Strength Prediction Method of High Performance Concrete Based on Stacking Model Fusion

HU Yichan; LIANG Ming; XIE Canrong; XIE Weiwei; WENG Yiling; CHI Hao; PENG Hao; LUO Xueshuang

[6] LI Z ZYOON JZHANG Ret al. Machine learning in concrete science: applicationschallengesand best practices［J］. NPJ Computational Materials20228: 127.

[7] CHOU J STSAI C FPHAM A Det al. Machine learning in concrete strength simulations: multi-nation data analytics［J］. Construction and Building Materials201473: 771-780.

[8] NUNEZ IMARANI AFLAH Met al. Estimating compressive strength of modern concrete mixtures using computational intelligence: a systematic review［J］. Construction and Building Materials2021310: 125279.

[9] MOHTASHAM M MSARADAR ARAHMATI Ket al. Predictive models for concrete properties using machine learning and deep learning approaches: a review［J］. Journal of Building Engineering202363: 105444.

[10] KHAMBRA GSHUKLA P. Novel machine learning applications on fly ash based concrete: an overview［J］. Materials Today: Proceedings202380: 3411-3417.

[11] BEN C WFLAH MNEHDI M L. Machine learning prediction of mechanical properties of concrete: critical review［J］. Construction and Building Materials2020260: 119889.

[12] YUAN X ZTIAN Y ZAHMAD Wet al. Machine learning prediction models to evaluate the strength of recycled aggregate concrete［J］. Materials202215(8): 2823.

[13] SHANG M JLI H JAHMAD Aet al. Predicting the mechanical properties of RCA-based concrete using supervised machine learning algorithms［J］. Materials202215(2): 647.

[14] IFTIKHAR BALIH S CVAFAEI Met al. Predictive modeling of compressive strength of sustainable rice husk ash concrete: ensemble learner optimization and comparison［J］. Journal of Cleaner Production2022348: 131285.

[15] FENG D CLIU Z TWANG X Det al. Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach［J］. Construction and Building Materials2020230: 117000.

[16] ASTERIS P GSKENTOU A DBARDHAN Aet al. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models［J］. Cement and Concrete Research2021145: 106449.

[17] FAROOQ FAHMED WAKBAR Aet al. Predictive modeling for sustainable high-performance concrete from industrial wastes: a comparison and optimization of models using ensemble learners［J］. Journal of Cleaner Production2021292: 126032.

[18] KANG M CYOO D YGUPTA R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete［J］. Construction and Building Materials2021266: 121117.

[20] NGUYEN K TNGUYEN Q DLE T Aet al. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches［J］. Construction and Building Materials2020247: 118581.

[34] WOLPERT D H. Stacked generalization［J］. Neural Networks19925(2): 241-259.

[35] CHEN T QGUESTRIN C. XGBoost: a scalable tree boosting system［C］//Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San FranciscoCaliforniaUSA. New York: ACM2016: 785-794.

[36] PROKHORENKOVA LGUSEV GVOROBEV Aet al. CatBoost: unbiased boosting with categorical features［EB/OL］. 2017: arXiv: 1706.09516. https://arxiv.org/abs/1706.09516.

[37] BREIMAN L. Random forests［J］. Machine Language200145(1): 5-32.

[38] WIDIASARI I RNUGROHO L EWIDYAWAN. Deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining［C］//2017 International Conference on Innovative and Creative Information Technology (ICITech). SalatigaIndonesia. IEEE2017: 1-5.

[39] LUNDBERG S MLEE S I. A unified approach to interpreting model predictions［C］//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long BeachCaliforniaUSA. New York: ACM2017: 4768-4777.

[40] YEH I C. Modeling of strength of high-performance concrete using artificial neural networks［J］. Cement and Concrete Research199828(12): 1797-1808.

[41] YEH I C. Modeling slump flow of concrete using second-order regressions and artificial neural networks［J］. Cement and Concrete Composites200729(6): 474-480.