Health Monitoring Of Civil Structures Research Articles

Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review

Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review

Role of Civionics in the Civil Structural Health Monitoring System

Civil structural health monitoring (CSHM) tracks different aspects of an infrastructure system’s service and safety condition by utilizing reliably measured data and physics-based model simulations. Data and physical models are coupled with heuristic experience to proactively represent current and expected future performance. In the past two decades, more bridges and dams have been instrumented and monitored during and after construction to determine their performances and responses to various loading, material, boundary, and environmental conditions. Furthermore, bridge and dam owners increasingly utilize civionics systems to obtain essential data for developing data-driven asset management programs and addressing the state of good repair requirements.

Unsupervised damage assessment under varying ambient temperature based on an adjusted artificial neural network and new multivariate covariance-based distances

Temperature variability is one of the critical environmental conditions that causes confusing changes in structural properties and dynamic responses of bridges similar to damage. In this case, false alarms and mis-detection are among the major errors in health monitoring of such civil structures. High damage detectability is another significant challenge in bridge health monitoring. To deal with these issues, this article proposes an unsupervised damage assessment technique comprising two steps of data normalization and novelty detection. For the first step, an adjusted artificial neural network is considered to remove the effects of temperature variability from dynamic features (modal frequencies). This process is carried out by an auto-associative neural network by adjusting its hidden layer neurons through a new hyperparameter selection algorithm. Using normalized features obtained from the first step, this article proposes three multivariate covariance-based distances called linear dissimilarity analysis, multivariate Kullback–Leibler divergence, and multivariate Bregman distance to compute damage indices or novelty scores for damage assessment. The fundamental principles of these distances lie in three aspects: dividing the normalized features into segments, estimating the covariances of segmented feature sets, and incorporating the estimated covariances into the proposed distance measures. The major contributions of this article include proposing three non-parametric distance measures and developing an unsupervised data normalization framework via a new hyperparameter tuning algorithm for adjusting an artificial neural network. A concrete box-girder bridge is considered to verify the proposed approach, along with several comparative studies. Results show that the method presented here can mitigate severe temperature variability and increase damage detectability with superiority over some traditional and state-of-the-art damage assessment techniques.

Monitoring of crack repair in concrete using spherical smart aggregates based on electromechanical impedance (EMI) technique

Cracks will inevitably occur in concrete structures or members during the construction process and service life due to aging, environmental factors, external loads, etc. To improve the strength and stability of the cracked concrete structures, many methods have been proposed to repair the cracks. However, the monitoring of the repairing process and repair quality has not been fully studied. The previous studies have proved that the spherical smart aggregates (SSAs) based on the electromechanical impedance (EMI) technique have outperformed the traditional smart aggregates (SAs) based on the EMI technique in structural health monitoring of civil structures, however, SSAs have not been applied to the monitoring of the concrete crack repair. In this work, the monitoring of the concrete crack repair using the SSAs based on the EMI technique was explored. A total of eight concrete specimens were prepared, and cracks in the concrete specimens were simulated by manually cutting under laboratory conditions. According to the principle of grouting method, two repair agents including cement paste and cement mortar were used to repair the cracks. The impedance signals of 28 d were measured, and three quantitative indicators, namely root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation were used to evaluate the quality of the concrete repair effect. The results indicate that the SSAs show excellent sensitivity and stability over the traditional SAs. In addition, the normalized values of the quantitative indicators were analyzed to distinguish the types of repair agents. A mathematical expression of exponential function was also proposed by fitting the experimental data to quantitatively evaluate and predict the repair effect of concrete cracks. Further, the influences of temperature, humidity, crack depth and width on the experimental results were analyzed and discussed. The numerical simulation was also presented to validate the reasonability of the experimental results.

