Displacement Response Reconstruction of Slender Flexible Structures Based on Cubic Spline Fitting Method

This paper presents an in-depth analysis of population-based structural health monitoring applied to offshore wind farms, with a specific focus on a real wind farm. The study quantifies the heterogeneity within the wind turbine population utilising advanced numerical methods. The Fréchet distance is introduced as a key metric to assess the similarity in terms of structural integrity among the turbines considering factors, such as geometry, material properties, topology, and operational conditions. The results revealed a predominant homogeneity with respect across the wind turbines, despite variations in environmental exposure and operational states. Furthermore, the scope of the study is extended to explore the implications of environmental influences, such as corrosion, scour, and marine growth, which are typically unmonitored yet crucial for comprehensive structural assessment. The present paper contributes to the field by demonstrating the feasibility of knowledge transfer in homogeneous wind turbine populations and highlighting the need for detailed similarity assessments in heterogeneous settings. The findings of this study pave the way for better-targeted, smart, and efficient structural health monitoring strategies in renewable energy systems.

Self-sensing properties of 3D printed continuous carbon fiber-reinforced PLA/TPU honeycomb structures during cyclic compression

Integrating direct-write (DW) devices into three-dimensional (3D) printed parts is key to continuing innovation in engineering applications such as smart material systems and structural health monitoring. However, this integration is challenging because: (1) most 3D printing techniques leave rough or porous surfaces if they are untreated; (2) the thermal sintering process required for most conductive inks could degrade the polymeric materials of 3D printed parts; and (3) the extensive pause needed for the DW process during layer-by-layer fabrication may cause weaker interlayer bonding and create structural weak points. These challenges are rather common during the insertion of conductive patterns inside 3D printed structures. As an avoidance tactic, we developed a simple ‘print-stick-peel’ method to transfer the DW device from the polytetrafluoroethylene or perfluoroalkoxy alkanes film onto any layer of a 3D printed object. This transfer can be achieved using the self-adhesion of 3D printing materials or applying additional adhesive. We demonstrated this method by transferring Aerosol Jet® printed strain sensors into parts fabricated by PolyJet™ printing. This report provides an investigation and discussion on the sensitivity, reliability, and influence embedding the sensor has on mechanical properties.

Now a day, earthquake engineers follow subjects such as structural health monitoring, warning announcement and prediction rather safe-making in the field of structure. In this regard, these three choices are of great goals of Iran in direction of many studies concentrated on. This research is centralized on real time health monitoring system of Iran bridges. In this regard, to evaluate smart real time health monitoring sensors, first all different types were determined using the library resources, and then all the important indices in evaluating these sensors were derived by interviewing experts in construction management fields. After that, to continue the survey, questionnaires were given to 18 experts to weight the effective indices. Through a decision-making method using new hybrid methodology based on SWARA and WASPAS, existential necessity degree of all indices and sensors were obtained and eventually the following result captured: applying piezoelectric sensors is optimal in smart health monitoring to be used in Iran bridges and optical fiber sensor was recognized as the second optimum option.

A multimode perturbation method for frequency response analysis of nonlinearly vibrational beams with periodic distribution parameters is proposed. The partial differential equation with spatial varying parameters for nonlinear vibration of beams with periodic parameters under harmonic excitations is derived. The procedure of the multimode perturbation method includes three main steps: first, the nonlinear partial differential equation is transformed into linear partial differential equations with varying parameters by applying perturbation method; second, the linear partial differential equations are transformed into ordinary differential equations with multimode coupling by applying Galerkin method, where multiple vibration modes of the beams are used and the equations are suitable to nonlinear vibration of periodic structures with high parameter-varying wave in wide frequency band; third, the ordinary differential equations are solved by applying harmonic balance method to obtain vibration response of the nonlinear periodic beam, which is used for characteristics analysis of frequency response and spatial mode. Furthermore, the stability problem of nonlinear harmonic vibration as multidegree-of-freedom system with periodic time-varying parameters is solved by applying direct eigenvalue analysis approach. The proposed method can incorporate multiple vibration modes into response analysis of nonlinear periodic structures and consider mode-coupling effects due to structural nonlinearity and parametric periodicity. Finally, a nonlinear beam with periodic supports under harmonic excitations is studied. Numerical results on frequency response of the beam are given to illustrate an application of the proposed method, new frequency response characteristics, and influences of periodic parameters on structural response. The results have potential application to nonlinear structural vibration control and support damage detection of nonlinear structures with periodic supports.

Smart structural health monitoring in civil engineering: A survey

To decrease the cost of instrumentation for the strain and displacement monitoring method that uses sensors as well as considers the structural health monitoring challenges in sensor installation, it is necessary to develop a machine vision-based monitoring method. For this method, the most important step is the accurate extraction of the image feature. In this article, the edge detection operator based on multi-scale structure elements and the compound mathematical morphological operator is proposed to provide improved image feature extraction. The proposed method can not only achieve an improved filtering effect and anti-noise ability but can also detect the edge more accurately. Furthermore, the required image features (vertex of a square calibration board and centroid of a circular target) can be accurately extracted using the extracted image edge information. For validation, the monitoring tests for the structural local mean strain and in-plane displacement were designed accordingly. Through analysis of the error between the measured and calculated values of the structural strain and displacement, the feasibility and effectiveness of the proposed edge detection operator are verified.

