Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

Civera, Marco; Mugnaini, Vezio; Fragonara, Luca Zanotti

doi:10.1002/stc.3028

Cited by 49 publications

(12 citation statements)

References 69 publications

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“…The results show that this method can match other mature SI technologies well, such as the Eigensystem Realization Algorithm (ERA), and can be used for damage assessment. On this basis, they combined machine learning and operational modal analysis to propose a novel automatic OMA multi-stage clustering algorithm and applied it to the damage detection and damage degree assessment of masonry arch bridges [150]. Conde et al [151] obtained the most probable distribution of the finite element model parameters by using a Bayesian method and the Markov chain Monte Carlo (MCMC) method based on the comparison between the finite element calculation results and the actual measured geometric data so as to reproduce the existing damage mode with the highest accuracy using the numerical model.…”

Section: Masonry Arch Bridge Damage Identification Methodsmentioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

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Section: Masonry Arch Bridge Damage Identification Methodsmentioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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“…In the face of the increasing number of arch bridge accidents, domestic and international scholars have carried out many useful scientifc studies [10]. Civera et al [11] tested and validated a novel multistage clustering algorithm for SHM applications of automatic OMA (AOMA), especially for damage detection and severity assessment of masonry arch bridges. Ge et al [12] presented an experimental validation for a high-precision vision-based displacement-infuenced line (DIL) measurement system for the purpose of damage detection on bridges.…”

Section: Introductionmentioning

confidence: 99%

Damage Identification Method of Tied‐Arch Bridges Based on the Equivalent Thrust‐Influenced Line

Zhou,

Li,

Shi

et al. 2024

Structural Control and Health Monitoring

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Early tied-arch bridges cannot satisfy the current traffic load demand due to their load grades and maintenance levels. Also, these tied-arch bridges have accumulated structural damage with increasingly prominent safety risks. In order to accurately evaluate the characteristics of tied-arch bridges’ structural-influenced lines and identify damage of their arch ribs and suspenders, two analytical solutions are derived and established in this paper for the thrust-influenced lines of parabola and catenary two-hinged arches with a tie beam. A new method and an index for identifying damage of arch ribs and suspenders of tied-arch bridges are proposed. The results of numerical simulation in this paper verified that the proposed analytical solution has good analytical accuracy in practice on those two-hinged nonflat arches. With the use of equivalent thrust-influenced line difference curvature, the effectiveness of damage identification and the verification method were verified on the tie beam, suspender, and arch rib of plane tie arch structure as well as the suspender and arch rib of tied-arch bridges in this research. Furthermore, combining with grey relation analysis, the noise immunity of the proposed index method can be verified. Also, thrust-influenced line recognition based on VMD (variational mode decomposition) is introduced, and a practical process of bridge health assessment based on the quasistatic-influenced line loading of three-axle heavy vehicles is established. Theoretical analysis and numerical verification show that the calculated error of the analytic solution of two-hinged arches with a rise-span ratio greater than 1/8 is less than 9.57%, and the error decreases with the increase of the rise-span ratio. Therefore, it can be applied to the calculation and analysis of tied-arch bridges with a rise-span ratio range between 1/4 and 1/5. With the equivalent thrust-influenced line index proposed in this paper, the damage of suspenders and arch ribs of tied-arch bridges can be accurately located. It has been found that the proposed method is more effective to identify the damage of suspenders than the damage of arch ribs does, showing good noise immunity. In summary, this research has provided theoretical support for arch bridge design and evaluation. Combining with existing bridge-monitoring methods, the new bridge damage identification method proposed in this paper has the prospect of realizing the normal health status assessment of existing tied-arch bridges.

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“…The monitoring approaches that are usually applied to masonry constructions are both vibration- and strain-based SHM methods; the monitoring of displacements is also widely adopted in common practice, especially to assess crack growth over time [ 11 , 12 ]. The potential applications of vibration-based SHM methods encompass the monitoring of slender structures, such as historic masonry towers [ 13 , 14 , 15 ] and buildings, such as palaces and churches [ 16 , 17 , 18 ], and bridges [ 19 , 20 , 21 ]. The practical applications of strain-based SHM methods to similar structural settings can be found in the literature [ 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

Meoni

D’Alessandro

Saviano

et al. 2023

Sensors

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A diffuse and continuous monitoring of the in-service structural response of buildings can allow for the early identification of the formation of cracks and collapse mechanisms before the occurrence of severe consequences. In the case of existing masonry constructions, the implementation of tailored Structural Health Monitoring (SHM) systems appears quite significant, given their well-known susceptibility to brittle failures. Recently, a new sensing technology based on smart bricks, i.e., piezoresistive brick-like sensors, was proposed in the literature for the SHM of masonry constructions. Smart bricks can be integrated within masonry to monitor strain and detect cracks. At present, the effectiveness of smart bricks has been proven in different structural settings. This paper contributes to the research by investigating the strain-sensitivity of smart bricks of standard dimensions when inserted in masonry walls subjected to in-plane shear loading. Real-scale masonry walls instrumented with smart bricks and displacement sensors were tested under diagonal compression, and numerical simulations were conducted to interpret the experimental results. At peak condition, numerical models provided comparable strain values to those of smart bricks, i.e., approximately equal to 10−4, with similar trends. Overall, the effectiveness of smart bricks in strain monitoring and crack detection is demonstrated.

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Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

Cited by 49 publications

References 69 publications

A Review on Damage Monitoring and Identification Methods for Arch Bridges

A Review on Damage Monitoring and Identification Methods for Arch Bridges

Damage Identification Method of Tied‐Arch Bridges Based on the Equivalent Thrust‐Influenced Line

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

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