Health Monitoring Of Civil Structures Research Articles

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.

Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures.

Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review

It is often computationally expensive to monitor structural health using computer models. This time-consuming process can be relieved using surrogate models, which provide cheap-to-evaluate metamodels to replace the original expensive models. Because of their high accuracy, simplicity, and efficiency, Artificial Neural Networks (ANNs) have gained considerable attention in this area. This paper reviews the application of ANNs as surrogates for structural health monitoring in the literature. Moreover, the review contains fundamental information, detailed discussions, wide comparisons, and suggestions for future research. Surrogates in this literature review are divided into parametric and nonparametric models. In the past, nonparametric models dominated this field, but parametric models have gained popularity in the recent decade. A parametric surrogate is commonly supplied with metaheuristic algorithms, and can provide high levels of identification. Recurrent networks, instead of traditional ANNs, have also become increasingly popular for nonparametric surrogates.

A literature review: Generative adversarial networks for civil structural health monitoring

Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

WSN-Based SHM Optimisation Algorithm for Civil Engineering Structures

With the development of economy and the improvement of architectural aesthetics, civil structure buildings show a trend of diversification and complexity, which brings great challenges to the Structural Health Monitoring (SHM) of civil structure buildings. In order to optimise the structural health monitoring effect of civil structures, reduce monitoring costs, and improve the ability of civil structures to deal with risks, a civil structure health monitoring method combining Variational Modal Decomposition (VMD) and the Gated Recurrent Unit (GRU) is proposed. The gated neural network algorithm of modal decomposition is used, and then a wireless sensor network (WSN) civil structure health monitoring model is constructed on this basis. Finally, the application effect of the model is tested and analysed. The results show that the network energy consumption of this model can reach a minimum of 0.05 J, which is 0.05 J less than that of the Gate Recurrent Unit (GRU) model. The minimum loss value is 0.08. Its Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), and Mean Absolute Percent Error (MAPE) are 0.03, 0.04, and 0.06, respectively; the prediction error is the smallest, the overall amplitude difference monitored by the model remains at a low level of less than 0.01, and the changes are closest to the real situation. This shows that the model improves the operation efficiency, improves the accuracy of health monitoring, enhances the adaptability of building structural health monitoring to complex structures, provides a new way for the development of building structural health monitoring technology, and is conducive to enhancing civil structures. The safety and stability of buildings promote the high-quality development of civil and structural buildings.

Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring

Owing to the outstanding sensing properties, especially high sensitivity and large stretchability, flexible piezoresistive strain sensors are advantageous for achieving intelligent sensing and have become a popular topic in the field of civil structural health monitoring (SHM). To explore advanced flexible strain sensors for civil SHM, this paper summarizes the recent research progress, achievements and challenges in flexible piezoresistive strain sensors. First, four common piezoresistive mechanisms are introduced theoretically. Sensor materials, including conductive materials, flexible substrates and electrodes, are explained in detail. Second, essential sensing parameters are interpreted and then followed by specific explanations of improvement strategies for the sensor performance in terms of each parameter. Third, applications of flexible piezoresistive strain sensors in the deformation measurement and damage detection of steel structures, concrete structures and fiber-reinforced composite structures are presented. Existing challenges and prospects in the practical application and large-scale production of flexible strain sensors are also reported. Last but not least, strategies for the selection of piezoresistive sensors for civil SHM are explained.

Generative adversarial networks for labeled acceleration data augmentation for structural damage detection

There have been major advances in the field of data science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have been utilized for civil structural health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training data set of a 1-D deep convolutional neural network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures.

Advances in Micro-Fabricated Fiber Bragg Grating for Detection of Physical, Chemical, and Biological Parameters—A Review

Fiber optic sensors are widely used in environmental sensing because of their high precision, compact size, remote operation, chemical inertness and multiplexing capabilities. This paper reviews the design, fabrication, and deployment of fiber optic sensors for various applications using grating technology. We conclude with a review of different applications, focusing on environmental factors, hazardous gases, volatile organic compounds biomolecules, marine salinity, and civil structure health monitoring. Our main objective is to provide a complete review of current solutions for humidity, temperature, and other environmental conditions using fiber grating sensors along with a summary of potential future research directions. This paper focuses on fiber grating-based devices and analyze the current state of precedent and existing research around the world as well as the hurdles that lie in the way of their speedy advancement in this field.

