Structural Health Condition Research Articles

A STUDY OF BUILDING PERFORMANCE INSPECTION BASED ON A COMBINATION OF SITE-SPECIFIC RESPONSE ANALYSIS AND STRUCTURAL ANALYSIS (A CASE STUDY OF THE LIGHTHOUSE VIEW TOWER IN BENGKULU CITY, INDONESIA)

This paper presents the implementation of site-specific analysis in observing the performance of a monumental building in Bengkulu City, Indonesia that is called the Lighthouse View Tower. This building was inaugurated in 2012. So far, it is still necessary to present the issue related to the performance of the building after 10 years. The objective of this study is to observe the performance of the building after the strong earthquake occurred. This study is first conducted by performing a site investigation. The information related to the soil profile; ground motion of the strong earthquake that occurred in Bengkulu City is collected. Furthermore, the site-specific response analysis is conducted on the site where the building stands. The results from site-specific response analysis, i.e. the actual spectral acceleration is obtained. The spectral acceleration is then used as the input parameter for finite element analysis to observe the performance of the building. The main goal of this study is to observe the structure health condition. The results show that during the strong earthquake, the structure of the view tower is still reliable and in good condition. Concern regarding the building maintenance should be emphasized. In general, the method implemented in this study could be used as the method to assess the performance of structures in other areas.

Spatiotemporal correlation analysis of the dynamic response of supertall buildings under ambient wind conditions

SummaryThe structural health monitoring systems of supertall buildings are typical big data systems with abundant and high‐dimensional data. Correlation analysis is a vital technique for data mining and analysis. This study aims to reveal the spatial and temporal correlation of 4 years of structural health monitoring data from the 632‐m‐high Shanghai Tower under normal wind and typhoon conditions. In this paper, the root mean square (RMS) value of the acceleration response is used to characterize the structural vibration intensity. The maximal information coefficient (MIC) and Pearson product‐moment correlation coefficient (PPMCC) are adopted to measure the spatial correlations along Shanghai Tower under different wind conditions. The temporal correlation of structural vibration is investigated with kernel density estimation and the density‐based spatial clustering of applications with noise (DBSCAN) algorithm. The results show that the structural vibrations of Shanghai Tower in different spatial positions are correlated. The correlation of the structural vibrations of the same floor is at a high level under both normal wind and typhoons, while that of different floors rises with increasing mean wind speed. Furthermore, the MIC is more stable than the PPMCC in measuring this spatial correlation. Based on temporal correlation analysis, a circadian rhythm tendency of the structural vibration intensity of Shanghai Tower on the daily scale is identified, and it is verified that the structural response is temporally correlated. These findings can provide a structural health state estimation index based on the correlation coefficient and provide a basis for the screening of modeling data.

Modelling and forecasting of SHM strain measurement for a large-scale suspension bridge during typhoon events using variational heteroscedastic Gaussian process

The modelling and forecasting (M&F) of strain measurement (as a kind of local structural responses) during typhoon events provides valuable insight into the structural condition assessment of large suspension bridges. However, the presence of time-dependent noise in reality can pose difficulties for forecasting the field data obtained by structural health monitoring (SHM) systems. Gaussian process regression (GPR), as a nonparametric model, can obtain probabilistic estimation outputs, but its constant noise assumption hampers the reliability of the forecasting model. In this study, Variational Heteroscedastic Gaussian Process (VHGP), a combination of variational approximation and heteroscedastic Gaussian process (HGP), is applied to perform modelling and forecasting for SHM strain field data during typhoon events because of its heteroskedasticity characteristics, higher forecasting accuracy and strong ability to quantify uncertainty. The proposed M&F method is exemplified by using SHM monitoring strain data acquired from the instrumented Tsing Ma Suspension Bridge during typhoon events. The results reveal that VHGP has a better regression accuracy and can obtain varying confidence intervals which reflect noise variations. Meanwhile, VHGP yields more robust forecasting results. The uncertainty analysis shows that VHGP is competent to evaluate the noise level change of strain responses brought by typhoons, providing a basis for conducting structural health condition assessment for large-scale bridges.

Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes

Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. Therefore, it is necessary to monitor the structural response to detect the damage in an early stage, evaluate the health condition of structures, and ensure the operation safety of structures. In fact, the structure and the evaluation can be considered as a special symmetry. Among several SHM methods, vibration-based SHM techniques have been widely adopted recently. Hence, this paper reviews the vibration-based SHM methods in terms of the vibrational parameters used. In addition, the technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications. Several related ISO standards and national codes have been developed and implemented, while more specific technical codes are still required to provide more detailed guidelines in practice to maintain structure safety and natural symmetry.

