首页|基于GWO-BPNN早期带伤混凝土冻后力学性能影响研究

基于GWO-BPNN早期带伤混凝土冻后力学性能影响研究

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为探究西北寒旱区水工混凝土结构早期受冻损伤后力学性能衰退的损伤规律和影响因素,设计混凝土早期受冻及冻融循环的室内加速试验,通过对混凝土试样进行冻融循环,探究温度为-10℃,起冻时刻为3.5 h的混凝土结构冻融损伤的劣化规律,研究水胶比、粉煤灰和引气剂对早期带伤混凝土冻后力学性能的影响.构建灰狼优化算法改进的反向传播神经网络(GWO-BPNN)对早期带伤混凝土的力学性能及抗冻性能进行模拟预测,并对各影响因素进行敏感性分析.结果表明,低水胶比混凝土的抗冻效果明显更优,引气剂可提升早期抗冻性能,最佳掺量为0.01％,粉煤灰在冻融循环后期对混凝土抗冻性有明显的提高,其最佳替代量为20％;GWO-BPNN模型的四个回归评估指标均优于传统神经网络模型,能够更准确地预测早期带伤混凝土的力学性能,影响早期带伤混凝土耐久性的最大变量为水胶比,最小变量为引气剂.
Study on the Effect of Post-Frost Mechanical Properties of Early Stripped Concrete Based on GWO-BPNN
To investigate the damage law and influencing factors of mechanical property degradation of hydraulic con-crete structures after early freezing damage in the cold and arid regions of Northwest China,the indoor accelerated test of early freezing and freeze-thaw cycle of concrete was designed.The deterioration law of freeze-thaw damage of concrete structure with a temperature of-10℃and a freezing moment of 3.5 h was investigated through the freeze-thaw cycle of concrete specimens.The impacts of water-cement ratio,fly ash and air entraining agent on the post-freezing mechanical properties of early-stage damage-induced concrete were studied.The back propagation neural network improved by gray wolf optimization algorithm was used to simulate and predict the mechanical properties and frost resistance of early-stage damaged concrete.The sensitivity analysis of each influencing factor was carried out.The results show that low water-to-cement ratio concrete has significantly better frost resistance,air-entraining agent can improve the early frost resistance,and the optimal dosage is 0.01%;The fly ash has significantly improved the frost resistance of concrete in the late freeze-thaw cycle,and the optimal substitution amount is 20%;Four regression evaluation indexes of the GWO-BPNN model are better than those of the traditional neural network model,and it is able to predict the mechanical properties of early-staged concrete more accurately.It was found that the largest variable affecting the durability of early-stage damaged con-crete was the water-cement ratio,and the smallest variable was the air-entraining agent.

grey wolf optimizerneural networksearly banded concretecompressive strengthinfluencing factor analysis

徐存东、曹骏、陈家豪、田俊姣、韩文浩、汪志航

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华北水利水电大学水利学院,河南 郑州 450046

浙江省农村水利水电资源配置与调控关键技术重点实验室,浙江 杭州 310018

灰狼优化算法 神经网络 早期带伤混凝土 抗压强度 影响因子分析

国家自然科学基金项目河南省高校科技创新团队支持计划中原科技创新领军人才支持计划河南省科技攻关项目河南省高等学校重点科研项目计划浙江省重点研发计划

5157910219IRTSTHN03020420051004821210231027320A5700062021C03019

2024

10.20040/j.cnki.1000-7709.2024.20231664

水电能源科学
中国水力发电工程学会 华中科技大学 武汉国测三联水电设备有限公司

水电能源科学

CSTPCD北大核心
影响因子：0.525
ISSN：1000-7709
年,卷(期)：2024.42(9)
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