A Real-Time Structural Damage Detection method for High-Pile Wharf Foundations

Structural health monitoring (SHM) based on long-term safety and efficacy has attracted much attention. The success of SHM methods depends on the information content of the measurements; in addition, there are challenges in processing SHM in terms of the optimal number and location of sensors. In this study, we provide a convenient method for structural damage detection using vibration data from plane steel frames. We apply 1-D convolutional neural networks (1D-CNNs) to present an influential sensor selection analysis to maintain performance and optimize the number and location of sensors. Finding the damage location based on the optimal number and location of sensors will significantly reduce the detection time and the number of installed sensors. The proposed structural damage detection method uses stress analysis to find positions with large displacements, then divides the detection zones, selects highly discriminate sensors, analyzes the influence of sensor combinations within each zone to achieve optimization of sensor number as well as position selection, and establishes CNN models of the overall structure for damage detection of the joints. The proposed method was practically validated on benchmark data from the Qatar University Grandstand Simulator (QUGS). The experimental results show that the sensor demand rate is only 16.67% compared to the previous optimal number and location of sensors with no restrictions on the mounting position. Moreover, only four CNN models are required to predict damage/undamaged for the entire structure (30 connections). The accuracy of damage detection is improved to 96.62% using sensor combinations with influence.

The [Formula: see text] regularization is usually used to deal with the problems of under-determinacy and measurement noise for the conventional sensitivity-based model updating damage detection methods. However, the [Formula: see text] regularization technique often provides overly smooth solutions and thus cannot exhibit the sparsity of the structural damage due to the promotion of the 2-norm term on smoothness. In the study, a structural damage detection method is proposed based on an improved modal flexibility sensitivity function and an iterative reweighted [Formula: see text] (IR[Formula: see text] regularization. Specifically, the sensitivity function is established by introducing changes in the mode shapes into the derivative of eigenvalue and can be applied to identify the localized damage more accurately. Additionally, IR[Formula: see text] regularization is proposed to deal with the ill-posed problem of damage detection in a noisy environment. The proposed IR[Formula: see text] regularization is compared with the [Formula: see text] and [Formula: see text] regularizations through a numerical and an experimental examples. The numerical and experimental results indicate that the IR[Formula: see text] regularization can more accurately locate and quantify the single and multiple damages under the noise situation. The maximum identification errors are only 5.16% and 5.67%, respectively. Moreover, compared to the basic modal flexibility sensitivity function, the improved function is more sensitive to the damage. The maximum identification error of the improved function is less than 6%, while the relative errors are significantly larger in the basic function.

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

The concept of using tunable piezoelectric transducer circuitry to enhance the performance of frequency-shift based damage detection has been recently explored. While previous studies have shown promising features, the advantage of such an approach has not been fully realized due to the limitations in the associated damage identification algorithms. This research aims at advancing the state-of-the-art of this concept, where an iterative eigenvector updating method is developed to improve the accuracy of the damage detection scheme. The basic principle of this method is to iteratively update the eigenvectors of the damaged structure such that the information of the damage-induced eigenvector change can be included in the damage detection formulation without actually measuring the vibration modes. The inductance tuning method is extended to structures with multiple transducers and circuits. Numerical analysis on a plate structure demonstrated the effectiveness and accuracy of the new method in detecting both the location and severity of the structural damage. Nomenclature K = generalized stiffness matrix of the electro-mechanical integrated system e j K = elemental stiffness matrix of the j th element c K = coupling matrix between the mechanical structure and piezoelectric circuit p K = inverse capacitance matrix of the piezoelectric circuit s K = stiffness matrix of the mechanical structure only M = generalized mass matrix of the electro-mechanical integrated system s M = mass matrix of the mechanical structure only q = displacement vector of the mechanical structure Q = electrical charge flow vector in the circuit i ω = i th resonant frequency of the healthy integrated electro-mechanical system () d i ω = i th resonant frequency of the damaged integrated electro-mechanical system i λ = i th eigenvalue of the healthy integrated electro-mechanical system

With the aim to decrease the uncertainties of structural damage detection, two fusion models are presented in this paper. The first one is a weighted and selective fusion method for combing the multi-damage detection methods based on the integration of artificial neural network, Shannon entropy and Dempster-Shafer (D-S) theory. The second one is a D-S based approach for combing the damage detection results from multi-sensors data sets. Numerical study on the Binzhou Yellow River Highway Bridge and an experimental of a 20-bay rigid truss structure were carried out to validate the uncertainties decreasing ability of the proposed methods for structural damage detection. The results show that both of the methods proposed are useful to decrease the uncertainties of damage detection results.

Damage detection for beam-like structures using the normalized curvature of a uniform load surface

In this study, a two-stage damage detection procedure using a new approximation of modal strain energy (MSE) value is developed to boost the accuracy of structural damage localization in plate-type structures. The MSE is calculated using the center differential method for both intact and damaged states. The new formulas to approximate MSE are developed based on the nine-node isometric quadrilateral element that eliminates boundary conditions’ effect on damage detection results. The modal assurance criteria (MAC) is used as an indicator to choose the mode shapes with the best damage detection results. A global and a local MSE are proposed to enhance the accuracy of detecting the location of structural damage. An aluminum plate is used to verify the effectiveness of the proposed procedure with several damage scenarios. The results show that the new estimation of MSE value combined with MAC and the two-step MSE procedure can improve the accuracy of identifying the damage location compared to the original MSE procedure.KeywordsDamage detectionModal assurance criteriaModal strain energyPlate-type structureStructural health monitoring

STOCHASTIC SUBSPACE-BASED STRUCTURAL IDENTIFICATION AND DAMAGE DETECTION AND LOCALISATION—APPLICATION TO THE Z24 BRIDGE BENCHMARK

A structural damage detection method by integrating data fusion and random forests was proposed. The original acceleration signals were translated into energy features by wavelet packet decomposition. Then the processed energy features were fused into new energy features by data fusion. This can further enlarge the differences among all types of damages. Finally, random forests as an effective classifier was used to detect the multiclass damage. Numerical study on the benchmark model and an eight-storey steel shear frame structure model was carried out to validate the accuracy of the proposed damage detection method. The experiment results indicate that the damage detection method based on random forests and data fusion can improve damage detection accuracy in comparison with random forests alone, support vector machine alone, and support vector machine and data fusion techniques. Moreover, the proposed method has significantly better stability than several other methods.

