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Using glass as a structural element that could obtain the role of the primary load-bearing elements of iconoclast structures has had a superficial history. Because of its aesthetic and admissible mechanical properties, glass has been considered one of the structural elements in the construction industry. Because of its extrasafety, laminated glass on top of the most widely used elements has been appraised with more depth. The study aimed to evaluate the failure modes in laminated glass and the possibility of detecting the pre-failure status of the surveyed elements, referring to the proffered approaches and image processing (IP) procedures in the literature. The outcomes have compared the peculiarity and strength points of various routes and have reached the inference for proper approaches in every procedure. According to the results, image processing (IP) is the most appropriate method of pre-failure detection in load-bearing laminated glass elements. Among the image processing (IP) techniques, digital image correlation has the widest application. The results of this study, in combination with other pieces of research, may propound a database for evaluating the failure status of glass structures. [ABSTRACT FROM AUTHOR]

The article provides information on the 20th International Conference on Condition Monitoring and Asset Management that will take place from June 17-20, 2024 at Milton Hill House, Abingdon, Oxfordshire, United Kingdom.

This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions. [ABSTRACT FROM AUTHOR]

Orthotropic steel decks (OSDs) are commonly used in the construction of bridges due to their load-bearing capabilities. However, they are prone to fatigue damage over time due to the cyclic loads from vehicles. Therefore, the early structural health monitoring of fatigue damage in OSDs is crucial for ensuring bridge safety. Moreover, Lamb waves, as elastic waves propagating in OSD plate-like structures, are characterized by their long propagation distances and minimal attenuation. This paper introduces a method of emitting high-energy ultrasonic waves onto the OSD surface to capture the nonlinear Lamb waves formed, thereby calculating the nonlinear parameters. These parameters are then correlated with the fatigue damage endured, forming a damage index (DI) for monitoring the fatigue life of OSDs. Experimental results indicate that as fatigue damage increases, the nonlinear parameters exhibit a significant initial increase followed by a decrease. The behavior is distinct from the characteristic parameters of linear ultrasound (velocity and energy), which also exhibit changes but to a relatively smaller extent. The proposed DI and fatigue life based on nonlinear parameters can be fitted with a Gaussian curve, with the R-squared value of the fitting curve being close to 1. Additionally, this paper discusses the influence of rib welds within the OSDs on the DI, whereby as fatigue damage increases, it enlarges the value of the nonlinear parameters without altering their trend. The proposed method provides a more effective approach for monitoring early fatigue damage in OSDs. [ABSTRACT FROM AUTHOR]

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth. [ABSTRACT FROM AUTHOR]

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures. [ABSTRACT FROM AUTHOR]

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left–right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively. [ABSTRACT FROM AUTHOR]

In 2018 a collaborative project between Politecnico di Milano (PoliMI) and Regione Lombardia (RL) was launched to join forces and expertise toward the improvement of the regional transport infrastructures maintenance management. One of the project goals was the development of regional guidelines aimed to support the design and implementation of monitoring systems for bridges. The focus of this paper is on the illustration of the Monitoring Regional (MoRe) guidelines and of their implementation on nine pilot monitoring systems designed and deployed within the project. The (MoRe) guidelines tackle the entire monitoring process from the analyses of the monitoring goals and the preliminary investigations needed to the identification of the phenomena and relevant indicators to monitor, up to the selection of monitoring devices and the presentation of results. A short illustration of the permanent monitoring systems installed on nine exemplary bridges in the Lombardia region concludes the paper. [ABSTRACT FROM AUTHOR]

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter. [ABSTRACT FROM AUTHOR]

