Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

IF 3.6 Q1 ENGINEERING, CIVIL
Dominik Merkle, Johannes Solass, Annette Schmitt, Julia Rosin, Alexander Reiterer, Alexander Stolz
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引用次数: 0

Abstract

Bridge inspection is a time-consuming, expensive, but indispensable task. In this work, a new semi-automatic workflow for a concrete bridge condition assessment system is developed and discussed. The workflow consists of three main parts merged in the new methodology. The elements are the data acquisition with cameras, the automated damage detection and localization using a neural network, and the resulting engineering condition assessment. Furthermore, a CAD model serves as a base for the later calculations for the condition assessment. Camera images are used for both sub-millimeter crack detection using semantic segmentation by an artificial neural network and a crack localization based on a combination of a photogrammetric workflow including structure from motion (SfM) and the projection as imprinted points directly onto the as-planned CAD mesh. Moreover, an approach for crack width derivation is given. The captured crack width, crack position, and the date of detection represent the input values for subsequent crack monitoring. Thereby, this new concept is proposed as an essential step towards a time-efficient and objective life-cycle assessment of reinforced concrete structures.
用于钢筋混凝土结构监测的半自动 3D 裂缝图生成和宽度评估
桥梁检查是一项耗时、昂贵但不可或缺的工作。本文提出并讨论了一种新的混凝土桥梁状态评估系统的半自动工作流程。工作流由三个主要部分组成,并在新方法中合并。这些要素包括摄像头的数据采集,使用神经网络的自动损伤检测和定位,以及由此产生的工程状况评估。此外,CAD模型为后期的工况评估计算提供了基础。相机图像用于亚毫米裂纹检测,使用人工神经网络进行语义分割,并基于摄影测量工作流程的组合进行裂纹定位,包括运动结构(SfM)和投影,作为直接刻印到计划的CAD网格上的点。并给出了裂缝宽度的求导方法。捕获的裂缝宽度、裂缝位置和检测日期代表后续裂缝监测的输入值。因此,提出这个新概念是实现钢筋混凝土结构高效、客观的生命周期评估的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.90
自引率
8.60%
发文量
44
审稿时长
26 weeks
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