基于 BPNN 的应力破坏轻质骨料混凝土抗冻性预测模型:比较研究

IF 1.8 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Materials Research Express Pub Date : 2024-08-29 DOI:10.1088/2053-1591/ad719b
Chun Fu, Qiushi Zhang
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引用次数: 0

摘要

随着自然资源的枯竭和对更高强度重量比的要求,轻骨料混凝土因其良好的热性能、耐火性能和抗震性能而受到越来越多的关注。然而,暴露在低温环境中会加速混凝土的老化,从而缩短轻骨料混凝土的使用寿命。更严重的是,在寒冷干旱地区,轻骨料混凝土在使用过程中经常会遭遇意外撞击、风蚀、地震等灾害,这些破坏严重影响了其抗冻性。因此,准确、定量地描述和预测轻骨料混凝土在特定灾害条件下的抗冻性至关重要。本研究以初始破坏程度和冻融循环为输入变量,以相对动态弹性模量(RDEM)为输出变量,建立了基于反向传播神经网络(BPNN)的应力破坏轻骨料混凝土抗冻性预测模型。结果表明,BPNN 模型的预测值与实验值吻合良好,同时还与另一位作者提出的修订版 Loland 模型进行了比较。结果表明,BPNN 预测值与实验值的平均相对误差仅为 1.69%,而修正的 Loland 模型的平均相对误差为 9.13%,这表明所提出的 BPNN 预测模型可以实现对轻质骨料混凝土灾后全生命周期抗冻性的较为准确的定量评估,同时也为应力破坏轻质骨料混凝土的抗冻性预测拓宽了思路,提供了参考。
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Frost-resistance prediction model for stress-damaged lightweight aggregate concrete based on BPNN: a comparative study
With the depletion of natural resources and the requirement of higher strength-weight ratio, lightweight aggregate concrete has attracted more and more attention because of its good thermal properties, fire resistance and seismic performance. However, exposure to low temperature environments accelerates deterioration of concrete, thereby, reduce the service life of lightweight aggregate concrete. Even worse, in cold and arid regions, lightweight aggregate concrete often experiences accidental impacts, wind erosion, earthquakes, and other disasters during service, these damage significantly impact its frost-resistance. Therefore, accurately and quantitatively describing and predicting the frost-resistance of lightweight aggregate concrete under specific disaster conditions is crucial. In this study, take the initial damage degree and freeze-thaw cycles as input variables, while the relative dynamic elastic modulus (RDEM) as an out variable, a frost resistance prediction models for stress-damaged lightweight aggregate concrete was established based on back propagation neural network (BPNN). The results show that the predicted values of BPNN model are in good agreement with the experimental values, and the results are also compared with the revised Loland model which is proposed by another author. Results demonstrate that the average relative error between predicted values of BPNN and experimental values is only 1.69%, whereas the one of revised Loland model is 9.13%, which indicating that the proposed BPNN prediction model can achieve a relatively accurate quantitative assessment of frost-resistance throughout the entire post-disaster lifecycle of lightweight aggregate concrete, it also broadened the idea and provided a reference for the frost resistance prediction of stress-damaged lightweight aggregate concrete.
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来源期刊
Materials Research Express
Materials Research Express MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
4.50
自引率
4.30%
发文量
640
审稿时长
12 weeks
期刊介绍: A broad, rapid peer-review journal publishing new experimental and theoretical research on the design, fabrication, properties and applications of all classes of materials.
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