Lifetime assessment of structural concrete - multi-scale integrated hygro-thermal-chemo-electrical-mechanistic approach and statistical evaluation

Lifetime assessment of structural concrete - multi-scale integrated hygro-thermal-chemo-electrical-mechanistic approach and statistical evaluation
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结构混凝土的寿命评估 - 多尺度集成湿热化学电机械方法和统计评估

DOI:
10.1080/15732479.2021.1995443
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发表时间:
2021
期刊:
Structures and Infrastructure Engineering
影响因子:
--
通讯作者:
Zhao Wang and Koichi Maekawa
Zhao Wang and Koichi Maekawa
中科院分区:
--
文献类型:
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作者:
Chenyu Wang;Kenta Kimura;Jincai Li;Joseph Jacob Richardson;Mitsuru Naito;Kanjiro Miyata;Takanori Ichiki;Hirotaka Ejima;Zhao Wang and Koichi Maekawa

文献摘要

相似文献

为了对服役中的钢筋混凝土基础设施进行评估,开发并改进了一种多尺度模拟方法,采用了综合的湿热-化学-电-力学方法。各种耐久性问题成功地评估了疲劳行为,包括由孔隙和裂纹中的水分运动引起的损伤、与温度相关的老化(如霜/火损伤)、由化学反应(如碱-硅反应)引起的退化,以及与多离子平衡(电化学现象)耦合的宏细胞腐蚀。此外,还成功进行了受干燥收缩率影响的长期性能评价。此外,该方法还结合了人工智能技术和数据同化技术。裂缝的位置和宽度被转换成空间平均应变,这些应变被建立在网络存储中,用于未来的性能预测。基于以往维修记录的大数据集,建立人工神经网络,反演出混凝土甲板表面裂缝形态的危害图。通过这两种方法,认识并提出了双眼诊断在结构混凝土寿命评估中的重要性。
To perform the assessment of reinforced concrete infrastructures in service, a multi-scale simulation has been developed and improved with an integrated hygro-thermal-chemo-electrical-mechanistic approach. Various durability issues are successfully evaluated in terms of fatigue behaviours with damage induced by moisture motion through pores and cracks, temperature-related deterioration such as frost/fire damage, degradation by chemical reaction like alkali-silica reaction, and the macro-cell corrosion coupled with multi-ion equilibrium which is electro-chemical phenomenon. Moreover, the long-term performance evaluation influenced by the drying shrinkage is also successfully conducted. Furthermore, the multi-scale integrated approach is coupled with AI technique and data assimilation techniques. The crack locations and their widths are converted to space-averaged strains, which are built inside the cyber storage for the futural performance prediction. The hazard map of crack patterns on the surface of concrete deck is inversely derived from the artificial neural network, which is built according to the big dataset of the past maintenance records. Through both methods, the importance of using both-eye diagnosis is realized and proposed for lifetime assessment of structural concrete.