bluuebunny/crossref_metadata_2025 huggingface dataset:
DEGRADATION MODEL UPDATING FOR FAILURE PROGNOSTICS USING A SEQUENTIAL LIKELIHOOD- FREE BAYESIAN INFERENCE METHOD AND VIDEO MONITORING DATA
Authors: ZENG JICE , TODD MICHAEL D. , ZHEN HU
Published: December 2023

Structural systems are inevitably subject to degradation that evolves progressively over time. Developing a degradation model to capture the physics of damage evolution is essential for failure prognostics, i.e., remaining useful life (RUL) prediction, to enable individualized predictive maintenance. Due to the lack of runto- failure data for large structural systems and natural variability across physical systems, uncertainty is inherent in the degradation model even if a degradation model can be constructed based on the physics of a certain damage mechanism. It is therefore necessary to update the degradation model over time based on measurements of quantities that are directly measurable. With the development of sensing and image processing techniques, it is possible to derive structural strain response from videos, which overcomes the limitations of the cumbersome and costly deployment of conventional contact sensors. While the strain video monitoring data provide rich information for structural health monitoring, the usage of this information for degradation model updating is challenging due to the implicit connection between the degradation model parameters and strain video monitoring data and the highly complicated model architectures. This research proposes a novel sequential Bayesian model updating framework for a degradation model using a likelihood-free Bayesian inference method and strain video monitoring data. In the proposed framework, strain video monitoring data are first compressed into lowdimensional latent time-series features using a convolutional autoencoder. Subsequently, a likelihood-free Bayesian inference method is employed to update the degradation model using a given time duration of the monitoring data. To enable continuous monitoring and model updating over a long time period, a sequential Bayesian model updating scheme is developed. Based on the updated degradation model, failure prognostics are performed sequentially and the associated uncertainty on RUL estimation is also quantified. The application of the developed framework to a miter gate structure demonstrates the efficacy of the proposed framework. Keywords: Remaining useful life; Degradation model; Likelihood-free Bayesian inference; Conditional invertible neural network
FINITE ELEMENT MODEL UPDATING USING PRIMAL-RELAXED DUAL GLOBAL OPTIMIZATION ALGORITHM
Authors: SCHREIBER TRENT E. , OTSUKI YU , WANG YANG
Published: December 2023

Finite element (FE) modeling has become a powerful tool in predicting the response of various engineering structures. However, predictions from the numerical model often differ from in-situ experimental measurements due to numerous approximations and inaccuracies in the model. The in-situ experimental data obtained from the as-built structure can be used to update selected model parameters to obtain a more accurate FE model that truly reflects the behavior of the as-built structure. This research investigates FE model updating by the modal property difference approach using eigenvalues and eigenvectors. The modal property difference approach is a nonconvex optimization problem, for which generic solvers cannot guarantee global optimality. However, the problem can be reformulated into a biconvex problem so that the global optimum can be found using a primal-relaxed dual (P-RD) decomposition approach. The formulation of the model updating algorithm and examples that demonstrate its functionality are presented in this paper.
DEVELOPMENT OF DEEP NEURAL NETWORK-BASED STRAIN VALUES PREDICTION MODELS FOR FULL-SCALE REINFORCED CONCRETE FRAMES USING HIGHLY FLEXIBLE SENSING SHEETS
Authors: ZHANG HUI , BESKHYROUN SHERIF
Published: December 2023

Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. These include abnormal changes due to strain fields and abnormal symptoms of the structure, such as damage and deterioration. At present, large-scale deployment of sensors in existing structures to cover large areas is still difficult to overcome, while increasing maintenance costs. In this study, a strain sensing sheet with high tensile strength is used to collect the strain data set generated on the concrete surface of the full-scale reinforced concrete (RC) frame structure when the cyclic load is applied to its limit. On this basis, two prediction models of deep neural network for frame beam and frame column are established. The training results show that they can predict the strain value accurately and have good generalization ability. These two deep neural network prediction models will also be deployed in SHM systems in the future as part of the intelligent strain sensor system.
ASSESSING THE PERFORMANCE CVM SENSORS FOR MONITORING THE 737 AFT PRESSURE BULKHEAD
Authors: ROACH DENNIS , LYNCH-STAUNTON TREVOR , SHAIGEC BRIAN , FORMOSA DERRICK
Published: December 2023

Current maintenance operations and integrity checks on aircraft require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be removed, sealant must be removed and disassembly processes must be completed. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Reliable Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition, and signal the need for specific maintenance actions. This paper presents an OEM-airline-SHM vendor-regulator effort to realize these benefits by moving Comparative Vacuum Monitoring (CVM) technology into routine use in airline maintenance programs. A certification program has been completed to validate CVM sensors for surface crack detection on the 737 Aft Pressure Bulkhead (APB). Formal and comprehensive CVM technology validation and certification was guided by a recently-released FAA Issue Paper which addresses the full spectrum of issues including design, deployment, durability and performance. For accurate SHM validation to occur, all relevant environments - which may include separate fatigue and environmental response components - were properly simulated in the tests. Flight tests also played an important role in assessing overall CVM system performance under normal aircraft operations. Validation tests were designed to address the CVM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, and the potential inspector population. The test results will be presented in light of the overall CVM certification plan. Such SHM deployment programs are allowing the aviation industry to confidently make informed decisions about the proper utilization of SHM. These programs also streamline the regulatory actions and certification measures needed to ensure the safe application of SHM solutions.
DAMAGE DETECTION OF RECTANGULAR SECTIONS BY 2D CURVELET, CONTOURLET AND WAVELET PACKET TRANSFORM
Authors: SHAHVERDI SAJAD
Published: December 2023

