Structural Damage Diagnosis Research Articles

Distribution Sub-Domain Adaptation Deep Transfer Learning Method for Bridge Structure Damage Diagnosis Using Unlabeled Data

Deep learning based bridge damage diagnosis methods can successfully use labeled data to detect bridge damage. These successful applications usually need that the training samples (source domain) and test samples (target domain) obey the same probability distribution. However, it is difficult to acquire a large amount of labeled data with damage information from actual bridges. It is also difficult to apply a model trained with bridge A to diagnose bridge B because of the distribution discrepancy of data from different bridges or environments. Therefore, transferring a well-trained damage diagnosis model to another bridge with unlabeled data remains a major challenge. Motivated by transfer learning, this paper proposes a new intelligent damage diagnosis method for bridges, namely, sub-domain adaptive deep transfer learning network (SADTLN), to solve the feature generalization problem in different bridges. In our method, a multi-kernel local maximum mean discrepancy (MK-LMMD) based sub-domain adaptation module, including a domain classifier for aligning the global distribution and a sub-domain multi-layer adaptation for aligning local distribution, is proposed for transfer learning, so that the learned features are domain-invariant. Experiments prove the effectiveness and advancement of the proposed method. This exploration will promote the practical application of intelligent bridge structural damage diagnosis.

Special Issue Editorial “Symmetry in Structural Health Monitoring”

Structural health monitoring refers to the strategy and process of the damage diagnosis and characterization of civil engineering structures, also showing the importance of structural health monitoring where symmetry is widely used, such as the building collapse accident in Changsha city, Hunan Province, in May 2022 [...]

Damage Diagnosis in Circular Structures using Cartesian Wavelet Analysis: A Comparison between Two Structural Signals

Circular structures are used in a wide variety of engineering mechanisms and devices. In this paper, an effective algorithm on the basis of the complex mappings is proposed to identify defects in circular structures using Cartesian damage detection techniques. The efficacy of the proposed algorithm is demonstrated through damage identification in circular plates using the Cartesian wavelet analysis. The vibration and thermal responses of the structure, as two important structural signals, are imported into the proposed algorithm to evaluate the abilities of the signals in identifying the damage location and severity. Finally, two experimental tests are conducted to explore the efficacy of the proposed algorithm in real applications.

Switching Kalman filter for damage estimation in the presence of sensor faults

Bayesian filtering based approaches for diagnosis of structural damage have been widely employed in structural health monitoring (SHM) research. The approach however may lead to an inaccurate alarm/decision due to the presence of faulty sensor/s. Nevertheless, sensor faults are inevitable during real field SHM in which sensor may malfunction or get detached from the structural surface, registering completely irrelevant information as measurement. Eventually, such erroneous information induce error in the estimation which leads to an inaccurate, sometimes divergent and impractical solution. The current study deals with Bayesian filtering based structural damage detection in the presence of one or multiple (consecutive) sensor faults. The damage detection is addressed with joint state-parameter estimation approach while a switching filtering strategy is employed for sensor fault detection. Switching approach employs multiple possible sensor fault models which are subsequently integrated to the measurement model of the joint estimation approach. The selection of the competent model (/switching between model ensembles) is undertaken recursively based on their likelihood against measured response. The proposed approach is tested on a numerical lumped mass model of a shear frame building, followed by a laboratory experiment on a cantilever beam. It has been perceived that estimation of health for structures measured with faulty sensors can actually lead to a false (positive and negative) alarm which can, however, be avoided by the employment of the proposed approach. The performance of the proposed approach is further established for healthy and damaged system with pre-existing and sudden sensor faults.

Possibilities of transmission electron microscopy in assessing the quality of bacterial cells and microbial population.

