We present a novel approach to evaluating mechanical features of structures using correlation coefficients and fast Fourier transform analysis. Although correlation coefficient is always a sensitive parameter to changes of mechanical properties of real structures, it is rarely used due to high complication in data collection. To overcome this drawback, we propose fast Fourier transform analysis to

Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed

Accurate finite element models play significant roles in the design, health monitoring and life-cycle maintenance of long-span bridges. However, due to uncertainties involved in finite element modelling, updating of the finite element model to best represent the real bridge is inevitable. This is particularly true after a long-span bridge experiences a moderate or severe earthquake and suffers some

Practical ultrasonic structural health monitoring systems must be able to deal with temperature changes and some signal amplitude/phase drift over time; these issues have been investigated extensively with low-frequency-guided wave systems but much less work has been done on bulk wave systems operating in the megahertz frequency range. Temperature and signal drift compensation have been investigated

Over the last several decades, structural health monitoring systems have grown into increasingly diverse applications. Structural health monitoring excels with large data sets that can capture the typical variability, novel events, and undesired degradation over time. As a result, the efficient storage and processing of these large, guided wave data sets have become a key feature for successful application

Structural health monitoring systems have been widely implemented to provide real-time continuous data support and to ensure structural safety in the context of structural integrity management. However, the quantification of the potential benefits of structural health monitoring systems has not yet attracted widespread attention. At the same time, there is an urgent need to develop strategies, such

The bearing vibration signal possesses nonlinear and non-stationary characteristics; hence; it is difficult to diagnosis the faults in the bearing under different working conditions. In this article, a new scheme has been proposed based on complete ensemble empirical mode decomposition with adaptive noise and corrected conditional entropy to recognize the different class of faults in bearing. The mode

In the application of structural health monitoring, the measured data might be temporarily or permanently lost due to sensor fault or transmission failure. The measured data with a high data loss ratio undermine its ability for modal identifications and structural condition evaluations. To reconstruct the lost data in the field of structural health monitoring, this study proposes a deep convolutional

Integrity testing of deep cast in situ concrete foundations is challenging due to the intrinsic nature of how these foundations are formed. Several integrity test methods have been developed and are well established, but each of these have strengths and weaknesses. A relatively recent integrity testing method is thermal integrity testing. The fundamental feature is the early age concrete release of

The interdigital transducer–based scanning laser Doppler vibrometer has recently been introduced to efficiently generate the symmetric mode for damage detection of shallow defects in thick plates. To measure shallow defects in a carbon steel plate, the excitation frequency is optimized based on the analysis of wavenumber sensitivity and degree of separation between modes. Even though the interdigital

In this article, a feasibility study for the visualization of hidden damage using an integrated high-speed camera system was carried out. A thin, planar, and low-modulus high-density polyethylene plate with surrogate damage was chosen to represent a damaged structure for the proof of concept, and two different damage scenarios (mimicked by attaching lightweight rectangular/circular masses to the back

Flood-induced scour is among the most common external causes of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fails every year, whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Scour assessments are currently based on visual inspections, which are time-consuming and expensive. Nowadays, sensor and communication

Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the

This article describes a new damage visualization method to investigate and analyze propagating guided Lamb waves using analyses of wavefield spatial gradients. A laser ultrasonic interrogation system was used to create full-field ultrasonic data measurements for ultrasonic wavefield imaging. The laser scanning process was performed based on both a raster scan and a circle scan. From the high-resolution

Delamination is a failure mechanism which is intrinsic of laminated fibre-reinforced plastics and possibly one of the major concerns of laminated composite structures, since, under certain conditions, delaminations can grow up to an hazardous extent without visible traces. In order to keep pace with recent condition-based maintenance requirements, proper validated diagnostic and prognostic methods

This article introduces the use of emerging augmented reality technology to enable the next generation of structural infrastructure inspection and awareness. This work is driven by the prevalence of visual structural inspection. It is known that current visual inspection techniques have multiple sources of variance that should be reduced in order to achieve less ambiguous visual inspections. Emerging

