Acoustic emission (AE) is widely used for identifying source mechanisms and the deformation stage of steel material. The effectiveness of this non-destructive monitoring technique heavily depends on the quality of the measured AE signals. However, the AE signals from deformation are easily contaminated by the signals from noise in a noisy environment. This paper presents a hybrid model for deformation stage identification, which combines a self-adaptive denoising technique and an Artificial neural network (ANN). In pursuit of model generality, AE signals were collected from tensile coupon tests with various steel materials and loading speeds. First, a decomposition-based denoising method is applied based on the singular spectral analysis (SSA) and variational mode decomposition (VMD), which is defined as SSA-VMD. Its effectiveness is demonstrated by simulated signals and experimental results. Following the use of the denoising technique, an ANN is constructed to identify the deformation stage of steel materials with the input of features extracted from the filtered AE signals. The results indicate that the ANN achieves a high prediction accuracy of 0.93 in the test set and 0.87 in unseen data. By applying this denoising method, the ANN-based approach enables accurate correlation of the collected AE signals to deformation stages. The finding can be used as the basis for the creation of new methodologies for monitoring structural health status of in-service steel structures.

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The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.

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Hole patterns are common in engineering design for connections and/or assembly purposes. Geometrical discontinuities can cause stress concentration in localized areas, making them more prone to fatigue crack initiation and influencing the fatigue life of the overall unit. In the past, much effort has been exerted on fatigue modelling of holed plates from both experimental and theoretical perspectives. However, most studied objects were aluminium or titanium thin plates for aviation purposes. In this work, the fatigue performance of a downscaled holed thick steel plate, extracted from a novel C1 Wedge Connection for wind turbine tower assembling, was tested and categorized according to commonly used industry codes. In particular, the influence of the surface size effect was experimentally observed and computationally discussed. Finally, a probabilistic fatigue model was proposed, which gives a favourable prediction on the fatigue behaviour of the surface polished holed thick steel plate with the help of the Smith–Watson–Topper (SWT) model.