With the increasing of tunnel mileage, one of the most vital problems of broad-scale structures is the intelligent identification and location of structural defects, which require massive data storage capacity and high data processing efficiency. The full-field point cloud data collected by terrestrial laser scanning (TLS) can be extracted to construct three-dimensional measured models more accurately, therefore, the fusion of TLS and the finite element method (FEM) is particularly beneficial to the deformation analysis of tunnel structures. This paper combines numerical simulation and full-field measurement to interactively verify the reliability of deformation analysis based on FEM simulation and point cloud processing. The innovation of this paper is that a three-dimensional FEM is validated through the measured point cloud data of the tunnel structure. The result of this study indicated that FEM simulation and the structural health monitoring analysis are consistent. [ABSTRACT FROM AUTHOR]

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork. [ABSTRACT FROM AUTHOR]

In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach's superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems. [ABSTRACT FROM AUTHOR]

It is important but still challenging to detect structural damage with limited instrumentation in the spatially large civil infrastructure. In this paper, a damage detection algorithm is proposed employing a global–local approach with limited sensors for detecting structural damage of spatially large civil engineering structures. The proposed algorithm is based on measuring frequency response functions (FRFs) at convenient yet selective locations on the structure with very limited sensors to localize the region of damage in the structure using the proposed global diagnostic approach. Once the local region of damage is established, local diagnostics are performed in the isolated small region of the structure using high fidelity sensing, still, a limited number of sensors in the small localized region (segment) of the structure, to precisely locate the damage. The principal component analysis combined with fractal dimension theory is employed for local diagnostics. Numerical studies have been conducted on a 6m simply supported beam and a 30-storey framed structure idealized as a shear building. Experimental verification of the proposed global–local damage diagnostic approach is also carried out. Studies presented in this paper indicate that the global–local approach for damage diagnosis of spatially large structures is very promising for precisely locating the damage with very limited sensors. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

This paper discusses the development of ultrasonic stress measurement (USM) technology as a potential tool for structural heath monitoring and evaluation of buildings and bridges. The proposed approach utilizes acoustic birefringence to assess the total stress in steel members. Acoustic birefringence describes the difference in velocity between orthogonally polarized shear waves as a result of anisotropy in the material. This anisotropy comes from texture produced during the manufacturing process and stresses (i.e., strains) in the plate under loading. The relationship between the birefringence and stresses may be a viable method to evaluate the total stress in a steel member, which includes stresses produced from all applied loads, including both dead loads and live loads. This paper investigates steel wide-flange (W-flange) rolled sections under tension and bending. The results indicate that birefringence shows a strong linear correlation with strain. The variation in the in situ birefringence was found to be significant and varied between different locations on the same cross section. If the acoustic constants (B0 and m1) that relate the birefringence to the strain are known, laboratory tests showed that stress can be approximated to within ±1.1 ksi (±7.6 MPa) (2% of the material yield strength, Fy). If average values of constants across all laboratory testing were used, the error increased to ±14.79 ksi (±102.0 MPa) (28% Fy). These results indicate that ultrasonic stress measurement based on acoustic birefringence measurements may be a viable method for the evaluation of total stress in steel members but needs additional research to identify and mitigate the sources of variation in critical acoustic constants that affect the accuracy of the approach. [ABSTRACT FROM AUTHOR]

Metallic samples are widely applied in modern industrial production. Due to non-uniformities in the stress load, such samples may become damaged and produce defects, which can cause unnecessary economic losses. In this paper, an online defect detection method is proposed for the quality monitoring of metallic plates. The research involves the design and optimisation of an electromagnetic tomography (EMT) sensor and the development of a fast tomography algorithm. Specifically, a planar array eddy current sensor is designed for in-situ structural health monitoring of metallic specimens. The parameters of the sensor are optimised using an orthogonal methodology and a response surface methodology to improve the uniformity of the sensitivity field. In addition, a second-order iterative Bregman reconstruction algorithm is investigated to reconstruct the defect image, which can improve the reconstruction speed for this ill-posed problem. Simulation and experimental results indicate that the proposed method can be applied to effectively evaluate the locations and sizes of defects in metallic specimens. The correlation coefficients of the reconstructed images using the proposed method are larger than 0.8. Compared with traditional reconstruction algorithms, the method proposed in this paper shows fast convergence speed and smaller estimation errors. [ABSTRACT FROM AUTHOR]