A cointegration-based approach for automatic anomalies detection in large-scale structures

In recent years, the development of structural health monitoring (SHM) solutions for the automatic evaluation of the health state of engineering structures is continuously growing. However, when considering real-world applications, structures are highly influenced by meteorological variations or human activities (like temperature, wind and traffic loading) which can overwhelm the changes induced by a damage. Thanks to its ability to remove the long-term trends from a set of variables of the same process, cointegration, a technique born in the field of econometrics, has been introduced about ten years ago in SHM applications as a valid method to project out the confounding influences, such as environmental and operational variations. Because of the few examples of implementation currently available, this paper provides an in-depth review of all the relevant aspects to consider when cointegration is used as damage detection strategy and data are acquired from real-world structures of large dimensions. The methodology is applied for the first time on a complex structure of a singular nature, i.e. the steel roof of the G. Meazza stadium in Milan, which consists of multiple modular elements referred to as rafts. The time series which measures the rotations of the rafts are used as input data for the development of the cointegration-based method. Then, Johansen procedure is adopted to create a unique feature from the multivariate dataset, namely cointegrating residual, in which the effects of environmental and operational variables are suppressed, while the effects due to damage remain evident. The obtained residual is therefore used for novelty detection by means of a control chart, demonstrating its effectiveness into identifying the presence of anomalies or modifications in the structure in a clear and timely manner.

Monitoring the Health Status of Rolling Bearings Based on Adaptive mean-shift

In order to monitor the structural health condition of rolling bearings in real time, an adaptive mean-shift algorithm was proposed to replace the fixed step size in the Mean Shift algorithm with adaptive variable step size. Wavelet packet energy entropy was used to extract the characteristics of rolling bearing vibration signals, and a series of centroids were clustered by adaptive mean-shift after dimensionality reduction. The centroid in normal state was used as the reference centroid to calculate the offsets of all centroids (including reference centroid) and reference centroid. Experiments show that the adaptive mean-shift algorithm converges quickly and has high accuracy. The centroid offset can be used to monitor the structural health effectively.

Computer vision based target-free 3D vibration displacement measurement of structures

Vibration displacement response is widely used in the field of structural health monitoring (SHM) to monitor the health condition of civil engineering structures. Traditionally, the vibration displacement response is acquired by physical sensors, such as linear variable differential transformers (LVDT) and laser displacement sensors (LDS), or more commonly by accelerometers to measure accelerations instead of directly measuring the displacement due to the fact that direct displacement measurement requires a fixed platform to install the sensors. Recently, to overcome the difficulties in direct displacement measurement, computer vision based methods have become a research hotspot. This paper proposes an advanced binocular vision system for target-free full-field three-dimensional (3D) vibration displacement measurement of civil engineering structures. The state-of-the-art key point detection and matching algorithm based on deep learning is employed to achieve target-free measurement, which greatly improves the quantity and quality of matching natural key points with low contrast. The performance of the proposed vision based approach for 3D vibration displacement measurement is evaluated through an experimental test on a steel cantilever beam in the laboratory. The obtained vibration displacement responses obtained from the proposed approach are compared with those measured by LVDTs and LDSs. The results demonstrate that the displacement responses obtained from the proposed vision based approach are accurate compared with the traditional sensors, while the proposed approach is more cost effective and much easier to achieve accurate 3D vibration displacement measurement.

Column Buckling of Corroded Steel Angles and Channels: Experiments and Failure Mode Analyses

Corrosion affects the sustainability of steel structures, especially those with improper maintenance and those exposed directly to the atmosphere and seashore environments. Corrosion-related accidents have been reported frequently recently; therefore, periodically assessing the structural health condition of steel structures is crucial. Clarifying the residual strengths of corroded steel structures with moderate to severe degrees of corrosion is important. Here, the uniaxial compressive strength of corroded steel angles and channels was investigated through experiments and nonlinear finite-element analyses. The behavior of these corroded specimens was found to depend strongly on the degree of corrosion, and had combined failure modes of global flexural buckling, local buckling, and cross-sectional yielding. The failure mode of the corroded steel specimens that largely behaved in a complex manner was examined. The relationship between the ultimate strength and degree of corrosion, and the applicability of a traditional global flexural buckling strength equation using the cross-sectional properties, were analyzed for strength evaluation. The strength equation provided relatively good strength estimates for lightly to moderately corroded specimens; however, it became less applicable for severely corroded specimens, possibly owing to the local buckling interaction. An empirical strength equation that reflects the trend of the experimental results was developed by modifying the traditional equation.

