基于BO-DNN模型的混凝土抗压强度尺寸效应研究

摘要/Abstract

摘要： 尺寸效应对混凝土材料力学性能和结构设计有重要影响。目前试验测试仍是混凝土尺寸效应研究主要手段,受限于样本制作周期及复杂的边界和加载条件,综合成本高,结果离散性较大。本文基于深度学习和贝叶斯优化算法,以大量试验数据为基础,建立了不引入任何简化计算假设的混凝土抗压强度尺寸效应深度神经网络模型(BO-DNN),并与已有尺寸效应模型进行了比较分析,通过改变选定特征参数的值来考察各参数对抗压强度尺寸效应的影响。结果表明:水胶比对抗压强度尺寸效应影响显著,水胶比越小,尺寸效应越明显;抗压强度尺寸效应随骨料粒径的增大呈递增趋势,但增幅随粒径的增大有所减缓;高宽比小于2的试件抗压强度尺寸效应随高宽比的增大而增大,超过2以后尺寸效应基本不再增大;试件形状对抗压强度尺寸效应的影响较小;龄期越大,尺寸效应越显著,但龄期超过90 d后尺寸效应现象趋于稳定。本文提出的预测模型泛化能力强,具有更高的精度和稳定性,能较好地挖掘各特征参数之间复杂的非线性关系,为混凝土材料和结构的工程设计提供理论依据和参考。

关键词: 混凝土, 抗压强度, 尺寸效应, 深度神经网络, 贝叶斯优化算法

Abstract: The size effect has an important influence on the mechanical properties and structural design of concrete materials. Currently, the test is still the main way to study the size effect of concrete. Limited by the sample preparation period, complex boundary, and loading conditions, the comprehensive cost is high, and the result is discrete. With the help of deep learning and the Bayesian optimization algorithm, based on a large number of test data, this paper established a deep neural network model (BO-DNN) of concrete compressive strength size effect without any simplified calculation assumptions. The model was compared with the existing size effect model, and the influence of each parameter on the size effect of compressive strength was investigated by changing the value of selected characteristic parameters. The results show that the size effect of the water-binder ratio on compressive strength is significant, and the smaller the water-binder ratio is, the more obvious the size effect is. The size effect of compressive strength increases with the increase of the aggregate size, but the increase slows down with the increase of the aggregate size. The size effect of compressive strength of specimens with an aspect ratio less than 2 increases with the increase of the aspect ratio, and the size effect tends to be stable after exceeding 2. The shape of the specimen has little influence on the size effect of compressive strength. The longer the curing age is, the more significant the size effect is, but the size effect tends to be stable after the curing age exceeds 90 d. The prediction model proposed in this paper has strong generalization ability, higher accuracy, and stability, and can better mine the complex nonlinear relationship between the characteristic parameters, providing a theoretical basis and reference for the engineering design of concrete materials and structures.

Key words: concrete, compressive strength, size effect, deep neural network, Bayesian optimization algorithm

中图分类号:

TU528

章伟琪, 王辉明. 基于BO-DNN模型的混凝土抗压强度尺寸效应研究[J]. 硅酸盐通报, 2023, 42(5): 1650-1660.

ZHANG Weiqi, WANG Huiming. Size Effect of Concrete Compressive Strength Based on BO-DNN Model[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2023, 42(5): 1650-1660.

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