In this research, the application of 2D curvelet, 2D wavelet, and Contourlet transform for damage detection of shell structure are presented. For this study, the equation of discrete curvelet transforms using unequally-spaced fast Fourier and wrapping transforms are employed. Four scenario models with one and more damages with different percentages and locations were considered for investigation of proposed methods. As real data has noise, and the existence of noise in the input data may affect the accuracy of damage detection methods. To consider the natural conditions, white noise has been applied to the data extracted from the FEM. First, the coefficients of transforms related to the noisy signal are calculated, then thresholding the previous coefficients calculated, and finally, the de-noise signal is reconstructed. Based on the presented results, the curvelet transform better identifies the location of damage than the wavelet transforms. Wavelet transform based on the direction of the damage element may be specified in some subbands. But in curvelet and Contourlet, it can better identify different damages in different directions due to the multi-directional transform. The curvelet transform has been modified based on polar space and poses challenges for non-polar (normal) discrete space. Therefore, using the Contourlet is divided into multiple directions because of each of the available general directions. According to the obtained results, the proposed methods are sensitive to the location and percentage of damage and can identify the location of damage with a low percentage of damage. Also, to confirm the results of the proposed methods, the results are evaluated using laboratory results. The results show that the proposed methods can identify the location of damages and with a percentage of damage of up to 3%.
VERSATILE SENSOR NODE WITH ACOUSTIC DATA COMMUNICATION CAPABILITIES FOR FSAT NETWORKS IN GUIDED WAVE-BASED STRUCTURAL HEALTH MONITORING APPLICATIONS
Authors: REYES OCTAVIO A.  MÁRQUEZ , ZONZINI FEDERICA , MOHAMMADGHOLIHA MASOUD , MOLL JOCHEN , MARCHI LUCA DE
Published: December 2023

Correct evaluation of structural integrity and the ability to predict the remaining useful life of structures is of critical importance in numerous applications, particularly for aerospace industry. The development of Structural Health Monitoring (SHM) systems and their integration into real-world structures has become a necessity to provide a robust and cost-effective solution for structural assessment. Normally structure inspection is done periodically after weeks or months. For machines such as aircrafts or trains, they are taken out of service for structural integrity evaluation. Indeed, one main advantage of modern SHM systems is the continuous, autonomous monitoring of structures, even during service. Therefore, dense networks of ultrasonic transducers are distributed over structures to perform reliable damage detection and localization. Unfortunately, powering these poses many challenges in terms of cabling and added weight, besides to long latency times if a single central processing unit is charged of managing all transducers in the network. To solve this issue, new systems rely on specialized sensor nodes (SNs) and transducers, such as frequency steerable acoustic transducers (FSATs), which offer inherent directional capabilities for data actuation/ sensing. These SNs can drive high voltages and various types of signals to and from transducers conforming a SHM network. They also perform signal treatment tasks and lead communication protocols, such as on-off keying (OOK) or frequency shift keying (FSK), and even help identifying defective nodes within the network. Given the specificities of the FSATs, a matching SN is required to ensure reliable data exchange, even in cases in which traditional radiofrequency-based wireless communication is not possible. In this contribution, we address this need by presenting the design, implementation and test of a new, lightweight and low-cost SN, capable of driving voltage signals as high as 60 Vpp and powering both: commonly used piezoelectric transducers and the more specialized FSATs, and perform communication tasks, using a regular 5V USB port as power source. With this SN, the weight, latency and cable complexity of regular sensor networks are addressed, while disclosing novel opportunities for a higher level of automation and, hence, safer embedded SHM systems for ultrasonic inspection.
HETEROGENEOUS SENSOR PLACEMENT UNDER UNCERTAINTY
Authors: JABINI AMIN , JOHNSON ERIK A.
Published: December 2023

