摘要
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。
To investigate the damage law and influencing factors of mechanical property degradation of hydraulic concrete 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-tocement 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 freezethaw 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 earlystaged concrete more accurately.It was found that the largest variable affecting the durability of early-stage damaged concrete was the water-cement ratio,and the smallest variable was the air-entraining agent.
作者
徐存东
曹骏
陈家豪
田俊姣
韩文浩
汪志航
XU Cun-dong;CAO Jun;CHEN Jia-hao;TIAN Jun-jiao;HAN Wen-hao;WANG Zhi-hang(School of Water Consevancy,North China University of Water Resources and Electric Power,Zhengzhou 450046,China;Key Laboratory for Technology in Rural Water Management of Zhejiang,Hangzhou 310018,China)
出处
《水电能源科学》
北大核心
2024年第9期93-97,共5页
Water Resources and Power
基金
国家自然科学基金项目(51579102)
河南省高校科技创新团队支持计划(19IRTSTHN030)
中原科技创新领军人才支持计划(204200510048)
河南省科技攻关项目(212102310273)
河南省高等学校重点科研项目计划(20A570006)
浙江省重点研发计划(2021C03019)。
关键词
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
grey wolf optimizer
neural networks
early banded concrete
compressive strength
influencing factor analysis