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This paper describes a non-intrusive method for collecting data about internal corrosion damages in AISI-304 stainless steel plates and classifying them according to severity. The mapping of the electric potential gradient is derived using the potential drop technique, which is then analyzed using image processing techniques including edge enhancement and segmentation. Simulations were run using finite element modeling to produce examples of damaged plates, with four types of defects that can be considered part of pitting corrosion. The image processing stage plays the role of an extractor of features that, when employed as inputs of machine learning algorithms, make it possible to determine the damage severity. With the Gradient Boosting regressor, the maximum absolute error of 0.879 mm was obtained in the estimate of the depth of the defects. Additionally, with the application of a Convolutional Neural Network, an accuracy of 94.84% was achieved to classify of the severity of the damages. [ABSTRACT FROM AUTHOR]

Coastal hazard events such as hurricanes pose a significant threat to coastal communities. Disaster relief is essential to mitigating damage from these catastrophes; therefore, accurate and efficient damage assessment is key to evaluating the extent of damage inflicted on coastal cities and structures. Historically, this process has been carried out by human task forces that manually take post-disaster images and identify the damaged areas. While this method has been well established, current digital tools used for computer vision tasks such as artificial intelligence and machine learning put forth a more efficient and reliable method for assessing post-disaster damage. Using transfer learning on three advanced neural networks, ResNet, MobileNet, and EfficientNet, we applied techniques for damage classification and damaged object detection to our post-hurricane image dataset comprised of damaged buildings from the coastal region of the southeastern United States. Our dataset included 1000 images for the classification model with a binary classification structure containing classes of floods and non-floods and 800 images for the object detection model with four damaged object classes damaged roof, damaged wall, flood damage, and structural damage. Our damage classification model achieved 76 % overall accuracy for ResNet and 87 % overall accuracy for MobileNet. The F1 score for MobileNet was also 9 % higher than the F1 score of ResNet at 0.88. Our damaged object detection model achieved predominant predictions of the four damaged object classes, with MobileNet attaining the highest overall confidence score of 97.58 % in its predictions. The object detection results highlight the model's ability to successfully identify damaged areas of buildings and structures from images in a time span of seconds, which is necessary for more efficient damage assessment. Thus, we show that this level of accuracy for our damage assessment using artificial intelligence is akin to the accuracy of manual damage assessments while also completing the assessment in a drastically shorter time span. [ABSTRACT FROM AUTHOR]

In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians. [ABSTRACT FROM AUTHOR]

Automated procedures for classifying vehicle damage are critical in industries requiring extensive vehicle management. Despite substantial research demands, challenges in the field of vehicle damage classification persist due to the scarcity of public datasets and the complexity of constructing datasets. In response to these challenges, we introduce a Three-Quarter View Car Damage Dataset (TQVCD dataset), emphasizing simplicity in labeling, data accessibility, and rich information inherent in three-quarter views. The TQVCD dataset distinguishes class by vehicle orientation (front or rear) and type of damage while maintaining a three-quarter view. We evaluate performance using five prevalent pre-trained deep learning architectures-ResNet-50, DenseNet-160, EfficientNet-B0, MobileNet-V2, and ViT-employing a suite of binary classification models. To enhance classification robustness, we implement a model ensemble method to effectively mitigate individual model dependencies' deviations. Additionally, we interview three experts from the used-car platform to validate the necessity of a vehicle damage classification model using the corresponding dataset from an industrial perspective. Empirical findings underscore the dataset's comprehensive coverage of vehicle perspectives, facilitating efficient data collection and damage classification while minimizing labor-intensive labeling efforts., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance. [ABSTRACT FROM AUTHOR]

Most modern Earth and Universe observation spacecraft are now equipped with large lightweight and flexible structures, such as antennas, telescopes, and extendable elements. The trend of hosting more complex and bigger appendages, essential for high-precision scientific applications, made orbiting satellites more susceptible to performance loss or degradation due to structural damages. In this scenario, Structural Health Monitoring strategies can be used to evaluate the health status of satellite substructures. However, in particular when analysing large appendages, traditional approaches may not be sufficient to identify local damages, as they will generally induce less observable changes in the system dynamics yet cause a relevant loss of payload data and information. This paper proposes a deep neural network to detect failures and investigate sensor sensitivity to damage classification for an orbiting satellite hosting a distributed network of accelerometers on a large mesh reflector antenna. The sensors-acquired time series are generated by using a fully coupled 3D simulator of the in-orbit attitude behaviour of a flexible satellite, whose appendages are modelled by using finite element techniques. The machine learning architecture is then trained and tested by using the sensors’ responses gathered in a composite scenario, including not only the complete failure of a structural element (structural break) but also an intermediate level of structural damage. The proposed deep learning framework and sensors configuration proved to accurately detect failures in the most critical area or the structure while opening new investigation possibilities regarding geometrical properties and sensor distribution.