This study presents a heterogeneous sensor placement optimization framework using deep reinforcement learning (DRL) that considers system parameter uncertainty. The sensor placement problem is a well-established combinatorial optimization problem characterized by inherent parameter uncertainties that affect system responses that sensors measure. These uncertainties render deterministic solutions insufficient and necessitate a computationally tractable approach to account for the uncertainties. The proposed method incorporates a Markov decision process (MDP) as a stochastic environment, and a sensor placement agent trained using DRL. The agent’s objective is to maximize the effectiveness of sensor placement within a system by selecting sensor types and locations. The agent’s sequential decision-making is guided by a reward function that is designed based on the observability Gramian, calculated using sampled parameter values from an a priori distribution. The proposed approach is validated through simulation of a case study involving heterogeneous sensors in a shear building model with results compared to those from an evolutionary algorithm. The results show that the sensors selected by DRL method match the CMA-ES algorithm with the advantage of having information about relative importance of the selected sensors.
UNSUPERVISED VEHICLE CLASSIFICATION USING A STRUCTURAL HEALTH MONITORING SYSTEM
Authors: MOALLEMI AMIRHOSSEIN A , ZANATTA LUCA , BURRELLO ALESSIO , SALVARO MATTIA , LONGO MONICA , DARO PAOLA , BARCHI FRANCESCO , BRUNELLI DAVIDE , BENINI LUCA , ACQUAVIVA ANDREA
Published: December 2023

The structural performance assessment of bridges is a crucial issue for managing transportation infrastructure systems in EU countries as traffic loads and structural ageing continues to increase. Weight-in-Motion (WiM) systems have been developed to estimate the gross weight of vehicles over a bridge and keep the bridge load under control. However, WiM systems are costly in procurement and installation; alternative approaches that aim to be more scalable and cost-effective are needed to respond to the need to monitor large-scale infrastructures. This work explores an innovative zeroincremental cost approach based on raw vibration data extracted from a system already deployed for Structural Health Monitoring (SHM) and based on MEMS accelerometers. A novel signal processing and classification pipeline has been developed to differentiate vehicles into three categories: light, i.e., less than 10 tons; heavy, i.e., between 10 and 30 tons; and super heavy, i.e., above 30 tons, using only features extracted from vibration data. The results show that this framework can distinguish vehicles with an accuracy of 96.87%, utilizing the mean-shift unsupervised clustering model. This method has the potential to be a significantly cost-effective and scalable solution for monitoring bridge loads compared to WiM systems, as it leverages existing SHM infrastructure and affordable MEMS sensors to provide real-time information on vehicular loads.
EXPLORING CROWDSENSING POTENTIALS FOR STRUCTURAL HEALTH MONITORING APPLICATIONS: CHALLENGES AND OPPORTUNITIES
Authors: IACUSSI LEONARDO , BRAMBILLA MATTEO , CHIARIOTTI PAOLO , MANZONI STEFANO , CIGADA ALFREDO
Published: December 2023

The use of devices equipped with sensing capabilities (ranging from smartphones to smartwatches) has become commonplace in our daily lives. These devices can measure various parameters such as acceleration and position, and have the potential to be leveraged for purposes beyond their intended use. This is the main goal of crowdsensing techniques. However, if on the one hand crowdsensing is spreading for its great appeal, on the other hand sometimes the importance of key concepts related to the robustness of the measurement data collected are underestimated. This is particularly true when crowdsensing techniques target structural dynamic problems. Indeed, the accurate extraction of dynamic information beyond the resonant frequencies of the structure is still an open issue and requires tackling the problem considering metrological aspects. Including effects like different sensitivities, sampling frequencies of the devices, as well as all the other interfering inputs that might affect a structural dynamic measurement, will provide the basis for extracting enriched structural data like mode shapes and damping. This may pave the way to a paradigm shift in monitoring civil infrastructures, as fixed installations could be correlated, compensated, or even substituted by mobile solutions. To prove these potentials, this paper discusses the main challenges arising in this application field of crowdsensing, such as the need of synchronizing the devices if targeting operational deflection shapes (ODSs) reconstructions, the lower sensitivity of sensors incorporated in mobile/wearable devices (typically based on micro electro-mechanical system – MEMS – technology), etc. These issues are demonstrated and tackled by performing a three-step analysis. At first, the metrological characterization of the selected hardware is carried out. In the second step, a first experiment is conducted on a laboratory case study, to verify whether the proposed system can capture the dynamic response of the structure. The final step consists of an experimental campaign conducted on a pedestrian bridge. The results obtained clearly demonstrate the need for a multidisciplinary approach which includes metrology science when targeting crowdsensing for the assessment of the dynamic behavior of a structure.
NON-CONTACT ACOUSTIC EMISSION MONITORING OF CORROSION-FATIGUE DAMAGE IN SUBMERGED STEEL STRUCTURES
Authors: RICCIOLI FILIPPO , PAHLAVAN LOTFOLLAH
Published: December 2023

