Robust damage detection in smart structures

In this paper, an improvement of the elliptical triangulation method for damage detection using Lamb waves is presented. The damage is the main cause of structural failure and often occurs on structures. In order to avoid sudden failure, a special attention was given in the past decades to the damage detection in structures. In order to obtain efficient damage detection techniques, the structural health monitoring has been the main research topic of many scientists worldwide. The elliptical triangulation method, proposed in this paper, is a non-destructive method based on measurement of Lamb waves. This method, through the calculation of the time of flight of the signals and the actuator-sensor positioning, allows to identify position and dimension of the damage. The application of the method to the metallic structures and to the composite material structures is presented in this paper. The complexity connected with the uncertainty of the waves’ propagation speed due to the anisotropy of the composite materials has been explored through an iterative approach. The initialization of the wave propagation speed at first tentative iteration is the key issue for the convergence of the method. Seven different conditions were used to validate the method on both metallic and composite structures combining two damage shapes, two damage dimensions (effective damaged area), and three different positions. Upon evaluating the effectiveness, the method has been applied at two composite panels in order to detect by test the post-impact damages. Tests results have been compared with the numerical ones. The feasibility of the elliptical triangulation method to detect the damage (evaluating the damage position and area) has been proved using the ultrasonic C-Scan.

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.

Structural health monitoring (SHM) and damage detection have attracted great interest in recent decades, in meeting the challenges of assessing the safety condition of large-scale civil structures. By wiring remote sensors directly to a centralized data acquisition system, traditional structural health monitoring systems are usually costly and the installation is time-consuming. Recent advances in wireless sensing technology have made it feasible for structural health monitoring; furthermore, the computational core in a wireless sensing unit offers onboard data interrogation. In addition to wireless sensing, the authors have recently developed a mobile sensing system for providing high spatial resolution and flexible sensor deployment in structural health monitoring. In this study, transmissibility function analysis is embedded in the mobile sensing node to perform onboard and in-network structural damage detection. The system implementation is validated using a laboratory 2D steel portal frame. Simulated damage is applied to the frame structure, and the damage is successfully identified by two mobile sensing nodes that autonomously navigate through the structure.

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

In the present work, the design of thermoplastic induced welded joints was carried out considering different aspects related to structural performance, manufacturability and structural health monitoring capability. Three lap-joints configurations were considered: two of them having welded plates with two different carbon fibers plies stacked layups and one obtained by welding carbon fibers fabric laminates. A numerical analysis was carried out by performing (i) structural simulations to assess the mechanical properties of the joints, (ii) induction welding process simulations to evaluate temperature distribution in the welded interface area and (iii) damage detection assessment by reproducing the response of a structural health monitoring sensor bonded in correspondence of the joint overlap area. Electromagnetic-based structural health monitoring techniques were employed to evaluate local variation of electromagnetic characteristics due to defect presence. The results obtained show that the design choices have a significant influence on the physical properties of the welded material such as the electrical conductivity anisotropy. The different induced currents distribution obtained during both the fabrication and damage inspection phase, affects respectively the manufacturing and detection capability. An experimental validation was carried out by mechanical tests under quasi-static loading for the case with the best tradeoff between structural performance, manufacturing feasibility and damage detection capability. The proposed approach show the importance of taking into account the requirement of damage detectability from an early stage of the design.

Experimental system identification of the dynamics of a vibro-impact beam with a view towards structural health monitoring and damage detection

Structural health monitoring and damage detection can help people avoid emergency casualties and take preventive measures, and it is very important in practical application. After consulting authoritative information, the structural health often affected by the surrounding environmental safety factors and structural safety factors. This paper begins with the harm of the inadequate building safety and the factors affecting building safety. To detect structural health problems and avoid many kinds of disasters, three monitoring methods have been mentioned, two new building safety detection methods and a damage detection scheme, called wireless sensor network subsystem, global navigation satellite system technology, and ultrasonic flaw detection technology. The principles of three methods are mainly expounded, and their advantages and disadvantages are analyzed, by putting forward suggestions for future research prospects and giving some practical examples. Finally, it is concluded that wireless sensor network method is simple to achieve but the monitoring data is single; GNSS system has high precision but cannot be widely used; And ultrasonic flaw detection technology can detect steel damage single efficiency.

