Online Data Fusion Using Incremental Tensor Learning

In structural health monitoring, damage detection results have uncertainty due to the measurement noise, modeling error and environment changes. In this paper, data fusion based on the Dempster-Shafer (D-S) evidence theory was employed to decrease the uncertainty level and improve the accuracy of damage identification. A weighted fusion damage index (WFDI) was proposed to perform damage identification. A two-story concrete frame was fabricated as the object for the structural health monitoring test. Experimental results have verified the effectiveness of the proposed method, which will improve damage identification accuracy and increase the reliability of damage identification.

Visualization of sensor networks, data, and metadata is becoming one of the most pivotal aspects of the structural health monitoring (SHM) process. Without the ability to communicate efficiently and effectively between disparate groups working on a project, an SHM system can be underused, misunderstood, or even abandoned. For this reason, this work seeks to evaluate visualization techniques in the field, identify flaws in current practices, and devise a new method for visualizing and accessing SHM data and metadata in 3D. More precisely, the work presented here reflects a method and digital workflow for integrating SHM sensor networks, data, and metadata into a virtual reality environment by combining spherical imaging and informational modeling. Both intuitive and interactive, this method fosters communication on a project enabling diverse practitioners of SHM to efficiently consult and use the sensor networks, data, and metadata. The method is presented through its implementation on a case study, Streicker Bridge at Princeton University campus. To illustrate the efficiency of the new method, the time and data file size were compared to other potential methods used for visualizing and accessing SHM sensor networks, data, and metadata in 3D. Additionally, feedback from civil engineering students familiar with SHM is used for validation. Recommendations on how different groups working together on an SHM project can create SHM virtual environment and convey data to proper audiences, are also included.

The dynamic performance of railway bridges under high-speed trains draws the attention of bridge engineers. The vibration issue for long-span bridges under high-speed trains is still not well understood due to lack of validations through structural health monitoring (SHM) data. This paper investigates the correlation between bridge acceleration and train speed based on structural dynamics theory and SHM system from three foci. Firstly, the calculated formula of acceleration response under a series of moving load is deduced for the situation that train length is near the length of the bridge span, the correlation between train speed and acceleration amplitude is analyzed. Secondly, the correlation scatterplots of the speed–acceleration is presented and discussed based on the transverse and vertical acceleration response data of Dashengguan Yangtze River Bridge SHM system. Thirdly, the warning indexes of the bridge performance for correlation scatterplots of speed–acceleration are established. The main conclusions are: (1) The resonance between trains and the bridge is unlikely to happen for long-span bridge, but a multimodal correlation curve between train speed and acceleration amplitude exists after the resonance speed; (2) Based on SHM data, multimodal correlation scatterplots of speed–acceleration exist and they have similar trends with the calculated formula; (3) An envelope line of polylines can be used as early warning indicators of the changes of bridge performance due to the changes of slope of envelope line and peak speed of amplitude. This work also gives several suggestions which lay a foundation for the better design, maintenance and long-term monitoring of a long-span high-speed bridge.

Structural identification (St-Id) using finite element models for optimum sensor configuration and uncertainty quantification

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can compensate for the temporal and spatial discontinuity of observation networks, but they suffer from coarse spatial resolutions and systematic biases. The objective of this study is to build a high-resolution precipitation oxygen isoscape in China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (δ18Op) using data fusion and bias correction methods. The temporal and spatial resolutions are month and 50–60 km for the isoscape, respectively. Prior to building the oxygen isoscape, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is compared after post-processing of eight iGCM simulations. Results show that the outputs of the Convolutional Neural Networks (CNN) fusion method exhibit the strongest correlation with observations with correlation coefficient mostly larger than 0.8, and the smallest bias with root mean square error mostly smaller than 2 ‰. The other two DFMs also perform slightly better than the two BCMs, which show similar performance. Thus, precipitation oxygen isoscape is generated by using the CNN fusion method for the 1969–2007 period in which all iGCMs have output and by using the bias correction methods for the remaining years. Based on the precipitation oxygen isoscape, the spatiotemporal patterns of δ18Op across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ18Op is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China, and be more specific in the Qinghai-Tibet Plateau of China. The δ18Op time series mirrors a fluctuating upward trend of the temperature or precipitation in most regions of China. The temporal and spatial distribution characteristics of the generated isoscape are consistent with the characteristics of atmospheric circulation and climate change, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built isoscape is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. The dataset is available in Zenodo at https://doi.org/10.5281/zenodo.5703811 (Chen et al., 2021).

