Automated, strain-based, output-only bridge damage detection

Rageh, Ahmed; Linzell, Daniel G.; Azam, Saeed Eftekhar

doi:10.1007/s13349-018-0311-6

Cited by 20 publications

(6 citation statements)

References 40 publications

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“…The results, although applied to different structures, are similar. The dependency of the results on the dynamic properties is in accordance with Rageh et al [29], who trained a neural network using a dataset of numerical time series solutions of a damaged bridge structure.…”

Section: Discussionsupporting

confidence: 81%

Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

Chiaia

Biagi

2020

Applied Sciences

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Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring.

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Section: Discussionsupporting

confidence: 81%

Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

Chiaia

Biagi

2020

Applied Sciences

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“…It can be argued that the classification approach is the most analytical approach implemented in the SHM system for bridges as can be seen in Table 5, which is expected because the SHM process is a classification problem from the machine learning point of view to compare between damaged and undamaged states of the structure. In the selected studies for this SLR, only 15.5% of the selected studies deploy regression for prediction operations [59,60,65,66,76,82,101]. For example, Xiao-Wei et al [59] propose a data-driven approach to predict the vibration amplitudes of girders and towers for early warning SHM.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…As can be seen in Table 8, 60% of the selected studies for this SLR deployed the data-driven approach, whereas the model-based approach was used in 40% only. For instance, Rageh et al [82] propose an SHM system for automated damage identification (location and intensity) by means of a continuous stream of data. The system deployed a number of strain sensors on the structure of the steel truss bridge.…”

Section: Discussion On Feature Extraction Techniquesmentioning

confidence: 99%

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements.

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“…The relative error of the NN is within 10%. Other studies using FNN for fatigue detection can be found in previous works 171,172,179–200 …”

Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

113

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Neural network (NN) models have significantly impacted fatigue‐related engineering communities and are expected to increase rapidly due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.

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Automated, strain-based, output-only bridge damage detection

Cited by 20 publications

References 40 publications

Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Fatigue modeling using neural networks: A comprehensive review

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