Determination of changes in the morphostructure of microorganisms under the influence of antibiotics, disinfectants, biocins, physical and chemical agents is carried out using the method of visualization of ultrathin sections in a transmission electron microscope. The aim is to develop methodological techniques for express preparation and visualization of the ultrastructure of bacteria in a transmission electron microscope to detect structural damage to cells and assess the quality of the microbial population. A technique of accelerated sample preparation for visualization of the fine structure of bacteria and diagnosis of structural damage to cells in a transmission electron microscope has been developed. The use of this method will make it possible to more effectively investigate the mechanisms of inactivation of microorganisms at different stages of contact with antibiotics, disinfectants, biocines, including various stressful, damaging and lethal physical and chemical effects.

Cracking Diagnosis in Fiber-Reinforced Concrete with Synthetic Fibers Using Piezoelectric Transducers

The addition of short fibers in concrete mass offers a composite material with advanced properties, and fiber-reinforced concrete (FRC) is a promising alternative in civil engineering applications. Recently, structural health monitoring (SHM) and damage diagnosis of FRC has received increasing attention. In this work, the effectiveness of a wireless SHM system to detect damage due to cracking is addressed in FRC with synthetic fibers under compressive repeated load. In FRC structural members, cracking propagates in small and thin cracks due to the presence of the dispersed fibers and, therefore, the challenge of damage detection is increasing. An experimental investigation on standard 150 mm cubes made of FRC is applied at specific and loading levels where the cracks probably developed in the inner part of the specimens, whereas no visible cracks appeared on their surface. A network of small PZT patches, mounted to the surface of the FRC specimen, provides dual-sensing function. The remotely controlled monitoring system vibrates the PZT patches, acting as actuators by an amplified harmonic excitation voltage. Simultaneously, it monitors the signal of the same PZTs acting as sensors and, after processing the voltage frequency response of the PZTs, it transmits them wirelessly and in real time. FRC cracking due to repeated loading ad various compressive stress levels induces change in the mechanical impedance, causing a corresponding change on the signal of each PZT. The influence of the added synthetic fibers on the compressive behavior and the damage-detection procedure is examined and discussed. In addition, the effectiveness of the proposed damage-diagnosis approach for the prognosis of final cracking performance and failure is investigated. The objectives of the study also include the development of a reliable quantitative assessment of damage using the statistical index values at various points of PZT measurements.

Optimal residual subspace model for structural damage diagnosis: an approach independent of operational and environmental variations

Optimal residual subspace model for structural damage diagnosis: an approach independent of operational and environmental variations

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

Experimental Study of Source Localization in Acoustic Emission using Triangulation Method

Conventional repair and rehabilitation techniques have proven effective only to treat damage and arrest it. Hence, the need to prevent the damage, to know the growing damage before a complete collapse is necessary. Thus, acoustic emission is a proven method for structural health monitoring, to find and rectify the damage at its early stage. The Acoustic emission technique could be directly applied for monitoring large structures made of concrete, aircrafts & bridges. It helps in recognition of structural damage prior, which helps in avoiding catastrophic failure. The proper and efficient detection of damage makes structure safer for the public. This is a low-cost & less intricate technique than conventional methods. Quantitative criteria of acoustic sensing will provide efficient diagnosis and prognosis of structural damage. This paper surmounts the triangulation method of source localization of acoustic emission. The data is gathered by the sensors and then used to know the location of damage/source and respective characteristics of the wave. MATLAB is used for defining the energy of the signals captured by the sensors. Thus, the signal parameter’s study and verification of the source’s location using sensors is justified to be correct in the acoustic emission technique.

