High Resolution Bridge Mode Shape Identification via Matrix Completion Approach

Eshkevari, Soheil Sadeghi; Takáč, Martin; Pakzad, Shamim N.; Eshkevari, Soheil Sadeghi

doi:10.12783/shm2019/32499

Cited by 2 publications

(5 citation statements)

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“…Matarazzo et al [ 33 , 34 ] introduced the “structural identification using expectation maximization (STRIDE)” method for mode shape identification from mobile sensors. Eshkevari et al [ 35 , 36 , 37 ] formulated mobile sensing data as a sparse matrix with missing values. They employed alternating least-square (ALS) for matrix completion, followed by principal component analysis (PCA) and structured optimization analysis (SOA) for modal identification.…”

Section: Introductionmentioning

confidence: 99%

“…They employed alternating least-square (ALS) for matrix completion, followed by principal component analysis (PCA) and structured optimization analysis (SOA) for modal identification. Matrix completion approaches have gained traction in recent years for health monitoring due to their data-driven nature, applicable to both fixed sensors [ 38 ] and mobile sensors [ 19 , 36 , 37 ]. Yang and Nagarajaiah [ 38 ] utilized nuclear norm minimization for matrix completion, and a comprehensive overview of such methods is presented in Nagarajaiah and Yang [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

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Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Jana,

Nagarajaiah

2023

Sensors

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In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Jana,

Nagarajaiah

2023

Sensors

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“…One limitation of the aforementioned two studies was that engineering judgment had to be applied to determine the signs of the mode shapes. Eshkevari et al 47–49 treated the mobile sensing data as a sparse matrix with missing values. In their work, alternating least‐square (ALS) was used to complete the matrix.…”

Section: Introductionmentioning

confidence: 99%

“…One major advantage of matrix completion methods is that they are fully data‐driven, which does not require prior knowledge about the bridges. They have been successfully applied to system identification of bridges using either fixed sensors 50 or mobile sensors 34,48,49 . Yang and Nagarajaiah 50 investigated two methods to recover the randomly missing values in structural vibration responses.…”

Section: Introductionmentioning

confidence: 99%

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Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

Mei

Shirzad-Ghaleroudkhani

Gül

et al. 2021

Struct Control Health Monit

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Summary Advances in smart infrastructure produces a natural demand of system identification techniques for structural health and performance monitoring that can be scaled to regions and large asset inventories. Conventional approaches require sensors to be installed, often in long‐term deployments, on the monitored infrastructure systems, which is a costly undertaking when thousands of systems (e.g., bridges) need to be monitored. This paper presents a novel mode shape identification method for bridges that uses data collected from moving vehicles as input—a paradigm that can overcome limitations associated with conventional approaches. The method consists of two steps. First, the data collected from moving measurement points are mapped to virtual fixed points to generate a sparse matrix. Then, a “soft‐imputing” technique is employed to fill the sparse matrix. Finally, a singular value decomposition is applied to extract the mode shapes of the bridge. Experiments with synthetic, yet realistic, data are conducted to verify the method. The sensitivity of the proposed approach to different factors, including the number of vehicles, car speed, road roughness, and measurement errors, are also investigated. The results show that the proposed method is capable of identifying the mode shapes of the bridge accurately.

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High Resolution Bridge Mode Shape Identification via Matrix Completion Approach

Cited by 2 publications

References 0 publications

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

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