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Freezing weather can introduce challenges in long-term structural health monitoring of civil structures, particularly bridges. A noticeable impact of freezing temperature is the emergence of sudden and sharp increases in structural modal frequencies, causing false alarm and mis-detection errors in change detection of civil structures. This paper proposes an innovative unsupervised data normalization method to mitigate freezing effects. The proposed method integrates locally robust principal component analysis (LRPCA) with Gaussian density distance (GDD) clustering, called GDD-LRPCA, which automatically determines the number of clusters. Initially, a training set of original modal frequencies is partitioned via the GDD clustering. Subsequently, an individual LRPCA model is fitted to each partition to extract new normalized modal frequencies insensitive to freezing effects. The groundbreaking nature of this research relies on developing an integrated unsupervised data normalizer by leveraging advanced machine learning algorithms such as local learning, robust learning, and hybrid unsupervised learning. The major advantage of the proposed method is its non-parametric nature obviating any supplementary technique for hyperparameter optimization. The validity of this method is benchmarked by real-world bridge structures along with several comparative analyses. Results demonstrate that GDD-LRPCA effectively removes the freezing effects from structural modal frequencies and outperforms its counterparts in unsupervised data normalization. • Proposing an unsupervised data normalizer for removing freezing effects from modal frequencies under statistical learning. • Leveraging cutting-edge machine learning algorithms including local learning, robust learning, and hybrid learning. • Suggesting a non-parametric framework for unsupervised data normalization without any hyperparameter optimization • Lacking the need for temperature sensor installation and measurement for removing freezing effects. [ABSTRACT FROM AUTHOR]

• Proposing a novel unsupervised meta-learning method for long-term SHM with large data and profound environmental effects. • Proposing a locally unsupervised feature selection approach called nearest cluster selection. • Proposing a locally robust Mahalanobis distance via local metric learning and robust statistics for anomaly detection. • Accurate warning of damage occurrence under severe environmental variability caused by freezing weather. 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. [ABSTRACT FROM AUTHOR]

The paper deals with the results of an experimental campaign carried out on a post tensioned concrete beam with the aim of investigating the possibility to detect early warning signs of deterioration based on static and/or dynamic tests. The beam was tested in several configurations aimed to reproduce 5 different phases of the ‘life’ of the beam: in the original undamaged state, under increasing loss of tension in the post tensioning cables, during and after the formation of cracks at mid span, after a strengthening intervention carried out by means of a second tension cable, during and after the formation of further cracks on the strengthened beam. Responses of the beam were measured by an extensive set of instruments consisting of accelerometers, inclinometers, displacement transducers, strain gauges and optical fibers. In this paper data from accelerometers and displacement transducers have been exploited. The paper presents the test program and the dynamic characterization of the beam in the different damage scenarios in terms of the first modal frequency, identified from dynamic tests and of the bending stiffness monitored during static tests. [ABSTRACT FROM AUTHOR]

Quantifying the uncertainty in the dynamic properties of large-scale complex engineering structures presents significant computational challenges. Monte Carlo simulation (MCS) method is extensively employed to perform uncertainty quantification (UQ) because of its generality, stability, and easy implementation. However, a brute-force MCS approach may be unaffordable and impractical when the target model contains a large number of uncertain parameters. In this circumstance, MCS requires a potentially burdensome (if not computationally intractable) number of model evaluations to obtain a credible estimate of the global statistics. In this study, a general framework for analytical UQ of model outputs using a Gaussian process (GP) metamodel is presented, where case inputs are characterized as normal and/or uniform random variables. A detailed derivation of important low-order statistical moments (mean and variance) is given analytically. This analytical method is adopted to characterize the uncertainty of modal frequencies of two bridges with assumed normally- and uniformly-distributed parameters. Meanwhile, the brute-force MCS approach is used for comparison of GP metamodel-derived statistics. Results show that the GP method outperforms the MCS methodology in terms of computational cost, with consistency in the "true" values obtained by MCS. It demonstrates that this GP method is feasible and reliable for modal frequency UQ of complex structures. [ABSTRACT FROM AUTHOR]

• The feasibility assessment of drive-by bridge inspection is investigated. • A novel index to quantify the transmissibility performance is proposed. • An induced change is identified through drive-by bridge inspection. • Successful identification of the first three modes of the bridge is verified. This paper presents results of numerical and experimental investigations for the feasibility assessment of drive-by bridge inspection, where the health status of a bridge is solely identified from the dynamic response of an inspection vehicle, passing over a target bridge. To this aim, a novel metric based on the transmission performance between the vehicle and the bridge is proposed, and it is demonstrated that the vehicle having a higher sensitivity index has superior performance, in moving state, to identify bridge frequencies, and consequently separate different states of a bridge from one another. In addition to numerical validation, experimental validation from a laboratory-based experiment is presented, where a two-axle vehicle model is adopted as an inspection vehicle and a single-span steel beam is employed as a target bridge. Vibration data are collected using three highly sensitive accelerometer sensors installed on the vehicle, one at the middle of each axle, i.e., the front axle, and the rear axle, and the third one at the center of gravity of the vehicle, to measure the pitching and bouncing modes of the vehicle. The contributions and the key findings of the work are as follows: First, this paper presents one of the early attempts of experimentally validating the feasibility of drive-by bridge inspection to identify an induced change in the structure of a bridge which has caused less than 2% variation in the fundamental frequency. Second, a novel index for quantifying the performance of transmission between the bridge and the vehicle is proposed. It is verified that a vehicle with a higher sensitivity index captures more bridge-related information, hence making the bridge modal frequencies more identifiable. Third, through experiments, successful identification of the first three vibration modes of the bridge, obtained from the moving vehicle tests, is verified under the condition of constant and slow speed, when there is ongoing excitation on the bridge. Fourth, the indices and criteria presented herein would be helpful in designing a real drive-by inspection system which can be potentially used for drive-by bridge inspection of a network of bridges with similar structural design in practice. [ABSTRACT FROM AUTHOR]