Towards Developing Reinforced Concrete Structures Digital Twins: A Multiscale Lattice Discrete Particle Model Approach

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-06-20 DOI:10.1177/13694332241260866
Yingbo Zhu, Alessandro Fascetti
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Abstract

Digital Twins (DT) provide a critical approach to connecting physical structures and corresponding virtual representations through constant observations-to-decision flows, enabling near real-time analysis and assessment of structural health. A critical component of DTs of reinforced concrete structures lies in the definition of prognostic capabilities to predict/infer the system response. This is achieved by devising efficient computational methods for the simulation of the mechanical behavior of the system. This study presents the first step in devising a Multiscale Lattice Discrete Particle Model (M-LDPM) approach to be embedded in a DT framework to allow for forward prediction of damage evolution in the structural system. In the DT framework, a modification of the M-LDPM is proposed to address well-known issues associated with linking the macroscopic mesh configuration and the corresponding representative volume elements, significantly reducing the total computational cost. The effectiveness of the proposed multiscale model is validated by comparing numerical results with the full-order solutions for plain concrete members under 3-point bending, and further investigated by comparison with experimental results on three reinforced concrete beams.
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开发钢筋混凝土结构数字双胞胎:多尺度晶格离散粒子模型方法
数字孪生(DT)提供了一种重要的方法,通过从观察到决策的不断流动,将物理结构和相应的虚拟表示连接起来,从而实现对结构健康状况的近实时分析和评估。钢筋混凝土结构数字孪生系统的一个重要组成部分是定义预测/推断系统响应的预报能力。要做到这一点,就必须设计出模拟系统机械行为的高效计算方法。本研究介绍了设计多尺度晶格离散粒子模型(M-LDPM)方法的第一步,该方法将嵌入 DT 框架,以便对结构系统中的损伤演变进行前瞻性预测。在 DT 框架中,建议对 M-LDPM 进行修改,以解决与连接宏观网格配置和相应代表体积元素相关的众所周知的问题,从而显著降低总计算成本。通过将数值结果与三点弯曲条件下素混凝土构件的全阶解决方案进行比较,验证了所提出的多尺度模型的有效性,并通过与三根钢筋混凝土梁的实验结果进行比较,进一步研究了该模型的有效性。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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