High-fidelity denoising for differential pulse-width pair brillouin optical time domain analyzer based on block-matching and 3D filtering

In the field of structural deformation and strain measurement for civil structural health monitoring, Brillouin optical time-domain analyzer (BOTDA) system with sub-meter high spatial resolution is required. Differential pulse-width pair Brillouin optical time-domain analyzer (DPP-BOTDA) is an excellent method to achieve the goal without the need to modify the system structure. But the signal-to-noise ratio (SNR) of DPP-BOTDA is poor due to the differential operation, which limits its practical applications. Here, we propose to use Block-Matching and 3D filtering (BM3D) algorithm to improve the SNR of DPP-BOTDA with less degradation of measurement accuracy and spatial resolution. The influence of each BM3D parameter on the denoising performance is fully analyzed in detail for the first time which helps the optimization of the algorithm. Three pulse pairs of 50/47 ns, 50/45 ns and 50/43 ns are used for the demonstration with a sensing range of 9.9 km. With the optimized BM3D, the experimental spatial resolution only degrades by 0.08 m, 0.05 m and 0.04 m after denoising when the SNR improvement is 9 dB, respectively. And the temperature uncertainty for the three cases is improved to be 0.79 °C, 0.36 °C and 0.24 °C, respectively. We believe that this research will improve the efficiency of the parameter optimization when using the BM3D method for noise reduction and contribute to the practical application of the DPP-BOTDA system.

Piezoelectric Impedance-Based Structural Health Monitoring of Wind Turbine Structures: Current Status and Future Perspectives

As an innovative technology, the impedance-based technique has been extensively studied for the structural health monitoring (SHM) of various civil structures. The technique’s advantages include cost-effectiveness, ease of implementation on a complex structure, robustness to early-stage failures, and real-time damage assessment capabilities. Nonetheless, very few studies have taken those advantages for monitoring the health status and the structural condition of wind turbine structures. Thus, this paper is motivated to give the reader a general outlook of how the impedance-based SHM technology has been implemented to secure the safety and serviceability of the wind turbine structures. Firstly, possible structural failures in wind turbine systems are reviewed. Next, physical principles, hardware systems, damage quantification, and environmental compensation algorithms are outlined for the impedance-based technique. Afterwards, the current status of the application of this advanced technology for health monitoring and damage identification of wind turbine structural components such as blades, tower joints, tower segments, substructure, and the foundation are discussed. In the end, the future perspectives that can contribute to developing efficient SHM systems in the green energy field are proposed.

Structural health monitoring of civil structures: A diagnostic framework powered by deep metric learning

Recent advances in learning systems and sensor technology have enabled powerful strategies for autonomous data-driven damage detection in structural systems. This work proposes a novel method for the real-time localization of damage relying on a Siamese convolutional neural network. The method exploits a learnable mapping of raw vibration measurements onto a low-dimensional space, wherein damage locations can be easily identified. The mapping is learned in a supervised pairwise fashion exploiting labelled data, to induce a task-specific metric that allows to encode the damage position in the structure. Training data are generated through a reduced-order numerical model of the monitored structure. The damage position is then identified by performing a regression in the resulting low-dimensional features space. The proposed method does not require to define a priori target classes and decision boundaries, thus requiring a limited amount of user-dependent assumptions. Results relevant to an L-shaped cantilever beam and a portal frame railway bridge demonstrate that the procedure can be effectively exploited for the purpose of damage localization. The method also proves to be insensitive to operational variability, measurement noise and modeling inaccuracies.

