scholarly journals Fast Dynamic Control of Damaged Historical Buildings: A New Useful Approach for Structural Health Monitoring after an Earthquake

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Sergio Vincenzo Calcina ◽  
Luca Piroddi ◽  
Gaetano Ranieri
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Conservation Status ◽  
Dynamic Control ◽  
Dynamic Monitoring ◽  
Damage State ◽  
Structural Dynamic ◽  
Damage Level ◽  
Structural Health ◽  
Material State

The structures damage conditions assessment requires numerous precautions to ensure the safety of people during site visits and inspections. Among several methods providing useful information about the conservation status of the structures, dynamic monitoring techniques are suitable to retrieve the global behavior of the buildings. The anomalous features diagnosis of the structural dynamic response is an index of alterations of the material state and, in the worst cases, is related to the presence of damaged structural elements. This paper proposes the use of remote control systems for the structural evaluation of the damage state of buildings and describes the results achieved in an interesting application: the experimental dynamic analysis carried out on the inaccessible damaged bell tower of the Church of Santi Giacomo and Filippo in Mirandola (Italy). The study is based on observations performed using the IBIS-S ground-based radar interferometer to remotely measure the displacements of several elements of the building above 0.01 mm amplitude. This totally noninvasive and nondestructive approach has proved to be reliably implemented as a useful method to structural health monitoring procedures and especially for extensive and fast inspection analyses aiming at the first evaluation of the damage level and the soundness of slender buildings after earthquakes.

scholarly journals Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1491-1509 ◽  
Author(s):  
Chuan-Zhi Dong ◽  
Ozan Celik ◽  
F Necati Catbas
Keyword(s):  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Single Point ◽  
Modal Identification ◽  
Dynamic Monitoring ◽  
Structural Dynamic ◽  
Multiple Camera ◽  
Structural Health ◽  
Displacement Sensors

In this study, a vision-based multi-point structural dynamic monitoring framework is proposed. This framework aims to solve issues in current vision-based structural health monitoring. Limitations are due to manual markers, single-point monitoring, and synchronization between a multiple-camera setup and a sensor network. The proposed method addresses the first issue using virtual markers—features extracted from an image—instead of physical manual markers. The virtual markers can be selected according to each scenario, which makes them versatile. The framework also overcomes the issue of single-point monitoring by utilizing an advanced visual tracking algorithm based on optical flow, allowing multi-point displacement measurements. Besides, a synchronization mechanism between a multiple-camera setup and a sensor network is built. The proposed method is first tested on a grandstand simulator located in the laboratory. The experiment is to verify the performance of displacement measurement of the proposed method and conduct structural identification of the grandstand through multi-point displacement records. The results from the proposed method are then compared to the data gathered by traditional displacement sensors and accelerometers. A second experiment is conducted at a stadium during a football game to validate the feasibility of field application and the operational modal identification of the stadium under human crowd jumping through the measured displacement records. From these experiments, it is concluded that the proposed method can be employed to identify modal parameters for structural health monitoring.

scholarly journals Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

scholarly journals A Wireless Strain Sensor Network for Structural Health Monitoring

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chengyin Liu ◽  
Jun Teng ◽  
Ning Wu
Keyword(s):  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Evaluation System ◽  
Strain Sensor ◽  
Modal Testing ◽  
Dynamic Strain ◽  
Structural Dynamic ◽  
Structural Health ◽  
Strain Monitoring

Structural strain under external environmental loads is one of the main monitoring parameters in structural health monitoring or dynamic tests. This paper presents a wireless strain sensor network (WSSN) design for monitoring structural dynamic strain field. A precision strain sensor board is developed and integrated with the IRIS mote hardware/software platform for multichannel strain gauge signal conditioning and wireless monitoring. Measurement results confirm the sensor’s functionality regarding its static and dynamic characterization. Furthermore, in order to verify the functionality of the designed wireless strain sensor for dynamic strain monitoring, a cluster-star network evaluation system is developed for strain modal testing on an experimental steel truss structure. Test results show very good agreement with the finite element (FE) simulations. This paper demonstrates the feasibility of the proposed WSSN for large structural dynamic strain monitoring.

scholarly journals Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 545 ◽  
Author(s):  
Xinlin Qing ◽  
Wenzhuo Li ◽  
Yishou Wang ◽  
Hu Sun
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Transducer ◽  
Piezoelectric Materials ◽  
Development Trend ◽  
Practical Implementation ◽  
Damage State ◽  
Structural Health ◽  
Safety And Reliability ◽  
Aircraft Application

