结构钢是一种重要的建筑材料，因其具有良好的机械、化学性能和使用特性被广泛应用于桥梁、机械、船舶等领域。然而，结构钢在生产或使用过程中可能发生组织结构变化，导致强度、韧性等性能下降，甚至引发断裂、腐蚀等事故。为了保证结构钢的安全性和可靠性，及时检测结构钢的组织结构变化是必要的。本研究旨在实现对结构钢表面和内部的电阻率分布检测，以反映其组织结构变化。本研究首次将电阻层析成像方法应用于低电阻金属材料领域，探索该方法在此领域的应用潜力。为实现这一目标，本研究以结构钢的电阻率检测为对象，构建基于电阻层析成像的结构钢电阻率检测方法，通过构建硬件系统和研究二维/三维图像重建算法，为金属材料的无损检测技术提供一种新思路。本文的主要工作内容如下：

基于电阻层析成像的结构钢电阻率分布检测方法构建。通过研究金属材料的无损检测技术发展现状，分析电阻成像成像技术原理，确定结构钢电阻率分布检测的基本思路，构建基于电阻层析成像的结构钢电阻率检测方法。

建立了结构钢的敏感场数学模型。并基于有限元法求解了正问题，得到了不同电极配置下的电压数据。为了提高正问题求解的准确性和效率，对结构钢有限元模型进行网格划分、电极材料与形状的确定以及测量策略的选取，为后面的反问题奠定了基础。最后通过仿真研究分析使用电阻层析成像技术检测结构钢电阻率分布的有效性。

建立了结构钢的反演模型。并采用了不同的重建算法进行了二维和三维图像重建。为了评估重建结果与实际结构钢电阻率分布的接近程度，引入了多个评价指标对重建结果进行了对比分析。通过二维研究，选择了适用于结构钢电阻率分布检测的重建算法。通过三维研究，使用GREIT法进行了三维重建，并验证了该方法能够提供结构钢电阻率分布检测的客观依据。

完成了结构钢电阻率分布成像实验，并验证了使用电阻层析成像技术检测结构钢电阻率分布的有效性。设计并搭建了结构钢电阻率分布检测实验平台，并对其进行了性能评价，确保采集数据的适用性；使用小波分析处理实验数据，实现对数据降噪目的，提高采集数据的精确性；使用GREIT算法对结构钢电阻率进行图像重建，重建结构钢异常体的位置与轮廓，验证了该方法能够检测出结构钢异常体的位置和轮廓，表明设计的结构钢电阻率检测实验平台具备对结构钢电阻率分布检测的能力。

Steel structures are an important building material widely used in fields such as bridges, machinery, and ships due to their excellent mechanical, chemical properties, and usage characteristics. However, changes in the structure of steel during production or use may lead to a decrease in performance such as strength and toughness, and even cause accidents such as fracture and corrosion. To ensure the safety and reliability of steel structures, timely detection of changes in their structure is necessary. This study aims to achieve detection of the distribution of electrical resistivity on the surface and inside of steel, to reflect changes in its structure. This study is the first to apply electrical resistivity tomography to the field of low-resistance metallic materials, exploring its potential for application in this field. To achieve this goal, this study focuses on detecting the electrical resistivity of steel, and constructs a detection method based on electrical resistivity tomography for steel by building a hardware system and researching two-dimensional/three-dimensional image reconstruction algorithms, providing a new approach to non-destructive testing technology for metallic materials. The main contents of this paper are as follows:

Construction of a detection method for the distribution of electrical resistivity in steel based on electrical resistivity tomography. By studying the development status of non-destructive testing technology for metallic materials and analyzing the principles of electrical resistivity imaging technology, the basic idea for detecting the distribution of electrical resistivity in steel is determined, and a detection method based on electrical resistivity tomography for steel is constructed.

A mathematical model of the sensitive field for steel structures was established. The forward problem was solved using the finite element method to obtain voltage data under different electrode configurations. In order to improve the accuracy and efficiency of solving the forward problem, the finite element model of steel was meshed, the electrode material and shape were determined, and the measurement strategy was selected, laying the foundation for the subsequent inverse problem. Finally, the effectiveness of using electrical resistivity tomography for detecting the distribution of electrical resistivity in steel was analyzed through simulation studies.

A inversion model for steel structures was established, and different reconstruction algorithms were used for two-dimensional and three-dimensional image reconstruction. To evaluate the degree of similarity between the reconstruction results and the actual distribution of electrical resistivity in steel, multiple evaluation criteria were introduced for comparative analysis of the reconstruction results. Through two-dimensional studies, a reconstruction algorithm suitable for detecting the distribution of electrical resistivity in steel was selected. Through three-dimensional studies, the GREIT method was used for three-dimensional reconstruction, and it was verified that this method can provide an objective basis for detecting the distribution of electrical resistivity in steel.

Structural Steel electrical resistivity imaging experiments were completed, and the effectiveness of using electrical resistivity tomography for detecting the distribution of electrical resistivity in steel was verified. A detection experiment platform for the distribution of electrical resistivity in steel was designed and built, and its performance was evaluated to ensure the applicability of the collected data. Wavelet analysis was used to process experimental data to achieve the purpose of data denoising and improve the accuracy of data collection. The GREIT algorithm was used to reconstruct images of the electrical resistivity of steel, and the position and contour of the abnormal body in the steel were reconstructed. This verifies that the method can detect the position and contour of the abnormal body in the steel, indicating that the designed detection experiment platform for the distribution of electrical resistivity in steel has the ability to detect the distribution of electrical resistivity in steel.

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