Finite Element–Based Machine-Learning Approach to Detect Damage in Bridges under Operational and Environmental Variations

Figueiredo, Elói; Moldovan, Ionuţ Dragoş; Santos, Adam; Campos, Pedro Silvestre da Silva; Costa, João B.

doi:10.1061/(asce)be.1943-5592.0001432

Cited by 50 publications

(31 citation statements)

References 20 publications

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“…20 Rather, a coupled approach of model-driven and datadriven algorithms can work together to achieve a reasonable damage identification level. 19…”

Section: Damage Definition and Identificationmentioning

confidence: 99%

“…Many researchers investigated these nonstationary sources of variations. Several works 15,19,[72][73][74] have shown that the dynamic performance of bridges varies significantly depending on the condition that the bridge is subjected to daily. The effect of these trends on damage-sensitive features could be removed by utilizing different linear and nonlinear correction models.…”

Section: Data Normalizationmentioning

confidence: 99%

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Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

166

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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“…20 Rather, a coupled approach of model-driven and datadriven algorithms can work together to achieve a reasonable damage identification level. 19…”

Section: Damage Definition and Identificationmentioning

confidence: 99%

Section: Data Normalizationmentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

166

View full text Add to dashboard Cite

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“…However, the process of finite element model updating is inherently highly uncertain (Simoen, De Roeck, & Lombaert, 2015). These uncertainties are not only caused by signal measurement errors and modeling errors but also by the variability in operational and environmental conditions (Figueiredo, Moldovan, Santos, Campos, & Costa, 2019; Sohn, 2006).…”

Section: Introductionmentioning

confidence: 99%

A decentralized unsupervised structural condition diagnosis approach using deep auto‐encoders

Jiang

Han

2021

Computer aided Civil Eng

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In this paper, the ideas of deep auto-encoder (DAE) and manifold learning are adopted to solve the problem of structural condition diagnosis. A scalable decentralized end-to-end unsupervised structural condition diagnostic framework is proposed. Three damage diagnosis mechanisms are clarified. The structural damage diagnosis approaches are presented from the latent coding domain and the time domain, respectively. In the latent coding domain, an undercomplete DAE is established to extract the distribution of the low-dimensional manifold of the signal. On the contrary, in the time domain, an overcomplete DAE is adopted to extract the reconstruction error of the signal. Subsequently, normalized damage quantitative indicators are developed in the two domains. The damage localization method is also clarified. The proposed method can extract features directly from original vibration data without the need for additional signal preprocessing techniques. More importantly, the algorithm relies only on the output signals and does not require a numerical or scale model. This framework can be used to identify, locate, and quantify structural damages. The validity of the diagnostic framework is verified using a well-designed laboratory benchmark structure. A large-scale grandstand structure is further used to prove the ability of the proposed method for identifying slight structural damage caused by the loosening of joint bolts. The results clearly demonstrate an elegant performance of the proposed damage detection algorithm in structural condition assessment and damage localization.How to cite this article: Jiang K, Han Q, Du X, Ni P. A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders. Comput Aided Civ Inf.

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“…Hwang et al and Kiani et al implemented certain algorithms such as the k-nearest neighbor, simple Bayes, random forest, and decision tree to analyze and predict the seismic response, and it was concluded that the dataset is the key to the learning performance, and accuracy of the classification models was better than those of the regression models in seismic response prediction [23,24]. Figueiredo et al explored a hybrid approach for damage detection with machine learning techniques [25]. Wang et al and Alwanas et al proposed a methodology which employs the machine learning methods to establish the constitutive models of structural member by using the test data [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Seismic Response Analysis and Evaluation of Laminated Rubber Bearing Supported Bridge Based on the Artificial Neural Network

Zhang

Wang

et al. 2021

Shock and Vibration

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Laminated rubber bearings are commonly adopted in small-to-medium span highway bridges in earthquake-prone areas. The accurate establishment of the mechanical model of laminated rubber bearings is one of most critical steps for the bridge seismic response analysis. A new constitutive model of bearing based on the artificial neural network (ANN) technique is established through the static cyclic test of laminated rubber bearings, considering the bearing initial stiffness, friction coefficient, and other parameters such as the bearing sectional area, height, loading velocity, vertical load, and aging time. Combined with the ANN method, the ANN-based bridge seismic demand model is built and applied to the rapid evaluation of the bridge seismic damage. The importance of the bearing affecting design factors in the bridge seismic demands are ranked. The results demonstrated that the dimensions of the bearing and vertical load are the main factors affecting the bearings constitutive model. Based on the partial dependency analysis with the ANN-based bridge seismic demand model, it is concluded that the height of bearing is the key design parameter which affects the bridge seismic response the most. The ANN seismic demands model can fit the complex function relationship between various factors and bridge seismic response with high precision, so as to achieve the rapid evaluation of bridge seismic damage.

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Finite Element–Based Machine-Learning Approach to Detect Damage in Bridges under Operational and Environmental Variations

Cited by 50 publications

References 20 publications

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

A decentralized unsupervised structural condition diagnosis approach using deep auto‐encoders

Seismic Response Analysis and Evaluation of Laminated Rubber Bearing Supported Bridge Based on the Artificial Neural Network

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