• Optimal reduced-order vehicle-bridge interaction model. • Reliable Structural Health Monitoring (SHM) and damage detection. • VBI computational framework with significantly reduced computational cost. • Enriched model with realistic rail irregularities. • Experimental validation over multiple operating speeds, loads, and damage scenarios. Bridge structures subjected to moving vehicle loads exhibit complex dynamic behavior influenced by structural flexibility and rail surface irregularities. The correct approach and optimal modeling of vehicle-bridge interaction effects are crucial for the accurate computational determination of dynamic bridge vibrations, for reliable Structural Health Monitoring (SHM) and damage detection. The purpose of this work is twofold. First, a novel computational framework is introduced to generate an optimal reduced-order Vehicle-Bridge Interaction (VBI) model that captures the essential dynamics of the system with significantly reduced computational cost. This process begins with the development of a high-fidelity finite element (FE) model of the bridge which is updated using experimental modal data. This model is then reduced using Component Mode Synthesis (CMS) to enable efficient simulation while preserving the essential dynamic behavior and is combined with a simplified vehicle model enriched with realistic rail irregularities, producing an accurate and computationally efficient VBI system. Second, the study introduces a classification framework based on a lightweight Convolutional Neural Network (CNN) trained on numerically generated data from the validated VBI model. The CNN operates on frequency-domain features derived from bridge acceleration responses, which reflect damage-induced changes in stiffness and energy distribution of the structure. Using these features and an ensemble of independently trained CNNs, the framework classifies the structural health condition of the bridge.

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 high structural deficient rate poses serious risks to the operation of many bridges and buildings. To prevent critical damage and structural collapse, a quick structural health diagnosis tool is needed during normal operation or immediately after extreme events. In structural health monitoring (SHM), many existing methods will have limited usefulness in the quick damage identification process because (1) the damage event needs to be identified quickly, and (2) postdamage information is usually unavailable. To address these drawbacks, we propose a new damage detection and localization approach based on stochastic time series analysis. Specifically, damage sensitive features, which are extracted from vibration signals, follow different distributions before and after a damage event. Hence, we use optimal change-point detection theory to find the time of damage occurrence. Because existing change-point detectors require the postdamage feature distribution, which is unavailable in SHM, we propose a maximum likelihood method for learning the distribution parameters from the time-series data. The proposed damage detection using estimated parameters achieves optimal performance. Also, we utilize the detection results to find damage location without any further computation. Validation results show highly accurate damage identification in American Society of Civil Engineers benchmark structures and two shake table experiments.

Structural health monitoring (SHM) is an important application area for wireless sensor networks. SHM techniques attempt to autonomously detect and localize damage in large civil structures. Structural engineers often implement and test SHM algorithms in a higher level language such as C/Matlab. In this paper, we describe the design and evaluation of NETSHM, a sensor network system that allows structural engineers to program SHM applications in Mat-lab or C at a high level of abstraction. In particular, structural engineers do not have to understand the intricacies of wireless networking, or the details of sensor data acquisition. We have implemented a damage detection technique and a damage localization technique on a complete NETSHM prototype. Our experiments on small and medium-scale structures show that NETSHM is able to detect and localized damage perfectly with very few false-positives and no false negatives, and that it is robust even in realistic wireless environments.

Vibration-based damage detection methods, leveraging structural response data to identify structural damage, play an important role in structural health monitoring, particularly for critical infrastructure like bridges and high-rise buildings. In recent years, deep learning, which can autonomously learn damage-sensitive features and classify damage from complex datasets, has been widely used in structural damage detection. This paper reviews deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders, applied in vibration-based structural damage detection for civil structures over the past ten years, highlighting their advantages and disadvantages. The challenges and future trends for vibration-based damage detection by using deep learning are summarized.

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Bolt loosening is a significant concern in structural health monitoring (SHM), and the nonlinear wave modulation (NWM) technique shows promise for its detection. However, temperature variations affect piezoelectric sensors, complicating detection. This study examines thermal stress effects on NWM-based damage detection by simulating a beam with boundary loosening under thermal stress. A temperature compensation method was developed to mitigate these effects. A multibolt structure was also analyzed using a hybrid deep learning model combining convolutional neural networks (CNNs) and Dezert-Smarandache Theory (DSmT) for multisensor data fusion. Results show that thermal stress significantly reduces classification accuracy. At 60 °C, the Damage Index dropped to zero, and CNN classification accuracy fell from 87.5% (at 25 °C) to 27.08%. Applying the compensation algorithm restored CNN accuracy to 87.27%. Statistical tests confirmed the compensated results at 60 °C were comparable to those at 25 °C. The DSmT-based fusion model achieved 98.92% accuracy on average, and even at 60 °C, maintained 98.80% accuracy with compensation for a multibolted plate. It combines the probabilistic outputs of multiple CNNs using the PCR6 rule, which redistributes conflicting evidence to improve decision reliability. This fusion strategy effectively handles uncertainty across sensors and significantly enhances classification robustness under temperature variations. In conclusion, thermal stress adversely impacts SHM damage detection, but the proposed compensation method effectively restores performance. Moreover, integrating multisensor fusion with DSmT greatly enhances accuracy, offering a robust solution for SHM in varying thermal environments.

An important aspect of the smart structures concept is the automated structural health monitoring of aircraft structures. This requires detecting damage and assessing its effect on structural performance. The authors present a new approach to detecting and defining large area damage on a structure. The approach is based on using a neural network to deduce the damage size and location from measured strain values at discrete locations. The neural network is trained using results from finite-element analyses. Several examples illustrating this approach are presented.