The paper presents a semi-analytical method, named the Double Legendre Polynomial Method (DLPM), which is extended for modeling the axisymmetric free vibration of a functionally graded piezoelectric (FGP) hollow cylinder resonator. The FGP resonator is composed of two-phase graded piezoelectric materials, Lead zirconate titanate (PZT4) and aluminum nitrid (AlN), on the top and bottom surfaces, respectively, where the material properties gradually change along the thickness direction. The integration of FGP materials with piezoelectric materials is an innovative aspect in material science, and this paper explores their dynamic behavior, providing insights into their mechanical and electrical properties and contributes to expand our understanding of the axisymmetric free vibration of FGP hollow cylinder resonators as a crucial factor for optimizing their performance across a wide range of applications, including sensors, actuators, energy harvesting, and structural health monitoring systems. The DLPM is applied to solve the wave governing differential equations of motion, offering a new tool for studying the dynamic behavior of FGP hollow cylinders under axisymmetric vibration. The study investigates the effects of the diameter-thickness ratio on the effective electromechanical coupling coefficient and the resonant and anti-resonant frequencies of the FGP hollow cylinder. The results show that as the diameter-thickness ratio increases, the frequency-diameter product appears to become more stable. This observation is a valuable insight for optimizing the design of these resonators. Furthermore, the study presents the electrical input admittance, the natural frequencies as well as the mechanical displacement and electric potential profiles of the resonator. All the provided results are based on the calculation of the resonant frequencies. The validity of the presented results was verified by the comparison with the existing ones reported in the literature. [ABSTRACT FROM AUTHOR]

Location of damage sources is an important aspect of structural health monitoring research. Acoustic emission (AE) technology is broadly concerned due to its potential advantages in damage monitoring and source localization. However, the traditional positioning method is based on the arrival of P wave, and the non-uniformity of concrete materials is not considered, resulting in poor accuracy at large distances. This paper describes a non-contact AE localization method using leaky Rayleigh waves via a new air-coupled MEMS microphones array unit. Compared with traditional contact detection, this method is convenient for rapid setup and monitoring in a wider range. The feasibility of the non-contact AE localization method was verified by numerical simulation and experiments. Azimuth (direction of arrival) of AE source is a key source parameter for damage location. The research shows that this method can determine the azimuth of AE source at different positions, the results are close to the actual coordinates. Non-contact monitoring method proposed in this paper is the basis for further research on the failure prediction of concrete plate-like structures such as tunnel lining and bridge deck. [ABSTRACT FROM AUTHOR]

Human–machine interface requires various sensors for communication, manufacturing and environmental control, and health and safety monitoring. Capacitive sensors have been used to detect touch, distance, geometry, electric property, and environmental parameters. However, highly sensitive proximity detection with a small form factor has always been a challenge. This paper presents a capacitive sensor composed of a nanostructured electrode array for contact and noncontact detection. In the sensor configuration, the nanostructured electrode is made of high aspect ratio cellulose fibers embedded with carbon nanotubes. The complementary electrode is designed to be smaller in surface area for high sensitivity. Based on the analysis, the unique sensing mechanism is shown to enhance the proximity sensitivity for target detection. A pair of asymmetrically designed electrodes are characterized and compared with the traditional symmetric electrodes for proximity and contact detection of human hands. The sensor performance is also characterized for detecting water mass in glass and metal cups. In the end, a smart pad that can recognize human gestures, gait, and water mass with unprecedented sensitivity is demonstrated. [ABSTRACT FROM AUTHOR]

Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors' knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles' vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges. [ABSTRACT FROM AUTHOR]

