Modularized Segmental Construction with CFRP-Reinforced Concrete Segments post-tensioned with CFRP-Tendons and Inline Quality Control by Computed Tomography

在该项目的第一阶段，研究了采用CFRP筋和CFRP筋的模块化节段桥梁的设计和受力性能，以及使用工业CT作为一种健壮的、有质量保证的制造方法的质量控制。因此，该项目促进了采用CFRP加固和预应力混凝土构件的固定制造和流动生产方法、连接方法、数字3D模型的生成以及SPP自适应模块化施工的质量控制。特别是，研究了异型干缝的承载能力、接缝接触特性以及基于CT的混凝土缺陷检测能力。在该项目的第二阶段，将在这些结果的基础上开发模块化节段桥的承载行为模型，以及基于集成质量保证的总体设计概念，以通过机器人辅助生成数字3D模型来精确预制大中型系列模块。这些将在桥式演示器的基础上进行验证，其中包括适合流动生产的模块(最大重量。1T)，用于计算机断层成像分析和承载能力试验，以确定实际的非线性承载性能，并考虑节段节点和CFRP筋的影响。由于桥梁演示器的模块比机械工程中使用的经典工业CT可以扫描的组件更大、更重，因此将使用机器人CT来生成固定模块的3D数据，方法是让源和探测器扫描模块的边缘和接口。通过融合基于X射线的3D CT数据和摄影测量3D表面数据，生成数字3D模型，同时提供关于表面状况(颜色、纹理、均匀性)和部件内部结构的质量信息。由于与基于实验室的CT扫描仪相比，机器人CT中X射线源和探测器的轨迹要复杂得多，因此产生的3D数据包含大量伪影，将使用深度神经网络(NN，Deep Learning)进行校正。考虑到机器人CT的可能轨迹以及机器人CT与结构设计(具有良好传输能力的横截面几何)之间的相互关系和要求，应为基于CT的结构设计制定合适的建议。最终，基于机器人CT用于质量保证的新可能性以及用于预测材料优化的模块化节段结构的承载行为的有限元模型的设计概念将被开发出来，该设计概念可应用于节段桥以及其他模块化建筑结构。

In the 1st phase of the project, the design and load-bearing behavior of modular segmental bridges with CFRP reinforcement and CFRP tendons as well as quality control using industrial computed tomography as a robust, quality-assured manufacturing method were investigated. Thus, the project contributed to stationary fabrication and flow production methods with CFRP-reinforced and -prestressed concrete elements, joining methods, the generation of digital 3D models as well as quality control for adaptive modularized construction in the SPP. In particular, the load-bearing capacity of profiled dry joints, their joint contact properties and the CT-based detectability of defects in the concrete were investigated. In the 2nd phase of the project, models for the load-bearing behavior of modular segmental bridges are to be developed on the basis of these results, as well as an overall design concept based on integrated quality assurance for precision prefabrication of the modules for large or medium series with robot-assisted generation of digital 3D models. These are to be validated on the basis of a bridge demonstrator consisting of modules suitable for flow production (weight max. 1t) and which is used both for analysis by means of computed tomography and for load-bearing capacity tests to determine the realistic, non-linear load-bearing behavior, taking into account the influences of segmental joints and CFRP tendons. Since the modules of the bridge demonstrator are larger and heavier than components that can be scanned by classical industrial CTs used in mechanical engineering, robotic CT will be used to generate 3D data of the stationary module by having the source and detector scan the edges and interfaces of the modules. By fusing X-ray-based 3D CT data with photogrammetric 3D surface data, digital 3D models are generated that simultaneously provide quality information about the surface condition (color, texture, homogeneity) and the internal structure of the component. Due to the much more complex trajectory of the X-ray source and detector in robot-based CT compared to laboratory-based CT scanners, the resulting 3D data contain significant artifacts, which will be corrected using deep neural networks (NN, Deep Learning). Considering the possible trajectories of robotic CT and the mutual relationships and requirements between robotic CT and structural design (cross-sectional geometry for good transmission ability), suitable recommendations for CT-based structural design shall be developed. Ultimately, a design concept based on the new possibilities of robotic CT for quality assurance as well as on FE models for the prediction of the load-bearing behavior for a material-optimized modularized segmental structure will be developed, which can be applied to segmental bridges as well as to other modularized building structures.