[1] [1] ABELLN-GARCA J. Four-layer perceptron approach for strength prediction of UHPC[J]. Construction and Building Materials, 2020, 256: 119465.

[2] [2] KUMAR M, BISWAS R, KUMAR D R, et al. Soft computing-based prediction models for compressive strength of concrete[J]. Case Studies in Construction Materials, 2023, 19: e02321.

[3] [3] KUMAR T R, PANCHAL V R, PANDYA K S. An ensemble approach to improve BPNN model precision for predicting compressive strength of high-performance concrete[J]. Structures, 2022, 45: 500-508.

[4] [4] ALYASEEN A, PODDAR A, KUMAR N, et al. Assessing the compressive and splitting tensile strength of self-compacting recycled coarse aggregate concrete using machine learning and statistical techniques[J]. Materials Today Communications, 2024, 38: 107970.

[5] [5] KHAN S A, KHAN M A, AMIN M N, et al. Sustainable alternate binding material for concrete using waste materials: a testing and computational study for the strength evaluation[J]. Journal of Building Engineering, 2023, 80: 107932.

[6] [6] LI Q F, SONG Z M. Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model[J]. Journal of Cleaner Production, 2023, 382: 135279.

[7] [7] ZHAO Y P, GOULIAS D, SAREMI S. Enhancing prediction accuracy of concrete compressive strength using stacking ensemble machine learning[J]. Comput Concrete, 2023, 32(3): 233-46.

[8] [8] ZHENG J Y, WANG M H, YAO T C, et al. Dynamic mechanical strength prediction of BFRC based on stacking ensemble learning and genetic algorithm optimization[J]. Buildings, 2023, 13(5): 1155.

[9] [9] CHEN H G, YANG J J, CHEN X H. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures[J]. Construction and Building Materials, 2021, 313: 125437.

[10] [10] ZHANG C B, TIAN X N, ZHAO Y, et al. Automated machine learning-based building energy load prediction method[J]. Journal of Building Engineering, 2023, 80: 108071.

[11] [11] CHEN W Y, ZHANG L M. An automated machine learning approach for earthquake casualty rate and economic loss prediction[J]. Reliability Engineering & System Safety, 2022, 225: 108645.

[12] [12] HARIRI A M A, POURKAMALI A F. An automated machine learning engine with inverse analysis for seismic design of dams[J]. Water, 2022, 14(23): 3898.

[13] [13] LUNDBERG S, LEE S I. A unified approach to interpreting model predictions[C]//Conference and Workshop on Neural Information Processing Systems. California, USA, 2017.

[14] [14] ZHAO W J, FENG S Y, LIU J X, et al. An explainable intelligent algorithm for the multiple performance prediction of cement-based grouting materials[J]. Construction and Building Materials, 2023, 366: 130146.

[15] [15] ERICKSON N, MUELLER J, SHIRKOV A, et al. AutoGluon-tabular: robust and accurate AutoML for structured data[J]. ArXiv e-Prints, 2020: 06505.

[16] [16] PHOEUK M, KWON M. Accuracy prediction of compressive strength of concrete incorporating recycled aggregate using ensemble learning algorithms: multinational dataset[J]. Advances in Civil Engineering, 2023, 2023: 5076429.

[17] [17] HUU NGUYEN M, NGUYEN T A, LY H B. Ensemble XGBoost schemes for improved compressive strength prediction of UHPC[J]. Structures, 2023, 57: 105062.

[18] [18] ABELLN G J. Study of nonlinear relationships between dosage mixture design and the compressive strength of UHPC[J]. Case Studies in Construction Materials, 2022, 17: e01228.

[19] [19] KIM K, KIM W, SEO J, et al. Prediction of concrete fragments amount and travel distance under impact loading using deep neural network and gradient boosting method[J]. Materials, 2022, 15(3): 1045.

[20] [20] WU X P, ZHU F, ZHOU M M, et al. Intelligent design of construction materials: a comparative study of AI approaches for predicting the strength of concrete with blast furnace slag[J]. Materials, 2022, 15(13): 4582.

[21] [21] MARANI A, JAMALI A, NEHDI M L. Predicting ultra-high-performance concrete compressive strength using tabular generative adversarial networks[J]. Materials, 2020, 13(21): 4757.

[22] [22] ALIMI O A, OUAHADA K, ABU-MAHFOUZ A M. A review of machine learning approaches to power system security and stability[J]. IEEE Access, 2020, 8: 113512-113531.

[23] [23] KASHEM A, KARIM R, MALO S C, et al. Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses[J]. Case Studies in Construction Materials, 2024, 20: e02991.

[24] [24] ALABDULJABBAR H, KHAN M, AWAN H H, et al. Predicting ultra-high-performance concrete compressive strength using gene expression programming method[J]. Case Studies in Construction Materials, 2023, 18: e02074.

[25] [25] QIAN Y F, SUFIAN M, ACCOUCHE O, et al. Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete[J]. PLoS One, 2022, 17(12): e0278161.

[26] [26] WONG H S, BUENFELD N R. Determining the water-cement ratio, cement content, water content and degree of hydration of hardened cement paste: method development and validation on paste samples[J]. Cement and Concrete Research, 2009, 39(10): 957-965.

[27] [27] ABBASS W, KHAN M I, MOURAD S. Evaluation of mechanical properties of steel fiber reinforced concrete with different strengths of concrete[J]. Construction and Building Materials, 2018, 168: 556-569.

[28] [28] JI Y J, CAHYADI J H. Effects of densified silica fume on microstructure and compressive strength of blended cement pastes[J]. Cement and Concrete Research, 2003, 33(10): 1543-1548.

[29] [29] YIP C K, LUKEY G C, VAN-DEVENTER J S J. The coexistence of geopolymeric gel and calcium silicate hydrate at the early stage of alkaline activation[J]. Cement and Concrete Research, 2005, 35(9): 1688-1697.

[30] [30] NOCHAIYA T, WONGKEO W, PIMRAKSA K, et al. Microstructural, physical, and thermal analyses of Portland cement-fly ash-calcium hydroxide blended pastes[J]. Journal of Thermal Analysis and Calorimetry, 2010, 100(1): 101-108.