The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can compensate for the temporal and spatial discontinuity of observation networks, but they suffer from coarse spatial resolutions and systematic biases. The objective of this study is to build a high-resolution precipitation oxygen isoscape in China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (δ18Op) using data fusion and bias correction methods. The temporal and spatial resolutions are month and 50–60 km for the isoscape, respectively. Prior to building the oxygen isoscape, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is compared after post-processing of eight iGCM simulations. Results show that the outputs of the Convolutional Neural Networks (CNN) fusion method exhibit the strongest correlation with observations with correlation coefficient mostly larger than 0.8, and the smallest bias with root mean square error mostly smaller than 2 ‰. The other two DFMs also perform slightly better than the two BCMs, which show similar performance. Thus, precipitation oxygen isoscape is generated by using the CNN fusion method for the 1969–2007 period in which all iGCMs have output and by using the bias correction methods for the remaining years. Based on the precipitation oxygen isoscape, the spatiotemporal patterns of δ18Op across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ18Op is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China, and be more specific in the Qinghai-Tibet Plateau of China. The δ18Op time series mirrors a fluctuating upward trend of the temperature or precipitation in most regions of China. The temporal and spatial distribution characteristics of the generated isoscape are consistent with the characteristics of atmospheric circulation and climate change, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built isoscape is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. The dataset is available in Zenodo at https://doi.org/10.5281/zenodo.5703811 (Chen et al., 2021).

Reliability of a ship structure has been investigated in this study under two factors of fatigue and corrosion failure using structural health monitoring data. In order to use structural health monitoring data in assessing the reliability, Bayesian inference method has been used to update the distribution of loads applied on the structure. This study used structural health monitoring data to assess the reliability of a ship construction under two conditions: fatigue and corrosion failure. A Bayesian inference method was utilized to update the distribution of loads applied to the structure to employ structural health monitoring data in determining reliability. The load distribution obtained from the equations was used to assess the reliability of a ship structure during corrosion. The proposed mathematical model was examined using the data output of the force sensors installed on the commercial ship in the laboratory, whose model has been scale tested. According to the reliability analysis, the reliability index of the structure decreases with time as a result of corrosion and fatigue failures. The utilization of structural health monitoring data has boosted confidence in the reliability index for estimating the structure's real-life as determined by the study. The findings suggest that using the reliability criterion and health monitoring data during the design stage can provide a better knowledge of the structure's performance throughout time, according to the environmental conditions.

Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using imputation methods. Different from common data, continuous probability density functions belong to functional data; the conventional distribution-to-distribution regression technique has significant potential in missing probability density function imputation; however, extrapolation and directly borrowing shape information from the covariate probability density function are the main challenges. Inspired by the warping transformation of distributions in the field of functional data analysis, a new distribution regression approach for imputing missing correlated probability density functions is proposed in this article. The warping transformation for distributions is a mapping operation used to transform one probability density function to another by deforming the original probability density function with a warping function. The shape mapping between probability density functions can be characterized well by warping functions. Given a covariate probability density function, the warping function is first estimated by a kernel regression model; then, the estimated warping function is used to transform the covariate probability density function and obtain an imputation for the missing probability density function. To address issues with poor performance when the covariate probability density function is contaminated, a hybrid approach is proposed that fuses the imputations obtained by the warping transformation approach with the conventional distribution-to-distribution regression approach. Experiments based on field monitoring data are conducted to evaluate the performance of the proposed approach. The corresponding results indicate that the proposed approach can outperform the conventional method, especially in extrapolation. The proposed approach shows good potential to provide more reliable estimation of distributions of missing structural health monitoring data.

In order to solve the problem of abnormal structural health monitoring data of large-span bridges, the construction of civil engineering structural health monitoring platform based on intelligent algorithm is proposed. This paper utilizes the Internet of Things RFID technology to deploy health monitoring sensor nodes and process the bridge structural health monitoring data, so as to realize the large-span bridge structural health monitoring. The experimental results show that the structural health monitoring method designed in this paper for large-span bridges is effective, and the accuracy rate between the measured data values and the real values is 90%, which is highly reliable and has certain application value. Conclusion: The designed structural health monitoring method for large-span bridges has a high degree of agreement between the main girder vertical deformation variables and the actual main girder vertical deformation variables at different monitoring points, which proves that the adopted structural health monitoring method for large-span bridges has a good accuracy and has certain application value.