Corrosion-fatigue is considered one of the main degradation mechanisms affecting the structural integrity of offshore support structures and mooring systems. These structures are typically covered with marine growth of various types. This paper highlights a feasibility assessment of detection and localization of corrosion-fatigue damage in submerged steel structures using non-contact Acoustic Emission (AE) technique. An accelerated corrosion-fatigue experiment was conducted on a dog-bone steel specimen. A dedicated experimental set-up was designed to simultaneously apply accelerated corrosion and cyclic loading on the dog-bone steel specimen submerged in artificial seawater. Ultrasound signals were continuously measured using an array of underwater AE transducers (in the frequency range between 50-450 kHz) placed at a fixed distance from the tested coupon. A source localization algorithm for corrosionfatigue- induced ultrasound signals was successfully implemented. The results of the accelerated corrosion-fatigue experiment suggest that corrosion-fatigue-induced ultrasound signals can be detected with a satisfactory signal-to-noise ratio using noncontact AE transducers. Four stages of corrosion-fatigue damage growth were identified which may be used as the basis for possible damage characterization.
ACOUSTIC EMISSION SOURCE LOCATION USING BAYESIAN OPTIMISATION FOR A COMPOSITE HELICOPTER BLADE
Authors: LINDLEY C. A. , JONES M. R. , DARDENO T. A. , MILLS R. S. , DERVILIS N. , WORDEN K.
Published: December 2023

It has been demonstrated that acoustic-emission (AE) based inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. These advantages have resulted in the use of AE-based systems in Structural Health Monitoring (SHM) applications becoming an increasingly popular research topic. The strong correlation between features of AE-waves and the sources that generate them can be exploited to reveal information of the health-state of a structure in a non-invasive framework. This idea has been demonstrated successfully in the early detection of crackgrowth, nucleation, corrosion, dislocation slips, and cavitation, among others. There are, however, numerous challenges associated with the analysis of AE-data. Some of these challenges include sensor placement, calibration, operational variations, sources of noise, and manipulation of large-size datasets. These challenges must be addressed carefully if one wishes to implement an AE-based system in SHM applications. Although the analysis of AE-data can be complex, its advantages can be meaningful in the development of an optimised maintenance schedule, reducing costs associated with unnecessary repairs, or by preventing potentially catastrophic outcomes from overlooking an emerging defect that can be detrimental to the safe operation of the structure. In this paper, AE data recorded from a full-scale helicopter blade are processed to develop a probabilistic model designed to predict the location of potential sources of damage. The following work addresses some of the challenges related to the construction of ΔT maps and proposes a novel strategy for identifying optimal sampling points, eliminating the need for extensive data collection for training.
POINTWISE DAMAGE MAPPING USING GUIDED WAVE SIGNALS FOR STRUCTURAL HEALTH MONITORING
Authors: GOPALAKRISHNAN KARTHIK , MATHEWS V. JOHN
Published: December 2023

This paper presents a point-wise damage mapping algorithm for composite aerospace structures wherein we classify each location of interest on a structure as belonging to a damaged part of the structure or otherwise. The system employs an array of transducers attached to the structure for inducing guided waves in the structure and recording resultant waveforms arriving at the sensors. We present a baseline signal-based supervised learning algorithm in which baseline measurements on the structure are made prior to deploying it in the field and signals recorded during inspection are compared with their baseline counterparts. For each location of interest on the structure, difference metrics and other characteristics for M closest actuator-sensor paths to the location (M is typically much smaller than the total number of actuator-sensor paths on the structure.) are used as input features to a machine learning classifier. The classifier outputs for all locations of interest are further processed through a series of morphological filters to produce the damage map for the structure. The algorithm was experimentally validated on data recorded from a unidirectional composite panel impacted at five locations. Experimental results show that, for sufficiently high sensor densities, the algorithm presented in this paper is capable of detecting and characterizing multiple damages accurately and with high resolution and Sørensen-Dice index greater than 0.9 for all test cases, and is comparable to results obtained from traditional C-scan ultrasound non-destructive inspection systems. The point-wise damage mapping algorithm offers an attractive high-resolution alternative to traditional grid-based tomographic reconstruction approaches.
NEXT GENERATION 3D-DIC TECHNIQUE WITH SENSOR-BASED EXTRINSIC PARAMETER CALIBRATION AND NATURAL PATTERN TRACKING
Authors: BOTTALICO FABIO , NIEZRECKI CHRISTOPHER , SABATO ALESSANDRO
Published: December 2023

Three-dimensional Digital Image Correlation (3D-DIC) is a computer vision technique capable of extracting full-field displacements of structures by processing images acquired from synchronized stereo cameras and performing triangulation. To properly reconstruct the 3D points in space, the stereo cameras must be calibrated to compute the lens distortion (i.e., intrinsic parameters) and the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking pictures of a calibration target (e.g., a checkerboard). However, the calibration target must occupy most of the cameras’ field of view (FOV) and have a size comparable to that of the object being tested, which becomes impractical for large-scale FOVs. Additionally, 3D-DIC requires the application of a stochastic speckle pattern on the surface to identify and track unique points across multiple images. This can be a problem when structures on which the application of a speckle pattern is impractical have to be analyzed (e.g., wind turbine blades). This research proposes a multi-sensor system using three inertial measurement units and a laser distance sensor to compute the extrinsic parameters of a stereo vision system and accelerate the calibration procedure. In addition, a feature-based natural pattern tracking algorithm is proposed to exploit features that are naturally present on the structure instead of applying a speckle pattern to perform 3D-DIC. Laboratory tests show that cameras calibrated using the multi-sensor system reconstruct displacements with an accuracy above 95% compared to displacements measured using a traditional calibration method. At the same time, the proposed feature-tracking algorithm can identify and reconstruct the 3D positions and displacements of preexisting features without requiring an applied speckle pattern. The research shows that the proposed methods can simplify and streamline the use of 3DDIC and open the way to the use of this technique to perform condition monitoring of large-scale civil and mechanical engineering structures.
AUTOMATED STRUCTURAL SURFACE DAMAGE IDENTIFICATION, CLASSIFICATION AND SEVERITY ESTIMATION USING DEEP LEARNING APPROACHES
Authors: DAS MRINMOY KUMAR , RAPOLU NIKHITA , MEDA DHATHRI , KALAPATAPU PRAFULLA , KUMAR VENKATA DILIP
Published: December 2023