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the structural load applied to the attachment and frame of a wheel loader by using pressure and acceleration, which have a load correlation, instead of strain. First, we propose a data-driven modeling methodology for load estimation. Load features as model input are derived based on cylinder pressure and frame acceleration. The relationship between features and the load is expressed through correlation coefficients and generalized by using the supervised machine learning technique. Next, to experimentally collect the large data set for learning, two wheel loaders of different classes are selected to build sensor cars. Then, the measurements on both wheel loaders are performed repeatedly with four experienced operators according to the test cases. Using conventional learning algorithms, model selection is performed for five parts that make up the attachment and frame. Then, the model learning and evaluation are performed. As a result of an out-of-sample test, the average estimation error for each part is approximately 5 % and the proposed methodology is experimentally verified that it is effective in estimating load for CUP monitoring. [ABSTRACT FROM AUTHOR]

This document provides a comprehensive overview of the integration of civil structural health monitoring and machine learning in the field of concrete structures. It discusses the importance of monitoring infrastructure condition and the challenges of manual inspection. The document highlights the increasing use of machine learning algorithms, such as computer vision methods, in optimizing maintenance and repairs. It presents case studies and applications of machine learning in civil engineering, including damage detection and load assessment. The document also emphasizes the challenges and limitations of using machine learning in this context and recommends further research and interdisciplinary collaboration. Additionally, it addresses ethical and privacy concerns, the importance of open data sharing, and aligning machine learning applications with sustainability efforts. The document includes a list of academic articles related to machine learning techniques in concrete structures, covering topics such as crack detection and structural health monitoring. It also compiles various studies and research papers on machine learning for structural health monitoring in concrete structures, exploring different methods and highlighting the potential of deep learning models and image processing techniques for crack detection. Overall, the document provides a comprehensive overview of the application of machine learning in the field of structural health monitoring for concrete structures. [Extracted from the article]

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

In April 2021 Stavå bridge, a main bridge on E6 in Norway, was abruptly closed for traffic. A structural defect had seriously compromised the bridge's structural integrity. The Norwegian Public Roads Administration (NPRA) closed it, made a temporary solution and reopened with severe traffic restrictions. The incident was alerted through what constitutes the bridge's Digital Twin processing data from Internet of Things sensors. The solution was crucial in online and offline diagnostics, the case demonstrating the value of technologies to tackle emerging dangerous situations as well as acting preventively. A critical and rapidly developing damage was detected in time to stop the development, but not in time to avoid the incident altogether. The paper puts risk in a broader perspective for an organization responsible for highway infrastructure. It positions online monitoring and Digital Twins in the context of Risk- and Condition-Based Maintenance. The situation that arose at Stavå bridge, and how it was detected, analysed, and diagnosed during virtual inspection, is described. The case demonstrates how combining physics-based methods with Machine Learning can facilitate damage detection and diagnostics. A summary of lessons learnt, both from technical and organisational perspectives, as well as plans of future work, is presented. [ABSTRACT FROM AUTHOR]

Translation abstract Deformation monitoring of bridges applying remote sensing satellite radar measurementsDeformations of the Earth's surface can be measured from space using remote sensing radar interferometry (InSAR). This paper demonstrates the use of these data for deformation monitoring on the Schottwienbrücke‐S6 Semmering motorway. The European Space Agency (ESA) recently began providing free research satellite data from the Sentinel‐1 mission. The approach uses historically recorded natural reflection points as well as corner reflectors artificially attached to the structure. These are measured coordinately and analyzed using remote sensing methods. Thermal displacements of the structure are compensated to increase accuracy. A new model predicts the temperature of the structure based on network weather data. This means that the deformation measurements from the satellites can be analyzed and thermally compensated without sensors on the building, and the deviations can be more than halved. The recorded and processed annual measurements from 2022 onwards show a standard deviation of 1.7 to 3.0 mm compared to the reference system (Liquid Leveling System). Good agreement with the long‐term settlement behavior of the bridge was also found for natural reflection points. Satellites can also be used to simultaneously record different structures at 6‐day intervals up to 2016. Due to the time‐delayed processing, they are currently well suited for long‐term deformation monitoring of long‐span bridges. [ABSTRACT FROM AUTHOR]

