Modelling and forecasting of SHM strain measurement for a large-scale suspension bridge during typhoon events using variational heteroscedastic Gaussian process

Wang, Qi‐Ang; Zhang, Cheng; Guo, Zhan; Ni, Yi

doi:10.1016/j.engstruct.2021.113554

Cited by 54 publications

(31 citation statements)

References 34 publications

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“…Data science as a new field has grown tremendously in the past decade due to its great potential to transform industries, the economy, health care, and scientific discovery. With data analyst skills becoming increasingly crucial for leveraging big data to gain a competitive edge, accelerate scientific discoveries, and advance public health [ 1 , 15 , 16 , 17 , 18 ], the inequitable representation of minority groups in the field, coupled with the lack of resources and infrastructure in minority-serving institutions, has become a major issue in the U.S.…”

Section: Discussionmentioning

confidence: 99%

A Case Study of Enhancing the Data Science Capacity of an RCMI Program at a Historically Black Medical College

Wang

Gupta

Cao

et al. 2023

IJERPH

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As data grows exponentially across diverse fields, the ability to effectively leverage big data has become increasingly crucial. In the field of data science, however, minority groups, including African Americans, are significantly underrepresented. With the strategic role of minority-serving institutions to enhance diversity in the data science workforce and apply data science to health disparities, the National Institute for Minority Health Disparities (NIMHD) provided funding in September 2021 to six Research Centers in Minority Institutions (RCMI) to improve their data science capacity and foster collaborations with data scientists. Meharry Medical College (MMC), a historically Black College/University (HBCU), was among the six awardees. This paper summarizes the NIMHD-funded efforts at MMC, which include offering mini-grants to collaborative research groups, surveys to understand the needs of the community to guide project implementation, and data science training to enhance the data analytics skills of the RCMI investigators, staff, medical residents, and graduate students. This study is innovative as it addressed the urgent need to enhance the data science capacity of the RCMI program at MMC, build a diverse data science workforce, and develop collaborations between the RCMI and MMC’s newly established School of Applied Computational Science. This paper presents the progress of this NIMHD-funded project, which clearly shows its positive impact on the local community.

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Section: Discussionmentioning

confidence: 99%

A Case Study of Enhancing the Data Science Capacity of an RCMI Program at a Historically Black Medical College

Wang

Gupta

Cao

et al. 2023

IJERPH

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“…The supervised learning algorithms, discussed in this SLR, include RF, DT, SVM and KNN. It is important to note that there are some other supervised learning algorithms such as Bayesian [120][121][122][123] and ensemble methods [124] that have been used in SHM systems. However, they have not been frequently deployed in the pattern recognition process of SHM systems for bridges.…”

Section: Discussion and Limitationsmentioning

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 main method is to use intelligent sensing instruments for the real-time monitoring, dynamic management, and trend analysis of engineering structures. Structural health monitoring can be carried out in real time or regularly to further evaluate the working state, load state, and damage state of engineering structures [1][2][3]. Through structural health monitoring, the health status of the structure can be monitored in real time, and the abnormal behavior of the structure can be identified in time so as to prevent potential structural failures and accidents.…”

Section: Introductionmentioning

confidence: 99%

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Ran,

Wang,

Wang

et al. 2024

Applied Sciences

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Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

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Modelling and forecasting of SHM strain measurement for a large-scale suspension bridge during typhoon events using variational heteroscedastic Gaussian process

Cited by 54 publications

References 34 publications

A Case Study of Enhancing the Data Science Capacity of an RCMI Program at a Historically Black Medical College

A Case Study of Enhancing the Data Science Capacity of an RCMI Program at a Historically Black Medical College

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

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

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