One of the primary challenges in civil engineering today is the safety assessment and management of existing infrastructures. In this context, structural health monitoring (SHM) plays a fundamental role in data acquisition and assessment of the safety of a given infrastructure. SHM data are instrumental for quantifying uncertainties, informing structural models, and updating structural reliability over time. In principle, an SHM system should provide the best information possible for assessing and updating structural reliability. Since infrastructure management resources are inevitably limited, SHM information quality assessment becomes a key issue. Several metrics are available in the scientific literature to measure the SHM information quality. Most of these are based on the accuracy of SHM information or the impact of monitoring on decision-making. This article introduces novel information quality metrics that quantify how SHM affects the structural reliability assessment. First, we state a comprehensive framework based on Bayesian networks for structural reliability updating. Based on this, we define a metric based on the measurement resolution concept. Next, according to structural reliability theory, we present an importance measure of the SHM observations. Finally, we propose two explanatory case studies.

Because of the critical role that bridges play in land transport networks and broader economy, the assessment of existing bridges is gradually becoming a global concern. Structural health monitoring (SHM) systems have been installed on many bridges to provide data for the evaluation of bridge performance and safety. The challenge for bridge engineers is now to make use of the data and convert them into usable information and knowledge. Integrating SHM data with reliability analysis procedures offers a useful and practical methodology for bridge assessment since reliability is an important performance index and reliability-based procedures have the capability of accommodating uncertainties in structural models, responses, loads and monitoring data. In this paper, an approach for integrating SHM data in a reliability assessment framework is proposed. The reliability of the bridge is quantified by incorporating SHM information in the resistance, load and structural models. Advanced modeling tools and techniques, such as finite element analysis, finite model updating and Bayesian updating, are used for the reliability computations. Data from the SHM system installed on the Newmarket Viaduct, a newly constructed, 12-span, post-tensioned box girder bridge erected by the balanced cantilever method in Auckland, New Zealand, are also presented in this paper and used to explain the proposed framework.

The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy.

Structural health monitoring (SHM) of bridges has gained rapid development in the past few years. This paper describes application of SHM on long-span bridges in China, with the aim to illustrate its practical value. A short review of its development and practice is firstly introduced. Three case studies are subsequently presented on utilization of SHM data in engineering practice. In the first case study, a ship collision incident is analyzed using SHM data. An alarm is sent and confirmed when the collision occurred, and mode parameters are identified with GPS measurements to evaluate the bridge condition. In the second case study, damage of expansion joints in a suspension bridge is assessed with girder end displacement measurements. Malfunction of viscous damper is found to correlate with cumulative displacement. The results show that cumulative displacement can be used for condition assessment of expansion joints. In the third case study, the performance of tuned mass dampers is evaluated with wind and vibration measurements before and after tuned mass damper installation. Through explanation of these case studies, the paper illustrates how to distill useful insights from SHM data, which could be instructive for further research in this field.

A case study is presented in which the first steps are made towards the development of a structural health monitoring (SHM) data fusion framework. For this purpose, a composite single-stiffener panel is subjected to compression-compression fatigue loading (R = 10). The carbon-epoxy panel contains an artificial disbond of 30 mm, which was created using a Teflon insert during manufacturing and placed between the skin and the stiffener foot. Under the applied fatigue load, the disbond is expected to grow and its propagation is monitored using two SHM techniques, namely acoustic emission (AE) and Rayleigh-scattering based distributed fiber optic strain sensing. Four AE sensors are placed on the skin, thereby allowing for disbond growth detection and localization. On each stiffener foot, fiber optic sensors are surface-bonded to monitor the growth of the disbond under the applied fatigue loading. The distributed strain measurements are used to localize and monitor the disbond growth. The strength of each technique is utilized by fusing the data from the AE sensors and the fiber optic sensors. In this manner, a data-driven approach is presented in which a data fusion of the different techniques allows for monitoring the damage in the stiffened panel on multiple SHM levels, including disbond growth detection and localization.