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In the past two decades, offshore wind has emerged as a new source of renewable energy. This highlights the requirement for the utilisation of larger and more efficient offshore wind turbines (OWTs). The connections used in support structures of OWTs are critical to ensure the excellent structural performance of OWFs. An alternative option is the C1 wedge connection (C1-WC) to join virtually all the wind turbine generator (WTG) towers to their foundations. This connection shows promising potential in reducing construction, installation, and maintenance costs by eliminating the ring flange and using smaller diameter bolts. Till now, C1-WC has undergone three generations of development. A more comprehensive research program is required to explore its implementation in a wind farm. The load transfer mechanism and critical component of C1 wedge connections are different to the conventional bolted ring flange (RF) connections. It is important to understand the mechanical behaviour of this connection. Meanwhile, support structures are exposed to the harsh environment during the service life of the OWTs. Material degradation and local cracks in the connection are inevitable to affect the serviceability of OWTs. A need for a reliable and rigorous structural health monitoring (SHM) system for the connection is evident. As one of the non-destructive techniques (NDT), Acoustic emission (AE) has been extensively used in early damage detection and real-time assessment of steel structures. Despite its successful applications, challenges still exist in using AE technique for monitoring applications, especially in analysing the recorded data. Therefore, the research aims to assist in understanding mechanical behaviour and evaluating the health status of the innovative connection. An extensive experimental program was conducted to evaluate the static and cyclic behaviour of the C1-WCs. Additionally, a detailed 3D non-linear finite element (FE) model of the C1-WCs has been developed. The incorporation of material non-linearity and ductile damage allows the FE model to model the post-necking and final fracture of the connection. The FE model replicates with a good agreement the experimental tensile static and cyclic tests up to the final damage and reproduces the joint behaviour correctly. Parametric studies investigate the influence of bolt grade, the friction coefficient between contact surfaces, and the preloading force level on mechanical behaviour. Moreover, a quantitative comparison between C1-WC and two types of connections (RF connection and RF connection with defined contacts) is performed to provide practical insights into the selection and application of such connections and further optimization. FE-assisted analyses were performed to examine the effect of applied boundary conditions, bolt pretension level, and steel grade on the behaviour of the connections. In addition to mechanical behaviour analysis. this research is also focused on developing data processing methods to address the challenges of AE monitoring for the C1-WCs. A hybrid model is proposed to identify the deformation stage of metal material. This method combines a self-adaptive denoising technique and an Artificial neural network (ANN). To reduce noise in the AE signals, a decomposition-based denoising method is proposed based on singular spectral analysis (SSA) and variable mode decomposition (VMD), referred to as SSA-VMD. After denoising, an ANN is constructed to identify the deformation stage of steel materials using features extracted from the filtered AE signals as input. Fatigue damage of the C1-WCs could result in catastrophic failure of OWTs. Due to space constraints, it can be challenging to detect surface cracks in the lower segment holes of the C1-WC using commercial sensors. Thin PZT sensors are lightweight and small, making them suitable for use in restricted-access areas. However, their poor signal-to-noise ratio can limit their effectiveness in AE monitoring. A criterion for selecting the optimal thin PZT sensors is proposed and a configuration is designed for multiple sensors. Two signal processing methods are then proposed in terms of this issue. Firstly, a data fusion-based method is proposed to enhance the functionality of thin PZT sensors in AE applications. Convolutional neural networks (CNNs) combined with principal component analysis (PCA) are employed for signal processing and data fusion. Secondly, a baseline-based method is proposed to provide early warning of the fatigue damage of C1-WCs using thin PZT sensors. A benchmark model correlating to the damage state is created by breaking pencil leads. Multi-variate feature vectors are extracted and then mapped to the Mahalanois distance for identification. Based on this research work, an efficient FE method has been developed to further improve the design of C1-WC. By providing an in-depth guideline for evaluating the mechanical performance of connections used in OWTs, this research has the potential to contribute to the development of more robust and reliable wind turbine structures. Moreover, the proposed signal processing methods for identifying the deformation stage and early fatigue damage can be further explored in structures with similar damage mechanisms. This can lead to the development of more accurate and effective methods for monitoring and assessing the health of offshore wind turbines, ultimately contributing to improved safety and reliability in the renewable energy industry. @en

The overall competitiveness of offshore wind turbine towers is significantly influenced by the selection of the connection. The following three types of connections: a conventional bolted ring flange (RF) connection, ring flange connection with defined contact surfaces (RFD), and C1 wedge connection (C1-WC) are considered. A quantitative comparison is made to enhance performance in a specific condition and enable further optimization of these connections in engineering practices. The study compares the tensile behaviour and fatigue performance of these connections by validated finite element (FE) simulation and analysis. The proposed FE modelling is based on a realistic geometry including all contacts present in the connections, steel full-range stress–strain relationship and ductile damage model. The efficiency and accuracy of the FE models are validated through the comparison with the performed tests. Then, a series of parametric FE analyses are carried out to examine the impact of the applied boundary conditions, bolt pretension level, and steel grade on the behaviour of connections. Load-displacement curves, bolt evolution curves, and stress responses are analysed to compare their tensile behaviour. The effectiveness of conventional segment specimen testing is evaluated thoroughly. For the fatigue performance of connections, the results indicate that the segment specimen testing substantially underestimates the fatigue performance of C1-WCs. This discrepancy is essential to be considered in the tower design. It is also noted that C1-WCs are rather insensitive to the variation of pretension force level, which show superiority to avoiding the difficulties associated with typically bolted joints. This research provides in-depth knowledge for the practical application of such connections and further optimization.

