Structural Health Assessment Research Articles

Structural Health Assessment of a Reinforced Concrete Building in Valparaíso Under Seismic and Environmental Shaking: A Foundation for IoT-Driven Digital Twin Systems

Structural health monitoring is vital for the safety and longevity of infrastructure, particularly in seismic zones. This study focuses on identifying the dynamic properties of a reinforced concrete building in Chile’s Valparaíso region. Using an experimental approach, the study compares ambient vibration records, seismic events (moment magnitude > 4), and data collected during adjacent construction activities. Force-balanced accelerometers were used for vibration measurements. The analysis employs the Stochastic Subspace Identification with Covariances (SSI-COV) method within an operational modal analysis framework to extract the building’s modal parameters without requiring artificial excitations. This technique effectively identifies modal characteristics under different vibration sources, making it suitable for evaluating the structural condition under diverse loading conditions. The findings reveal the building’s modes and frequencies, offering critical insights for maintenance and management of infrastructure. Little to no variations were observed in the identified frequencies of the building when working with different types of input data. These data support the integration of real-time IoT systems for continuous monitoring, providing a foundation for future digital twin applications. These advancements facilitate early deterioration detection, enhancing resilience in seismic environments.

Novel Visualization of Building Earthquake Response Recorded by a Dense Network of Sensors.

The strong motion records collected in full-scale structures provide the ultimate evidence of how real structures, in situ, respond to earthquakes. This paper presents a novel method for visualization, in three dimensions (3D), of the collective motion recorded by a dense array of sensors in a building. The method is based on one- and two-dimensional biharmonic spline interpolation of the motion recorded by multiple sensors on the same or multiple floors. It is demonstrated on novel data that have been recorded recently in a 50-story skyscraper, uniquely instrumented with multiple triaxial accelerometers per floor, approximately at every five floors above ground and at two basement levels, and with rotational seismometers and two borehole arrays measuring the motion of the soil very near the building foundation. The method is computationally efficient and suitable for real-time application and rapid assessment of structural health. The animations provide invaluable insight into the 3D structural response of the building as a whole, including wave propagation through the structure and the interplay between translations and rotations, which will be useful for testing existing and developing new methods for structural health monitoring of buildings and for the further development of building design codes. Animations of selected earthquakes can be found on YouTube at @TPYC-seismic.

A geometrical morphology-enhanced computer vision approach for structural health assessment

Images contain abundant valuable information about the health state of the photographed infrastructures. However, local defects are mostly detected in vision-based structural health monitoring (SHM), while global safety and risk at a larger scale are rarely assessed. To fill up this gap, a geometrical morphology-based image analysis framework is developed for structural global health assessment. A structured random forest edge detector is adopted to extract the edges in an image, and the morphological operations are subsequently used to highlight the edge skeleton, from which the continuous line segments are estimated by the Hough transform. Via these operations, the intrinsic geometrical features of different structures can be extracted and highlighted in an unsupervised manner and at a real-time speed. A global health indicator, line-to-edge ratio, is finally calculated to assess the structural state according to the fact that the deterioration of material will introduce abundant irregular edges in an image and break the lines at the structure boundaries. A destructive beam loading test video and a set of images from different bridge piers are analyzed for verification. The images are taken from arbitrary, uncontrolled, and backlighting views. The results show that the proposed framework can correctly quantify the health condition and detect the occurrence of damage for the in-field infrastructures.

Approach Towards the Development of Digital Twin for Structural Health Monitoring of Civil Infrastructure: A Comprehensive Review

Civil infrastructure assets’ contribution to countries’ economic growth is significantly increasing due to the rapid population growth and demands for public services. These civil infrastructures, including roads, bridges, railways, tunnels, dams, residential complexes, and commercial buildings, experience significant deterioration from the surrounding harsh environment. Traditional methods of visual inspection and non-destructive tests are generally undertaken to monitor and evaluate the structural health of the infrastructure. However, these methods lack reliability due to the need for instrumentation calibration and reliance on subjective visual judgments. Digital twin (DT) technology digitally replicates existing infrastructure, offering significant potential for real-time intelligent monitoring and assessment of structural health. This study reviews the existing applications of DTs across various sectors. It proposes an approach for developing DT applications in civil infrastructure, including using the Internet of Things, data acquisition, and modelling, together with the platform requirements and challenges that may be confronted during DT development. This comprehensive review is a state-of-the-art review of advancements and challenges in DT technology for intelligent monitoring and maintenance of civil infrastructure.