This study demonstrates the use of multi-temporal LiDAR data to extract collapsed buildings and to monitor their removal process in Minami-Aso village, Kumamoto prefecture, Japan, after the April 2016 Kumamoto earthquake. By taking the difference in digital surface models (DSMs) acquired at pre- and post-event times, collapsed buildings were extracted and the results were compared with damage survey data by the municipal government and aerial optical images. Approximately 40% of severely damaged buildings showed a reduction in the average height within a reduced building footprint between the pre- and post-event DSMs. Comparing the removal process of buildings in the post-event periods with the damage classification result from the municipal government, the damage level was found to affect judgements by the owners regarding demolition and removal.

Damage types are important for structural condition assessment, however, for conventionally guided wave-based inspections, the characteristics extracted from the guided wave packets are usually used to detect, locate and quantify the damages, but not classify them. In this work, the data-driven method is proposed to classify the common damages in the pipe utilizing the guided wave signals obtained from numerous damage detection tests. The fundamental torsional mode T(0,1) is selected to conduct the guided wave-based damage detection to reduce the complexity of signal processing for its almost non-dispersive property. A total of 520 groups of experimental data under different degrees of damage were obtained to verify the proposed method. Finally, with help of a deep neural network (DNN) algorithm, all response data from the damages in the pipes were all clearly classified with quite high probability.

Harnessing the advantages of geotextile sand containers (GSCs), numerous submerged breakwaters and shoreline protection structures have been constructed worldwide. But an emerged breakwater structure with geotextile armour units, capable of replacing the conventional structures, is rarely discussed. A 1:30 scaled physical experimentation is chosen as a preliminary investigation to test the feasibility of using GSCs as breakwater armour units. Structural design is evolved based on a comprehensive literature survey. The paper focuses on the stability parameters and damage characteristics of the proposed structure. Four different configurations are subjected to waves, confining to Mangaluru's wave parameters. Effect of armour unit size and sand fill ratio on the stability of the structure is analysed and it is concluded that changing sand fill ratio from 80% to 100% shot up the structural stability to a maximum of 14%. Increasing bag size also resulted in the increased stability up to 8%. Experiments revealed that the best performing configuration could withstand wave heights up to 2.7 m. Stability curves for all tested configurations are discussed and can serve as an effective guideline for designing GSC breakwaters. • Feasibility of using geotextile sand containers (GSCs) as armour units of breakwaters is investigated. • Existing GSC protection works and its design recommendations are extensively reviewed. • Novel stability curves developed through experimentation can be used as a design guideline for GSC structures. • Effect of GSC size, sand fill ratio and water depth in the stability of the structure is identified. • Serves as pilot data for numerous studies that may be adopted by changing various structural parameters. [ABSTRACT FROM AUTHOR]

Summary: Damage fracture characteristics of composite slabs with different connection configurations were analyzed using acoustic emission (AE) with cast‐in‐place (CIP) reinforced concrete (RC) slabs as contrast. AE characteristic parameters were obtained during static experiments and compared with each other. The bearing capacity was directly proportional to cumulative AE energy, rise time, and duration time, of which the correlation coefficient of rise time was the highest and reached 99.99%. The whole fracture process could be divided into three stages. Compared to CIP slabs, the rates of energy release in the former two stages were slower, which caused the worse ductility. Meanwhile, many other differences among the tested slabs were captured in aspects of energy release, crack development, and severity. Finally, the quantitative analysis of damage classifications was performed under various load levels. The experimental results were beneficial to advance the application of AE in fields of damage analysis and health monitoring for composite slabs. [ABSTRACT FROM AUTHOR]

The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency.