In order to strengthen and repair existing concrete structural elements, fibre-reinforced polymer composites are often externally bonded using structural adhesives. It is thus desirable to monitor the in situ performance of the sandwiched adhesive layer in such fibre-reinforced polymer–strengthened systems via its stiffness and strength gain throughout the curing process. The electromechanical impedance

For leakage identification in water distribution networks, if each node is used as a category label of the classifier model, the accuracy of the classifier model will be low because of similar leakage characteristics. By clustering the nodes with similar leakage characteristics and using all the possible combinations of leakages as the category labels of the classifier model, the accuracy of the classifier

Assuming the distance between two nodal points of a specific cable vibration mode as the effective length of a pinned–pinned cable, mode shape–aided cable tension estimation methods are employed to estimate the cable force. This article proposes a framework based on digital image correlation technique for remote measurement of the dynamic displacement time history of cables in cable structures. Frequency

Accurate temperature measurement is a crucial aspect of structural health monitoring and prognosis. Conventional temperature measurement devices are either incapable of measuring subsurface temperatures in solids or need to be invasively installed. This study investigates the use of an ultrasonic technique for non-invasive measurement of subsurface temperatures in steel components; the temperature

This work dives into the spectral realm of acoustic emission waveforms. The acoustic emission waveforms carry a footprint of source, its mechanism, and the information of the medium through which it travels. The idiosyncrasies of these waveforms cannot be visualized from the time-domain parameters. The complex fracture process of the heterogeneous composite, such as concrete, reflects in the spectral

The excellent properties of advanced composite materials provide great opportunities for making industrial structures large-scale and intelligent. Liquid composite molding process is suitable for manufacturing complex large-scale composite structures and has the potential for low cost and mass production. In present work, the concept of Networked Elements for Resin Visualization and Evaluation network

This study presents a novel method for composite damage identification using Lamb wave. A probabilistic integration of the elliptical loci method and the RAPID (reconstruction algorithm for probabilistic inspection of defects) in a Bayesian framework is proposed. The proposed method allows for the incorporation of multiple damage sensitive features in a rational manner to improve the reliability and

The holy grail of structural health monitoring is the quantitative linkage between data and decisions. While structural health monitoring has shown continued growth over the past several decades, there is a persistent chasm between structural health monitoring and the ability of structure owners to make asset management decisions based on structural health monitoring data. This is in part due to the

The goal of this study is to develop a structural health monitoring methodology for smart self-sensory carbon-based textile reinforced concrete elements. The self-sensory concept is based on measuring the electrical resistance change in the carbon roving reinforcement and by means of an engineering gage factor, correlating the relative electrical resistance change to an integral value of strain along

Axles are widely used mechanical components in rotating machines. In many applications, axles are exposed to varying environmental and operational conditions, including for example, temperature, load and rotational speed. To apply acousto-ultrasonics–based structural health monitoring for axles, it is in some cases not economically feasible to collect data and to construct a baseline model for each

For structural health monitoring, estimating the external load is a typical ill-posed problem but significant. Because with the external force and the structural finite element model, any required response can be calculated, which is advantageous for further structural health monitoring works. This article first defines an underdetermined equation using a limited number of in-field measurements and

Considerable amount of electromechanical admittance data needs to be collected, transmitted and stored during in-situ and long-term structural health monitoring applications, and data loss could be inevitably met when processing the monitoring electromechanical admittance signals. In this article, an innovative compressed sensing–based approach is proposed to implement data recovery for electromechanical

Vibration-based structural monitoring is a fundamental tool to assess the conditions of existing structures, in their real operating state. In particular, as concerns masonry buildings, although a large part of the Italian and European historical heritage is composed of vaulted structures, comparatively few papers in the literature are targeted to the study of the dynamic behavior of vaulted systems

In this article, a hierarchical approach is proposed for the design and assessment of a guided wave-based structural health monitoring system for the detection and localisation of barely visible impact damage in composite airframe structures. The hierarchical approach provides a systemic and practical way to establish guided wave-based structural health monitoring systems for different structures in

