The Modal Identification of Structure Using Distributed ERA and EFDD Methods

2010 ◽  
Vol 163-167 ◽  
pp. 2532-2536
Author(s):  
Ying Lei ◽  
Zhi Lu Lai
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Volume ◽  
Modal Identification ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Civil Infrastructure ◽  
Complicated Structure ◽  
Data Set ◽  
Structural Health

Structural health monitoring (SHM) is an emerging field in civil engineering, offering the potential for continuous and periodic assessment of the safety and integrity of civil infrastructure. In this paper, a distributed computing strategy for modal identification of structure is proposed, which is suitable for the problem of solving large volume of data set in structural health monitoring. Numerical example of distribute computing the modal properties of truss illustrates the distributed out-put only modal identification algorithm based on NExT / ERA techniques and EFDD. This strategy can also be applied to other complicated structure to determine modal parameters.

scholarly journals An Iterative Modal Identification Algorithm for Structural Health Monitoring Using Wireless Sensor Networks

2013 ◽  
Vol 29 (2) ◽  
pp. 339-365 ◽  
Author(s):  
Siavash Dorvash ◽  
Shamim N. Pakzad ◽  
Liang Cheng
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Estimation Method ◽  
Energy Resource ◽  
Modal Identification ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Identification Algorithm ◽  
Structural Health ◽  
Time And Energy

A novel modal identification approach for the use of a wireless sensor network (WSN) for structural health monitoring is presented, in which the computational task is distributed among remote nodes to reduce the communication burden of the network and, as a result, optimize the time and energy consumption of the monitoring system. Considering the need for having an agile system to capture the earthquake response and also the limited energy resource in WSN, such algorithms for speeding the analysis time and preserving energy are essential. The algorithm of this study, called iterative modal identification (IMID), relies on an iterative estimation method that solves for unknown parameters in the absence of complete information about the system. Applying IMID in WSN-based monitoring systems results in significant savings in time and energy. Validation through implementation of the algorithm on numerically simulated and experimental data illustrates the superior performance of this approach.

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.

scholarly journals Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6894
Author(s):  
Nicola-Ann Stevens ◽  
Myra Lydon ◽  
Adele H. Marshall ◽  
Su Taylor
Keyword(s):  
Survival Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Transport Networks ◽  
Data Set ◽  
Structural Health

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

scholarly journals Wireless Sensor Networks Composed of Standard Microcomputers and Smartphones for Applications in Structural Health Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2070 ◽  
Author(s):  
Guido Morgenthal ◽  
Jan Frederick Eick ◽  
Sebastian Rau ◽  
Jakob Taraben
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Health Monitoring ◽  
Modal Identification ◽  
System Level ◽  
Wireless Sensor ◽  
Raspberry Pi ◽  
Structural Health

Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.

scholarly journals Recent Progress of Fiber-Optic Sensors for the Structural Health Monitoring of Civil Infrastructure

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4517
Author(s):  
Tiange Wu ◽  
Guowei Liu ◽  
Shenggui Fu ◽  
Fei Xing
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Electromagnetic Interference ◽  
Fiber Optic ◽  
Materials Science ◽  
Fiber Optic Sensors ◽  
Civil Infrastructure ◽  
Basic Principles ◽  
Structural Health ◽  
Sensing Systems

In recent years, with the development of materials science and architectural art, ensuring the safety of modern buildings is the top priority while they are developing toward higher, lighter, and more unique trends. Structural health monitoring (SHM) is currently an extremely effective and vital safeguard measure. Because of the fiber-optic sensor’s (FOS) inherent distinctive advantages (such as small size, lightweight, immunity to electromagnetic interference (EMI) and corrosion, and embedding capability), a significant number of innovative sensing systems have been exploited in the civil engineering for SHM used in projects (including buildings, bridges, tunnels, etc.). The purpose of this review article is devoted to presenting a summary of the basic principles of various fiber-optic sensors, classification and principles of FOS, typical and functional fiber-optic sensors (FOSs), and the practical application status of the FOS technology in SHM of civil infrastructure.

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