To study the effects of declining mechanical parameters caused by blasting excavation on slopes, we introduced the damage degree index Ds, and established a relationship between Ds and the disturbance factor D in the Hoek–Brown criterion, along with a basic quality index BQ for the rock mass. We then explored the change law for the degree of rock damage Ds as a function of the disturbance factor D. We also used a phosphate mine slope in Guizhou Province for reference, and analyzed the process of damage evolution of the slope based on the RHT (Riedel-Hiermaier-Thoma Constitution) in LS-DYNA (Yunnan, China). Results showed a direct relationship between the GSI value in the Hoek–Brown criterion and the initial damage degree of the slope. As Ds increases, D increases exponentially. However, the compressive strength, elastic modulus, cohesion, and internal friction angle decreased nonlinearly, and the tensile strength of the rock mass decreased linearly. Among these parameters, the compressive strength decreased the most rapidly, while the internal friction angle decreased slowly. We also established a new grade for rock self-stabilization with Ds as the evaluation standard. Thus, these results may provide a theoretical basis for determining the mechanical properties of rocks for future slope protection and stability evaluations.

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.

Coastal hazard events such as hurricanes pose a significant threat to coastal communities. Disaster relief is essential to mitigating damage from these catastrophes; therefore, accurate and efficient damage assessment is key to evaluating the extent of damage inflicted on coastal cities and structures. Historically, this process has been carried out by human task forces that manually take post-disaster images and identify the damaged areas. While this method has been well established, current digital tools used for computer vision tasks such as artificial intelligence and machine learning put forth a more efficient and reliable method for assessing post-disaster damage. Using transfer learning on three advanced neural networks, ResNet, MobileNet, and EfficientNet, we applied techniques for damage classification and damaged object detection to our post-hurricane image dataset comprised of damaged buildings from the coastal region of the southeastern United States. Our dataset included 1000 images for the classification model with a binary classification structure containing classes of floods and non-floods and 800 images for the object detection model with four damaged object classes damaged roof, damaged wall, flood damage, and structural damage. Our damage classification model achieved 76% overall accuracy for ResNet and 87% overall accuracy for MobileNet. The F1 score for MobileNet was also 9% higher than the F1 score of ResNet at 0.88. Our damaged object detection model achieved predominant predictions of the four damaged object classes, with MobileNet attaining the highest overall confidence score of 97.58% in its predictions. The object detection results highlight the model’s ability to successfully identify damaged areas of buildings and structures from images in a time span of seconds, which is necessary for more efficient damage assessment. Thus, we show that this level of accuracy for our damage assessment using artificial intelligence is akin to the accuracy of manual damage assessments while also completing the assessment in a drastically shorter time span.

Precast pre-stressed concrete composite slabs have been widely used in recent years owing to excellent mechanical properties and cost savings. The low accuracy of visual observation and the complexity of actual measurement make the determination of the precise information of structural safety difficult. This article presents an acoustic emission study on the precast pre-stressed concrete composite slab under static loading. First, the correlation between acoustic emission hits (or energy) and crack width (or deflection) was obtained and compared in detail. Second, the stiffness of this composite slab was predicted using the acoustic emission amplitude. Third, a novel analysis method for damage fracture classification is proposed, which is based on the traditional parameter and fuzzy C -means clustering. This method can be used to determine visual crack emerging, rebar yielding, and eventual failure mode by an acoustic emission parametric analysis without involving visual inspection or mechanical measurement. It can be effectively used for fracture characterization and health monitoring of composite slabs. [ABSTRACT FROM AUTHOR]

Competition in the industrial environment is increasingly intense, so it is of utmost importance that organizations keep their assets in operation as much as possible (in order to produce more). In this context, there is a need for predictive maintenance, a technique that detects the health of assets in real time, allowing failures to be diagnosed before they can interrupt the operation of the assets, avoiding high financial losses. This study uses a sixteen-motor experimental setup with four different known operating conditions. The vibration signal of these motors, through signal analysis, both in time and frequency domains, is performed to evaluate the types and severities of the defects. An artificial neural network (ANN) is used to classify these defects. Considering the vibration analysis, mechanical faults can be identified quickly and conveniently. For the development of the ANN, it was necessary to perform a preprocessing of the vibration signal (response in time) due to the data size, which overwhelms the network. Thus, statistical data were used to extract key information from the vibration signal. Finally, the neural network created based on this study's methodology presents extremely reliable results, allowing a quick and robust diagnosis of the motor operating condition. [ABSTRACT FROM AUTHOR]