To enhance the real-time performance and accuracy of damage detection in conveyor belts with poor image quality, insufficient sample feature information, and numerous scattered minor damages, we propose an improved visual detection method based on YOLOv5, incorporating Improved Depth-Separable Convolution (IDSC) and Scale Sequence Feature Fusion (SSFF). The IDSC method significantly reduces network parameters, achieving a lightweight model while maintaining detection accuracy, whereas the SSFF method effectively integrates spatial and multi-scale feature information, enhancing the identification of minor damage. Compared to existing methods, our approach provides a superior balance between detection speed and accuracy. The proposed IDSC-SSFF model achieves a mean average precision (mAP) of 95.65% while maintaining an inference speed of 139.1 FPS, meeting the stringent requirements of real-time defect detection in industrial environments.

Effect of response type and excitation frequency range on the structural damage detection method using correlation functions of vibration responses

The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data. First, the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments. In particular, the damage location is identified using two parameters: the pile location (PL) and the distance from the pile cap (DS). This study investigates the influences of various acoustic emission parameters, numbers of sensors, sensor installation locations, and the time difference on the prediction accuracy of PL and DS. In addition, correlations between the damage location and acoustic emission parameters are investigated. Second, the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments. Finally, a new damage detection and evaluation method for pile foundations is proposed. This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.

When using convolutional neural networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice [two-dimensional (2D)] or whole volumes [three-dimensional (3D)]. One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost. In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs, and to triplanar orthogonal 2D CNNs. The standard pseudo-3D method regards the neighboring slices as multiple input image channels. We additionally design and evaluate a novel, simple approach where the input stack is a volumetric input that is repeatably convolved in 3D to obtain a 2D feature map. This 2D map is in turn fed into a standard 2D network. We conducted experiments using two different CNN backbone architectures and on eight diverse data sets covering different anatomical regions, imaging modalities, and segmentation tasks. We found that while both pseudo-3D methods can process a large number of slices at once and still be computationally much more efficient than fully 3D CNNs, a significant improvement over a regular 2D CNN was only observed with two of the eight data sets. triplanar networks had the poorest performance of all the evaluated models. An analysis of the structural properties of the segmentation masks revealed no relations to the segmentation performance with respect to the number of input slices. A post hoc rank sum test which combined all metrics and data sets yielded that only our newly proposed pseudo-3D method with an input size of 13 slices outperformed almost all methods. In the general case, multislice inputs appear not to improve segmentation results over using 2D or 3D CNNs. For the particular case of 13 input slices, the proposed novel pseudo-3D method does appear to have a slight advantage across all data sets compared to all other methods evaluated in this work.

A damage detection process can be significantly enhanced if the nonlinear effects can be used when extracting damage-sensitive features from measured signals. The coherence function is typically used for nonlinearity identification by determining the extent of the output power linearly correlated with the input. However, the excitations are usually difficult to measure in actual tests. To overcome this limit, this article presents a signal-segments cross-coherence method for nonlinearity identification. By defining a signal-segments cross-coherence matrix and signal-segments cross-coherence index, the method can visually and quantitatively indicate the presence of nonlinearity. The innovation of the new method is that the coherence analysis process only depends on a single output signal, where input and baseline signals are not required. Then, a novel structural damage localization index is constructed by multi-point comparing of the signal-segments cross-coherence indices, based on the assumption that all the measured signals from different points on the structure have the same frequency bandwidth and components. To meet this requirement, a newly proposed signal decomposition method called analytic mode decomposition method is adopted. Numerical studies on a duffing oscillator and a 10 degree-of-freedom spring-damping-mass system were performed to demonstrate the nonlinear identification process and investigate the effectiveness and robustness of the signal-segments cross-coherence-based damage detection method. The results show that the signal-segments cross-coherence method can effectively indicate the appearance of nonlinearity by the signal-segments cross-coherence matrix and signal-segments cross-coherence index with strong noise robustness. And the proposed damage localization index can accurately detect the weak nonlinear damage even with severely noise-polluted signals. To further investigate the applicability of the new method, an experimental study was conducted on a steel simplified scale model of a monopile offshore wind turbine support structure. The results demonstrate that the proposed signal-segments cross-coherence method and the new damage localization index can be used to detect the bolt-loosening damage of the steel structure only with output signals.

In civil engineering, health monitoring of structure have been treated as an important subject. Health monitoring by using experimental measurements such as strain, acceleration, velocity, displacement, rotation and other parameters has been used for detecting changes, which may indicate damage or degradation. In recent, the applications of health monitoring of large structure have been facilitated by the developments in measurement, communications and computational technology gradually. Especially, large civil structures as bridges are damaged by fatigue loads or unexpected load such as earthquakes and finally reach to failure. Thus, a structural health monitoring and damage detection methods have been developed and attracted a great deal of public attention in recent decades. Actually many research efforts have been conducted to develop a health monitoring and damage detection techniques of existing civil structures. However, these methods are needed to improve to apply to a real civil structure. The purpose of this study is to develop a practical instrument to identify dynamic parameters of real civil structural systems.