Bridge Safety for a Secure Society Using AI and IoT for Infrastructure Monitoring

The Bridge and Boat Monitoring System is intended to improve safety and operational efficiency of both bridges and waterways, through two integrated modules: the Bridge and the Boat. It is a Bridge Module made by Arduino, equipped with IR sensors for detecting any vehicle approaching, a load cell to monitor weight, a water level sensor and motorized gates to gain access under control. The Boat Module, which is also Arduino-based, contains a wet sensor and a tilt sensor (ADXL345) to detect water entry and ensure stability. For crack detection and structural health assessment of the bridge, the system utilizes YOLO v5, which is a state-of-the-art deep learning algorithm that can accurately and timely identify problems. Zigbee technology provides seamless wireless communication, allowing for real-time data sharing to support proactive maintenance and informed decision-making. Key Words: Crack detection, YOLOv5, Structural health, Zigbee, Arduino.

Digital Twins in Structural Health Assessment and Monitoring: Applications in Historical Timber Buildings

ABSTRACT This paper explores the application of Digital Twins (DTs) for supporting the structural health monitoring (SHM) of timber buildings, with a particular emphasis on historical timber structures. It reviews technologies and methods for DT creation, examining how geospatial data and Building Information Modeling (BIM) serves as foundational elements in developing digital twins. The paper discusses integrating these elements with various data and models to enhance SHM tasks, detailing actionable DT approaches for documenting, analyzing, simulating and predicting structural behavior. The study highlights that while high-fidelity geometric models are well developed, their application for advanced SHM tasks remains limited. Integration of sensor data into BIM frameworks is promising but still faces challenges such as limited automation and difficulties in handling real-time and long-term data. Additionally, the paper notes that the implementation of DTs for the simulation of the effects of hygrothermal phenomena on the long-term behavior and durability of timber structures is underexplored. Multi-dimensional BIM models and machine and deep learning techniques in data-driven, physics-based models are promising approaches for timber cultural heritage management and beyond.

Performance Analysis on Surface Crack Detection in Buildings & Bridges Using Image Processing Method

Surface crack detection in buildings and bridges is critical for ensuring structural integrity and preventing potential failures. It presents a performance analysis of surface crack detection using image processing techniques, offering an efficient and non-invasive solution. Traditional manual inspection methods are labor - intensive, subjective, and prone to human error, making automated systems more desirable. In this study, we utilize digital image processing techniques such as edge detection, thresholding, and morphological operations to identify and quantify surface cracks. Key performance indicators such as detection accuracy, computational efficiency, and robustness to varying environmental conditions are evaluated. The experimental results demonstrate the system's effectiveness in detecting fine cracks with high accuracy and low false positive rates, providing a reliable alternative for infrastructure maintenance. Additionally, the proposed method reduces inspection time and improves decision-making in the assessment of structural health. This study contributes to advancing automated crack detection systems, which are crucial for modernizing maintenance practices. Keywords: Automated inspection, Edge detection, Structural integrity, Thresholding

Structural health assessment of existing dams based on non-destructive testing, physics-based models and machine learning tools

The safe operation of dams is ensured by monitoring systems that collect periodic information on environmental conditions (for example, temperature and water level) and on the structural response to external actions. In newly built or retrofitted facilities, large networks of sensors can take daily measurements that are automatically transferred to servers. In other cases, additional information can be acquired, occasionally or systematically, through emerging drone-based non-contact full-field techniques.The measurements are processed by various analytical and machine learning tools trained on historical data sets, capable of highlighting any anomalous recordings. Monitoring data can also support the accurate calibration of a physics-based model of the structure, usually built in the finite element framework. The analyses carried out by the digital twin allow the experimental database to be expanded with the displacements evaluated in the event of extreme environmental conditions, damage or collapse mechanisms never occurred before.This contribution illustrates an integrated approach to the safety assessment of existing dams that combines experimental, computational and data processing methodologies. Attention is particularly focused on model calibration procedures and on the uncertainties that influence the characteristics of the joints. The presented results of the validation studies performed by the Authors on benchmark and real-scale problems highlight the merits and limitations of alternative approaches to data exploitation and remote measurement.

Inversion Method for Material Parameters of Concrete Dams Using Intelligent Algorithm-Based Displacement Separation

Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in foundation properties. This paper proposes a concrete dam displacement separation model that distinguishes between deformation caused by foundation restraint and that induced by external loads. By combining this model with intelligent optimization techniques and long-term observational data, we can identify the actual mechanical parameters of the dam and conduct structural health assessments. The proposed model accommodates multiple degrees of freedom and is applicable to both two- and three-dimensional dam modeling. Consequently, it is well-suited for parameter identification and health diagnosis of concrete gravity and arch dams with extensive observational data. The efficacy of this diagnostic model has been validated through computational case studies and practical engineering applications.