Concrete gravity dams are one of the most common water retention structures, some of which are located in seismically active regions. Determination of damage level after earthquakes plays an important role in assessing the safety behavior of concrete dams. Compared with the traditional performance parameters obtained from numerical simulations, such as the damage coefficient, energy dissipation, failure modes, and stress state, etc., the displacement of dams can be acquired from daily monitoring data conveniently and quickly. It is of great significance for the rapid and effective evaluation of dam properties after earthquakes. The residual displacement in the concrete gravity dam crest was adopted as the performance parameter in the paper, and the linear mapping function between the residual displacement and the damage coefficient was established based on the concrete damaged plasticity model (CDP). Based on the traditional classification method with damage coefficient, a residual displacement-based seismic damage classification method with corresponding level limits was proposed. The seismic fragility analysis of Guandi concrete gravity dam was conducted as an example to illustrate the presented methodology. The results indicate that the proposed method is reasonable, effective, and can be easily applied to different projects after slight modifications. [ABSTRACT FROM AUTHOR]

Seismic vulnerability assessment of bridges is essential for both pre-earthquake planning and post-earthquake rapid loss assessment to facilitate maintenance planning and recovery initiatives. Given the impracticality of creating numerical models for every conceivable attribute variation within bridge classes, data-driven approaches offer a versatile solution to address such variations. While data-driven techniques are widely adopted in bridge engineering, many existing models rely on numerical or experimental datasets. However, such models may not adequately capture the dynamic and complex nature of seismic response of bridges. This presents an opportunity to evaluate the effectiveness of data-driven approaches using post-earthquake damage data. This paper assesses the efficacy of machine learning models and develops a robust framework for the classification of earthquake-induced damage incurred to some bridge classes. We deployed damage data collected following the 2015 Gorkha earthquake, incorporating taxonomical, stiffness, and excitation variables as predictor variables. Considering three distinct damage classes—minor, major, and critical—in a heterogeneous bridge class environment, we evaluated multiple machine learning classifiers. To account for the influence of heterogeneous bridge classes in the dataset, we conducted separate assessments for the reinforced concrete (RCC) bridge class. The Random Forest classifier achieved a substantial accuracy improvement, reaching 83% when compared to the mixed bridge class accuracy of 79% reflected by the ANN classifier. Similarly, a minor improvement was observed when deploying soft voting classifiers for RCC only bridge data. The sum of observations suggest that tree-based classifiers consistently outperform other classifiers when considering single bridge classes. This study identifies stiffness variables as the most critical factor in bridge damage classification. • Parameterization of bridge attributes for machine learning applications. • Formulation of machine learning and soft voting ensemble approaches for bridge damage classification. • Quantification of data homogeneity/heterogeneity effect on machine learning applications. • Hierarchization of categorical and numerical variables for bridge damage classification. [ABSTRACT FROM AUTHOR]

On February 6, 2023, Türkiye experienced a significant earthquake with a magnitude of M w 7.8 1 1 Note these values are based on initial assessment by USGS. AFAD reported differently. , followed by four additional earthquakes throughout the day, measuring M w 5.8 and above, including one with a M w 7.5. As a result, over 100 dams in the region experienced low to high seismic loads. The impact of these seismic events resulted in a range of damages to dams, varying from light to severe. The authors participated in several post-earthquake reconnaissance missions to collect perishable data regarding the damage. This paper is three-fold: First, it explores the seismic performance and observed damage in 17 dams (majority embankments) based on data collected from the field. During the seismic activity, embankment dams underwent failure modes such as strength deterioration, yielding, settlement, tension crack formation, as well as sliding along planar or circular surfaces. Second, the paper offers an updated seismic risk analysis for ten selective damaged dams in the regions. Total risk factors are quantified using two different methods. The results show that the majority of the studied dams are classified as high and very high-risk dams. Third, the paper evaluates 11 other dams in the region that were equipped with sensors for monitoring the ground motions. The recorded acceleration at the crest and the abutment are further used to investigate the amount of amplification based on transfer functions. These recordings will be crucial for subsequent model calibration and system identification. Overall, the paper's objective is to contribute to sharing post-earthquake reconnaissance mission information and to establish the groundwork for in-depth numerical analyses and quantitative risk assessments of dams located in seismically vulnerable areas. [ABSTRACT FROM AUTHOR]