The maintenance of civil infrastructure has become a major challenge for modern engineers due to its continuous growth, especially buildings and bridges which are susceptible to structural damage caused by factors like aging, design flaws, and natural disasters, which can seriously jeopardize their safety, health, and structural soundness. The conventional inspection methods are not only costly and timeconsuming, but also pose safety hazards, rendering them ineffective. The paper introduces an automated method for detecting and assessing the severity of damages in buildings and bridges, which aims to address and mitigate the limitations of traditional techniques. A collection of 5000 images, ranging in size from 416x416 to 640x640 pixels, were gathered from damaged sites and annotated in polygon annotation format, with damages categorized into three classes: spalling, corrosion, and crack. To expand the dataset and enhance the precision of the deep learning models, data augmentation techniques from the data Albumentation library were utilized for image processing. Several object-based instance segmentation deeplearning models, including Yolo V5, V7, V8 Instance Segmentation, and Mask- RCNN, were trained on 80% of the dataset to obtain the coordinates of the damaged area's outline and generate masks for the detected damages. The area of these masks and the percentage of damage severity in an image were computed. The trained models achieved an accuracy range of approximately 60% to 70%, indicating the potential effectiveness of instance segmentation deep learning models. The proposed approach offers a quick and efficient method for determining the severity of structural damages, resulting in improved safety and decreased maintenance costs for buildings and bridges. To enhance the accuracy and precision of the model, future research could include a larger dataset with additional types of structural failures. Furthermore, the automated approach can be integrated into mobile monitoring and inspection devices, such as drones, as well as street camera systems to identify damaged buildings following natural disasters. An alarm can be triggered based on a specific threshold value, and appropriate actions can be taken to reduce the response time for emergency management and improve the structures' resilience to natural disasters.
ENHANCING DAM INSPECTION WITH PIXEL-LEVEL CNN-FCN APPROACH VIA 3D TEXTURE MAPPING
Authors: CHAIYASARN KRISADA , BUATIK APICHAT , JANGID VISHAL , KONGSILP SIRISILP , KHADEMI NAVID
Published: December 2023

Inspecting damage through visual inspection is an outdated and inefficient method, especially for large concrete dam structures. Furthermore, inspecting for damage to dam structures is costly, time-consuming, and hazardous. This research proposes a novel method for detecting and measuring cracks in the concrete structure of dams by utilizing a combination of the Convolutional Neural Network (CNN)-Fully Convolutional Network (FCN) algorithm and image-based 3D modeling. The method enables the identification of cracks on large structures at a pixel level, facilitating detection over a wide area and with high accuracy. Specifically, the approach detects cracks on the threedimensional surface of the dam, providing a comprehensive and effective means of identifying potential structural issues. To accurately determine the size of the detected damage, the local thickness (LT) algorithm is introduced. This algorithm utilizes the pixelprecise detection capabilities of crack detection to provide highly precise crack size measurements. The CNN-FCN system segments cracks at the pixel level on the texture space obtained from a 3D model created through photogrammetry techniques. Then the trained CNN is employed to detect crack patches, which are then imported into a trained FCN system for pixel-level segmentation. The predicted crack pixels are converted to a physical scale by the LT algorithm to quantitatively measure the morphological features of cracks. The resulting crack map is then projected onto a 3D model, providing a comprehensive visual representation of the cracks. The proposed crack detection method can achieve an accuracy of over 90%. The study revealed that the presented system could efficiently and precisely detect, locate, and measure the size of cracks in large dam structures, highlighting the potential of the proposed method to enhance the efficiency and effectiveness of crack inspection. Furthermore, the visualization of cracks on 3D models can help increase inspectors’ inspection efficiency.
ACCELERATING CORROSION SURFACE-AREA MEASUREMENTS WITH COMPUTER VISION AND DEEP LEARNING: AN ENSEMBLE APPROACH
Authors: NGUYEN HAI D. , WANG SHENGYI , WILSON REBEKAH , EICK BRIAN , BECERRA-STASIEWICZ NATALIE
Published: December 2023