Flexible strain sensors have a wide range of applications in the field of health monitoring of seismic isolation bearings. However, the nonmonotonic response with shoulder peaks limits their application in practical engineering. Here we eliminate the shoulder peak phenomenon during the resistive-strain response by adjusting the dispersion of conductive nanofillers. In this paper, carbon black (CB)/methyl vinyl silicone rubber (VMQ) composites were modified by adding a silane coupling agent (KH550). The results show that the addition of KH550 eliminates the shoulder peak phenomenon in the resistive response signal of the composites. The reason for the disappearance of the shoulder peak phenomenon was explained, and at the same time, the mechanical properties of the composites were enhanced, the percolation threshold was reduced, and they had excellent strain-sensing properties. It also exhibited excellent stability and repeatability during 18,000 cycles of loading–unloading. The resistance-strain response mechanism was explained by the tunneling effect theoretical model analysis. It was shown that the sensor has a promising application in the health monitoring of seismic isolation bearings. [ABSTRACT FROM AUTHOR]

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures' health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task. [ABSTRACT FROM AUTHOR]

Composite adhesive rivet hybrid connection structure has been more and more recognized and applied in aerospace, automobile, and other industries due to its high strength, corrosion resistance, and good stability. However, due to the particularity of the composite structure and the complex nonlinear coupling factors existing in the bearing process of the adhesive rivet hybrid connection structure, it is difficult to carry out structural health monitoring of the composite adhesive rivet hybrid connection structure. In this paper, a new fabrication technique is proposed to fabricate MXene thin film sensors with excellent performance. The MXene sensor can be tailored to the required size and arrangement to monitor the hybrid connection structure. The multi‐stage loading‐unloading test verifies the feasibility of using the MXene sensor to monitor the hybrid connection structure. The monitoring curves of the MXene sensor for composite adhesive rivet mixed connection structure specimens under low‐speed impact test and vibration fatigue test were obtained, and the sensing mechanism of the MXene sensor was explained. The proposed monitoring method based on the MXene sensor is suitable for the health monitoring of adhesive rivet hybrid connection structures under tensile‐compression, impact, and vibration conditions. Highlights: MXene sensors can be integrated into composite hybrid bonded‐riveted joints without defects.MXene sensors combined with flexible circuits can form a high‐stability monitoring array.Monitor the mechanical behavior of bonded and riveted areas by the resistance change rate of MXene sensors.Real‐time monitor the micro damage and acceleration changes under the vibration environments. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) composites with self‐sensing and self‐healing capabilities was developed in this work. Uniform dispersion of CNTs within a core‐shell nanofiber structure was achieved through a combination of electrospinning and subsequent surface spraying of CNTs. The resulting core‐shell nanofibers were incorporated into CFRP composites using a vacuum‐assisted resin transfer molding process. Mechanical testing demonstrated improved mechanical properties in the composites treated with CNT spraying. Real‐time, precise monitoring of structural damage was enabled through the measurement of electrical signal changes caused by composite deformation in static loading experiments. Furthermore, self‐healing experiments demonstrated that the thermally conductive network formed by CNTs facilitated more uniform heat transfer within the composites. During the healing process, resistive heating of damaged specimens was applied, resulting in a remarkable healing efficiency of 95.2%. The experiments indicate that the composite developed in this paper possess excellent self‐sensing and self‐healing capabilities. Highlights: Damage self‐sensing and self‐healing CFRP composites were developed.CNTs‐modified nanofibers were applied to endow the composite specific abilities.Resistivity signals were collected for real‐time damage monitoring.The self‐healing efficiency of the designed composite reached up to 95.2%.Self‐sensing and self‐healing occurred autonomously within closed‐loop structure. [ABSTRACT FROM AUTHOR]

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods. [ABSTRACT FROM AUTHOR]

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process. [ABSTRACT FROM AUTHOR]