Structures and components need to be constantly monitored to ensure good service life and avoid failures. Structural health monitoring is a wide-ranging concept that tries to ensure the safety of components and structures. Vibration responses of small and large structures contain information about these damages. In the present study, this information is extracted and used to classify the location and severity of cracks. For this purpose, an FE model of Glass Fibre Reinforced Plastic (GFRP) free-free beam is created for beams without any crack and beams with three crack locations and three crack severities. Finite Element (FE) model is validated using modal analysis and is used to generate vibration responses from simulated hammer strikes at different locations on the beam. The vibration data consists of responses of beams that have only one crack present at a time and when two cracks are present. A 1D-CNN network is trained on the generated vibrational responses to classify damage labels assigned for each crack location and crack severity. The network was tested using datasets containing only single crack instances and only dual crack instances. The trained networks were found to be 95% and 93% accurate in determining the damage class respectively. Furthermore, datasets of these two instances combined, containing responses when single cracks and dual cracks are present, were also investigated. These networks had an accuracy of 92%. Hence, in all these instances, the 1D-CNN network classifies the healthy and damaged conditions satisfactorily. There are two advantages of using such a technique. Firstly, this approach simplifies the two-step approach: one for feature extraction and another one for damage prediction into a single step. Secondly, the use of raw vibration data for damage prediction means that real-time damage detection is possible. [ABSTRACT FROM AUTHOR]

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

The problem of characterizing the structural residual life is one of the most challenging issues of the damage tolerance concept currently applied in modern aviation. Considering the complexity of the internal architecture of composite structures widely applied for aircraft components nowadays, as well as the additional complexity related to the appearance of barely visible impact damage, prediction of the structural residual life is a demanding task. In this paper, the authors proposed a method based on detection of structural damage after low-velocity impact loading and its classification with respect to types of acting stress on constituents of composite structures using the developed processing algorithm based on segmentation of 3D X-ray computed tomograms using the rebmix package, real-oriented dual-tree wavelet transform and supporting image processing procedures. The presented algorithm allowed for accurate distinguishing of defined types of damage from X-ray computed tomograms with strong robustness to noise and measurement artifacts. The processing was performed on experimental data obtained from X-ray computed tomography of a composite structure with barely visible impact damage, which allowed better understanding of fracture mechanisms in such conditions. The gained knowledge will allow for a more accurate simulation of structural damage in composite structures, which will provide higher accuracy in predicting structural residual life.

The accurate evaluation of landslide-induced damage is a necessity for planning of proper and effective mitigation measures. It requires the implementation of field investigations to identify structural failures to more effectively trace landslide boundaries. Many methods have been proposed to classify landslide-induced damage of buildings. The existing methods demonstrate several advantages and drawbacks depending on the parameters considered, as lack of some important features and difficulties in applicability. A new classification approach of landslide-induced damage of facilities is proposed, which specifically focuses on assessing of damage degree and its relationship to the ground motion intensity and impact severity. The new approach is designed in two steps: a chart utilized during surveys to quantify cracks on structures and ground surface; an a posteriori ranking of structures performed using a cell-grid matrix. Furthermore, a damage recording scheme useful for field surveying is proposed. This approach considers several parameters derived from different existing methodologies by smoothing out drawbacks and homogenizing the considered features. The resulting approach provides a new procedure of landslide-induced damage assessment adoptable in case of private dwellings, as it does not require internal accessibility, and it is exploitable for different landslide events and for different kinds of structures and facilities.

Landslides are among the most important and frequent natural calamities that cause severe socio-economic and human losses. After earthquakes, landslides are responsible for the greatest number of casualties and the largest amount of damage to man-made structures. On average, southern Italy is affected by a high spatial density of landslides due to its complex geological setting, which often predisposes it to slope instability phenomena under both natural and anthropogenic influences. Structurally complex formations are widespread in the southern Apennines and are characterized by high heterogeneity and very poor mechanical properties. Thus, these formations represent one of the main factors contributing to the predisposition of slopes to landsliding. In this paper, landslide-induced damage was investigated and analyzed in an area within the municipality of Agnone (Molise region), which is affected by a complex landslide that involves a structurally complex formation. The approaches used were based on six different methods that have previously been described in the literature, and a comparison of the results was made. Data regarding the damage, which consists largely of cracks observed in buildings and at the ground, were compiled through field surveys. The results were critically analyzed to note the advantages and constraints of each classification scheme. The aim of the work was to apply and compare different approaches in order to test the best and most accurate procedures for assessing damage due to landslides at the scale of individual buildings as well as to provide an objective assessment of the degree of landslide damage to structures and facilities. [ABSTRACT FROM AUTHOR]

