scholarly journals Structural reliability assessment based on optical monitoring system: case study

2016 ◽  
Vol 9 (2) ◽  
pp. 297-305 ◽  
Author(s):  
E. Mesquita ◽  
P. Antunes ◽  
A. A. Henriques ◽  
A. Arêde ◽  
P. S. André ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Reliability ◽  
Extreme Values ◽  
Low Cost ◽  
Reliability Assessment ◽  
Water Reservoir ◽  
Optical Systems ◽  
Structural Health

ABSTRACT Optical systems are recognized to be an important tool for structural health monitoring, especially for real time safety assessment, due to simplified system configuration and low cost when compared to regular systems, namely electrical systems. This work aims to present a case study on structural health monitoring focused on reliability assessment and applying data collected by a simplified optical sensing system. This way, an elevated reinforced concrete water reservoir was instrumented with a bi-axial optical accelerometer and monitored since January 2014. Taking into account acceleration data, the natural frequencies and relative displacements were estimated. The reliability analysis was performed based on generalized extreme values distribution (GEV) and the results were employed to build a forecast of the reliability of the water elevated reservoir for the next 100 years. The results showed that the optical system combined with GEV analysis, implemented in this experimental work, can provide adequate data for structural reliability assessment.

scholarly journals Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5126
Author(s):  
Lili Pang ◽  
Junxiu Liu ◽  
Jim Harkin ◽  
George Martin ◽  
Malachy McElholm ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Extraction Methods ◽  
Structural Health ◽  
Sensory Data ◽  
Performance Benchmarking ◽  
Hardware System

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.

scholarly journals Advances in the Structural Health Monitoring of Bridges Using Piezoelectric Transducers

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4312 ◽  
Author(s):  
Yunzhu Chen ◽  
Xingwei Xue
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Materials ◽  
Low Cost ◽  
Rapid Development ◽  
Concrete Strength ◽  
Steel Corrosion ◽  
Fast Response ◽  
Future Application ◽  
Structural Health

With the rapid development of the world’s transportation infrastructure, many long-span bridges were constructed in recent years, especially in China. However, these bridges are easily subjected to various damages due to dynamic loads (such as wind-, earthquake-, and vehicle-induced vibration) or environmental factors (such as corrosion). Therefore, structural health monitoring (SHM) is vital to guarantee the safety of bridges in their service lives. With its wide frequency response range, fast response, simple preparation process, ease of processing, low cost, and other advantages, the piezoelectric transducer is commonly employed for the SHM of bridges. This paper summarizes the application of piezoelectric materials for the SHM of bridges, including the monitoring of the concrete strength, bolt looseness, steel corrosion, and grouting density. For each problem, the application of piezoelectric materials in different research methods is described. The related data processing methods for four types of bridge detection are briefly summarized, and the principles of each method in practical application are listed. Finally, issues to be studied when using piezoelectric materials for monitoring are discussed, and future application prospects and development directions are presented.

scholarly journals Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

2018 ◽  
Vol 7 (3) ◽  
pp. 30 ◽  
Author(s):  
Chiara Bedon ◽  
Enrico Bergamo ◽  
Matteo Izzi ◽  
Salvatore Noè
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Full Scale ◽  
Shaking Table ◽  
Efficient Design ◽  
Dynamic Identification ◽  
Structural Health ◽  
Cable Stayed Bridge ◽  
Mems Accelerometers

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

scholarly journals Reliable Data Acquisition System for a Low-Cost Accelerograph Applied to Structural Health Monitoring

2020 ◽  
Vol 3 (2) ◽  
pp. 181-194
Author(s):  
Milton Muñoz ◽  
Remigio Guevara ◽  
Santiago González ◽  
Juan Carlos Jiménez
Keyword(s):  
Structural Health Monitoring ◽  
Data Acquisition ◽  
Health Monitoring ◽  
Seismic Event ◽  
Low Cost ◽  
Recording System ◽  
Continuous Recording ◽  
Remote Communication ◽  
Structural Health ◽  
Hydroelectric Dam