Data Mining-based Structural Damage Identification of Composite Bridge using Support Vector Machine

A structural health monitoring system contains two components, i.e. a data collection approach comprising a network of sensors for recording the structural responses as well as an extraction methodology in order to achieve beneficial information on the structural health condition. In this regard, data mining which is one of the emerging computer-based technologies, can be employed for extraction of valuable information from obtained sensor databases. On the other hand, data inverse analysis scheme as a problem-based procedure has been developing rapidly. Therefore, the aforesaid scheme and data mining should be combined in order to satisfy increasing demand of data analysis, especially in complex systems such as bridges. Consequently, this study develops a damage detection methodology based on these strategies. To this end, an inverse analysis approach using data mining is applied for a composite bridge. To aid the aim, the support vector machine (SVM) algorithm is utilized to generate the patterns by means of vibration characteristics dataset. To compare the robustness and accuracy of the predicted outputs, four kernel functions, including linear, polynomial, sigmoid, and radial basis function (RBF) are applied to build the patterns. The results point out the feasibility of the proposed method for detecting damage in composite slab-on-girder bridges.

A new LNG wharf health assessment model considering structural weak areas

The structural health state of liquefied natural gas (LNG) wharf is important for the docking and operation of LNG ships. Based on the finite element modal analysis (FEMA) and evidential reasoning (ER) rule, a new health assessment model for the LNG wharf is proposed in this paper, where structural weak areas are considered. Firstly, combining the structural mechanism and FEMA, the LNG wharf is simulated and modeled. The weak areas of the wharf in common working modes are analyzed and obtained. Secondly, based on the analysis results, the sensor deployment is guided and the wharf health information is collected. Finally, based on the ER rule, the sensor monitoring information is fused to realize the health assessment of the wharf. This paper takes an LNG wharf in the Hainan Yangpu area of China as an example. The experimental results show that the proposed method can accurately and effectively assess the health state of the wharf.

Data Anomaly Detection of Bridge Structures Using Convolutional Neural Network Based on Structural Vibration Signals

Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns.

HOW TO START GENTRIFICATION PROCESS USING INTERFEROMETRIC STACK OF SENTINEL-1

Abstract. In Turkey, gentrification has gained importance in the major cities such as Istanbul because of the rapid urbanization. The establishment of the construction land suitability is one of the main issues raised here. However, it is not easy to start gentrification process in Istanbul where there is a developing transportation and already dense housing. Up-to-date structural health information is then required for an optimum gentrification process. The traditional way of obtaining the structural health conditions on building is generally with the stationary measurements. However, in-situ based information can only provide a small amount of information, and it is impossible to conduct for the entire city. In this context, remotely sensed images, specifically synthetic aperture radar (SAR) ones, can easily provide data to monitor not only ground subsidence but also deformation on the urban sites. The main purpose of the study is to monitor the actual condition of the urbanization on unsuitable and important sites and to guide in determining pioneer areas for gentrification process using freely available remote sensing images, in particular Sentinel-1 SAR images. The potential of these data set will be evaluated with the land suitability map (LSM) produced by The Istanbul Metropolitan Municipality (IMM). In this study, we carried out multi-scale interferometric analysis to understand the spatial relationship between the LSM and the freely available satellite-based measurements for detecting urban sites in danger. The capability and usability of PSI and SBAS methods as a guide before gentrification were investigated using Sentinel-1 data covering 2015 and 2018 years.

Health and Health Service Needs: Comparison of Older and Younger Women with Criminal-Legal Involvement in Three Cities.

Objectives: We profiled the health and health services needs of a sample of older adult women (age 50+) with criminal-legal system (CLS) involvement and compared them with younger women (age 18-49), also CLS-involved. Methods: Using survey data collected from January to June 2020 from adult women with CLS involvement in three US cities, we profiled and compared the older adult women with younger women on behavioral and structural risk factors, health conditions, and health services access and use. Results: One-third (157/510) were age 50+. We found significant differences (p < .05) in health conditions and health services use: older women had more chronic conditions (e.g., hypertension and stroke) and more multimorbidity and reported more use of personalized care (e.g., private doctor, medical home, and health insurance). Discussion: Although older women with CLS involvement reported good access to health services compared with younger women, their chronic health conditions, multimorbidity, and functional declines merit attention.

The installation of embedded ultrasonic transducers inside a bridge to monitor temperature and load influence using coda wave interferometry technique

This article presents a unique method of installing a special type of embedded ultrasonic transducers inside a 36-m-long section of an old bridge in Germany. A small-scale load test was carried out by a 16 ton truck to study the temperature and load influence on the bridge, as well as the performance of the embedded transducers. Ultrasonic coda wave interferometry technique, which has high sensitivity in detecting subtle changes in a heterogeneous medium, was used for the data evaluation and interpretation. The separation of two main influence factors (load effect and temperature variation) is studied, and future applications of wave velocity variation rate for structural health condition estimation are discussed. As a preliminary research stage, the installation method and the performance of the ultrasonic transducer are recognized. Load- and temperature-induced weak wave velocity variations are successfully detected with a high resolution of 10−4%. The feasibility of the whole system for long-term structural health monitoring is considered, and further research is planned.