Application of Digital Twin in Structural Health Monitoring of Civil Structures: A Systematic Literature Review Based on PRISMA

Structural Health Monitoring (SHM) systems are critical for ensuring the safety and longevity of structures. Digital twin technology has emerged as a promising tool to improve the accuracy and efficiency of SHM systems. This paper presents a systematic literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the applications of digital twin for SHM of civil structures. And this systematic literature review examines the current trends and developments in the application of digital twin technology in SHM systems for structures. The review identified 45 relevant articles published between 2010 and 2023 that covered various applications of digital twin in SHM systems, including buildings, bridges, wind turbines, trains, and gas turbines. Key themes identified from the literature review included the integration of physical models and machine learning algo-rithms, the use of distributed sensor networks, and the importance of real-time data analysis for effective SHM. The findings suggest that digital twin technology has the potential to significantly improve the accuracy and efficiency of SHM systems, but further research is needed to develop more advanced and integrated Digital Twin-Based SHM systems for different types of civil structures.

Damage Detection and Condition Assessment of Civil Engineering Structures Using Structural Health Monitoring

Damage identification is critical in evaluating the condition and performance of the existing civil infrastructure. This requires not just routine or critical event-based inspections, but rather a means of continuous monitoring of a structure to provide an assessment of changes as a function of time and an early warning of an unsafe condition using real-time data. Health monitoring plays an important role in the construction and maintenance of the infrastructure. It employs in situ, continuous, or regular measurements and analyses of key structural and environmental parameters under the operating conditions to identify if a damage has occurred, define its location, and estimate its severity, evaluate its consequences on the residual life of the structure also to provide warning of impending abnormal states or accidents to avoid casualties and to inform maintenance and rehabilitation decisions. SHM takes advantage of the new technologies in sensing, instrumentation, communication, and modelling to integrate them into an intelligent system. Structural Health Monitoring for civil structures is becoming increasingly popular in Europe and worldwide because of the opportunities that it offers in the fields of construction management and maintenance. Reduction of inspection costs, research, with the possibility to better understand the behaviour of structures under dynamic loads, seismic protection, observation, in real or near real-time, of the structural response and of evolution of damage, so that it is possible to produce post-earthquake scenarios and support rescue operations, are the main advantages related to the implementation of such techniques.

Self-sensing concrete masonry structures with intrinsic abilities of strain monitoring and damage detection

Self-sensing cementitious materials are emerging technologies for Structural Health Monitoring (SHM) of civil structures. Their application for SHM of concrete masonry elements was not investigated in previous studies. The effects of triaxial strain/stress states of masonry joints and units on their self-sensing response are still unknown. Therefore, this work aimed to investigate mechanical and self-sensing properties of masonry elements produced with self-sensing mortar joints, solid concrete bricks and/or hollow concrete blocks fabricated with carbon black nanoparticles. The intrinsic abilities of strain monitoring and damage detection were evaluated in piezoresistive tests of masonry prisms, based on variations in the type of unit, mortar bedding approach, bonding arrangement, relative strength of mortar and units, joint thickness and location of self-sensing regions. These variations did not affect significantly the gauge factor of cementitious sensors embedded into the prism units. In contrast, the gauge factor of self-sensing horizontal or vertical joints was statistically affected by most of these variations. Stages of balance and abrupt increases in fractional changes in electrical resistivity (FCRs) during the plastic regime of the prisms provided an anticipated warming associated with the propagation of vertical and diagonal cracks associated with tensile splitting and crushing failure of units. The strong non-linearity on the electrical output of self-sensing joints provided an evidence of the mortar pore collapse. In conclusion, the self-sensing units and mortar joints were found to be promising alternatives for strain monitoring and damage detection in smart concrete masonry structures.

A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks

Finite Element Model Updating (FEMU) is a common approach to model-based Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) of civil structures and infrastructures. Its application can be further utilized to produce effective digital twins of a permanently monitored structure. The FEMU concept, simple yet effective, involves calibrating and/or updating a numerical model based on the recorded dynamic response of the target system. This enables to indirectly estimate its material parameters, thus providing insight into its mass and stiffness distribution. In turn, this can be used to localize structural changes that may be induced by damage occurrence. However, several algorithms exist in the scientific literature for FEMU purposes. This study benchmarks three well-established global optimization techniques—namely, Generalized Pattern Search, Simulated Annealing, and a Genetic Algorithm application—against a proposed Bayesian sampling optimization algorithm. Although Bayesian optimization is a powerful yet efficient global optimization technique, especially suitable for expensive functions, it is seldom applied to model updating problems. The comparison is performed on numerical and experimental datasets based on one metallic truss structure built in the facilities of Cranfield University. The Bayesian sampling procedure showed high computational accuracy and efficiency, with a runtime of approximately half that of the alternative optimization strategies.

