scholarly journals Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems

2019 ◽  
Vol 9 (14) ◽  
pp. 2840 ◽  
Author(s):  
Ekin Ozer ◽  
Maria Q. Feng
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Reliability ◽  
Time History ◽  
Fem Analysis ◽  
Modal Identification ◽  
Performance Criteria ◽  
Participatory Sensing ◽  
Structural Health ◽  
Demand Distribution

With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective.

scholarly journals Diseño e implementación de un sistema de adquisición y monitoreo de datos (shm) para un rectificador de protección catódica usado en ductos.

Respuestas ◽  
2016 ◽  
Vol 21 (1) ◽  
pp. 45 ◽  
Author(s):  
Daniel Alejandro Rodríguez-Caro ◽  
Enrique Vera-López ◽  
Helver Mauricio Muñoz-Barajas
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Cathodic Protection ◽  
Structural Integrity ◽  
Protection System ◽  
Performance Criteria ◽  
Correct Operation ◽  
Structural Health ◽  
El Sistema ◽  
Cathodic Protection System

Antecedentes: La protección catódica por corriente impresa es uno de los métodos para prevenir la corrosión de tuberías o tanques, preservando el estado estructural y la integridad del material. Para que un sistema de protección catódica funcione correctamente debe existir un control sobre las variables eléctricas que intervienen en el proceso, es por ello que se hace necesario monitorear variables tales como (Voltaje, Corriente y Potencial de protección). Objetivo: De esta manera se desarrolla un sistema de adquisición y monitoreo de datos en tiempo real, con el propósito de aumentar la accesibilidad a las variables eléctricas y de esta forma mejorar el funcionamiento del sistema de protección catódica. Métodos: El sistema de monitoreo y análisis de la información se basa en el concepto de SHM (Structural Health Monitoring), el cual consta de; un sistema electrónico de adquisición y envío remoto de señales (micro controlador y sistema GSM de comunicaciones) y un sistema de visualización y análisis de la información en un sistema móvil (celular), usando un servidor web para ello. Teniendo en cuenta que la condición de integridad estructural del ducto está determinada por el correcto funcionamiento del rectificador. Resultados: se logró implementar un sistema de monitoreo y visualización remota de las variables principales de un sistema de protección catódica. Se desarrolló un algoritmo basado en el concepto de SHM, el cual permite correlacionar, generar tendencia y establecer criterios de funcionamiento del sistema de protección catódica que permiten establecer si el sistema está asegurando la integridad estructural del ducto de transporte de crudo. Conclusión: lo novedoso del presente trabajo consiste en mostrar el comportamiento en tiempo real de las variables necesarias para analizar si el ducto está siendo correctamente protegido y generar las alarmas e informes sobre protección catódica, lo cual es la base del concepto de SHM (Structural Health Monitoring).AbstractBackground: Cathodic protection by impressed current is one of the methods to prevent corrosion of pipes or tanks, preserving the structural state and integrity of the material. For a cathodic protection system to function properly there must to be a control over the electrical variables involved in the process, which is why it is necessary to monitor variables such as (voltage, current and potential protection). Objective: to develop a system of data acquisition and monitoring in real time, in order to increase accessibility to electrical variables and thus improve the operation of the cathodic protection system. Methods: The monitoring and information analysis system is based on the concept of SHM (Structural Health Monitoring), which consists of an electronic system for remote acquisition and sending of signals (micro controller and GSM communications system) and a system for visualization and analysis of information in a mobile system (cell) using a web server for it. Given that the condition of structural integrity of the pipeline is determined by the correct operation of the rectifier. Results: It was possible to implement a monitoring and remote viewing system of the main variables of a cathodic protection system. An algorithm based on the concept of SHM was developed, allowing to correlate, generate trend and establish performance criteria for the cathodic protection system which allows to establish whether the system is ensuring the structural integrity of the crude transportation pipeline. Conclusion: the novelty of this work is to show the realtime behavior of the variables needed to analyze whether the pipeline is being properly protected and generate alarms and reports regarding cathodic protection, which is based on the concept of SHM (Structural Health Monitoring).Palabras Clave: corriente, corrosión, Innovación, monitoreo, SHM (Structural Health Monitoring)

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.

An Innovative Structural Health Monitoring System for Large Transmission Towers Based on GPS

2019 ◽  
Vol 19 (04) ◽  
pp. 1971002 ◽  
Author(s):  
X. X. Cheng ◽  
Y. J. Ge
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Time History ◽  
Time Behavior ◽  
Structural Health ◽  
Transmission Towers ◽  
Strong Winds ◽  
Force Equilibrium

In this paper, we propose an innovative structural health monitoring (SHM) system for large transmission towers that are frequently subjected to strong winds. The system is based on the strategy of using a static force equilibrium equation to calculate the whole structure’s real-time stress distribution according to its real-time behavior, as captured by the global positioning system (GPS). The reason for adopting this approach is that large transmission towers are fundamentally quasi-static structures and they are not prone to resonance under wind excitations. A case study is used to present the SHM system, then its effectiveness is validated by comparing the simulated SHM results with the exact solution obtained by a realistic time-history dynamic analysis. Additionally, we discuss the use of a new reliability analysis method based on the Ditlevsen’s bounds to assess the real-time structural conditions.

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.

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 Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach

2019 ◽  
Vol 19 (1) ◽  
pp. 293-304 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Applied Mathematics ◽  
Modal Identification ◽  
Data Reconstruction ◽  
Wireless Data ◽  
Sensing Data ◽  
Structural Health

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the optimal solution for sparse optimization. In the past several years, many algorithms have been proposed in the field of applied mathematics. In this article, we propose a machine learning–based approach to solve the compressive-sensing data-reconstruction problem. By treating a computation process as a data flow, the solving process of compressive sensing–based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e. the basis matrix and the compressive sensing–sampled signals, is used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; and the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original compressive sensing–sampled signals and the masked reconstructed signals with a common optimization algorithm. In addition, the proposed network is able to handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning–based approach. In addition, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the compressive-sensing data-reconstruction neural network interpretable.

Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks

2020 ◽  
pp. 147592172091688
Author(s):  
Gao Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Dynamic Response ◽  
Health Monitoring ◽  
Network Architecture ◽  
Experimental Studies ◽  
Modal Identification ◽  
Structural Health ◽  
Convolutional Networks ◽  
Response Reconstruction ◽  
Training Efficiency

This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques are carefully applied in the designed network architecture, which greatly facilitates the information flow, and increases the training efficiency and accuracy of feature extraction and propagation with fewer parameters in the network. Sub-pixel shuffling and dropout techniques are used in the designed network and applied to reduce the computational demand and improve training efficiency. The network is trained in a supervised manner, where the input and output are the measurements of the available channels at response available locations and desired channels at response unavailable locations. The proposed densely connected convolutional networks automatically extract the high-level features of the input data and construct the complicated nonlinear relationship between the responses of available and desired locations. Experimental studies are conducted using the measured acceleration responses from Guangzhou New Television Tower to investigate the effects of the locations of available responses, the numbers of available and unavailable channels, and measurement noise. The results demonstrate that the proposed approach can accurately reconstruct the responses in both time and frequency domains with strong noise immunity. The reconstructed response is further used for modal identification to demonstrate the usability and accuracy of the reconstructed responses. The applicability of the proposed approach for structural health monitoring is further proved by the highly consistent modal parameters identified from the reconstructed and true responses.

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