Optimal Three-Dimensional Sensor Placement for Cable-Stayed Bridge Based on Dynamic Adjustment of Attenuation Factor Gravitational Search Algorithm

Structural health monitoring (SHM) is essential when detecting damage in large and complex structures in order to provide a comprehensive assessment of the structural health state. Optimal sensor placement (OSP) is critical in the structural health monitoring system, which aims to use a limited number of sensors to obtain high-quality structural health diagnosis data. However, the current research mainly focuses on OSP for structures, without considering the values contributed by different modes to the bridge structure. In this article, an optimal sensor placement method based on initial sensor layout, using the dynamic adjustment of attenuation factor gravitational search algorithm (DGSA), is proposed. The effective modal mass participation ratio is introduced to ensure the validity of the initial data of optimal sensor placement. In view of the insufficient developmental ability of the gravitational search algorithm, the attenuation factor α adjusted dynamically aids the global search in the early iteration and the local fine search in the late iteration. The double coding method is used to apply the DGSA algorithm to OSP; taking cable-stayed bridges as an example, the feasibility of the algorithm is verified. The results show that the improved algorithm has a good optimization ability and can accurately and efficiently determine the optimal placement of sensors.

Review of fiber optic sensors for corrosion monitoring in reinforced concrete

Monitoring corrosion in reinforced concrete is critical to assess the health condition of structures at an early stage and enable effective and efficient asset management. Various novel fiber optic sensors have been developed and demonstrated many advantages in monitoring corrosion in reinforced concrete under different conditions. However, currently, there is a gap between sensor technology innovation and engineering practices, and the gap hinders further development and wide applications of fiber optic sensors. This paper reviews representative types of fiber optic sensors for monitoring corrosion in reinforced concrete. The reviewed types of sensors include grating sensors, interferometer sensors, distributed sensors, and reflectometer sensors. For each type of sensors, the reviewed contents include the sensing principle and key features of applications, such as sensor design and installation, sensitivity, resolution, and sensor life. Emphasis is placed on discussing the functions and performance of the sensors in different corrosion stages. Based on the review, technical challenges and future research needs are identified and discussed. This review is expected to advance sensor development and promote effective applications of fiber optic sensors for intelligent structures and asset management.

Conditionmonitoring of composite overwrap pressure vessels based on buckypaper sensor and MXene sensor

Composite overwrap pressure vessel (COPV) with their excellent specific properties and fatigue resistance are used for high-pressure storage of compressible liquid and gaseous fluids in aerospace industries. To predict the life of COPV or avoid disasters, MXene and buckypaper (BP) sensors are embedded in COPV to manufacture a smart structure that can help COPV to know its strain and pressurization state. Hydrostatic pressure tests are carried out. The changes in resistance measured by MXene and BP sensors are compared. The experimental results show MXene and BP sensors embedded in composite overwrap have the same sensitivity. But BP sensor is more sensitive to the initiation and propagation of microcrack in COPV than MXene sensor. For MXene and BP sensors embedded in the interface between the aluminum liner and composite overwrap, MXene is more sensitive to plastic deformation or compressive residual strains than BP sensor due to the different microstructure of the two kinds of the sensor. In a word, BP sensors are effective for real-time damage Condition monitoring of composite. MXene sensors are more suitable for the performance and structural health condition monitoring of aluminum liner.

A multi-scale attention neural network for sensor location selection and nonlinear structural seismic response prediction

Monitoring and predicting seismic responses of a civil structure can be used to assess its behavior under dynamic loading and to determine its structural health condition. In practice, the employed number of sensors is generally limited by the cost and functionality issues. This paper develops a practical solution of four-stage procedures, focusing on prediction of seismic displacement responses at all building floors using acceleration measurements at the optimized sensor locations. In this paper, a novel multi-scale attention-based recurrent neural network is proposed. In particular, the attention mechanisms in the network effectively focuses on more relevant input data among bidirectional ground accelerations and multivariate acceleration responses. The seismic response data for training the developed neural network is generated by performing nonlinear time historical analysis of a three-dimensional finite element model. A floor displacement warping loss is designed to numerically measure the discrepancy between the prediction and the ground truth. A case study is performed using the numerical and real-world data of a high-rise building to systematically evaluate the prediction performance of the proposed methodology. Results demonstrate that the proposed method outperforms the compared state-of-the-art methods in terms of prediction accuracy and reliability.

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.