Defect reconstruction is essential in non-destructive testing and structural health monitoring with guided ultrasonic waves. This paper presents an algorithm for reconstructing notches in steel plates, which can be seen as artificial defects representing cracks by comparing measured results with those from a simulation model. The model contains a parameterized notch, and its geometrical parameters are to be reconstructed. While the algorithm is formulated and presented in a general notation, a special case of guided wave propagation is used to investigate one of the simplest possible simulation models that discretizes only the cross section of the steel plate. An efficient simulation model of the plate cross section is obtained by the semi-analytical scaled boundary finite element method. The reconstruction algorithm applied is gradient-based, and algorithmic differentiation calculates the gradient. The dedicated experimental setup excites nearly plane wave fronts propagating orthogonal to the notch. A scanning laser Doppler vibrometer records the velocity field at certain points on the plate surface as input to the reconstruction algorithm. Using two plates with notches of different depths, it is demonstrated that accurate geometry reconstruction is possible. [ABSTRACT FROM AUTHOR]

Despite the significant advances in structural health monitoring (SHM) technology and the widespread use of monitoring in other industries, its use on bridges remains relatively limited. Bridge owners seem to consider that the cost and difficulty of installing and maintaining sensors and data acquisition equipment outweigh the potential benefits that might be gained. In addition, owners realise that monitoring and managing the data also requires resources. There is a difference in philosophy and approach when considering SHM on new complex bridges and on existing bridges. On new bridges, SHM can assist engineers validate design assumptions. On existing bridges, the use of SHM has typically been on a reactive, usually temporary basis, to address specific concerns. Some of the challenges surrounding the use of SHM, particularly on existing bridges, are examined in this paper and a new development in SHM that uses risk prioritisation as part of the wider asset management of bridges is described. [ABSTRACT FROM AUTHOR]

In the realm of structural health monitoring (SHM), understanding the expected behavior of a structure is vital for the timely identification of anomalous activities. Existing methods often model only the physical quantities of monitoring data, neglecting the corresponding temporal information. To address this, this paper presents an innovative deep learning framework that synergistically combines a BiLSTM model, fortified by a temporal pattern attention (TPA) mechanism, with time-encoded temperature and traffic-induced deflection-temporal patterns. The arithmetic optimization algorithm (AOA) is employed for optimal hyperparameter tuning, and incremental learning was implemented to enable real-time updates of the model. Based on the proposed framework, an anomaly detection method was subsequently developed. This method is bidirectional: it uses quantile loss to provide expected ranges for structural behavior, identifying isolated anomalies, while the windowed normalized mutual information (WNMI) based on multivariate kernel density estimation (MKDE) helps detect trend variability caused by decreases in structural stiffness. This framework and the anomaly detection method were validated using data from an operational cable-stayed bridge. The results demonstrate that the method effectively predicts structural behavior and detects anomalies, highlighting the critical role of temporal information in SHM. [ABSTRACT FROM AUTHOR]

Ultrasonic guided waves (UGWs) based structural health monitoring (SHM) systems are widely used in several engineering fields for real‐time damage detection, since they are particularly sensitive to material local changes, induced, i.e., by a damage. This paper aims to investigate on the use of SHM systems in additively manufactured laminate. Specifically, a flat panel made of XSTRAND GF30‐PA6 material, fabricated by Ultimaker S5 fused filament fabrication (FFF) based 3D printer, is used as a case study. Signals recorded by a piezoelectrics (PZTs) array are analyzed to highlight the dispersive behavior of symmetric, S0, mode along different measurement paths in the excitation frequency range of 100–300 kHz. [ABSTRACT FROM AUTHOR]

