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

Sarmadi, Hassan; Entezami, Alireza; Behkamal, Bahareh; Michele, Carlo De

doi:10.1007/s13349-022-00596-y

Cited by 24 publications

(13 citation statements)

References 47 publications

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“…140 To reduce the impact of environmental variability, the one-class nearest neighbor search-based unsupervised feature selection concept is used to select relevant features and remove irrelevant features that are affected by various variabilities. 141 Subsequently, the enhanced local MSD could be used to determine the distance values or anomaly indices.…”

Section: Vibration Response-oriented Methodsmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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Section: Vibration Response-oriented Methodsmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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“…Among these, frstly, environmental efect normalization methods have been used, such as linear or nonlinear principal component analysis [17,18] and cointegration analysis [19], so as to separate environmental variables. In addition, some feature matching methods have also been used, such as outlier discrimination [20], supervised classifcation [21], and unsupervised clustering [22,23]. Notably, these feature matching methods are generally used to identify structural damage directly, rather than separating environmental and operational efects from features.…”

Section: Introductionmentioning

confidence: 99%

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

Han

Dang

et al. 2023

Structural Control and Health Monitoring

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A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.

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“…Apart from the simplicity and unsupervised nature, the other important advantage of this technique is related to its great performance for mitigating and removing the negative effects of the EOV conditions. 26,27 A one-class NN searching algorithm can be developed by three main steps: (i) calculating pairwise distances (dissimilarities) between features, (ii) arranging them in an ascending way, and (iii) selecting adequate NNs depending on the problem under study. In addition to the first two steps, the major challenge is to determine the number of adequate NNs.…”

Section: Introductionmentioning

confidence: 99%

“…22 The input–output data normalization relies on both structural responses/features and EOV data to model their relationship by various regression models 23,24 and machine learning algorithms. 25 In contrast, the output-only data normalization considers the intrinsic characteristics of the structural responses/features to detect variability patterns and then remove their effects by diverse methods such as one-class nearest neighbor (NN) searching, 26,27 nonparametric unsupervised learners, 9,28,29 artificial neural networks, 30,31 and some statistical algorithms such as principal component analysis (PCA) 32 and its variants, 33,34 factor analysis, 35 etc.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

Sarmadi

Entezami

Magalhães

2023

Structural Health Monitoring

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Continuous dynamic monitoring brings an important opportunity to evaluate the health and integrity of civil structures in a long-term manner. However, high dimensionality and sparsity of data caused by long-term monitoring and negative influences of environmental and/or operational variability are major challenges in this process. To address these important issues, this article proposes an innovative unsupervised data normalization method based on a novel hybrid feature weighting-selection algorithm and the idea of natural nearest neighbor (NN) searching emanated from the theory of mutual friendships in human societies. The proposed hybrid algorithm is a combination of global feature weighting with a new weighting measure and local feature selection. For this algorithm, this article leverages the natural NN searching that seeks to find adequate NNs automatically. The main objective of the proposed method is to remove the environmental and/or operational effects and provide normalized weighted features for reliable continuous dynamic monitoring. Using such features, an anomaly detector based on the Mahalanobis-squared distance is developed to assess and detect structural damage. The key innovations of this paper contain proposing a fully nonparametric unsupervised learning technique in two parts of data normalization and anomaly detection and developing a novel hybrid algorithm for removing the environmental and/or operational variations. Long-term dynamic features (modal frequencies) of a three-span box-girder concrete bridge (Z24 Bridge) and a long-span concrete arch bridge (Infante Dom Henrique Bridge) are considered to verify the proposed technique with several comparisons. Results indicate that this technique is successful and reliable in mitigating the environmental and/or operational effects and notifying accurate structural states.

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

Cited by 24 publications

References 47 publications

Review of artificial intelligence-based bridge damage detection

Review of artificial intelligence-based bridge damage detection

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

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