Controlling the Formation of Electroactive Graphene-Based Cementitious Composites: Towards Structural Health Monitoring of Civil Structures.

The development of composites combining multiple components each one imparting a specific function to the ensemble is highly sought after for disruptive applications in chemistry and materials science, with a particular importance for the realization of smart structures. Here, we report on the development of an unprecedented multifunctional cementitious composite incorporating reduced graphene oxide (rGO). By design, this material features significantly enhanced electrical properties while retaining the excellent cement's hydration and microstructure. The multiscale investigation on the chemical and physical properties of the dispersion made it possible to establish an efficient preparation protocol for rGO aqueous dispersion as well as rGO-based cementitious composites using a commercial poly(carboxylate ether)-based superplasticizer. The conduction mechanisms within the matrix of rGO containing mortars were unraveled by electrochemical impedance spectroscopy revealing conductive paths originating from bulk cement matrix and rGO nanosheets in composites with rGO loadings as low as 0.075 wt. %. For this rGO loading, we observed the reduction of the resistivity of bulk cement mortar layers from 18.3 MΩ cm to 2.8 MΩ cm. Moreover, the addition of 0.2 wt. % of rGO resulted in the formation of rGO conductive paths with the resistivity of 51.1 kΩ cm. These findings represent a major step forward towards the practical application of graphene-based materials in structural health monitoring of concrete structures.

Beamforming: a spatial de-noising approach for civil structural health monitoring

In the civil structural health monitoring fields, monitored data suffer from noise and sensor faults. In practice, redundant sensors are usually deployed to monitor structural condition to obtain more accurate and robust information. This paper proposes a beamforming-based spatial filtering method to improve the data quality by using the information redundancy within sensor networks. Data pre-processing is first implemented, including missing data imputation and thermal response separation. Subsequently, short-term Fourier transform is used to transform the measured time sequences into time–frequency domain to obtain more useful features. Finally, signals in the time and frequency domain are processed using the beamforming algorithm. In the beamformers, a linear filter is applied to suppress noise signals, which is formulated as a constrained optimization problem. Herein, interior point algorithm is used to optimize the allocation of the linear filter, wherein the objective function is to minimize the power of the noise component at the beamformer output. The effectiveness of the proposed method is verified by using signals from strain gauges installed on steel deck plates of the 3rd Nanjing Yangtze River Bridge. Results through the case study show that signals after spatial filtering have a satisfactory de-noising, which indicates the effectiveness of the proposed beamforming algorithm. We believe that the proposed beamforming algorithm has substantial potential applications, such as providing high quality data source for further investigations.

Deformation of High Rise Cooling Tower through Projection of Coordinates Resulted from Terrestrial Laser Scanner Observations onto a Vertical Plane

The appearance and development of new construction materials, technology and accurate geodetic instruments have led to the necessity of their inevitable use in the health monitoring, maintenance, and restoration of civil structures and special structures such as high-rise cooling towers. This paper presents an accurate practical and analytical method for determining the structural deformation and axis inclination of high rise cooling towers using terrestrial laser scanner (TLS) observations through the projection of tower surface points coordinates on a vertical plane. Four cooling towers in El-Mahla El-Kubra city, Egypt are observed and studied. Two of them with height 56 m, and the others were 36 m height. The studied four towers have different cross-section diameters along the tower height. Each tower has a cone shape with a curved wall. The equation of the tower wall is determined using TLS observations and least squares adjustment techniques. The equations of cone projection with a curved wall are derived and presented in this paper. From TLS observations, the radii and accuracy of each 2 m tower height are determined with center coordinates, and then the inclination of the tower axis is calculated and drawn. From the results of TLS observations, data processing, and analysis using the suggested techniques, it is deduced that there is a deformation in tower walls with small values. The specified technique for observations collection and TLS data analysis through projection on a vertical plane is significant and valuable for determining the structural deformation of circular high rise buildings and towers. From the results, there are obvious deformation values in some cooling towers, so maintenance work must be included. The towers also must be checked and monitored several times at brief intervals.