At this time, the system with advanced automatic detection capability has consistent execution results and has been verified that it can be used as a necessary tool for cost reduction and the quality control of the product, which is the value of the customer. It will contribute to reducing human error and minimizing defects by designing a system that relies on more mechanization automation, taking into account the characteristics of the machine as well as the role of humans and machines. [ABSTRACT FROM AUTHOR]

Highlights • A stochastic hybrid blade tip timing approach is proposed and demonstrated. • Turbomachine blade damage is identified and classified using this approach. • Probabilistic damage identification is achieved through natural frequency tracking. • Damage classification utilises a K-means clustering approach. • The proactive scheduling of a turbomachine outage is thus demonstrated. Abstract Blade Tip Timing (BTT) has been in existence for many decades as an attractive vibration based condition monitoring technique for turbomachine blades. The technique is non-intrusive and online monitoring is possible. For these reasons, BTT may be regarded as a feasible technique to track the condition of turbomachine blades, thus preventing unexpected and catastrophic failures. The processing of BTT data to find the associated vibration characteristics is however non-trivial. In addition, these vibration characteristics are difficult to validate, therefore resulting in great uncertainty of the reliability of BTT techniques. This article therefore proposes a hybrid approach comprising a stochastic Finite Element Model (FEM) based modal analysis and Bayesian Linear Regression (BLR) based BTT technique. The use of this stochastic hybrid approach is demonstrated for the identification and classification of turbomachine blade damage. For the purposes of this demonstration, discrete damage is incrementally introduced to a simplified test blade of an experimental rotor setup. The damage identification and classification processes are further used to determine whether a damage threshold has been reached, therefore providing sufficient evidence to schedule a turbomachine outage. It is shown that the proposed stochastic hybrid approach may offer many short- and long-term benefits for practical implementation. [ABSTRACT FROM AUTHOR]

Concrete gravity dams are one of the most common water retention structures, some of which are located in seismically active regions. Determination of damage level after earthquakes plays an important role in assessing the safety behavior of concrete dams. Compared with the traditional performance parameters obtained from numerical simulations, such as the damage coefficient, energy dissipation, failure modes, and stress state, etc., the displacement of dams can be acquired from daily monitoring data conveniently and quickly. It is of great significance for the rapid and effective evaluation of dam properties after earthquakes. The residual displacement in the concrete gravity dam crest was adopted as the performance parameter in the paper, and the linear mapping function between the residual displacement and the damage coefficient was established based on the concrete damaged plasticity model (CDP). Based on the traditional classification method with damage coefficient, a residual displacement-based seismic damage classification method with corresponding level limits was proposed. The seismic fragility analysis of Guandi concrete gravity dam was conducted as an example to illustrate the presented methodology. The results indicate that the proposed method is reasonable, effective, and can be easily applied to different projects after slight modifications.

This paper investigates the effects of replacing cement with ground granulated blast furnace slag (GGBFS), adding hooked steel fibers, and reducing the number of prestressing strands on the behavior of prestressed concrete (PSC) sleepers subjected to multiple impacts. Five PSC sleepers with and without GGBFS and steel fibers were fabricated and tested using a drop-weight impact test machine. Test results indicate that the hammer weight and loading protocol strongly influenced the multiple impact resistance of PSC sleepers. In terms of peak reaction load, maximum deflection, and residual deflection, the multiple impact resistance of PSC sleepers was enhanced by including GGBFS and 0.75 vol% steel fibers and increasing the number of strands. The maximum crack width was reduced by increasing the number of strands and including steel fibers, and the steel fibers also effectively limited crack propagation, the number of cracks formed, and concrete spalling. Lastly, the flexural strength of PSC sleepers damaged by multiple impacts was improved by including steel fibers and more strands. Consequently, the overall performance of PSC sleepers subjected to multiple flexural impacts was improved by adding 0.75 vol% steel fibers and increasing the number of strands. [ABSTRACT FROM AUTHOR]

