Vibration-based damage detection techniques for structural health monitoring of civil infrastructure systems

2009 ◽  
pp. 177-212 ◽  
Author(s):  
V M Karbhari ◽  
L S- W Lee
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Civil Infrastructure ◽  
Infrastructure Systems ◽  
Detection Techniques ◽  
Structural Health

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

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.

A hybrid learning strategy for structural damage detection

2020 ◽  
pp. 147592172096694
Author(s):  
Lorena Andrade Nunes ◽  
Rafaelle Piazzaroli Finotti Amaral ◽  
Flávio de Souza Barbosa ◽  
Alexandre Abrahão Cury
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Real Time ◽  
Health Monitoring ◽  
Time Domain ◽  
Structural Damage ◽  
Machine Learning Techniques ◽  
Hybrid Classifier ◽  
Detection Techniques ◽  
Structural Health

Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised ( k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

2-D Directional Phased Array Using Piezoelectric Paint to Detect Damages in Isotropic Plates

2009 ◽  
Author(s):  
Byungseok Yoo ◽  
Darryll J. Pines ◽  
Ashish S. Purekar
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Ultrasonic Testing ◽  
Phased Array ◽  
Guided Wave ◽  
In Situ Monitoring ◽  
Mode Shapes ◽  
Detection Techniques ◽  
Structural Health

Research interests in structural health monitoring have increased due to in-situ monitoring of structural components to detect damage. This can secure personal safety and reduce maintenance effort for mechanical systems. Conventional damage detection techniques known as nondestructive evaluation (NDE) have been conducted to detect and locate damaged area in structures. Ultrasonic testing, using ultrasonic transducers or electromagnetic acoustic transducers, is one of the most widespread NDE techniques, based on monitoring changes in acoustic impedance. Although the ultrasonic testing has advantages such as high sensitivity to discontinuities and evaluation accuracy, it requires testing surface accessibility, close location to the damaged area, and decent skill and training of technicians. In recent years, modal analysis techniques to capture changes of mode shapes and natural frequency of structures have been investigated. However, the technique is relatively insensitive to small amount of damage such as an initial crack which can rapidly grow in structures under cyclic loadings. In addition, structural health monitoring based on guided waves has become a preferred damage detection approach due to its quick examination of large area and simple inspection mechanisms. There are many techniques used to analyze sensor signals to bring out features related to damage. A phased array coupled with the guided wave approach has been introduced to effectively analyze complicated guided wave signals. Phased array theory as a directional filtering technique is usually used in antenna applications. By using phased array signal processing, virtually steering the array to find the largest response of source, the desired signal component can be enhanced while unwanted information is eliminated.

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