Bridge structural health monitoring (BSHM) is extensively employed to assess whether bridge damage exceeds maintenance limits and helps maintenance decision-making. Frequency identification is an essential part of BSHM, which utilizes the first-order modal frequency as an evaluation indicator of bridge health status. This paper proposes a method for bridge damage identification based on extracting the first-order modal frequency of the bridge from dynamic responses of vehicle bogies. Firstly, the feasibility of extracting bridge modal frequency from bogie accelerations is theoretically deduced via a vehicle–bridge interaction model. The result indicates that the bogie accelerations encompass three frequency components, namely vehicle driving frequency, vehicle pitching frequency and bridge first-order modal frequency. Then, a multi-domain conjoint analysis method incorporating the time domain, frequency domain and time–frequency domain is proposed to extract the first-order frequency of damaged bridge from bogie accelerations. By defining a damage sensitivity index of the sliding window, the time-domain sensitive boundaries of bogie acceleration to bridge damage are pinpointed. Leveraging prior knowledge of the bridge modal frequency range, the frequency-domain sensitive boundaries bogie acceleration to bridge frequency are determined. On this basis, the sensitive region of bogie acceleration to bridge frequency can be precisely localized in the Hilbert Huang transform spectrum, ultimately identifying bridge frequency through searching local maximum instantaneous energy points within this region. Finally, comprehensive experiments, considering different vehicle speeds and track spectra scenarios, are carried out to investigate the effectiveness and robustness of the proposed method. The results affirm the performance of the method to accurately identify the first-order modal frequency of the damaged bridge, further underscoring the promising prospect of on-board BSHM. [ABSTRACT FROM AUTHOR]

This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) a precise displacement projection method considering target deformation was exploited, and a case study of the Fifth Nanjing Yangtze River Bridge (FNYRB) GB-radar campaign shows that a centimeter-level compensation can be achieved; (3) a post-construction settlement phenomenon was found during the FNYRB static load tests, characterized by 0.31 mm/min, which accumulated up to 25 mm. In addition, the dynamic monitoring capabilities of GB-radar for the bridge tower and girder were verified, highlighting its potential for bridge structural health monitoring (SHM). The insights gained from this study offer valuable recommendations for future GB-radar bridge displacement monitoring. [ABSTRACT FROM AUTHOR]

In the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle loads, a spatio‐temporal correlation model of the girder strain and tension forces of all suspenders is constructed based on the combined application of stacked denoising autoencoder and convolutional neural networks‐long short‐term memory model, so as to realize the preliminary identification of suspenders tension. On this basis, by using the actual monitoring data of suspenders tension and the strain monitoring data obtained through the distributed optical fiber sensors, the delicate identification of tension forces of all suspenders is realized based on the error interpolation of preliminary identification results. The results of the example bridge show that the method in this paper can effectively identify tension forces of all suspenders of the suspension bridge, and identification results are more accurate than the method using only the monitoring data of suspenders. [ABSTRACT FROM AUTHOR]

Current traffic simulation approaches analyze vehicle loads and load effects from a statistical perspective; however, they fail to reproduce the spatiotemporal distribution of bridge loads and resultant load effects at every moment, hindering real‐time bridge health management. This paper proposes a two‐step hybrid virtual–real traffic simulation (HvrTS) approach to reproduce the spatiotemporal distribution of bridge loads. Vehicle load sequences are first identified using computer vision based on traffic video from two surveillance cameras installed along the bridge. Next, traffic microsimulation is optimized with the identified vehicle load sequences from the two cameras serving as the known input and validated output. Using a cable‐stayed bridge under free‐flowing traffic, the HvrTS approach achieved a weighted mean square matching error of ≤0.3 m for the vehicle longitudinal location and a matching error of ≤7% for the vehicle lane position, whereas with congested traffic, the matching errors were much higher due to the inherent complexities and challenges associated with reproducing vehicle behavior in heavily congested situations. The traffic load effects calculated via HvrTS presented excellent spatiotemporal matching with those measured by a structural health monitoring system, especially in free‐flow traffic conditions. Applications on a continuous rigid frame girder bridge further validate these findings. Hence, the proposed HvrTS approach can overcome the challenge of spatiotemporal matching between vehicle loads and load effects in field monitoring. [ABSTRACT FROM AUTHOR]

