Page 112 - 2022年第53卷第1期
P. 112
[ 18] NGUYEN T,KASHANI A,NGO T,et al . Deep neural network with high-order neuron for the prediction of
foamed concrete strength[J]. Computer-Aided Civil and Infrastructure Engineering,2019,34(4):316-332 .
[ 19] HAN Q H,GUI C Q,XU J,et al . A generalized method to predict the compressive strength of high-performance
concrete by improved random forest algorithm[J]. Construction and Building Materials,2019,226:734-742 .
[ 20] ZHANG M X,LI M C,SHEN Y,et al . Multiple mechanical properties prediction of hydraulic concrete in the
form of combined damming by experimental data mining[J]. Construction and Building Materials,2019,207:
661-671 .
[ 21] 李火坤,王刚,魏博文,等 . 基于敏感性分析与粒子群算法的拱坝原型动弹性模量反演方法[J]. 水利学
报,2020,51(11):1401-1411 .
[ 22] 陈晓东,陈斌,刘国华 . 基于 BP ANN-GA 混合型算法的混凝土配合比优化设计研究[J]. 水力发电学报,
2007(5):59-63,52 .
[ 23] CHENG M Y,PRAYOGO D,WU Y W . Novel genetic algorithm-based evolutionary support vector machine for
optimizing high-performance concrete mixture[J]. Journal of Computing in Civil Engineering,2014,28(4):
06014003 .
[ 24] 徐毅慧 . 基于人工智能的混凝土配合比优化设计[J]. 漳州职业技术学院学报,2011,13(4):15-18 .
[ 25] PARICHATPRECHA R,NIMITYONGSKUL P . An integrated approach for optimum design of HPC mix propor⁃
tion using genetic algorithm and artificial neural networks[J]. Computers and Concrete,2009,6(3):253-268 .
[ 26] 王仁超,朱品光 . 基于随机森林回归方法的爆破块度预测模型研究[J]. 水力发电学报,2020,39(1):
89-101 .
[ 27] YEH I C . Modeling of strength of high-performance concrete using artificial neural networks[J]. Cement and
Concrete Research,1998,28(12):1797-1808 .
[ 28] LIM C H,YOON Y S,KIM J H . Genetic algorithm in mix proportioning of high-performance concrete[J]. Ce⁃
ment and Concrete Research,2004,34(3):409-420 .
[ 29] MOUSAVI S M,AMINIAN P,GANDOMI A H,et al . A new predictive model for compressive strength of HPC
using gene expression programming[J]. Advances in Engineering Software,2012,45(1):105-114 .
[ 30] BUI D K,NGUYEN T,CHOU J S,et al . A modified firefly algorithm-artificial neural network expert system for
predicting compressive and tensile strength of high-performance concrete[J]. Construction and Building Materi⁃
als,2018,180:320-333 .
[ 31] BREIMAN L . Random forests[J]. Machine Learning,2001,45(1):5-32 .
[ 32] 余为韬,潘欣,史彬 . 考虑低碳排放的混凝土配合比多目标优化[J]. 计算机与应用化学,2014,31(3):
307-310 .
[ 33] 李明超,杨琳,任秋兵,等 . 多目标约束下的土石坝抗液化措施数值建模与优化分析方法[J]. 水利学报,
2020,51(12):1462-1472 .
[ 34] 陈 民 铀 ,张 聪 誉 ,罗 辞 勇 . 自 适 应 进 化 多 目 标 粒 子 群 优 化 算 法[J]. 控 制 与 决 策 ,2009,24(12):
1851-1855,1864 .
[ 35] COELLO C A C,PULIDO G T,LECHUGA M S . Handling multiple objectives with particle swarm optimization
[J]. IEEE Transactions on Evolutionary Computation,2004,8(3):256-279 .
[ 36] 贾善坡,伍国军,陈卫忠 . 基于粒子群算法与混合罚函数法的有限元优化反演模型及应用[J]. 岩土力学,
2011,32(S2):598-603 .
[ 37] 徐琛,刘晓丽,王恩志,等 . 基于组合权重-理想点法的应变型岩爆五因素预测分级[J]. 岩土工程学报,
2017,39(12):2245-2252 .
[ 38] GANDOMI A H,ALAVI A H,SHADMEHRI D M,et al . An empirical model for shear capacity of RC deep
beams using genetic-simulated annealing[J]. Archives of Civil and Mechanical Engineering,2013,13(3):
354-369 .
[ 39] CHOU J S,PHAM A D . Enhanced artificial intelligence for ensemble approach to predicting high performance
concrete compressive strength[J]. Construction and Building Materials,2013,49:554-563 .
[ 40] CHOU J S,CHONG W K,BUI D K . Nature-inspired metaheuristic regression system:Programming and imple⁃
mentation for civil engineering applications[J] . Journal of Computing in Civil Engineering,2016,30(5):
04016007 .
— 107 —