Acoustic assessment of pavement structural health

Determining the in-situ asset condition of a pavement is necessary to programme pavement renewal and life extension work. Concrete pavements can develop different forms of structural faults including variable types of cracking, spalling, slab rocking, corrosion of reinforcement and softening of subgrade. This paper reports on work designed to detect deterioration before it manifests at the surface, in the form of defects that would only then have been picked up through visual inspection. The approach involves non-destructive, real-time monitoring with a Close Proximity (CPX) trailer, utilising both microphones and a tri-axial accelerometer. Dependent on its condition, the concrete pavement and subgrade will respond differently to traffic induced acoustic noise and vibration from the tyres. The variability in response is analysed using specialist spectral and acoustic signal processing techniques, to detect any internal and external cracks in the pavement, as well as to assess the condition of the subgrade. By characterising the pavement into uncracked, lightly cracked and heavily cracked areas, the collected data is used to map crack locations and their severity. Repeated surveys will allow temporal changes to be monitored and its rate of decay to be predicted, enabling the scheduling of renewal and life extension to be optimised.

Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach

Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, pre-processing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The presented approach enhances accuracy by detecting anomalies at any strain level, computational time using multi-sensor data fusion, and reliability of structural health assessment through anomaly evaluation. It also provides a framework for proactive decision-making, ultimately improving the longevity and structural integrity of critical infrastructure.

Design of eddy current and capacitance dual-mode sensor for thickness detection of thermal barrier coatings

Abstract Thermal barrier coatings (TBCs) can markedly enhance the service temperature of high-temperature alloys, thereby enhancing the engine thrust-to-weight ratio. However, TBCs must be capable of withstanding a multitude of harsh environments, including high temperatures, high pressures, and high-speed particle impacts, which can potentially lead to damage and failure. Consequently, nondestructive testing of TBCs is of particular importance for the assessment of structural health. As eddy current detection is applicable to conductive materials and capacitive detection is applicable to non-conductive materials, this paper proposes a novel, eddy current/capacitance detection, dual-mode sensor, which combines the advantages of the two detection modes. This study examines the structural configuration and excitation characteristics of the eddy current and capacitance dual-mode sensor. Furthermore, the interaction between the capacitive electrode and the eddy current coil is examined, and the viability of the dual-mode sensor is demonstrated through experimentation. The experimental results demonstrate that the dual-mode sensor exhibits a sensitivity of 2.67 mΩ mm−1 for bond coating thickness detection in eddy current mode with an excitation frequency of 5 MHz, and a maximum sensitivity of 80.31 pF mm−1 for top coating thickness detection in capacitive mode.

Damage identification of statically indeterminate trusses based on Bayesian updating

Amidst China's rapid development, most structures and edifices have advanced into the mid-service life phase, where components and frameworks gradually undergo aging and deterioration. Ensuring the safety and functionality of buildings necessitates a heightened emphasis on structural health assessments. This study aims to explore a more cost-effective and time-efficient diagnostic strategy for detecting damage in statically indeterminate trusses, while exploring techniques to uphold data precision by reducing loading nodes and measurement parameters. Employing OpenSees for model construction, this paper utilizes Bayesian updating through Monte Carlo rejection sampling to pinpoint damage in statically indeterminate trusses. Moreover, it unveils the fundamental principles behind optimal loading and measurement strategies. Findings from conducted experiments advocate for a comprehensive approach integrating displacement measurements across multiple axes for enhanced accuracy, surpassing single-direction measurements. Validation of the virtual work principle underscores this method's efficacy. Ultimately, this methodology holds promise for curbing engineering expenses and amplifying operational efficiency in real scenarios.

Integrated Structural Health Assessment of Industrial Buildings in Areas of High Seismic Risk

Integrated Structural Health Assessment of Industrial Buildings in Areas of High Seismic Risk

An enhanced stochastic subspace identification incorporating time variables and its application to large-span sea-crossing suspension bridges

