Numerical investigation on lateral monotonic and cyclic responses of scoured rigid monopile based on an integrated bounding surface model

Zheng, Hanbo; Zhang, Hao; Liang, Fayun; Li, Lin

doi:10.1016/j.compgeo.2023.105997

Cited by 20 publications

(5 citation statements)

References 60 publications

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“…As shown in Figure 6, the combination of monitoring data and numerical simulation was carried out to update the finite element model of OWTs, allowing for further analysis of their dynamic response and damage conditions [85,86]. During the simulation process, an integrated bounding surface model can be employed for the soil model under dynamic loads [87], while a simplified numerical model, such as the lumped parameter model [88], can be utilized for OWTs. Iliopoulos et al (2016) utilized limited monitoring displacement and acceleration data in combination with finite element software to estimate the response at the unmonitored location [89].…”

Section: Hybrid Fault Diagnosis Methodsmentioning

confidence: 99%

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

Yang,

Liang,

Zhu

et al. 2024

JMSE

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The service environment of offshore wind turbine (OWT) support structures is harsh, and it is extremely difficult to replace these structures during their operational lifespan, making their failure a catastrophic event. The structural health monitoring (SHM) of OWT support structures is a crucial aspect of operational maintenance for OWT support structures, aiming to mitigate significant financial losses. This paper systematically summarizes the current monitoring methods and technologies for OWT support structures, including towers and foundations. Through the review of monitoring content and the evolution of monitoring techniques for supporting structures, it delves deeper into the challenges faced by wind turbine monitoring and highlights potential avenues for future development. Then, the current damage identification techniques for OWT towers and foundations are analyzed, exploring various methods including model-based, vibration-based, artificial intelligence and hybrid fault diagnosis methods. The article also examines the advantages and disadvantages of each approach and outlines potential future directions for research and development in this field. Furthermore, it delves into the current damage identification techniques for OWT towers and foundations, discussing prevalent challenges and future directions in this domain. This status review can provide reference and guidance for the monitoring design of OWT support structures, and provide support for the fault diagnosis of OWT support structures.

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Section: Hybrid Fault Diagnosis Methodsmentioning

confidence: 99%

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

Yang,

Liang,

Zhu

et al. 2024

JMSE

Self Cite

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“…To achieve the installation of multiple sensors at desired depths and make use of existing test techniques, some implementation ideas are proposed for onshore and offshore piles [37][38][39]. For example, as depicted in Figure 1a, if a reinforced concrete (RC) pile is qualified for the cross-hole sonic logging (CSL) test, a series of hydrophones (commonly used in the PS method) can be plugged into one of the water-filled sonic logging tubes located below the known defect (e.g., an enlarged pile top or a detected defect); Figure 1b demonstrates a series of transducers installed along the outer surface of an offshore pile to conduct the MSPDD test, where the sensors below the superstructure can remove the effects of the above structure after signal post-processing (as will be introduced in the ensuing section).…”

Section: Operation Stepsmentioning

confidence: 99%

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

Wu,

El Naggar,

Wang

2024

Sensors

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Machine learning (ML) algorithms are increasingly applied to structure health monitoring (SHM) problems. However, their application to pile damage detection (PDD) is hindered by the complexity of the problem. A novel multi-sensor pile damage detection (MSPDD) method is proposed in this paper to extend the application of ML algorithms in the automatic identification of PDD. The time-series signals collected by multiple sensors during the pile integrity test are first processed by the traveling wave decomposition (TWD) theory and are then input into a hybrid one-dimensional (1D) convolutional and recurrent neural network. The hybrid neural network can achieve the automatic multi-task identification of pile damage detection based on the time series of MSPDD results. Finally, the analytical solution-based sample set is utilized to evaluate the performance of the proposed hybrid model. The outputs of the multi-task learning framework can provide a detailed description of the actual pile quality and provide strong support for the classification of pile quality as well.

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“…In this study, the specifications of the BPANN models align with the previously mentioned BPANN model. The ADA-BPANN algorithm offers advantages such as straightforward operation, cost-effective computation, superior predictive accuracy, and generalization capability [78,79].…”

Section: Ada-bpannmentioning

confidence: 99%

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Hu,

Li,

Wang

et al. 2024

Water

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In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.

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Numerical investigation on lateral monotonic and cyclic responses of scoured rigid monopile based on an integrated bounding surface model

Cited by 20 publications

References 60 publications

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

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