An exploratory study of underwater bolted connection looseness detection using percussion and a shallow machine learning algorithm

He, Sihong; Chen, Jian; Chen, Zheng; Song, Gangbing

doi:10.1007/s10409-023-22360-x

Cited by 7 publications

(7 citation statements)

References 40 publications

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“…Some machine learning models have shown a remarkable performance in the field of engineering. For example, they have been used to identify the degree of bolt looseness based on the sound generated when tapping the bolts [15], or for rapidly assessing the seismic damage status of steel frames [16]. Additionally, in the realm of stress distribution prediction, researchers have also employed models such as convolutional neural networks to create stress distribution prediction models tailored to different objects, e.g., reference [17][18][19], and notable results have been achieved, thereby circumventing the need for complex numerical computation processes.…”

Section: Application Of Machine Learning Models In Structural Design ...mentioning

confidence: 99%

“…Rahman [11] eXtreme Gradient Boosting (XGboost) Prediction of shear strength of steel fiber reinforced concrete beams Kameshwar [13] Decision Tree (DT) Earthquake recovery model of a bridge He [15] K-Nearest Neighbor (KNN) Prediction of loosening state of underwater bolt connection Nguyen [16] Random Forest (RF) Damage assessment of buildings after an earthquake Bolandi [17] Convolutional Neural Networks (CNN) Stress distribution of damaged structural components with stress concentration Liang [18] Deep Learning Model (DL) Stress distribution in human aorta…”

Section: Authors Machine Learning Model Used Application Scenariomentioning

confidence: 99%

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Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Dai,

Du,

Luo

et al. 2023

Materials

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The flexible high-neck flange is connected to the circular hollow section (CHS) tube through welding, and the placement of the weld seam and corresponding stress concentration factor (SCF) are crucial determinants of the joint’s fatigue performance. In this study, three hybrid models combining ant colony optimization (ACO), a genetic algorithm (GA), and grey wolf optimization (GWO) with a random forest (RF) model were developed to predict the stress distribution on the inner and outer walls of the CHS tube under different flange parameter combinations. To achieve this, an automated finite element (FE) analysis program for flexible high-neck flange joints was initially developed based on ABAQUS 2020 software. Parameter combinations were randomly selected within a reasonable range to simulate the nonlinear mechanical behavior of the joint under uniform tension, generating a dataset comprising 5417 sets of data. The accuracy of the FE model was validated through experimental data from the literature. Based on this, feature importance analysis was conducted to reveal the influence of different variable parameters on the stress distribution in the tube of the joint. The flange parameters and tube stress distribution are considered as inputs and outputs, respectively. Three hybrid RF models, specifically ant colony optimization-based random forest (ACO-RF), genetic algorithm-based random forest (GA-RF), and grey wolf optimization-based random forest (GWO-RF), are trained for regression prediction. The results demonstrate that the three hybrid models outperform the original machine learning model in predictive accuracy. The ACO-RF model achieved the highest accuracy with average coefficients of determination (Rmean2) of 0.9983 and 0.9865 on the testing and training sets, respectively. Building upon this foundation, the study developed a corresponding open-source graphical user interface (GUI) as a tool for facilitating computations and visualizing results. Finally, a case study on fatigue damage assessment of a flexible high-neck flange joint in a wind-turbine tower is presented to demonstrate the application of the proposed model in this study.

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Section: Application Of Machine Learning Models In Structural Design ...mentioning

confidence: 99%

Section: Authors Machine Learning Model Used Application Scenariomentioning

confidence: 99%

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Dai,

Du,

Luo

et al. 2023

Materials

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“…With the development of artificial intelligence in the last decade, it has been widely used in fields such as unmanned vehicles, voice processing, face recognition, medical imaging, and structural health monitoring [23,24] and damage recognition [25][26][27]. Machine learning, as one of the important branches, has been applied in ultrasonic nondestructive testing [28][29][30] and fracture recognition [31,32]. Niu and Srivastava used convolutional neural network (CNN) to classify and recognize the ultrasonic echo signals of internal fractures in structures directly without extracting any features [33].…”

Section: Introductionmentioning

confidence: 99%

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Liu,

Luo,

et al. 2024

Smart Mater. Struct.

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Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a Self-model Emission Detection (SED), we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a Convolutional Neural Network (CNN) model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

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“…Traditional percussion detection methods rely on the operator's experience, and because of the limitation of individual human's hearing, the accuracy of the detection varies. The recent advances in artificial intelligence and machine learning accelerate the research and implementation of the percussion detection method [19][20][21][22]. Zheng et al [23] combined the percussion detection method with support vector machines to detect the moisture content of concrete.…”

Section: Introductionmentioning

confidence: 99%

A pipeline corrosion detecting method using percussion and residual neural network

Yang,

Ji,

Wang

et al. 2024

Meas. Sci. Technol.

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Corrosion of pipeline walls can lead to serious safety accidents such as leaks, fires and even explosions. This paper proposes a corrosion detection method using deep learning based on percussion sound for pipelines. The percussion induced acoustic signals are processed by wavelet threshold noise reduction and double threshold endpoint detection to generate the Mel spectrograms, and then an 18-layer Residual Network (ResNet18) is used to mine the depth information and classify the degree of pipeline corrosion. We conducted experiments to verify the validity of the approach. Seven working conditions are generated by electrochemical corrosion of a pipe specimen, and percussions are applied at five different positions under the same working conditions to collect the impact acoustic signals. The test results show that the method can quickly, efficiently and accurately detect the degree of pipeline corrosion, classify the degree of pipe corrosion without being affected by the striking position Therefore, the model has great potential for application in detecting the internal corrosion of pipelines based on percussion sounds.

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An exploratory study of underwater bolted connection looseness detection using percussion and a shallow machine learning algorithm

Cited by 7 publications

References 40 publications

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

A pipeline corrosion detecting method using percussion and residual neural network

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