Structural health monitoring and assessment are essential to large-span sea-crossing bridges, and modal identification is the most effective method for achieving them, with the stochastic subspace identification (SSI) method dominating. However, when analyzed in open traffic scenarios with SSI for a large-span sea-crossing bridge, the contour of the vibration mode is distorted. Related affecting factors such as the measuring conditions, user-defined parameters, and traffic are explored, and traffic is verified as the essential component. To address this issue, an enhanced SSI algorithm incorporating time variables is created to automatically identify and rectify vibration mode shapes. Feature heterotopic signals are first matched with a common reference signal to perform modal identification. Advanced particle swarm optimization is applied to estimate critical parameters. The SSI algorithm with the density-based spatial clustering of applications with noise (DBSCAN) algorithm is then utilized to extract the modal characteristics. Both simulated misalignment and real records are applied to verify the suggested approach's performance. Comparative analysis with state-of-the-art techniques, such as the error norm, cross correlation, phase transform-β, mean phase deviation, and genetic algorithm, shows that the recommended approach is dependable and successful at identifying the large-span sea-crossing bridge's vibration mode shapes and computationally more accurate and efficient.

Quantification of Lamb Wave-based SHM technique through Ground Test under simulated flight environment for commercial airplane

This research introduces an advanced Structural Health Monitoring (SHM) methodology for in-service aircraft, with a specific focus on a case study involving 737NG Aft Pressure Bulkhead (APB) coupons. Aimed at achieving FAA first targeted application for FAA certification, the study leverages SMART layer technology to construct a sensor network, facilitating real-time damage assessment through Damage Index (DI) analysis for precise crack detection and localization. The core of our approach involves the utilization of DI values, obtained from the sensor data, to assess crack progression and pinpoint structural anomalies, highlighting the capability of SHM systems in enhancing maintenance efficiency and aircraft safety. Additionally, the Probability of Detection (PoD) analysis incorporated in this study assesses the SHM system's reliability, ensuring compliance with FAA standards. Our findings underscore the potential of SMART layer-based SHM systems in revolutionizing aircraft maintenance protocols by enabling proactive and accurate structural health assessments, particularly at the 4.5mm threshold. This case study not only demonstrates the feasibility of SHM in critical aircraft components but also sets a precedent for the wider application and development of SHM technologies in the aviation industry, promising to elevate safety standards and operational efficiency.

Vibration measurement technique by using OFDR with in-line interferometers

Due to their compact size, electromagnetic interference immunity, and compatibility with embedding in composite materials, optical fiber sensors have emerged as a versatile and integral solution for structural health monitoring (SHM) in various structures. Among the key contributors to the evolution of this field is the significant progress in optical frequency domain reflectometry (OFDR), a technology that offers unparalleled high resolution and sensitivity in distributed strain sensing, thereby establishing itself as an indispensable tool in the realm of SHM. Through the implementation of OFDR, we devised a distributed fiber optic sensor tailored for the precise measurement of vibration or strain within structures. In previous works, vibration monitoring has been achieved using an OFDR with an array of 20 in-line interferometers, serving as weak reflectors that not only facilitated vibration monitoring but also furnished valuable strain information. In this study takes a step further by introducing an innovative technique wherein in-line interferometers are strategically arranged in a non-uniform fashion along the optical fiber. To validate the practical utility of this technology, extensive simulations and experiments were conducted, utilizing a cantilever beam as a representative structure. The results obtained from these endeavors showcase the tangible potential and reliability of optical fiber sensors in the practical implementation of SHM across various applications. By enhancing the precision and scope of structural monitoring, optical fiber sensors equipped with non-uniform interferometer arrangements pave the way for more effective and comprehensive structural health assessment in diverse settings.

Integrated analysis of instrumentation data for structural health assessment and behavior prediction of arch dams

Integrated analysis of instrumentation data for structural health assessment and behavior prediction of arch dams

Towards Developing Reinforced Concrete Structures Digital Twins: A Multiscale Lattice Discrete Particle Model Approach

Digital Twins (DT) provide a critical approach to connecting physical structures and corresponding virtual representations through constant observations-to-decision flows, enabling near real-time analysis and assessment of structural health. A critical component of DTs of reinforced concrete structures lies in the definition of prognostic capabilities to predict/infer the system response. This is achieved by devising efficient computational methods for the simulation of the mechanical behavior of the system. This study presents the first step in devising a Multiscale Lattice Discrete Particle Model (M-LDPM) approach to be embedded in a DT framework to allow for forward prediction of damage evolution in the structural system. In the DT framework, a modification of the M-LDPM is proposed to address well-known issues associated with linking the macroscopic mesh configuration and the corresponding representative volume elements, significantly reducing the total computational cost. The effectiveness of the proposed multiscale model is validated by comparing numerical results with the full-order solutions for plain concrete members under 3-point bending, and further investigated by comparison with experimental results on three reinforced concrete beams.

A Low-Cost Sensing Solution for SHM, Exploiting a Dedicated Approach for Signal Recognition.

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.