Effect of electric impedance on the electromechanical characteristics of tunable spherical piezoelectric transducer

Spherical piezoelectric transducers are a critical component of ultrasonic vibration systems with significant applications in various practical scenarios such as underwater acoustic detection and structural health monitoring. The existing spherical piezoelectric transducers have fixed electromechanical characteristics that limit their applications in scenarios with multi-frequency or frequency variation requirements. It would therefore be intriguing, from the viewpoints of both science and technology, to break through this limit by introducing external electric load impedance. Here we propose a tunable spherical piezoelectric transducer (TSPT) capable of adjusting the electromechanical characteristics by changing the resistance, inductance, and capacitance applied to the passive piezoceramic shell. The theoretical and numerical results show that when the resistance is exerted on the inner and outer piezoceramic shells, the resonance is accompanied by large variations at low values of resistance, while it becomes almost constant at high values of resistance. When the inductance is exerted on the outer piezoceramic shell, the resonance frequency obtained by the finite element method can be changed from 72730 Hz to 42928 Hz by adjusting the inductance, indicating that the TSPT can realize a rich and selective resonance frequency region. The resonance/antiresonance frequency decreases while the effective electromechanical coupling coefficient increases, when the capacitance applied on the inner and outer piezoceramic shells increases. The experiments are conducted to verify the effectiveness of the proposed TSPT, which is in agreement with the simulated results and theoretical predictions. Our methodology will offer possibilities to extend the working frequency range of the existing piezoelectric spherical transducer for underwater acoustic detection, hydrophones, and the spherical smart aggregate for civil structural health monitoring.

Three-dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi-rate Kalman filter

Structural displacements play an important role in the health monitoring of civil structures; however, the accurate measurement of structural displacements remains a difficult task. Previous efforts have combined a monocular camera and an accelerometer to estimate structural displacement, but only in-plane displacements could be estimated in this way. In this study, the fusion of a monocular camera and an accelerometer was further extended for out-of-plane or three-dimensional displacement estimation. A computer vision algorithm and an adaptive multi-rate Kalman filter were integrated to efficiently estimate high-sampled displacements from low-sampled vision images and high-sampled acceleration measurements. All parameters associated with the computer vision algorithm were automatically calibrated without using any user-defined thresholds. Experimental validation was performed on two building structures and a 10-m-long bridge structure, and the proposed method accurately estimated the displacement for all three structures with a root mean square error of less than 1 mm.

Investigation of cross-sensitivities of the potential drop method for structural health monitoring of civil structures

Abstract Currently, structural health monitoring (SHM) systems are not in widespread use for monitoring civil structures because of low defect sensitivity and high cross-sensitivities of most SHM techniques available. The potential drop method (PDM), commonly used in material testing, is a possible method for SHM of large metallic civil structures combining high defect sensitivity and high area monitoring capability. The current state of the art lacks experimental evidence of the applicability to large specimens under the demanding operating conditions of SHM. Here, we investigated the PDM as an SHM system experimentally by analyzing the cross-sensitivity to temperature changes and performed a temperature compensation. We present an optimized method for suppressing the unwanted influence of mechanical loads and increasing the defect sensitivity. The temperature influence was separated from the defect-induced impedance change and effectively suppressed by compensation. Thus, cross-sensitivity does not limit PDM in SHM for large metallic civil structures with temperature compensation. PDM is a promising technique for SHM which could facilitate the widespread use of SHM of conductive civil structures.