The corrosion of infrastructure and facilities poses a significant challenge for the United States Department of Defense (DoD) in terms of cost and military readiness. To tackle this challenge, our research introduces a data-driven corrosion segmentation method that combines three deep learning-based models and an ensemble learning approach for the automatic identification and segmentation of corroded regions within high-resolution images. The method involves several stages, such as data annotation, preprocessing, augmentation, model implementation, and performance evaluation. The deep learning models used include Feature Pyramid Network (FPN) with Residual Network (ResNet)-34 encoder, UNet with ResNet-34 encoder, and UNet++ with Visual Geometry Group (VGG)-19 encoder. Ensemble learning, a technique that integrates these deep learning models, was employed to improve prediction accuracy and overall performance. The proposed method is evaluated using both the Dice score and the Intersection over Union (IoU) score metrics. Experimental results demonstrate that the ensemble learning approach outperforms individual models, achieving a Dice score of 90.1% and an IoU score of 83.9%. The approach shows promise to automatically detect and measure corrosion, which can reduce inspection costs and identify major issues to aid in prevention of structural failure. The tool developed in this study will be expanded to provide similar capabilities for large-scale civil infrastructure.
UNLEASHING THE POWER OF MULTI-SOURCE DATA FOR BUILDING ATTRIBUTE PREDICTION BASED ON DEEP LEARNING IN FLOOD RISK ASSESSMENT
Authors: SUBEDI ABHISHEK , JAHANSHAHI MOHAMMAD R. , JOHNSON DAVID
Published: December 2023

Knowledge of a structure’s physical attributes is critical for analyzing the risks associated with natural disasters such as floods and hurricanes. However, collecting these data over a wide geographical area is a difficult task because it often requires manual labor. Even in Louisiana, which is at considerable risk for flood-related natural disasters, the state government has sparse structure level data about the composition of building type, number of stories, foundation type, foundation height, presence of a basement, and presence of a garage, all of which are significant predictors of flood risk in many flood risk assessment methods. In this study, an automated model is proposed to predict these six attributes by utilizing a data fusion-based methodology. The model extracts feature from a building’s Google Street View (GSV) image through a Convolutional Neural Network (CNN) architecture. Likewise, an Artificial Neural Network (ANN) architecture is employed to extract features from community data taken at the census-block-group level. Another ANN is used to extract features from structure-level data obtained through a real estate database. These three feature streams are then fused and processed with a fusion module to predict a building’s attributes needed for flood risk assessment. Through this technique, accuracies close to or surpassing 90% are achieved on all five classification tasks. Likewise, the Mean Absolute Error (MAE) for foundation height estimation is small enough to make usable improvements to flood risk estimates over existing data sources and modeling assumptions. Although CNN and ANN models are used in this study, the basic framework can be applied with other machine learning models as well, if features can be extracted from them. Likewise, all the experiments are performed in the context of flood risk assessment in Louisiana. However, the framework can be easily extended to other natural disasters and geographical regions.
MULTIVIEW VISION-BASED DISPLACEMENT MEASUREMENT OF FULL-SCALE MITER GATE
Authors: LEE JUNHWA , WANG SHUO , FILLMORE TRAVIS , SPENCER BILLIE F.
Published: December 2023

Miter gates are critical components of locks and dams of inland waterways for facilitating movement of vessels between different water elevations. Closure of locks and dams due to gate failure or unexpected maintenance stalls barge traffic, leading to goods hardly reaching the market as well as huge economic loss. Therefore, continuous condition monitoring of miter gates while not affecting their operation is especially important for the stakeholders. Measured displacements of the gates under load are known to be informative of their condition, as well as for the updating of numerical models of the structure. Considering the need for remaining operational, as well as inaccessibility of contact-type sensors, this study proposes a multiview vision-based displacement measurement strategy tailored for full-scale miter gates. A displacement measurement strategy using multiple cameras is developed specialized for the miter gate under harsh field conditions such as non-negligible camera motion induced by strong wind and lack of reliable reference points. The proposed method will be demonstrated on the full-scale miter gate at The Dalles Lock and Dam by collecting displacements of the gate during the water-fill of the lock chamber. The measured displacements can be compared to the corresponding numerical analysis outputs to inform the updating of the model, thus providing the potential to improve the condition monitoring of the miter gates on inland waterways.
DEVELOPMENT OF AN ARTIFICIAL INTELLIGENCE (AI) FOR THE PREDICTION OF FATIGUE FAILURE IN NAVAL STRUCTURES: A DIGITAL TWIN APPLICATION
Authors: BANERJEE SOURAV , MEHTAJ NAFISA , GRISSO BENJAMIN L.
Published: December 2023

Many of the high speed and high-performance naval vessels use aluminum as a primary structural material. Welded aluminum stiffened structures are very common in these vehicles, but even with much research, performance of these structures under fatigue is not completely known. Many experimental investigations on similar structures were performed over the past two decades. Meanwhile, the desire for a comprehensive naval structural health monitoring (SHM) system is receiving greater interest. Recently, with re-advent of machine learning and artificial intelligence (AI) for structural digital twin, older experimental sensor dataset from crack growth in welded aluminum structure could be better utilized and exploited for crack predictions with few interventions from the sensor data. In this study, piezoelectric wafer active sensors are utilized for SHM of a welded aluminum structure and sensor data were collected at multiple frequencies from multiple specimens. Crack initiation to growth pattern were also recorded. Through machine learning, sensor data from four specimens were exploited to develop an AI algorithm for predicting the crack growth. It is shown that ML and AI frameworks are suitable for ship structure digital twin applications.
DEEP LEARNING-BASED AUTOMATIC BLIND IDENTIFICATION PROCEDURE FOR STRUCTURAL MODAL IDENTIFICATION
Authors: ZHANG CONGGUANG , SHU JIANGPENG , GUO YUTONG , XU YIFEI , NIU YANBO
Published: December 2023