A Novel Real-Time Method for Structural Damage Detection Using Kalman Filter and Sensitivity Analysis

Damage detection is a critical aspect of structural health monitoring. The Kalman filter (KF) has been used in various methods for detecting structural damage based on online measurement data. Due to noise in the sensor responses and errors in the damage detection process, the damage detection process’s accuracy is reduced. This study proposes a novel method for resolving this issue by integrating the Kalman filter and sensitivity analysis (KFSA). The damage detection problem has been considered as a state estimation problem in this method, with the system’s state estimated using the KF. In this problem, the damage is considered a random phenomenon in the state equation, and the damage detection equation is used as the observation equation via sensitivity analysis (SA). Due to the randomization of the state equation and the resulting increase in accuracy, damages are accurately detected among the suspected damaged elements obtained via modal strain energy. The numerical validation of structures is carried out under various damage scenarios. The comparative results demonstrate that the proposed method for detecting damage is exact and can be used for the structural health monitoring of in-service civil structures.

Development of piezoelectric-based technology for application in civil structural health monitoring

Piezoelectric material, as one of the great potential materials, had attracted lots of attention all over the world due to its distinguish advantages. In this paper, the development of piezoelectric-based technology for application in the field of civil structural health monitoring (CSHM), was summarized and discussed. Based on the different identification mechanisms, the piezoelectric transducer-based technology can be divided into two main approaches as the active or passive sensing and detection methods. This paper summarized the development of these two approaches and discussed their applications in the area of civil structural health monitoring, such as structural and concrete engineering, bridge engineering, pipeline engineering, protection engineering for geological hazards and earthquake disasters, and so on. In addition, the electrical mechanical impedance (EMI) technique, as one of the active identification methods, was also detailly presented. Finally, its great potential for the piezoelectric-based technique was presented based on the detail discussion, especially in the areas of civil structural health monitoring.

Strain data correction of distributed optical fiber sensors using strain transfer model with variable shear lag parameters

Distributed optical fiber sensing techniques have been widely utilized in the field of structural health monitoring (SHM) of civil structures. However, strain measurements from distributed optical fiber sensors cannot always accurately reflect the strain of the host material of structures due to the coupled effects of the composite protective layer of the cables and the quality of the surface bonding construction. To address this issue, a method is proposed for correcting the strain data for civil structures measured by surface bonded distributed optical fiber sensors. The layered analysis method is used to establish a strain transfer model from the fiber core, through the inner protective layer, rigid protective layer, outer protective layer, and adhesive layer to the host material of the structure. With the new method, the strain transfer coefficient from the fiber core to the host material of the structures is calculated by updating the shear lag parameters based on a finite element model with a specific bonding length. The results of an experimental model and an actual bridge show that, comparing with the current methods, the proposed method effectively improves the accuracy of strain data from the distributed optical fiber sensors. The proposed method is benefit for the application of distributed optical fiber sensing techniques in monitoring the condition of actual civil structures.

Theoretical and Experimental Studies of Micro-Surface Crack Detections Based on BOTDA.

Micro-surface crack detection is important for the health monitoring of civil structures. The present literature review shows that micro-surface cracks can be detected by the Brillouin scattering process in optical fibers. However, the existing reports focus on experiment research. The comparison between theory and experiment for Brillouin-scattering-based optical sensors is rarely reported. In this paper, a distributed optical fiber sensor for monitoring micro-surface cracks is presented and demonstrated. In the simulation, by using finite element methods, an assemblage of a three-dimensional beam model for Brillouin optical time domain analysis (BOTDA) was built. The change in Brillouin frequency (distributed strain) as a function of different cracks was numerically investigated. Simulation results indicate that the amplitudes of the Brillouin peak increase from 27 με to 140 με when the crack opening displacement (COD) is enlarged from 0.002 mm to 0.009 mm. The experiment program was designed to evaluate the cracks in a beam with the length of 15 m. Experimental results indicate that it is possible to detect the COD in the length of 0.002~0.009 mm, which is consistent with the simulation data. The limitations of the proposed sensing method are discussed, and the future research direction is prospected.

