A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing stateof-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage

Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models

Temperature is an important environmental factor for long-span bridges because it induces thermal loads on structural components that cause considerable displacements, stresses, and structural damage. Hence, it is critical to acquire up-to-date information on the status, sustainability, and serviceability of long-span bridges under daily and seasonal temperature fluctuations. This paper intends to investigate the effects of temperature variability on structural displacements obtained from remote sensing and represent their relationship using supervised regression models. In contrast to other studies in this field, one of the contributions of this paper is to leverage hybrid sensing as a combination of contact and non-contact sensors for measuring temperature data and structural responses. Apart from temperature, other unmeasured environmental and operational conditions may affect structural displacements of long-span bridges separately or simultaneously. For this issue, this paper incorporates a correlation analysis between the measured predictor (temperature) and response (displacement) data using a linear correlation measure, the Pearson correlation coefficient, as well as nonlinear correlation measures, namely the Spearman and Kendall correlation coefficients and the maximal information criterion, to determine whether the measured environmental factor is dominant or other unmeasured conditions affect structural responses. Finally, three supervised regression techniques based on a linear regression model, Gaussian process regression, and support vector regression are considered to model the relationship between temperature and structural displacements and to conduct the prediction process. Temperature and limited displacement data related to three long-span bridges are used to demonstrate the results of this research. The aim of this research is to assess and realize whether contact-based sensors installed in a bridge structure for measuring environmental and/or operational factors are sufficient or if it is necessary to consider further sensors and investigations

Online hybrid learning methods for real-time structural health monitoring using remote sensing and small displacement data

Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods

Detection of Partially Structural Collapse Using Long‐Term Small Displacement Data from Satellite Images

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long‐term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR‐based SHM. Conversely, the major challenge of the long‐term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis‐squared distance. The first method presented in this work develops an artificial neural network‐based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher– student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long‐term displacement samples extracted from a few SAR images of TerraSAR‐X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR‐based SHM applications

Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions

Health monitoring of large‐scale civil structures: An approach based on data partitioning and classical multidimensional scaling

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high‐dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data‐driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low‐dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low‐dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high‐dimensional datasets and environmental variability. Results related to two large‐scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data‐driven approach

Big data analytics and structural health monitoring: A statistical pattern recognition-based approach

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and effciency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data

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 se- lection 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 fre- quencies 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

Partially online damage detection using long-term modal data under severe environmental effects by unsupervised feature selection and local metric learning

Distance-based anomaly detectors are among the most efficient unsupervised learning methods due to their non-parametric properties, inexpensive computational requirements, and simplicity. However, some major challenges including severe variability in data, low detectability, and a reliable decision threshold seriously affect overall performances of such techniques for long-term structural health monitoring (SHM). This article proposes a new distance-based anomaly detection method for partially online damage detection using the concepts of unsupervised feature selection and local metric learning. The main objective of unsupervised feature selection is to exploit one-class nearest neighbor search for extracting relevant features and removing irrelevant ones by a local Mahalanobis-squared distance (MSD). Because the choice of adequate nearest neighbors is critical to estimate local covariance matrices needed for distance metric learning, this issue is addressed by developing a hyperparameter optimization algorithm based on a statistical hypothesis test. An enhanced local MSD is also proposed to compute anomaly scores for decision-making. To estimate a reliable decision threshold, this article utilizes the peak-over-threshold technique under extreme value theory and generalized Pareto distribution. Due to the importance of selecting optimal extreme values and a probability rate of false alarm for threshold estimation, two hyperparameter optimization algorithms are designed to choose these unknown parameters. The major contribution of this article is to propose an innovative anomaly detector in conjunction with an enhanced multivariate distance under the concept of distance metric learning. Full-scale concrete and steel bridges under severe environmental variability are considered to validate the proposed method along with several comparisons. Results demonstrate that this method succeeds in long-term SHM under strong environmental variations in modal data and it also outperforms some well-known anomaly detection techniques in terms of effectiveness and efficiency

On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method

Design of an automated and continuous framework is of paramount importance to structural health monitoring (SHM). This study proposes an innovative multi-task unsupervised learning method for early assessment of damage in large-scale bridge structures under long-term monitoring. This method entails three main tasks of data cleaning, data partitioning, and anomaly detection. The first task includes discarding missing data and providing outlier-free samples by developing an approach based on the well-known DBSCAN algorithm. Accordingly, this approach enforces the DBSCAN to generate two clusters, one of which contains outlier-free samples and the other one comprises outlier data. In the second task, the outlier-free samples are fed into spectral clustering to partition them into local clusters. Subsequently, a cluster with the maximum cumulative local density is selected as the optimal partition whose features are extracted as the representative data. Finally, local empirical measures under the theory of empirical learning are used to compute anomaly indices for SHM. Long-term modal frequencies of two full-scale bridges are incorporated to verify the proposed method alongside comparative analyses. Results prove that this method can effectively detect damage by providing discriminative anomaly scores and mitigating the negative influences of severe environmental variability