This article presents a design procedure for structural health monitoring systems based on bulk wave ultrasonic sensors for structures fabricated from polycrystalline materials. When designing a monitoring system, maximum coverage per transducer is a general requirement in order for the system to be economic. For coarse-grained polycrystalline materials, monitoring is often made challenging by low

By providing accurate and efficient crack detection and localization, image-based crack detection methodologies can facilitate the decision-making and rehabilitation of the roadway infrastructure. Deep convolutional neural network, as one of the most prevailing image-based methodologies on object recognition, has been extensively adopted for crack classification tasks in the recent decade. For most

Bearings are used as the most important load-carrying transmission components in various machines, thus subjecting to a number of faults including wear, fatigue pitting, cracks and so on. Fault detection and diagnosis of bearings can effectively prevent the machine from such typical failures and subsequent consequences. The faults in bearings can lead to the vibration signals that exhibit cyclostationary

Vibration-based damage identification is a non-destructive method that enables the health monitoring of civil infrastructures. It aims to detect the presence and location of damage by measuring changes in the vibration characteristics of these structures. Unfortunately, the most popular vibrational parameters – natural frequencies and modal displacements – have a low sensitivity to certain types of

The model errors are often inevitably encountered when a practical structure is analyzed by a mathematical baseline model, and such model errors would have undesired effects on damage identification. Aiming to alleviate the effect of the model errors, a new Measurement-Changes-Correction strategy that simply uses measurement changes to correct the measured data is developed in this article. To this

Since bearing fault signal under complex running status is usually manifested as the characteristics of nonlinear and non-stationary, which implies it is difficult to extract accurate affluent features and achieve effective fault identification via conventional signal processing tools. In this article, a hybrid intelligent fault identification scheme, the combination of hierarchical dispersion entropy

Modern infrastructure systems, such as bridges, dams, power generation stations, and buildings increasingly have an intrinsic cyber-physical nature to them. Infrastructure now commonly, includes actuators, network connections, sensors, control systems, and computational resources. It is of increasing concern that modern infrastructure is vulnerable to cyber-attacks that can damage both the cyber and

Structural health monitoring of thin plate and beam structures using ultrasonic guided wave techniques has been widely studied and demonstrated advanced capabilities dependent on detailed analysis of specific guided wave modes. A common setup employs the d31 electromechanical coupling of piezoelectric wafer active sensors mounted on the surface of a beam or plate. Analysis of output signals from these

Ultrasonic guided waves are used in non-destructive testing and structural health monitoring solutions for long-range inspection, in applications ranging from Civil Engineering to Aerospace. In order to ease the inspection process, it is generally preferable to generate a carefully selected single mode. Although single mode Lamb wave generation is not difficult to achieve in infinite plate-like structures

While machine learning has been increasingly incorporated into structural damage detection, most existing methods still rely on hand-crafted damage features. For a given structure, the performance of detection is heavily impacted by the quality of features, and choosing the optimal features may be difficult and time-consuming. Various time series classification algorithms studied in machine learning

The acoustic source localization in 3D structures is a challenging task especially if the structure is heterogeneous. A large number of unknown parameters in a 3D heterogeneous structure require a large number of sensor arrays for accurate source localization. In this article, a localization technique is proposed using triangular pyramid shaped sensor clusters. The time difference of arrival of acoustic

A real-time and durable system for scour process monitoring with sufficient spatial precision is in pressing need for bridge safety management. In light of this, an innovative bundled time domain reflectometry sensing cable was recently proposed to enhance the time domain reflectometry technique for scour monitoring. However, current development only dealt with the construction of bundled sensing cable

Rolling element bearings eventually become worn and develop surface defects, such as spalls, dents, and pits. Previous researchers have used bearings with defects that have sharp 90° rectangular edges to develop methods for the estimation of the size of a defect. However, these idealized rectangular defects do not occur in the real world, they are in fact sloped. Tests on bearings with sloped edges

In this research work, the acoustic emission results obtained from testing double cantilever beam specimens with carbon fibre reinforced plastic laminates are analysed. The acoustic emission descriptors such as amplitude, frequency centroid, counts, duration and risetime are clustered using k-means++ algorithm. An unconventional and innovative way of using the acoustic emission descriptors, after the