Sonographic and Duplex Evaluation of Synovial activity in Wrist and Hand Joints in Rheumatoid Arthritis

Abstract Background Rheumatoid arthritis is a long-term autoimmune disease and inflammatory disorder. The damaging effect on cartilage, bone, ligaments, and tendons has raised the importance of the accurate diagnosis of structural damage, disease activity and severity assessment to enable therapeutic decisions and to evaluate disease outcome. US is non invasive, relatively cheap and widely accessible imaging tool that facilitates the detection of synovitis joint effusin and erosions more better than clinical, laboratory investigations alone and x-ray. Aim of the Work assess the role of of gray-scale and Doppler ultrasound early detection and characterization of synovitis and structural bone changes in wrist and hand joints in patients with Rheumatoid Arthritis by comparing qualitative and quantitative US parameters with clinical and laboratory indicators of disease activity. Patients and Methods A cross sectional study conducted onthirty five adult patients with RA . All the patients were diagnosed according to the 2010 ACR/European League . The patients were recruited from the Rheumatology and Rehabilitation Outpatient Clinic, Faculty of Medicine, Ain Shams Hospitals .The study was approved by the ethical committee, and consents were taken from all patients enrolled in the study. Patients assessed by history taking, Clinical examination, Laboratory investigations and US gray scale and US power Doppler was done for each patient. Results This study included 35 patients with established RA, 27 females (77.1%) and 8 males (22.9%). Ranged in age between 20-70 years with mean age [45.66±12.76],the mean CDAS28 was [4.62±1.37], ESR [44.93±20.79], CRP [15.92±9.03], 33 patients (94.3%) were found to have synovial thickening, 14 patient (40%) were found to have increased vascularity by PD, joint effusion was detected in 12 patient (34.3%) particularly in MCP joints, 13 (37.1%) had bone erosion and only 9 patients (25.7%) had Tenosynovitis, most of them were detected at the extensor compartment of the wrist joint.positive correlations found between the clinical disease activity indices, laboratory investigations, duration of illness and components of the US findings. Conclusion Ultrasound is non invasive, relatively cheap and widely accessible imaging tool that facilitates the detection of synovitis and erosions,, high resolution gray scale images combined with Doppler technique have made it possible to detect slow low volume blood flow, thus improving the outcome of the standard clinical assessment in Rheumatoid synovial inflammation.

On the mitigation of the RAPID algorithm uneven sensing network issue employing averaging and Gaussian blur filtering techniques

Ultrasonic guided waves-based Structural Health Monitoring (SHM) is a promising solution to perform damage diagnosis in plate-like structures. In this field, the Reconstruction Algorithm for Probabilistic Inspection (RAPID) is one of the most widely used tomographic algorithms to perform active damage detection and localisation. Even though this algorithm is easily implementable, very versatile and satisfactorily accurate, it presents some disadvantages, such as the production of artefacts determined by non-uniform distributions of the sensing network density. In the present article, a processing and spatial filtering technique is proposed, which strongly mitigates the aforementioned problem. The proposed methodology is validated using a numerical database created for a large carbon fibre reinforced polymer (CFRP) with a through-the-thickness hole.

Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study.

Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing displacement transducers between the ground and floors in real-world buildings is unrealistic due to lack of reference points and structural scales and complexity. Alternatively, structural displacements can be acquired using computer vision-based motion extraction techniques. These extracted motions not only provide vibrational responses but are also useful for identifying the modal properties. In this study, three methods, including the optical flow with the Lucas–Kanade method, the digital image correlation (DIC) with bilinear interpolation, and the in-plane phase-based motion magnification using the Riesz pyramid, are introduced and experimentally verified using a four-story steel-frame building with a commercially available camera. First, the three displacement acquiring methods are introduced in detail. Next, the displacements are experimentally obtained from these methods and compared to those sensed from linear variable displacement transducers. Moreover, these displacement responses are converted into modal properties by system identification. As seen in the experimental results, the DIC method has the lowest average root mean squared error (RMSE) of 1.2371 mm among these three methods. Although the phase-based motion magnification method has a larger RMSE of 1.4132 mm due to variations in edge detection, this method is capable of providing full-field mode shapes over the building.