The demand for fiber-reinforced polymers (FRPs) has significantly increased in various industries due to their attributes, including low weight, high strength, corrosion resistance, and cost-efficiency. Nevertheless, FRPs, such as glass and Kevlar fiber composites, exhibit anisotropic properties and relatively low interlaminar strength, rendering them susceptible to undetected damage. The integration of real-time damage detection processes can effectively mitigate this issue. This paper introduces a novel method for fabricating embedded capacitive sensors within FRPs using a coating technique. The study encompasses two types of fibers, namely glass and Kevlar fiber/epoxy composites. The physical vapor deposition (PVD) technique is employed to coat bundle fibers with conductive material, thus creating embedded electrodes. The results demonstrate the uniform distribution of nanoparticles of gold (Au) along the fibers using PVD, resulting in a favorable resistance of approximately ≈100 Ω. Two sensor configurations are explored: axial and lateral embedding of the coated yarn (electrodes) to investigate the influence of load direction on the coating yarn. Axial-sensor configuration specimens undergo tensile testing, showcasing a linear response to axial loads with average sensitivities of 1 for glass and 1.5 for Kevlar fiber/epoxy composites. Additionally, onset damage is detected in both types of fiber composites, occurring before final fracture, with average stress at the turning point measuring 208 MPa for glass and 144 MPa for Kevlar. The lateral-sensor configuration for glass fiber-reinforced polymer (GFRP) exhibits good linearity towards strain until failure, with average gauge factors of 0.25 and −2.44 in the x and y axes, respectively. [ABSTRACT FROM AUTHOR]

Summary: Structural performance assessment is a critical stage in the health monitoring for the maintenance and risk management of engineering structures, in which the interstory drift is a key indicator. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of steel structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the steel dynamic, while the unknown measurement noise covariance is adapted based on a fixed‐lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modelling uncertainties in each time increment by solving a minimax game: one player tries to select an "actual" model far from the proposed ACJM with an exponential decay tolerance, while its "hostile" player, namely, the optimum filter, is designed by minimizing the estimation error according to the selected "actual" model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Numerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given the paucity of monitoring data in field settings. This paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying Bayesian model averaging to several representative prediction models. By accounting for both model and parameter uncertainties, this approach enables more realistic predictions of ground deformations than individual models. Specifically, our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements, while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal deformations. Importantly, our analysis reveals that when monitoring data are sparse, model uncertainties may contribute up to 78.7% of the total uncertainties. Thus, obtaining sufficient data for parameter fitting is crucial for accurate predictions. The proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations. [ABSTRACT FROM AUTHOR]

Modal parameter estimation of a bridge with the vibration responses measured from an instrumented vehicle moving at a controlled speed is an active area of research. It is challenging to determine bridge natural frequencies and their associated mode shapes, as the measured output-only responses of an instrumented vehicle contain the desired dynamic responses of the bridge, along with the other confounding components related to vehicle dynamics, driving frequency component, and dynamics associated with the roughness profile of the road. These bridge responses are often masked by the dynamic responses associated with the vehicle, and road surface profile. Measurement noise components add to the existing problem of separating bridge frequency from various other said components. In this paper, an attempt has been made to extract bridge frequencies and mode shapes through the output-only responses collected from a traversing vehicle and using singular value decomposition (SVD) combined with the Teager-Kaiser energy operator (TKEO). Numerical investigations are made on the proposed SVD-TKEO-based modal identification technique in the presence of measurement noise. Parametric studies are conducted to investigate the influence of vehicle speed and road surface roughness on the quality of the identified bridge modal parameters using the proposed technique Numerical simulations carried out, show that the proposed SVD-TKEO-based algorithm performs well in identifying bridge mode shapes, even with relatively higher vehicle traveling speed, and handles even roughness of the road surface profile reasonably well. Lab-level experimental studies using vehicle bridge interaction setup, are also carried out using the SVD-based modal parameter estimation technique to explore its practical use. [ABSTRACT FROM AUTHOR]