Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Delgadillo, Rick M.; Casas, Joan R.

doi:10.1002/stc.2966

Cited by 14 publications

(9 citation statements)

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“…Then, unsupervised learning methods were used for damage type identification. Delgadillo and Casas 124 used real data from the Warren truss bridge to verify the effectiveness of clustering methods in detecting and locating bridge damage.…”

Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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“…Various techniques (methods) have been proposed to process structural vibration responses recorded along the bridge under truckloads to locate structural damage. Some of the most important ones that could work with acceleration responses are transformer-based methods (such as the Wavelet transformer or Hilbert–Huang transformer) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ], blind source separation-based methods [ 9 , 10 , 11 , 12 , 13 , 14 ], and special averaging-based methods (e.g., random decrement technique) [ 15 , 16 , 17 , 18 , 19 , 20 ]. Although these techniques (methods) are very popular, they have difficult-to-understand mathematical backgrounds.…”

Section: Introductionmentioning

confidence: 99%

An Investigation into the Application of Acceleration Responses’ Trendline for Bridge Damage Detection Using Quadratic Regression

Kordestani,

Zhang,

Arab

2024

Sensors

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It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method.

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“…Pnevmatikos et al proved that the discrete wavelet coefficients extracted from the seismic acceleration response show spikes when damage occurs [7]. Delgadillo and Casas [28] used the results of the Hilbert-Huang transform in a machine learning algorithm and successfully located the damage in a real bridge under a moving load in Japan. Kildashti et al [29] numerically investigated the effect of various vehicle parameters on the accuracy of their damage detection method in a cable-stayed bridge.…”

Section: Introductionmentioning

confidence: 99%

An Output-Only, Energy-Based, Damage Detection Method Using the Trend Lines of the Structural Acceleration Response

Kordestani,

Zhang,

Arab

2023

Buildings

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Using the trendlines of an acceleration response as a tool to decompose a structural response is a new topic that was proposed by authors in 2020. This paper provides a numerical/experimental investigation of using a Savitzky–Golay filter (SGF) in a method to calculate the trendline and decompose building acceleration responses when subjected to a seismic load. Hence, this paper proposes an output-only, energy-based, damage detection method in which the trend lines of a building’s structural acceleration responses are used to locate the damage. For this purpose, an adjusted SGF is utilized to calculate an especial trend line for each floor’s acceleration response of the building structural model. The energy of these trend lines is then calculated and normalized. Two damage indices are used, of which, the second one is being proposed for the first time in this paper. The accuracy of the proposed method is numerically and experimentally investigated using a five-floor building structural model subjected to white noise excitation through a shake table. The results prove that the proposed method is capable of accurately locating and quantifying structural damages with a severity of more than 10% in a noisy environment. In view that the proposed method locates the damage with no need of determining the structural modal properties or parameters, it can be categorized as an online and quick structural damage detection method.

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Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Cited by 14 publications

References 50 publications

Review of artificial intelligence-based bridge damage detection

Review of artificial intelligence-based bridge damage detection

An Investigation into the Application of Acceleration Responses’ Trendline for Bridge Damage Detection Using Quadratic Regression

An Output-Only, Energy-Based, Damage Detection Method Using the Trend Lines of the Structural Acceleration Response

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