Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an imag...
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MDPI AG
2025-01-01
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| author | Kyung-Wan Seo Junwon Park Sang I. Park Jeong-Hoon Song Young-Cheol Yoon |
| author_facet | Kyung-Wan Seo Junwon Park Sang I. Park Jeong-Hoon Song Young-Cheol Yoon |
| author_sort | Kyung-Wan Seo |
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| description | Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments. |
| format | Article |
| id | doaj-art-90d8d17c9740406083fed7c92dfea80a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-90d8d17c9740406083fed7c92dfea80a2025-01-24T13:48:49ZengMDPI AGSensors1424-82202025-01-0125241010.3390/s25020410Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing TechniqueKyung-Wan Seo0Junwon Park1Sang I. Park2Jeong-Hoon Song3Young-Cheol Yoon4Department of Civil Engineering, Myongji College, Seoul 03656, Republic of KoreaDepartment of Civil Engineering, Myongji College, Seoul 03656, Republic of KoreaResearch Institute for Safety Performance, Korea Authority of Land & Infrastructure Safety, Jinju 52856, Republic of KoreaDepartment of Civil Environmental and Architectural Engineering, University of Colorado, Boulder, CO 80309, USADepartment of Civil Engineering, Myongji College, Seoul 03656, Republic of KoreaConventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.https://www.mdpi.com/1424-8220/25/2/410hybrid structural analysiscontinuum point cloud methoddigital image processingpolynomial regressionessential boundary conditionboundary value problem |
| spellingShingle | Kyung-Wan Seo Junwon Park Sang I. Park Jeong-Hoon Song Young-Cheol Yoon Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique Sensors hybrid structural analysis continuum point cloud method digital image processing polynomial regression essential boundary condition boundary value problem |
| title | Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique |
| title_full | Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique |
| title_fullStr | Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique |
| title_full_unstemmed | Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique |
| title_short | Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique |
| title_sort | image driven hybrid structural analysis based on continuum point cloud method with boundary capturing technique |
| topic | hybrid structural analysis continuum point cloud method digital image processing polynomial regression essential boundary condition boundary value problem |
| url | https://www.mdpi.com/1424-8220/25/2/410 |
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