020, 384: 121322.

[37] [37] ASHRAF M, IQBAL M F, RAUF M, et al. Developing a sustainable concrete incorporating bentonite clay and silica fume: mechanical and durability performance［J］. J Clean Prod, 2022, 337: 130315.

[38] [38] VAN QUAN TRAN. Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials［J］. Constr Build Mater, 2022, 328: 127103.

[39] [39] GOMAA E, HAN T H, ELGAWADY M, et al. Machine learning to predict properties of fresh and hardened alkali-activated concrete［J］. Cem Concr Compos, 2021, 115: 103863.

[40] [40] HODHOD O A, AHMED H I. Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete［J］. HBRC J, 2013, 9(1): 15-21.

[41] [41] PARICHATPRECHA R, NIMITYONGSKUL P. Analysis of durability of high performance concrete using artificial neural networks［J］. Constr Build Mater, 2009, 23(2): 910-917.

[42] [42] SONG H W, KWON S J. Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure［J］. Cem Concr Res, 2009, 39(9): 814-824.

[43] [43] SAFARZADEGAN GILAN S, BAHRAMI JOVEIN H, RAMEZANIANPOUR A A. Hybrid support vector regression-Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin［J］. Constr Build Mater, 2012, 34: 321-329.

[45] [45] KHAN M I. Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks［J］. Autom Constr, 2012, 22: 516-524.

[46] [46] MOHAMED O, KEWALRAMANI M, ATI M, et al. Application of ANN for prediction of chloride penetration resistance and concrete compressive strength［J］. Materialia, 2021, 17: 101123.

[47] [47] GHAFOORI N, NAJIMI M, SOBHANI J, et al. Predicting rapid chloride permeability of self-consolidating concrete: A comparative study on statistical and neural network models［J］. Constr Build Mater, 2013, 44: 381-390

[48] [48] NEVES R, SILVA A, DE BRITO J, et al. Statistical modelling of the resistance to chloride penetration in concrete with recycled aggregates［J］. Constr Build Mater, 2018, 182: 550-560.

[49] [49] HOANG N D, CHEN C T, LIAO K W. Prediction of chloride diffusion in cement mortar using multi-gene genetic programming and multivariate adaptive regression splines［J］. Measurement, 2017, 112: 141-149.

[50] [50] CHEN H Y, DENG T T, DU T, et al. An RF and LSSVM-NSGA-II method for the multi-objective optimization of high-performance concrete durability［J］. Cem Concr Compos, 2022, 129: 104446.

[52] [52] ABDULALIM ALABDULLAH A, IQBAL M, ZAHID M, et al. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis［J］. Constr Build Mater, 2022, 345: 128296.

[53] [53] HENDI A L, BEHRAVAN A, MOSTOFINEJAD D, et al. A step towards green concrete: effect of waste silica powder usage under HCl attack［J］. J Clean Prod, 2018, 188: 278-289.

[55] [55] JIN L B, DONG T Y, FAN T, et al. Prediction of the chloride diffusivity of recycled aggregate concrete using artificial neural network［J］. Mater Today Commun, 2022, 32: 104137.

[56] [56] NAJIMI M, GHAFOORI N, NIKOO M. Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm［J］. J Build Eng, 2019, 22: 216-226.

[57] [57] LIU Q F, IQBAL M F, YANG J, et al. Prediction of chloride diffusivity in concrete using artificial neural network: modelling and performance evaluation［J］. Constr Build Mater, 2021, 268: 121082.

[58] [58] AL-SWAIDANI A M, KHWIES W T, AL-BALY M, et al. Development of multiple linear regression, artificial neural networks and fuzzy logic models to predict the efficiency factor and durability indicator of nano natural pozzolana as cement additive［J］. J Build Eng, 2022, 52: 104475.

[59] [59] LIU K H, ZHENG J K, PACHECO-TORGAL F, et al. Innovative modeling framework of chloride resistance of recycled aggregate concrete using ensemble-machine-learning methods［J］. Constr Build Mater, 2022, 337: 127613.

[61] [61] FELIX E F, CARRAZEDO R, POSSAN E. Carbonation model for fly ash concrete based on artificial neural network: development and parametric analysis［J］. Constr Build Mater, 2021, 266: 121050.

[62] [62] LEE H, LEE H S, SURANENI P. Evaluation of carbonation progress using AIJ model, FEM analysis, and machine learning algorithms［J］. Constr Build Mater, 2020, 259: 119703.

[63] [63] NUNEZ I, NEHDI M L. Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs［J］. Constr Build Mater, 2021, 287: 123027.

[64] [64] LONDHE S N, KULKARNI P S, DIXIT P R, et al. Predicting carbonation coefficient using Artificial neural networks and genetic programming［J］. J Build Eng, 2021, 39: 102258.

[65] [65] BISWAS R, LI E M, ZHANG N, et al. Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete［J］. Constr Build Mater, 2022, 346: 128483.

[66] [66] LIU K H, ALAM M S, ZHU J, et al. Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms［J］. Constr Build Mater, 2021, 301: 124382.

[67] [67] TAFFESE W Z, SISTONEN E, PUTTONEN J. CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods［J］. Constr Build Mater, 2015, 100: 70-82.

[68] [68] LIU K H, ZOU C Y, ZHANG X C, et al. Innovative prediction models for the frost durability of recycled aggregate concrete using soft computing methods［J］. J Build Eng, 2021, 34: 101822.

[69] [69] WU X G, ZHENG S Y, FENG Z B, et al. Prediction of the frost resistance of high-performance concrete based on RF-REF: a hybrid prediction approach［J］. Constr Build Mater, 2022, 333: 127132.

[70] [70] CEMALGIL S, GL E, ONAT O, et al. A novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELM［J］. Constr Build Mater, 2022, 341: 127856.

[71] [71] HODHOD O, SALAMA G A. Analysis of sulfate resistance in concrete based on artificial neural networks and USBR4908- modeling［J］. Ain Shams Eng J, 2013, 4(4): 651-660.

[72] [72] ALANI A M, FARAMARZI A. An evolutionary approach to modelling concrete degradation due to sulphuric acid attack［J］. Appl Soft Comput, 2014, 24: 985-993.

[73] [73] SAHOO S, MAHAPATRA T R. ANN Modeling to study strength loss of Fly Ash Concrete against Long term Sulphate Attack［J］. Mater Today Proc, 2018, 5(11): 24595-24604.

[74] [74] LIU K H, DAI Z H, ZHANG R B, et al. Prediction of the sulfate resistance for recycled aggregate concrete based on ensemble learning algorithms［J］. Constr Build Mater, 2022, 317: 125917.

[76] [76] SALAMI B A, RAHMAN S M, OYEHAN T A, et al. Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete［J］. Measurement, 2020, 165: 108141.