Study on Bridge Structure Damage and Health Diagnosis Method Based on Health Monitoring

Study on Bridge Structure Damage and Health Diagnosis Method Based on Health Monitoring

Statistical model-based optimization for damage extent quantification

Damage localization and quantification constitute different aspects of structural damage diagnosis, which are of particular interest in the Structural Health Monitoring field. Therein, a classical solution is model updating, where the parameters of a finite element model of the possibly damaged structure are optimized to match with the corresponding parameters estimated from its vibration responses. To avoid ill-posedness of the classical finite element updating problem, damage localization and quantification can be treated separately. First, the information about regions or clusters of possibly damaged elements in the structure is obtained by a damage localization method. Then, this information is used to reduce the number of parameters for damage quantification. A framework combining the advantages of methods for damage localization with model optimization is proposed in this paper. For the exploration of the clustered physical model space, a stochastic optimization algorithm is coupled with the evaluation of the statistical properties of the MAC and frequency differences between the numerical model and the estimated modes for an adequate treatment of the data-based uncertainties. Herein, the development of the statistical properties of the MAC estimate is an important step, which is based on a recent quadratic framework that is adapted to the context of the inner product between an estimated mode shape and a numerical mode shape. This statistical information is used in the formulation of the objective function as well as in a data-driven stopping criterion for the optimization search. The proposed framework is validated on numerical simulations of a beam model, where damage at multiple locations is quantified up to the clustering precision.

A Novel Bridge Damage Diagnosis Algorithm Based on Deep Learning with Gray Relational Analysis for Intelligent Bridge Monitoring System

In recent years, intelligent structural damage diagnosis algorithms using machine learning have achieved much success. However, because of the fact that in real bridge applications, the working environment (load, temperature, and noise) is changing all the time, degradation of the performance of intelligent structural damage diagnosis methods is very serious. To address these problems, a novel bridge diagnosis algorithm based on deep learning is proposed. Our contributions include: First, we proposed an improved denoising auto‐encoder‐based deep neural networks, which is optimized by the gray relational analysis. It is able to automatically extract high‐level features from raw signals via a multi‐layer extraction to satisfy any damage diagnosis objective and thus does not need any time consuming denoising prepossessing. The model can achieve high accuracy under noisy environment. Second, the algorithm does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working environment is changed. Numerical simulations and experimental investigations on real bridges conducted to present the accuracy and efficiency of the proposed algorithm, comparing with other commonly machine learning‐based algorithms. The result shows it is deemed as an ideal and effective method for damage diagnosis of bridge structures. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A decentralized unsupervised structural condition diagnosis approach using deep auto‐encoders

AbstractIn this paper, the ideas of deep auto‐encoder (DAE) and manifold learning are adopted to solve the problem of structural condition diagnosis. A scalable decentralized end‐to‐end unsupervised structural condition diagnostic framework is proposed. Three damage diagnosis mechanisms are clarified. The structural damage diagnosis approaches are presented from the latent coding domain and the time domain, respectively. In the latent coding domain, an undercomplete DAE is established to extract the distribution of the low‐dimensional manifold of the signal. On the contrary, in the time domain, an overcomplete DAE is adopted to extract the reconstruction error of the signal. Subsequently, normalized damage quantitative indicators are developed in the two domains. The damage localization method is also clarified. The proposed method can extract features directly from original vibration data without the need for additional signal preprocessing techniques. More importantly, the algorithm relies only on the output signals and does not require a numerical or scale model. This framework can be used to identify, locate, and quantify structural damages. The validity of the diagnostic framework is verified using a well‐designed laboratory benchmark structure. A large‐scale grandstand structure is further used to prove the ability of the proposed method for identifying slight structural damage caused by the loosening of joint bolts. The results clearly demonstrate an elegant performance of the proposed damage detection algorithm in structural condition assessment and damage localization.