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This paper shows a part of the analysis of the development of the second generation of the C1 wedge connections for use in offshore wind turbine supporting towers. The novelty of this connection is that bolt failure is avoided under static and fatigue loads. This study aims to investigate the tensile behaviour of the connection by combining the findings of experiments and finite element (FE) analysis. Two specimens subjected to uniaxial and cyclic tensile loading tested until failure are used for illustration. Advanced quasi-static FE analysis results, considering the most detailed geometry and using an explicit dynamic solver, are compared to the experimental results. The FE analysis results agree well with the experimental results. Based on the FE model, a parametric study is carried out to analyse the influence of the bolt grade, friction coefficient between contact surfaces, and preloading force level on mechanical behaviour. Failure modes, bolt force development, and the evolution of gap opening between contacted segments are analysed. Results demonstrate that the tensile fracture of the C1 wedge connection mainly appears in the lower segment. All the investigated parameters have a negligible effect on the connection's ultimate resistance and failure mode. However, the friction coefficient between contact surfaces and bolt preload level significantly affects the connection's local deformation capacity and the response of the bolt stress range. The FE simulation provides practical guidance for designing this connection without bolt failure.

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Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area, but the AE source location strategies are rather complicated for real engineering structures. In this paper, an effective method for source localization in realistic structures is presented based on the application of artificial neural networks (ANN), using finite element (FE) simulation results of Lamb waves as the modelling basis. Pencil lead break experiments and related FE simulations on a steel-concrete composite girder are conducted to evaluate the performance of the method. The identification of different wave modes is carried by comparing alternative onset time detection methods. Numerical results are found to be matching closely with the experimental results. To get a reliable ANN model, the validated FE model is used to create a comprehensive database with five different sensor arrangements. It is found that the proposed method is superior to the classical Time of Arrival (TOA) method with the same input data. The results indicate that using trained neural networks based on numerical data is a viable option for AE source location in the case of the I-shaped girder, increasing the likelihood of design and optimization of the AE technique in monitoring realistic structures.

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In recent years, wire arc additive manufacturing (WAAM) has increasingly attracted attention in the construction sector because of its ability to optimize the production of large metallic structural parts, and for use in connections suitable for easy execution and potential reuse. The technology has become mature leading to shorter fabrication times and less expensive total costs due to lower raw material costs. Therefore, it is timely to conduct the material tests to evaluate the plastic flow and fracture of the WAAM steel plate to gain material characterization for an efficient design of connections. In this paper, the coupon specimens are cut from the WAAM plates in different directions in relation to the printing orientation to investigate possible material anisotropy. Results of uniaxial coupon specimens, the stress-strain curve, are analyzed in three stages: the elastic stage, the plastic stage and the coupled plastic-damage stage. The FE simulation is performed to calibrate the true stress and strain curves in different stages.@en

S460 steel is increasingly used in civil engineering, especially in a harsh environment such as offshore structures [1]. Material damage is inevitable for structures subjected to static and dynamic loads during their technical life time. Thus, monitoring material damage is important for providing information regarding critical damage of in-service structures. Non-destructive testing (NDT) techniques have been widely used for damage detection in recent years. Acoustic emission (AE), one of the efficient NDT techniques, can identify material damage based on the rapid release of strain energy as bursts of transient elastic waves. Previous research showed that the AE technique is sensitive and reliable in the detection of the material damage [2,3]. Specifically, AE signals contain information on a number of damage factors, such as material types, plasticity level, loading conditions and microdefects [4].@en

Acoustic Emission (AE) is used in various fields of engineering and recently has been gained increasing attention in crack detection of cyclically loaded engineering structures. The crack location strategies are rather complicated for realistic structures with complex geometry. In this paper, pencil leads breaking (PLB) experiments and finite element simulation of lamb wave propagation are conducted to give a rational interpretation on acoustic wave propagation within composite structures. Identification of the fundamental extensional mode (S0) and flexural mode (A0) is successfully carried out. The proposed FE models are proven to be a suitable alternative/complement to the experiments, in the monitoring of realistic structures.@en