Traditional modal identification approaches require too much user interaction, which results in low implementation efficiency. This study develops an automatic approach that combines deep learning and blind source separation to determine the modal parameters of structures. The core object of the proposed approach is to establish a multi-task deep neural network (MTDNN) that enables it to automatically obtain independent modes from multi-mode vibration responses of structures. Then modal frequencies and damping ratios of structures can be extracted from independent modes by employing the traditional random decrement technique and curve fitting approach. The weights between the last two layers of MTDNN represent the corresponding mode shapes. The approach is implemented in a long-span cable-stayed bridge in engineering practice for validation. For the field test, five accelerometers are employed to derive acceleration responses of the structure. The results indicate the ability of the proposed approach to automatically determine the modal parameters of structures with reliable accuracy, which provides a promising new solution for online modal parameter identification and modal tracking of structures.
EXPLORING THE POTENTIAL OF TRANSFER LEARNING APPLICATIONS FOR STRUCTURAL DAMAGE CLASSIFICATION
Authors: DURAN BURAK , AZAM SAEED EFTEKHAR
Published: December 2023

Structural Health Monitoring (SHM) is essential for ensuring the safety and maintaining the functionality of structures and infrastructure systems, and Machine Learning (ML) techniques have shown great potential in rendering SHM as an automated process. However, unlike many other application areas of ML, when dealing with infrastructure SHM, there is little to no data available from damaged states to be used for ML training. To cope with this issue, a typical Finite Element Model (FEM) of a representative bridge beam structure is used for generating a damaged dataset. Secondly, creating a generalized network that can successfully work and perform a classification task for a variable set of structures seems to be one of the biggest challenges in the field of structural damage detection. In this work, the capability of Transfer Learning (TL) via Feature Extraction (FE) and Joint Training (JT) in generalizing the network is explored. Modeling uncertainties for supervised damage classification can be mitigated by utilizing a Deep Neural Network (DNN) comprised of Fully Connected (FC) and Convolutional (CONV) layers. In this regard, FEMs of three simply-supported beam structures with varying lengths were constructed, and acceleration time-history data was obtained from the interior nodes under the applied load history at the midpoint. The damage states were represented by the change in flexural stiffness within a range from 10% to 90%. Subsequently, the potential knowledge transfer was successfully implemented from the source domain to the target domains via TL and JT. Then, the proposed two-dimensional (2D) CNN network was tested with a target dataset that was not included in the training from another beam with a different length. The results indicate that FEMs could generate numerous source domains to achieve a generalized model even with including uncertainties.
POPULATION-BASED SHM UNDER ENVIRONMENTAL VARIABILITY USING A CLASSIFIER FOR UNSUPERVISED DAMAGE DETECTION
Authors: BEL-HADJ YACINE , WEIJTJENS WOUT
Published: December 2023

In this paper, we introduce a novel deep learning technique for anomaly detection in the context of Population-Based Structural Health Monitoring (PB-SHM). The proposed method eliminates manual feature engineering by utilizing Power Spectral Density (PSD) as input, allowing examination of the entire spectrum. It is based on an auxiliary classification task, wherein the model is trained to discriminate between different systems according to their dynamic response. The classifier confidence is then used during inference for damage detection. The neural network extracts discriminative features commonly impacted by damage, which are employed to create a normality model. The efficacy of our method is demonstrated on a simulated population of 20 individual 8- DOF systems influenced by a latent environmental variables, emphasizing its potential for PB-SHM under diverse conditions. Our technique achieves performance comparable to resonance frequency-based methods while potentially exhibiting higher capability in complex structures with multiple modes. Anomalies caused by a 5% decrease in stiffness are successfully detected, yielding an AUC of 0.94.
Javier Buzón, la naturaleza y la luz
Authors: Luque Teruel Andrés
Published: December 2023

El artículo presenta un primer estudio de la pintura de Javier Buzón, mediante el análisis formal de una serie de obras representativas de sus distintos períodos, con los que se establece el perfil creativo y la evolución del artista, señalando los rasgos morfológicos que lo distinguen. El interés de este pintor, debido a su capacidad creativa, fundamentada en dos principios básicos, la precisión de los elementos figurativos y la incidencia directa de la luz sobre ellos y como foco que reclama la atención visual, tiene, pues, el reconocimiento debido.
Análisis de la pintura de Torsten Jovinge.
Authors: Iglesias Cumplido Alicia
Published: December 2023