Statistical analysis of the modal properties of a seismically-damaged five-story RC building identified using ambient vibration data

Damage detection (DD) is one of the primary goals of health monitoring of civil structures. Vibration-based techniques aim to identify the modal properties (or vibration characteristics) of structures and are one of the most popular approaches for structural damage detection. The use of vibration characteristics (natural frequencies, damping ratios, and mode shapes) for DD purposes is based on the premise that these characteristics change when the structure suffers damage, since the modal properties depend on the physical properties (mass, stiffness, and damping) of the structure. The use of output-only measurements (e.g., ambient vibration or AV) is the most popular approach for damage detection of civil structures. AV data recorded before and after the structure has potentially suffered damage can be used for DD. However, application of vibration-based DD using AV requires an accurate and reliable estimation of the modal properties and their variability (or uncertainty) in order to genuinely determine the existence of damage. This study presents a comprehensive statistical analysis of the identified modal properties of a full-scale five-story reinforced concrete building using AV data. The building specimen was tested on the NHERI@UCSD shake table in base-isolated and fixed-base configurations. On April 6, 2012, about two weeks before the start of the seismic tests in the base-isolated configuration, a dense array of twenty accelerometers was deployed on the structure and AV data were recorded continuously until May 18, 2012, three days after completion of the seismic tests in the fixed-base configuration. In its fixed-base configuration, the building was subjected to a sequence of six earthquake motions that progressively damaged the structure. Two popular methods of operational modal analysis are used to automatically identify the modal properties of the fixed-base building at different damage states using the recorded AV data. A statistical analysis of the identified modal parameters is performed to investigate the statistical variability and accuracy of the system identification results. The variability of the identified modal parameters due to environmental conditions is also investigated.

Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling

This article proposes a novel probabilistic machine learning method based on unsupervised novelty detection for health monitoring of civil structures. The core of this method is based on extreme value theory (EVT) and mixture quantile modeling. Accordingly, a mixture quantile value by combining non-parametric and parametric quantile estimators is proposed as a new decision-making or novelty score. The non-parametric estimator relies on an empirical quantile function, while the parametric estimator stems from modeling a generalized extreme value distribution. Generally, the proposed method is composed of some main steps; that is, calculating distances between feature samples, sorting the distances in ascending order, changing their signs for providing negated quantities, selecting some negated maximum distances or extreme values in an iterative algorithm by using a goodness-of-fit test based on Kullback-Leibler information, and computing a mixture quantile. Furthermore, an EVT-based approach is proposed to estimate an alarming threshold. The major innovation in this article is to develop a novel probabilistic novelty detector with a new score for decision-making. The advantages of the proposed method contain preparing discriminative novelty scores for damage detection, dealing with the major challenge of environmental and/or operational variability, estimating a reliable threshold, and implementing both the procedures of decision-making and threshold estimation within a single framework. Dynamic and statistical features of two full-scale bridge structures are utilized to validate the proposed method along with comparative studies. Results demonstrate that the method is a reliable and influential tool for health monitoring of civil structures under varying various environmental and/or operational conditions.

Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years.

Strain is one of the most frequently monitored parameters in civil structural health monitoring (SHM) applications, and strain-based approaches were among the first to be explored and applied in SHM. There are multiple reasons why strain plays such an important role in SHM: strain is directly related to stress and deflection, which reflect structural performance, safety, and serviceability. Strain field anomalies are frequently indicators of unusual structural behaviors (e.g., damage or deterioration). Hence, the earliest concepts of strain sensing were explored in the mid-XIX century, the first effective strain sensor appeared in 1919, and the first onsite applications followed in the 1920′s. Today, one hundred years after the first developments, two generations of strain sensors, based on electrical and fiber-optic principles, firmly reached market maturity and established themselves as reliable tools applied in strain-based SHM. Along with sensor developments, the application methods evolved: the first generation of discrete sensors featured a short gauge length and provided a basis for local material monitoring; the second generation greatly extended the applicability and effectiveness of strain-based SHM by providing long gauge and one-dimensional (1D) distributed sensing, thus enabling global structural and integrity monitoring. Current research focuses on a third generation of strain sensors for two-dimensional (2D) distributed and quasi-distributed sensing, based on new advanced technologies. On the occasion of strain sensing centenary, and as an homage to all researchers, practitioners, and educators who contributed to strain-based SHM, this paper presents an overview of the first one hundred years of strain sensing technological progress, with the objective to identify relevant transformative milestones and indicate possible future research directions.