• The damage process of axially loaded concrete-filled steel tubular column can be divided into 5 stages. • The strongest signal monitored before peak load indicated the concrete reached failure strain. • Cumulative acoustic emission energy, amplitude, hits were linearly related to load. • Stages Ⅰ-Ⅲ1 reflected the damage properties of plain concrete, and stages Ⅲ2-Ⅴ reflected the confined concrete. • Specimens with small sizes and thin walls were more obviously confined. Concrete-filled steel tubular (CFST) columns are widely used as major load-bearing components in structures due to their excellent load-bearing properties. However, the presence of steel tube makes it difficult to accurately assess the damage level of concrete by traditional mechanical measurements alone. This paper investigated the efficiency of acoustic emission (AE) technique in assessing the damage characteristics of the concrete in CFST columns. The damage properties of four axially compressed square CFST columns with the widths of 500–1000 mm and the width-to-thickness ratios of 40 and 50 were analyzed through AE signals. According to the AE results, the axial compression progress of specimens could be divided into five stages. Then, through the correlation analysis, the cumulative AE energy, amplitude, and hits were found to be linear related to the applied load. Finally, the damage percentage of each specimen at different stages was discovered to be influenced by width and width-to-thickness ratio. [ABSTRACT FROM AUTHOR]

The accurate evaluation of landslide-induced damage is a necessity for planning of proper and effective mitigation measures. It requires the implementation of field investigations to identify structural failures to more effectively trace landslide boundaries. Many methods have been proposed to classify landslide-induced damage of buildings. The existing methods demonstrate several advantages and drawbacks depending on the parameters considered, as lack of some important features and difficulties in applicability. A new classification approach of landslide-induced damage of facilities is proposed, which specifically focuses on assessing of damage degree and its relationship to the ground motion intensity and impact severity. The new approach is designed in two steps: a chart utilized during surveys to quantify cracks on structures and ground surface; ana posterioriranking of structures performed using a cell-grid matrix. Furthermore, a damage recording scheme useful for field surveying is proposed. This approach considers several parameters derived from different existing methodologies by smoothing out drawbacks and homogenizing the considered features. The resulting approach provides a new procedure of landslide-induced damage assessment adoptable in case of private dwellings, as it does not require internal accessibility, and it is exploitable for different landslide events and for different kinds of structures and facilities. [ABSTRACT FROM AUTHOR]

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) of aeronautic structures by means of Lamb waves opens promising perspectives in terms of maintenance costs reduction and safety increases. Lamb waves interactions with damages are known to be nonlinear, a property still largely underexploited in SHM. Difficulties in this context are (i) to be able to distinguish between nonlinearities due to the waves spatial propagation (i.e. material or geometrical nonlinearities) and those located at the damage position, (ii) to handle computational complexity associated with spatio-temporal nonlinear models, and (iii) to be able to physically link recorded signals with actual damage state. This work proposes to rely on the Green–Volterra formalism to build up a semi-analytical spatio-temporal framework describing longitudinal waves propagation and damage interaction able to physically represent both types of nonlinearities, and computationally simple enough to be tractable in real-time for SHM purposes. This approach is detailed here for longitudinal waves, which corresponds in the low frequency × thickness range to the S 0 Lamb wave mode propagating in a damaged beam. A spatio-temporal semi-analytical model of the nonlinear longitudinal waves propagation is first derived, where the damage is represented by a polynomial stiffness characteristic acting via boundary conditions at a given position in the beam. This model is then used to derive the Green–Volterra series describing the nonlinear input–output relationship of the system. A modal decomposition of the Green–Volterra series is also provided to ease implementation and reduce computational cost. The proposed spatio-temporal semi-analytical approach is then successfully compared to state-of-the-art nonlinear Lamb waves simulation methods based on finite-element models. It is finally shown on a simulated example and discussed in detail how such a nonlinear framework could potentially be relevant for SHM purposes. • Lamb waves interactions with damages are known to be nonlinear, a property still largely underexploited in structural health monitoring. • An analytical spatio-temporal framework describing 1D-longitudinal waves propagation and nonlinear damage interaction relying on the Green–Volterra formalism is proposed here. • The proposed spatio-temporal analytical approach is successfully compared to state-of-the-art nonlinear simulation methods based on finite-element models. • It is finally shown on a simulated example and discussed in detail how such a nonlinear framework could potentially be relevant for SHM purposes. [ABSTRACT FROM AUTHOR]