Development of a Structural Health Monitoring Benchmark Problem for High-Rise Slender Structures

2008 ◽  
Vol 56 ◽  
pp. 489-494 ◽  
Author(s):  
Yong Xia ◽  
Yi Qing Ni ◽  
Jan Ming Ko ◽  
Hua Bin Chen

Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST) and the International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII), a structural health monitoring benchmark problem for highrise slender structures is being developed by taking the instrumented Guangzhou New Television Tower as a test bed. The benchmark problem consists of the following four tasks: (i) output-only modal identification and finite element model updating, (ii) damage detection using simulated data, (iii) optimal sensor placement for structural health monitoring, and (iv) damage detection using field measurement data. This paper will address some key issues related to the development of this first benchmark problem for high-rise structures. More details of the study can be found in the website: http://www.cse.polyu.edu.hk/benchmark/index.htm

2016 ◽  
Vol 16 (04) ◽  
pp. 1640025 ◽  
Author(s):  
Wensong Zhou ◽  
Shunlong Li ◽  
Hui Li

A full-scale bridge benchmark problem was issued by the Center of Structural Monitoring and Control at the Harbin Institute of Technology. The data used in this problem were collected by an in situ structural health monitoring system implemented into a full-scale cable-stayed bridge before and after the bridge was damaged, which is very rare in structural health monitoring field. This benchmark problem will help to verify and/or make comparison of the condition assessment and the damage detection methods, which are usually validated by numerical simulation and/or laboratory testing of small-scale structures with assumed deterioration models and artificial damage. With respect to damage detection of girder, one of the benchmark problems, using the monitored and field testing acceleration data, this paper describes a damage detection method, based on a residual generated from a subspace-based covariance-driven identification method, to detect the damage, and give relative quantitative damage indexes. This method was applied on both two parts of the given benchmark problem, and then detailed discussions and results on this problem are reported in this paper.


2019 ◽  
Vol 55 (7) ◽  
pp. 1-6
Author(s):  
Zhaoyuan Leong ◽  
William Holmes ◽  
James Clarke ◽  
Akshay Padki ◽  
Simon Hayes ◽  
...  

Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.


2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.


Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.


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