Automatic modal identification via eigensystem realization algorithm with improved stabilization diagram technique

Efficient and automatic identification of modal parameters becomes increasingly important for real-time structural health monitoring (SHM) of civil structures. As spurious modes usually exist as a key problem for most output-based identification methods, great efforts have been made to eliminate them typically via stabilization-diagram techniques. However, the quality of traditional stabilization diagrams depends on preset parameters whose values usually vary from one case to another, which makes the method to be less objective and low efficient. This article proposes an improved stabilization-diagram technique, through combined usage of Monte-Carlo sampling simulation, as well as fuzzy C-means (FCM) clustering and three-stage sifting manipulations. While the Monte-Carlo simulation aims to generate more robust stable-axis, the sifting and clustering manipulations can further remove outliers and discriminate true modal results. The improved stabilization-diagram technique is then applied to two mainstream modal identification methods, i.e., eigensystem realization algorithm (ERA) and stochastic subspace identification (SSI) under the context of both a simulation study on a dynamic system and a field research about a super-tall building. Results through comparison demonstrate that the improved stabilization-diagram technique can facilitate ERA and SSI to identify modal parameters automatically and effectively at a comparably good accuracy. However, ERA outperforms SSI evidently in terms of computational efficiency (upmost 15 times faster), which is attractive for real-time SHM. Parametric analysis has been also conducted to examine detailed performance of ERA aided by the proposed stabilization-diagram technique. Overall, the aforementioned method can be adopted to achieve a good balance between identification effectiveness and computational efficiency in an automatic working pattern, and has application prospect for real-time SHM of civil structures.

Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring.

This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

Low-Cost Civil Structure Health Monitoring Using Wireless Sensor Network

This research presents a low-cost, real-time, reliable, and scalable solution for Structural Health Monitoring.(SHM) using an IoT-based multi-node system. The study's objective is to detect and identify damages timely andweaknesses in civil structures such as bridges, buildings, and dams, which environmental factors, overuse, or natural phenomena may cause. Each sensor node, fabricated using NodeMCU, is equipped with sensors for measuring vibration, shock, tilt, strain, humidity, and temperature. The data collected by multiple sensor nodes is transmitted to Amazon Web Services (AWS) cloud platform via Wi-Fi and analyzed for damage detection and predictive maintenance. The field testing on a pedestrian steel bridge showed over 99% success in data transmission with a frequency of 1 second per reading, resulting in 18,000 readings over 5 hours. With a fabrication cost of $40 per sensor node and scalable AWS resources, the proposed solution is highly scalable and can operate for 15 days without human intervention.

Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning

Long-term monitoring brings an important benefit for health monitoring of civil structures due to covering all possible unpredictable variations in measured vibration data and providing relatively adequate training samples for unsupervised learning algorithms. Despite such merits, this process may encounter large data with missing values and also yield erroneous results caused by severe environmental changes, particularly those emerge as sharp increases in modal frequencies during freezing weather. To address these challenges, this article proposes a novel unsupervised meta-learning method that entails four steps of an initial data analysis, data segmentation, subspace searching by a novel approach called nearest cluster selection, and anomaly detection. The first step intends to initially analyze measured data/features for cleaning missing samples. Next, the second step exploits spectral clustering to divide clean data into some segments. In the third step, the proposed nearest cluster selection is utilized to measure dissimilarities between the segments by a distance metric and select a cluster with the minimum distance as the representative of the main segment. Finally, a locally robust Mahalanobis-squared distance is applied by merging the concepts of robust statistics and local metric learning for online anomaly detection. The key innovations of this research contain developing a new unsupervised learning strategy alongside a locally robust distance and proposing the idea of nearest cluster selection. Long-term modal frequencies of full-scale concrete and steel bridges are used to verify the proposed method. Results demonstrate that this method succeeds in mitigating severe environmental effects and accurately detecting damage.

Development of a low-cost vision-based real-time displacement system using Raspberry Pi

Vision-based real-time displacement measurement systems can measure the structural displacements without contact and output the results immediately, overcoming some drawbacks of traditional contact-type sensors and off-line vision sensors in field applications. However, high cost and complexity of installation restrict the widespread application of the off-the-shelf vision-based real-time displacement systems. In this work, a fast image processing software is developed for operation in single board computers (SBC), firstly, based which a low-cost and portable vision-based real-time displacement measurement system using Raspberry Pi is constructed for civil structural health monitoring. To evaluate the performance of the system in field applications, a new method for validation of on-site measurement accuracy is proposed. The experimental results show that the constructed system accurately measured multipoint structural displacements of a long-span suspension bridge in real-time, with at least 1/17 pixel accuracy, providing a cost-efficient, convenient, high-precision, and contactless approach to obtain the displacement responses of infrastructures in the field.