[1] Communiqué on the state of China’s ecology and environment in 2022[R]. Ministry of Ecology and Environment of the People’s Republic of China, 2022: 69 (in Chinese).
[2] WANG Y Y, YUAN H, CHEN S Y. Study on preparation of alkali-activated cementing materials using solid wastes with different blending ratios[J]. Industrial Minerals & Processing, 2022, 51(12): 1-6 (in Chinese).
[3] CHEN L L, CAO X Y, WANG Z Y, et al. Study on the application of the technical route for producing recycled concrete from construction solid waste[J]. Shanxi Architecture, 2023, 49(8): 29-32 (in Chinese).
[4] DING Q J, SUN X P, SHI J J, et al. C50 high wear-resisting pavement concrete prepared by large amount of solid waste cementitious material[J]. Concrete, 2022(4): 176-181 (in Chinese).
[5] DUAN X P, CHEN J, YAN C W, et al. Effect of steel slag on hydration of solid waste belite sulfoaluminate cement[J]. Journal of Inner Mongolia University of Technology (Natural Science), 2023, 42(6): 549-554 (in Chinese).
[6] CAI L X, HE L M, LV Y L, et al. Hole-bottom slurry mechanical properties of horizontal directional drilling in pipeline crossing project[J]. Oil & Gas Storage and Transportation, 2011, 30(1): 25-29+4 (in Chinese).
[7] WANG H X, CHEN Z, CHENG X M, et al. Prediction research of concrete compressive strength based on optimal support vector regression[J]. Construction Technology, 2023, 52(4): 117-121+138 (in Chinese).
[8] XU X H, HU Z L, LIU J P, et al. Concrete strength prediction of the Three Gorges Dam based on machine learning regression model[J]. Materials Reports, 2023, 37(2): 45-53 (in Chinese).
[9] WANG Q H, ZHANG T R, LI Y J, et al. Shear capacity of studs in steel-spontaneous-combustion coal gangue concrete composite beams using machine learning[J]. Journal of Shenyang Jianzhu University (Natural Science), 2023, 39(2): 227-233 (in Chinese).
[10] LI D Z, QI Y S, LIU L Q. PV power prediction based on LSTM-ATTENTION fusion neural network[J]. Journal of Inner Mongolia University of Technology (Natural Science Edition), 2023, 42(4): 350-354+384 (in Chinese).
[11] ZHANG X S, GAO X X. Prediction of ADMET properties of anti-breast cancer drugs by random forest-based logistic regression[J]. Journal of Inner Mongolia University of Technology (Natural Scienc), 2023, 42(6): 481-487 (in Chinese).
[12] FAN X Q, LIU J D, SHI C Y, et al. Innovative idea on fracture analysis of FRP reinforced concrete using artificial neural network[J]. Journal of Disaster Prevention and Mitigation Engineering, 2023, 43(3): 626-636 (in Chinese).
[13] JIN J W, DONG C F, FENG G H. Prediction of concrete compressive strength based on grey relational-support vector machine[J]. Journal of Zhengzhou University (Natural Science Edition), 2015, 47(3): 59-63 (in Chinese).
[14] WANG J M, YE Y R, RAO C M, et al. Prediction on composite interface bonding strength between ceramsite lightweight aggregate concrete and normal concrete based on GBDT algorithm[J]. Journal of Building Materials, 2023, 26(2): 150-155+171 (in Chinese).
[15] SONG H W, AHMAD A, FAROOQ F, et al. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms[J]. Construction and Building Materials, 2021, 308: 125021.
[16] WU X G, LIU P C, CHEN H Y, et al. Characteristic screening and prediction of high-performance concrete compressive strength based on random forest method[J]. Concrete, 2022(1): 17-20+24 (in Chinese).
[17] CHEN G, ZHU D L, WANG X A, et al. Prediction of concrete compressive strength based on the BP neural network optimized by random forest and ISSA[J]. Journal of Function Spaces, 2022, 2022: 1-20.
[18] BESKOPYLNY A N, STEL’MAKH S A, SHCHERBAN’ E M, et al. Concrete strength prediction using machine learning methods CatBoost, k-nearest neighbors, support vector regression[J]. Applied Sciences, 2022, 12(21): 10864.
[19] WANG F C, LIU Y M, CUI Q A. Hybrid parameter modeling and optimization based on Gaussian process regression[J]. Statistics & Decision, 2023, 39(1): 34-39 (in Chinese).
[20] YUAN G L. Study on hydration characteristics of carbide slag-based geopolymer[D]. Hohhot: Inner Mongolia University of Tehchnology, 2022 (in Chinese).
[21] ZHENG G Y. Study on genetic support vector machine method in strength prediction of high strength concrete for constructing large granary[J]. Journal of Henan University of Technology (Natural Science Edition), 2014, 35(3): 88-91+104 (in Chinese).
[22] LI J F. Experimental study and application of coal gangue-carbide slag geopolymer cementing material for curing soft soil[D].Guangzhou: Guangzhou University, 2021 (in Chinese).
[23] BAI G L, LIU H Q, LIU H, et al. Study on physicochemical properties of coal gangue and mechanical properties of coal gangue concrete[J]. Journal of Building Structures, 2023, 44(10): 243-254 (in Chinese).
[24] YANG Z B, HUANG S L, YAO J K, et al. Effect of curing method on the compressive strength of high-volume fly ash concrete[J]. Concrete, 2022(10): 151-155 (in Chinese).