El artículo analiza la pintura de Torsten Jovinge, artista vinculado a las Vanguardias Históricas parisinas, procedente del círculo de André Lhote, cuyos planteamientos cubistas superó con una nueva figuración en contacto con los planteamientos de importantes pintores internacionales, como Fernand Léger, Le Corbusier y Amédee Ozenfant, en consonancia con la Pintura Metafísica, y con ciertos paralelismos con las estructuras directas de Magritte.
Ana Barriga, la pintura como re dimensión del objeto
Authors: Luque Teruel Andrés
Published: December 2023

El artículo plantea el inicio de la carrera artística de una joven pintora, Ana Barriga, cuya actividad en Sevilla, comprendida entre 2010 y 2017, supuso la revelación de un método muy personal, con el que los logros plásticos y el alcance de la carga de sentido quedaron en perfecto equilibrio. La continuidad de su carrera en Madrid, y con ello la amplitud de sus éxitos, la confirman como uno de los más firmes valores de la pintura española actual. Aquí se recogen sus principales datos biográficos; el perfil de su actividad; sus explicaciones sobre los fundamentos de su pintura; las opiniones de historiadores y teóricos del Arte; y un análisis formal que permite establecer sus claves creativas.
Fundamentos teóricos de la abstracción en las primeras vanguardias artísticas, los casos previos de monet, matisse y Picasso
Authors: Olazábal Lucía
Published: December 2023

El artículo trata sobre los orígenes de la abstracción en las Vanguardias Históricas a través de las aportaciones de una serie de pintores que propusieron conceptos novedosos que renovaron el espacio de representación, los principales el nuevo tratamiento del color y la yuxtaposición de las pinceladas de Monet; el valor del plano mediante el uso de un solo color de Matisse; y el facetado del espacio y el volumen de Picasso.
La evolución plástica en una serie pictórica: la sociedad líquida de Joaquín González Quino
Authors: Báñez Simón José
Published: December 2023

Nos proponemos continuar con el estudio de la serie ‘Sociedad líquida’ del artista extremeño Joaquín González “Quino”, cuyos primeros planteamientos avanzamos en un estudio anterior. Incluimos cinco nuevas piezas en el contexto de una evolución conceptual que responde al trabajo y a la experimentación constante, factores que lo consagran como uno de los más importantes pintores españoles del panorama actual.
Los encuentros de Max Ernst, Yves Tanguy y Óscar Domínguez.
Authors: Parra Romero Carlos
Published: December 2023

El artículo trata sobre el contexto en el que se dio el encuentro de la obra de Max Ernst, Yves Tanguy y Óscar Domínguez y su relación con el movimiento surrealista. Fue el punto de partida de una cierta poética, coincidente en conceptos y mecanismos perceptivos, pese a las grandes diferencias formales y estilísticas entre los tres.
Informes criminológicos para el acceso a libertades anticipadas en Neuquén, Argentina
Authors: Petraglia Luciana , Amaral Machado Bruno
Published: December 2023

El objetivo del artículo es identificar cuál es el marco teórico y el modelo que se utiliza para la elaboración de los informes criminológicos para evaluar libertades anticipadas en Neuquén. Asimismo, analiza cómo los informes impactan en las decisiones judiciales y cómo opera la maquinaria judicial en relación a ellos. Para eso se utiliza, inicialmente, el análisis documental de la normativa que dio origen y marco a la institución que confecciona los informes criminológicos en la provincia de Neuquén. En seguida, analiza los informes y las decisiones judiciales que se adoptaron entre enero y julio de 2022 en la ciudad de Neuquén, un total de veintisiete casos. Finalmente, para conocer las percepciones de los profesionales en relación al tema, se realizaron entrevistas en profundidad a operadores judiciales y de la ejecución de la pena en la Primera Circunscripción de Neuquén.
Reseña de 'La cuestión carcelaria. La pena legal y la pena real'
Authors: Giraldo Cano Natalia
Published: December 2023

Reseña del libro 'La cuestión carcelaria. La pena legal y la pena real', Iñaki Rivera Beiras, Tirant lo Blanch 2023, 834 pp.
Los dos maestros. Las camisas azules contra Antoni Benaiges
Authors: Bernal Sergi
Published: December 2023

La voz de la Bureba fue un semanario independiente, defensor de la agricultura y de los intereses de la comarca de la Bureba, que se publicó en la ciudad de Briviesca (Burgos) entre abril de 1934 y julio de 1936. En la redacción de este periódico escribían con regularidad dos maestros, uno socialista y otro falangista. ¿Pero quiénes eran estos dos maestros que coincidieron durante unos pocos años en la ciudad de Briviesca? ¿Y qué responsabilidad tuvo uno en la desaparición del otro? Se ha hablado mucho de los maestros de la 2a República, pero poco de aquellos otros maestros que estaban contra ella. Este trabajo saca a la luz una historia no contada, también, pone cara a cara a esos dos modelos de entender la educación y como también, hubo docentes, como nuestro protagonista que trabajó para acabar con el modelo pedagógico defendido por los «Maestros de la República».