ACOMPUTATIONAL method for design sensitivity analysis of an eigenvalue and an eigenvector of a beam under nonlinear forced vibration is presented in this paper. The nonlinear vibration problem is only analyzed once in the proposed method. The geometric nonlinearity of concern results from the large deflection of a beam. The finite element system equation for nonlinear vibration is symmetric. However, it is found that the equation for computing the design sensitivity of an eigenvector is linear and unsymmetric. A numerical example is included to validate the proposed computational procedure. Contents The nonlinear vibrations studied herein refer to the periodic (though not necessarily harmonic) and stable motions of a nonlinear system. Recently, Hou and Yuan1 addressed the design sensitivity analysis of eigenvalues and eigenvectors of a beam under nonlinear free vibration. In that paper, the longitudinal inertia was not considered in the nonlinear vibration formulation, so that the problem could be expressed in terms of the lateral deflection alone. In the present work the design sensitivity analysis has been extended successfully to a nonlinear forced vibration problem whose formulation includes the in-plane inertia and displacement. The variational formulation2 for the nonlinear forced vibrations of beams under harmonic excitation is given as

Structural dynamic displacement is an important indicator of the health condition of long-span bridges. Monitoring of bridge dynamic displacement has been a critical task within the context of structural health monitoring (SHM). Traditionally, the structural displacement measurement methods are contact, wired and time-consuming. With the great progress of image processing technique and vision robot science, the visionbased monitoring systems have been developed rapidly and received a huge amount of attentions in the field of SHM. However, in the practical applications, the environmental factors will affect the operational performance of the vision-based system. In this study, a series of comparative experiments are conducted to explore the robustness of vision-based structural dynamic displacement monitoring under varying illumination conditions by use of various types of vision targets (quick response (QR) code and LED lamp). The experimental results indicate that the robustness of the vision-based system is weakened under poor illumination conditions when the QR code is used to be the vision target in comparison with the vision target of LED lamp.

Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.

An intelligent multisensor integration and fusion model that uses fuzzy logic is developed. Measurement data from different types of sensors with different resolutions are integrated and fused based on the confidence in them derived from information not usually used in data fusion, such as operating temperature, frequency range, fatigue cycles, etc. These are fed as additional inputs to a fuzzy inference system (FIS) that has predefined membership functions for each of these variables. The output of the FIS are weights that are assigned to the different sensor measurement data that reflect the confidence in the sensor's behavior and performance. A modular approach is adopted. It allows adding or deleting a sensor, along with its fuzzy logic controller (FLC), anytime without affecting the entire data fusion system. This paper presents a preliminary model that fuses the data from three different types of sensors that monitor the strain at a single location in a cantilever beam. This will be later extended to sensors that will be fixed at different locations on the same beam. The results from the proposed work are a stepping stone toward the development of generic autonomous sensor models that are capable of data interpretation, self-calibration, data fusion from other sources, and even learning so as to improve their performance with time. This work is aimed at the development of smart structural health monitoring systems, but has applications in diverse fields such as robotics, controls, target tracking, and biomedical imaging.

Geometrical nonlinear and stability analysis for slender frame structures of crawler cranes

To avoid potential life and economic loss, structural health monitoring has become increasingly important for bridge systems. As a key component of structural health monitoring, structural system identification (SSI) tries to identify the structural parameters through the measured response. However, due to the ill-posedness of inverse analysis, the estimation of parameters is greatly affected by measurement errors. To alleviate the adverse effect of errors, the relations among the displacements in beam structures (for instance bridges), compatibility conditions, are derived analytically via a symbolical approach. Then a set of compatible displacements is obtained by an optimization procedure that finds the closest compatible deformation shape to the measured one. The final estimations of the parameters are obtained by providing the compatible displacements to SSI. The proposed method is validated by the numerical simulations on a simply supported bridge and on a continuous bridge.

This paper proposes a multiclass nonlinear relevance vector machine (MNRVM) model for health monitoring of smart structures equipped with magnetorheological (MR) dampers. The proposed model will be used to classify the damage statuses of the integrated structure-control systems subjected to ambient excitations. A numerical model of a three-story building equipped with an MR damper is studied to demonstrate the effectiveness of the proposed health monitoring schemes. Dynamic responses of the smart structures subjected to random excitations are measured. Discrete wavelet transform is applied to the obtained data to compress and filter noises of the measured data. As a next step, the compressed and de-noised signals are used for developing autoregressive (AR) models. Then the MNRVM is applied to the AR-coefficient data to classify them with respect to the damage statuses. As a baseline, the support vector machine (SVM) algorithm is considered. It is demonstrated that the proposed MNRVM framework is effective in classifying various damage statuses of the nonlinear smart structures subjected to ambient excitations. Simulation results also show that the MNRVM performs similar to the SVM with faster computation time.

Structural Health Monitoring (SHM) is essential for ensuring the safety and longevity of critical infrastructure. Traditional methods face limitations in spatial coverage, susceptibility to environmental factors, and complex data interpretation. To address these challenges, this research proposes an intersection of mechanical metamaterials with embedded piezoelectric sensors. Metamaterials enable distributed sensing while maintaining the load-bearing capacity of a structure. Piezoelectric materials provide passive strain/vibration sensing and vibration-damping capabilities. Integrating piezoelectric elements with a tailored re-entrant mechanical metamaterial design enables smart support system beam condition monitoring. Experimental validation was conducted on an aluminium beam supported by a pin and roller supports, exploring the feasibility and potential of this smart SHM system. This approach offers distributed sensing, multi-functionality, and more resilient built environments that continuously monitor their structural health and adapt as needed. This paper demonstrates the potential of a smart engineering support system for SHM.