State-of-the-art review on advancements of data mining in structural health monitoring

被引：94

作者：

Gordan, Meisam ^{[1
,2
]}

Sabbagh-Yazdi, Saeed-Reza ^{[2
]}

Ismail, Zubaidah ^{[1
]}

Ghaedi, Khaled ^{[1
]}

Carroll, Paraic ^{[3
]}

McCrum, Daniel ^{[3
]}

Samali, Bijan ^{[4
]}

机构：

[1] Univ Malaya, Dept Civil Engn, Kuala Lumpur 50603, Malaysia

[2] KN TOOSI Univ Technol, Dept Civil Engn, Tehran, Iran

[3] Univ Coll Dublin, Sch Civil Engn, Dublin, Ireland

[4] Western Sydney Univ, Ctr Infrastruct Engn, Sydney, NSW, Australia

来源：

MEASUREMENT | 2022年 / 193卷

关键词：

Structural health monitoring; Data mining; Artificial intelligence; Machine learning; Deep learning; Industry; 4; 0; ARTIFICIAL NEURAL-NETWORK; PRINCIPAL COMPONENT ANALYSIS; IMPERIALIST COMPETITIVE ALGORITHM; EMPIRICAL WAVELET TRANSFORM; BEAM-LIKE STRUCTURES; DAMAGE DETECTION; FAULT-DIAGNOSIS; KNOWLEDGE DISCOVERY; GENETIC ALGORITHM; ANOMALY DETECTION;

D O I：

10.1016/j.measurement.2022.110939

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

To date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in SHM. For this purpose, an intensive review is carried out to classify the aforementioned techniques. In doing so, a brief background, models, functions, and classification of DM techniques are presented. To this end, wide range of researches are collected in order to demonstrate the development of DM techniques, detect the most popular DM techniques, and compare the applicability of existing DM techniques in SHM. Eventually, it is concluded that the application of artificial intelligence has the highest demand rate in SHM while the most popular algorithms including artificial neural network, genetic algorithm, fuzzy logic, and principal component analysis are utilized for damage detection of civil structures.

引用

页数：38

共 50 条

[31] State-of-the-art overview on data mining in power systems
Mori, Hiroyuki
2006 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION. VOLS 1-5, 2006, : 33 - 37
[32] Structural intelligent health diagnosis state-of-the-art
Dong, C.
Zhao, M.
Jiang, Jianjing
9th International Conference on Inspection Appraisal Repairs & Maintenance of Structures, 2005, : 217 - 224
[33] State-of-the-art overview on data mining in power system
Mori, Hiroyuki
2006 POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-9, 2006, : 3419 - 3423
[34] Sublevel Shrinkage (SLSh) Mining—A State-of-the-art Review
Brand, Lukas
Haider, Katharina
BHM Berg- und Huttenmannische Monatshefte, 2023, 168 (06): : 274 - 280
[35] Destress Blasting in Coal Mining - State-of-the-Art Review
Konicek, Petr
Saharan, Mani Ram
Mitri, Hani
ISMSSE 2011, 2011, 26
[36] ROOF BOLTING IN UNDERGROUND MINING - A STATE-OF-THE-ART REVIEW
PENG, SS
TANG, DHY
INTERNATIONAL JOURNAL OF MINING ENGINEERING, 1984, 2 (01): : 1 - 42
[37] The State-of-the-art Review of Structural Control Strategy
Liu, Jianjun
Xia, Kaiquan
Zhu, Caixia
IEEE: 2009 INTERNATIONAL CONFERENCE ON E-LEARNING, E-BUSINESS, ENTERPRISE INFORMATION SYSTEMS AND E-GOVERNMENT, 2009, : 211 - +
[38] A state-of-the-art review of structural control systems
Saaed, Tarek Edrees
Nikolakopoulos, George
Jonasson, Jan-Erik
Hedlund, Hans
JOURNAL OF VIBRATION AND CONTROL, 2015, 21 (05) : 919 - 937
[39] Sensing Techniques for Structural Health Monitoring: A State-of-the-Art Review on Performance Criteria and New-Generation Technologies
Mardanshahi, Ali
Sreekumar, Abhilash
Yang, Xin
Barman, Swarup Kumar
Chronopoulos, Dimitrios
SENSORS, 2025, 25 (05)
[40] State-of-the-Art Review on Recent Advancements on Lateral Control of Autonomous Vehicles
Biswas, Archishman
Reon, M. A. Obayed
Das, Prangon
Tasneem, Zinat
Muyeen, S. M.
Das, Sajal K.
Badal, Faisal R.
Sarker, Subrata Kumar
Hassan, Md Mehedi
Abhi, Sarafat Hussain
Islam, Md Robiul
Ali, Md Firoj
Ahamed, Md Hafiz
Islam, Md Manirul
IEEE ACCESS, 2022, 10 : 114759 - 114786

← 1 2 3 4 5 →