Damage detection of underwater foundation of a Chinese ancient stone arch bridge via sonar-based techniques

Bin, Chen; Yang, Yang; Jie, Zhou; Easa, Said M.; McFarland, Michael B.

doi:10.1016/j.measurement.2020.108283

Cited by 28 publications

(14 citation statements)

References 33 publications

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“…Underwater robot detection Free and flexible, able to accurately identify and detect underwater diseases of bridges It is greatly affected by the underwater environment, such as water turbidity, water flow speed method of underwater non-destructive testing and identification, underwater detection and imaging technology has attracted more and more attention, and many countries are developing research on turbidity water detection and imaging technology. Bin et al [10] used sonar technology to detect the underwater foundation damage of ancient Chinese stone arch Bridges, used a multi-beam echo sounder [11] to measure the terrain under the bridge, scanned the sediment under the bridge and submerged obstacles on both sides, and investigated the exposed state of wooden piles and the appearance of strip stone foundation. Combined with the results of multi-beam sounding, the erosion of the underwater foundation is analyzed.…”

Section: Sonar Technology Detectionmentioning

confidence: 99%

“…Bin et al. [10] used sonar technology to detect the underwater foundation damage of ancient Chinese stone arch Bridges, used a multi‐beam echo sounder [11] to measure the terrain under the bridge, scanned the sediment under the bridge and submerged obstacles on both sides, and investigated the exposed state of wooden piles and the appearance of strip stone foundation. Combined with the results of multi‐beam sounding, the erosion of the underwater foundation is analyzed.…”

Section: Introductionmentioning

confidence: 99%

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A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network

Sun

Song

et al. 2022

IET Image Processing

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The problem of the underwater structure disease of the bridge is increasingly obvious, which has seriously affected the safe operation of the bridge structure, so it is necessary to detect the underwater structure regularly. There are many kinds of bridge underwater structure diseases. This paper targets the bridge underwater structural crack diseases adopts multiple image recognition networks for verification, compares the advantages of different networks, and takes the YOLO-v4 network as the main body to build a lightweight convolutional neural network.Mobilenetv3 replaced CSPDarkent as the backbone feature extraction network, while the feature layer scale of Mobilenetv3 was modified, and the extracted preliminary feature layer was input into the enhanced feature extraction network for feature fusion. The PANet networks are replaced by the depthwise separable convolution. Using ablation experiments to compare the performance of four algorithm combinations in lightweight networks. At the same time, the disease identification accuracy of each network and the performance of the network are tested in various experimental environments, and the feasibility of the lightweight network is verified in the application of bridge underwater structure damage identification.

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Section: Sonar Technology Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network

Sun

Song

et al. 2022

IET Image Processing

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“…PROCESSING In recent decades, there has been a significant advance in UAS processing, both for civilian and military purposes. For civilian purposes, passive SONAR may be used for the detection and monitoring of animals using imaging techniques [34,45], oil and gas exploration [36], and damage detection in underwater structures [46].…”

Section: Introduction To Underwater Acoustics Signalmentioning

confidence: 99%

Empirical Mode Decomposition: Theory and Applications in Underwater Acoustics

Filho

Santos

Sinezio

et al. 2022

JCIS

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Empirical mode decomposition (EMD) is a signal processing method that produces a data-driven multiresolution representation in time-domain suited to characterize time-varying and nonlinear phenomena. In EMD, intrinsic mode functions (IMF) are sequentially estimated from the signal of interest to represent different intrinsic oscillation modes and produce an orthogonal representation of the original information. Different algorithms have been proposed for IMF estimation to deal with limitations such as mode-mixing and noise sensitivity. EMD is usually associated with the Hilbert transform to obtain a frequency-domain representation. In this case, the method is referred to as the Hilbert-Huang transform (HHT). This paper presents a theoretical review of the fundamental aspects of both EMD and HHT, such as the IMF estimation procedure. Variations of the original EMD algorithm are also presented. Both simulated and experimental underwater acoustic signals are used to illustrate the efficiency of EMD/HHT in revealing relevant characteristics from time-varying and nonlinear phenomena.

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“…Nas últimas décadas houve um aumento considerável na utilizac ¸ão de sistemas sonar (Sound navigation and ranging) no âmbito submarino [1]. É possível encontrar aplicac ¸ões em diversas áreas como, por exemplo, detecc ¸ão e monitoramento de peixes e animais mamíferos usando sonar de imagem [2], detecc ¸ão de alvos em ambiente subaquático [3] e detecc ¸ões de danos em estruturas subaquáticas [4] .…”

Section: Introduc ¸ãOunclassified

Simulador de Ruído de Cavitação de Embarcações utilizando uma Rede Neural Realimentada

Santos¹,

Filho²,

Filho³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Sistemas de sonar passivo são utilizados tanto em aplicações civis quanto militares, para tarefas como monitoramento do ambiente submarino e detecção de animais e embarcações. A aquisição de sinais acústicos submarinos requer um complexo sistema de medição e envolve custos consideráveis para instalação e manutenção. Aliado a isso, a grande variedade de classes de embarcação e condições climáticas aumenta consideravelmente a diversidade destes tipos de sinais. Neste contexto, esse trabalho propõe-se a desenvolver um simulador de sinais acústicos submarinos que combina informações da cavitação de embarcações com ruído ambiente a partir de um sistema com um bloco fenomenológico e um bloco baseado em aprendizado de máquinas. O primeiro produz a combinação de ruído de cavitação de embarcações com ruído ambiente a partir da descrição dos fenômenos físicos utilizando modelos matemáticos. A seguir, uma rede neural realimentada (estrutura NARX) é utilizada para produzir um mapeamento que aproxima o resultado do simulador do comportamento de sinais experimentais. O simulador proposto foi capaz de produzir sinais com grande fidedignidade a sinais reais.

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Damage detection of underwater foundation of a Chinese ancient stone arch bridge via sonar-based techniques

Cited by 28 publications

References 33 publications

A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network

A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network

Empirical Mode Decomposition: Theory and Applications in Underwater Acoustics

Simulador de Ruído de Cavitação de Embarcações utilizando uma Rede Neural Realimentada

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