Using an Improved Ant Lion Optimizer Algorithm and Wireless Network to Explore Nondestructive Testing Technology for Water Conservancy Engineering

The damage diagnosis of traditional hydraulic engineering structures is largely based on the modal analysis of contact approach, which makes it difficult to carry out a rapid diagnosis of the damage following an earthquake. In view of enhancing the efficiency of water conservancy engineering damage detection, this study was aimed at improving the traditional ant lion optimizer algorithm, combining it with wavelet transform to detect image denoising and effectively extract the features of the damage. Meanwhile, in terms of signal transmission and collection, the study adopted a geological radar as the carrier. In addition, the study verified the application prospects of nondestructive testing technology in the field of water conservancy engineering by evaluating the quality of a specific water conservancy project. Finally, the effectiveness of the algorithm was analyzed via experimental research. The results demonstrate that the proposed algorithm has clear advantages over traditional detection techniques.

Performance comparison of non-adaptive and adaptive optimization algorithms for artificial neural network training applied to damage diagnosis in civil structures

The structural health monitoring of civil structures has been highlighted by the perception that the cost involved in the prevention of structural accidents is lower than the cost of correcting them. In addition, in the case of large structures (e.g. bridges, dams and industrial sheds), it is desired that the damage diagnosis should occur quickly from real-time monitoring without interrupting the use of the structure. The structural diagnosis may be performed based on vibration measurements, considering that the structural damages modify modal parameters of the structure (e.g. frequencies and mode shapes). With these dynamic responses, the application of appropriate computational tools to recognize and classify structural damage is intended, and artificial neural networks (ANN) have gained a lot of attention in achieving these goals. In this work, the methodology of diagnosing structures by real-time monitoring is originally developed and based on initial definition of the optimal topology, avoiding both the use of multiple hidden layers and the combination of several neural networks, and by applying of non-adaptive and adaptive first-order algorithms for agile network training, in order to be mathematically suitable for continuous and real-time monitoring, which allows updating both the dataset and neural parameters without greater computational effort. As some of these algorithms were not addressed in the diagnosis of civil structures with the aforementioned hypotheses, until this research, the performance of each algorithm was verified in case studies that simulate classic structural systems adopted in most civil buildings. Finally, the results endorse the feasibility of improving the structural diagnosis based on the training of a simple neural network, with one hidden layer, associated with non-adaptive or adaptive first-order optimizers that guarantee the agile assessment of structural integrity in real time.

Nonparametric nonlinear restoring force and excitation identification with Legendre polynomial model and data fusion

Identification of nonlinear restoring force and dynamic loadings provides critical information for post-event damage diagnosis of structures. Due to high complexity and individuality of structural nonlinearities, it is difficult to provide an exact parametric mathematical model in advance to describe the nonlinear behavior of a structural member or a substructure under strong dynamic loadings in practice. Moreover, external dynamic loading applied to an engineering structure is usually unknown and only acceleration responses at limited degrees of freedom of the structure are available for identification. In this study, a nonparametric nonlinear restoring force and excitation identification approach combining the Legendre polynomial model and extended Kalman filter with unknown input is proposed using limited acceleration measurements fused with limited displacement measurements. Then, the performance of the proposed approach is first illustrated via numerical simulation with multi-degree-of-freedom frame structures equipped with magnetorheological dampers mimicking nonlinearity under direct dynamic excitation or base excitation using noise-polluted measurements. Finally, a dynamic experimental study on a four-story steel frame model equipped with a magnetorheological damper is carried out and dynamic response measurement is employed to validate the effectiveness of the proposed method by comparing the identified dynamic responses, nonlinear restoring force, and excitation force with the test measurements. The convergence and the effect of initial estimation errors of structural parameters on the final identification results are investigated. The effect of data fusion on improving the identification accuracy is also investigated.

The Architecture Design and Implementation of Aircraft Structural Fault Assistant Decision System Based on Data Analysis

That the aircraft structural damage diagnosis and prediction is very important for flight safety. In this paper, the aircraft structural damage can be predicted based on the maintenance data analysis and the structure fault diagnosis and assistant decision are realized. Thus, the maintenance management of aircraft structure damage can be developed. According to the actual service data, both the structural damage prediction and decision assistant support system are designed, which apply the micro-service architecture and Vue.Js front-end development technology. Thus, the effective container server and excellent usability are achieved.