This paper presents and evaluates a continuous recording system designed for a low-cost seismic station. The architecture has three main blocks. An accelerometer sensor based on MEMS technology (Microelectromechanical Systems), an SBC platform (Single Board Computer) with embedded Linux and a microcontroller device. In particular, the microcontroller represents the central component which operates as an intermediate agent to manage the communication between the accelerometer and the SBC block. This strategy allows the system for data acquisition in real time. On the other hand, the SBC platform is used for storing and processing data as well as in order to configure the remote communication with the station. This proposal is intended as a robust solution for structural health monitoring (i.e. in order to characterize the response of an infrastructure before, during and after a seismic event). The paper details the communication scheme between the system components, which has been minutely designed to ensure the samples are collected without information loss. Furthermore, for the experimental evaluation the station was located in the facilities on a relevant infrastructure, specifically a hydroelectric dam. The system operation was compared and verified with respect to a certified accelerograph station. Results prove that the continuous recording system operates successfully and allows for detecting seismic events according to requirements of structural health applications (i.e. detects events with a frequency of vibration less than 100 Hz). Specifically, through the system implemented it was possible to characterize the effect of a seismic event of 4 MD reported by the regional seismology network and with epicenter located about 30 Km of the hydroelectric dam. Particularly, the vibration frequencies detected on the infrastructure are in the range of 13 Hz and 29 Hz. Regarding the station performance, results from experiments reveals an average CPU load of 51%, consequently the processes configured on the SBC platform do not involve an overload. Finally, the average energy consumption of the station is close to 2.4 W, therefore autonomy provided by the backup system is aroud of 10 hours.

Structural Health Monitoring With Piezoelectric Active Sensors

2000 ◽  
Author(s):  
Howard A. Winston ◽  
Fanping Sun ◽  
Balkrishna S. Annigeri
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Electromechanical Coupling ◽  
Low Cost ◽  
Piezoelectric Element ◽  
Impedance Spectrum ◽  
Active Sensors ◽  
Electromechanical Impedance ◽  
Structural Health

A technology for non-intrusive real-time structural health monitoring using piezoelectric active sensors is presented. The approach is based on monitoring variations of the coupled electromechanical impedance of piezoelectric patches bonded to metallic structures in high-frequency bands. In each of these applications, a single piezoelectric element is used as both an actuator and a sensor. The resulting electromechanical coupling makes the frequency-dependent electric impedance spectrum of the PZT sensor a good mapping of the underlying structure’s acoustic signature. Moreover, incipient structural damage can be indicated by deviations of this signature from its original baseline pattern. Unique features of this technology include its high sensitivity to structural damage, non-intrusiveness to the host structure, and low cost of implementation. These features have potential for enabling on-board damage monitoring of critical or inaccessible aerospace structures and components, such as aircraft wing joints, and both internal and external jet engine components. Several exploratory applications will be discussed.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Use of low-cost GNSS receivers for Structural Health Monitoring

2018 ◽  
Author(s):  
Nicolas Manzini ◽  
Andre Orcesi ◽  
Antoine Clément ◽  
Miguel Ortiz ◽  
John Dumoulin
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Structural Health ◽  
Gnss Receivers

Enhancing Vibration-Based Structural Health Monitoring via Edge Computing: A Tiny Machine Learning Perspective

2021 ◽  
Author(s):  
Federica Zonzini ◽  
Francesca Romano ◽  
Antonio Carbone ◽  
Matteo Zauli ◽  
Luca De Marchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Network Architecture ◽  
Binary Classification ◽  
Low Cost ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Structural Health

Abstract Despite the outstanding improvements achieved by artificial intelligence in the Structural Health Monitoring (SHM) field, some challenges need to be coped with. Among them, the necessity to reduce the complexity of the models and the data-to-user latency time which are still affecting state-of-the-art solutions. This is due to the continuous forwarding of a huge amount of data to centralized servers, where the inference process is usually executed in a bulky manner. Conversely, the emerging field of Tiny Machine Learning (TinyML), promoted by the recent advancements by the electronic and information engineering community, made sensor-near data inference a tangible, low-cost and computationally efficient alternative. In line with this observation, this work explored the embodiment of the One Class Classifier Neural Network, i.e., a neural network architecture solving binary classification problems for vibration-based SHM scenarios, into a resource-constrained device. To this end, OCCNN has been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 95% and 94%, respectively.

Sign in / Sign up

Export Citation Format

Share Document