State-of-the-art in structural health monitoring of large and complex civil infrastructures

2015 ◽  
Vol 6 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Hong-Nan Li ◽  
Liang Ren ◽  
Zi-Guang Jia ◽  
Ting-Hua Yi ◽  
Dong-Sheng Li
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
State Of The Art ◽  
Structural Health ◽  
Civil Infrastructures

Structural health monitoring of civil infrastructure

2006 ◽  
Vol 365 (1851) ◽  
pp. 589-622 ◽  
Author(s):  
J.M.W Brownjohn
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Communication Systems ◽  
State Of The Art ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Civil Infrastructures ◽  
Economic Operation ◽  
History Of ◽  
Computer Based

Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued ‘fitness for purpose’ of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Si-Yao Wu ◽  
Li-Qiang Dong
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Threshold ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Structural Health ◽  
Civil Infrastructures ◽  
Standard Particle Swarm Optimization

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

scholarly journals Piezoelectric Sensing Techniques in Structural Health Monitoring: A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3730 ◽  
Author(s):  
Pengcheng Jiao ◽  
King-James I. Egbe ◽  
Yiwei Xie ◽  
Ali Matin Nazar ◽  
Amir H. Alavi
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Smart Materials ◽  
State Of The Art ◽  
Piezoelectric Materials ◽  
Great Promise ◽  
Monitoring Systems ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Health Monitoring Systems

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.

Design of Structural Health Monitoring System Based on WSNs

2014 ◽  
Vol 681 ◽  
pp. 47-50
Author(s):  
Yue Zhou ◽  
Shuai Liu ◽  
Li Xin Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Large Scale ◽  
State Of The Art ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
Large Scale Networks ◽  
Monitoring Application

The structural health monitoring technology has been one of the most important issues. In this paper, the design of wireless sensor network for structural health monitoring application is studied. The basic concept, significance, state of the art of structural health monitoring, the architecture and the principle of the wireless structural health monitoring system are described. The hardware and software of the overall system are designed and built. The WLANonSAN architecture network is particularly proposed as a solution for the large-scale networks.

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