Laser cladding technology has become a central issue in industrial research and application recently. Due to the complexity of the processing and the difficulty of quantitative analysis, it is particularly important to effectively and reliably detect the forming quality of the cladding layer. Therefore, the laser cladding processing condition of metallic panels is monitored and identified based on

After a seismic event, buildings need to be inspected to confirm their safety prior to reoccupation. As such, the rapid evaluation of the condition of individual buildings is important for minimizing disruption to lives and business. However, traditional manual inspection by experts is laborious and time-consuming. Structural health monitoring provides the potential to accelerate the required evaluation

System identification aims at updating the model parameters (e.g. mass and stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to

Reliable damage detection over large areas of structures can be achieved by spatially quasi-continuous structural health monitoring enabled by two-dimensional sensing sheets. They contain dense arrays of short-gauge sensors, which increases the probability to have sensors in direct contact with damage (e.g. crack opening) and thus identify (i.e. detect, localize, and quantify) it at an early stage

This research investigates a framework for the efficient development of vision-based dense three-dimensional displacement measurement algorithms to support reliable structural health monitoring of civil structures. The framework exploits the use of a photo-realistic synthetic model, termed a physics-based graphics model, to simulate the entire process of vision-based measurement. At the same time,

A new identification technique is proposed to evaluate the tension of a cable using a static inverse approach that couples a universal cable model with displacement sensors, strain gauges and added masses that should preserve operational affordability. An inverse problem is formulated as the minimization of a data misfit functional based on the differences in terms of vertical displacements and axial

In this study, different alternatives to detect and localize damage from linear and nonlinear vibration responses were investigated. These alternatives were compared on two concrete slabs subjected to different damage extents. Linear and nonlinear modal test configurations were considered. The variations of linear resonant frequencies, modal assurance criterion values, and nonlinear hysteretic parameters

Subwavelength defect imaging using guided waves has been known to be a difficult task mainly due to the diffraction limit and dispersion of guided waves. In this article, we present a noncontact super-resolution guided wave array imaging approach based on deep learning to visualize subwavelength defects in plate-like structures. The proposed approach is a novel hierarchical multiscale imaging approach

Rotor shafts subjected to severe operating stresses are prone to develop transverse fatigue cracks at the localized stress raisers. Therefore, the ability to identify and locate the incipient fatigue crack is imperative in order to avoid catastrophic failure. The literature on rotor crack detection discussed the importance of monitoring the steady-state 1X, 2X and 3X harmonic response components of

The article proposes a methodology for assessing the development of damage in building structures, subjected to differential settlement and uplift, using the analysis of Interferometry Synthetic Aperture Radar (InSAR) data. The proposed methodology is targeted towards general applicability, capable of providing assessment results for measurements over wide geographic areas and for varying structural

The concept of structural health monitoring has been introduced to ensure structural integrity during the design lifetime of a structure. The main objectives of structural health monitoring are to detect, locate, quantify, and predict any damage that occurs during this lifetime of the structure so that effective and efficient maintenance and repair procedures can be performed. The location of structural

Crack is an important indicator for evaluating the damage level of concrete structures. However, traditional crack detection algorithms have complex implementation and weak generalization. The existing crack detection algorithms based on deep learning are mostly window-level algorithms with low pixel precision. In this article, the CrackUnet model based on deep learning is proposed to solve the above

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to the computer vision technology application advantages such as non-contact, long distance, rapid, low cost and labor, and low interference to the daily operation of structures, it is promising to consider computer

Cracks on concrete structures are an important indicator for assessing concrete durability and structural safety. Although such cracks are typically monitored by manual visual inspection, this method has drawbacks in terms of inspection time, safety, cost-effectiveness, and measurement accuracy. An innovative alternative is digital image processing, which can be used to obtain crack information from

Delamination is one of the most common and dangerous failure modes for composites because it takes place and grows in the absence of any visible surface damage. The successful implementation of delamination detection in aerospace composite structures is always challenging due to the general anisotropic behavior of composites and multilayer delamination scenarios. This article presents a numerical and