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

Study on New Structural Health Monitoring Sensor of Aircraft

2014 ◽  
Vol 530-531 ◽  
pp. 62-65
Author(s):  
Yu Long Zhang ◽  
Wei Fang Zhang ◽  
Ai Ai Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Preparation Method ◽  
Core Component ◽  
Damage State ◽  
Film Sensor ◽  
Health Monitoring System ◽  
Structural Health ◽  
Damage Monitoring

Sensor is the core component of structural health monitoring system, which can collect the data of structural damage. The structural damage state can be gained after further processing. Aircraft serves in rigorous environment, and existing sensors cant meet the demand of its structural damage monitoring for inherent defect. A preparation method of partial poling piezoelectric film sensor was proposed in the paper, which can be used for structural damage monitoring of aircraft in combination with lamb wave.

scholarly journals Structural Health Monitoring Applications in Tall Buildings

2020 ◽  
Vol 198 ◽  
pp. 02020
Author(s):  
Yifan Zhao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Tall Buildings ◽  
Modal Parameter ◽  
Index Method ◽  
Structural Dynamic ◽  
Modal Parameter Identification ◽  
Structural Health ◽  
Whole Process ◽  
Neural Network Algorithm

Since there is not much research on structural health monitoring (SHM) applications in tall buildings nowadays, this paper gives a proposal of how it can be applied on skyscrapers. Covering the whole process of SHM, this paper focuses more on the diagnostic algorithms, including Structural dynamic index method, Modal parameter identification method Neural network algorithm and Genetic algorithm and how these algorithms can be used in SHM. After introducing the basic process of SHM, an example is given to show how these principles can be applied in this over 400m building. And after all these introductions, a conclusion can be drawn that the structural health monitoring system can be applied properly in tall buildings following the way proposed in this paper.

An Evaluation of Damage Features Extraction from Attractors in Structural Health Monitoring

2011 ◽  
Vol 105-107 ◽  
pp. 738-741
Author(s):  
Chao Xu ◽  
Dong Wang
Keyword(s):  
Structural Health Monitoring ◽  
State Space ◽  
Health Monitoring ◽  
Prediction Error ◽  
Structural Response ◽  
Accurate Information ◽  
Variance Ratio ◽  
Damage Level ◽  
Structural Health ◽  
Local Variance

Structural health monitoring provides accurate information about structure’s safety and integrity. The vibration-based structural health monitoring involves extracting a feature which robustly quantifies damage induced change to the structure. Recent work has focused on damage features extracted from the state space attractor of the structural response. Some of these features involve prediction error and local variance ratio. In the present paper, a five degree of freedom spring damper system forced by a Lorenz excitation is used to evaluate these two typical damage features. Their ability of identification damage level and location is characterized and compared.

Structural Health Monitoring on Real Scale Model of a Masonry Triumphal Arch

2007 ◽  
Vol 347 ◽  
pp. 279-284
Author(s):  
Giovanni Damonte ◽  
Stefano Podestà ◽  
Giuseppe Riotto ◽  
Sergio Lagomarsino ◽  
Georges Magonette ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Dynamic Behaviour ◽  
Structural Safety ◽  
Scale Model ◽  
Research Centre ◽  
Structural Dynamic ◽  
Measurement Variability ◽  
Structural Health ◽  
Real Scale

Monitoring represents one solution for the safeguard of historical buildings. The need for a non-destructive and comprehensive monitoring methodology suggests using related to Structural Health Monitoring. This paper is intended to present the outcomes of an experimental campaign on a masonry triumphal arch representing a real scale model of a church part, which was built outside ELSA laboratory at the Joint Research Centre of European Commission. This study aims to evaluate the damage pattern of the structure through simplified dynamic methods producing a quick evaluation of structural safety, easy to use on real cases. As in traditional monitoring, both the instrumentation precision and the measurement variability due to the different testing condition (e.g. ambient conditions) have to be considered. The related effects on the structural dynamic behaviour were analysed and evaluated in order to distinguish an effective change in the “structural health” (a real damage) from an alteration caused by external conditions (a “false positive”). Once studied such effects, settlements were induced to one column base through an “ad hoc” device. Varying the settlement width, three damage levels were obtained in the structure. For each state the structural dynamic properties and their variation were evaluated. Sensitivity of dynamic behaviour to structural damage and to its changes was analysed comparing the results for each level.

DETECTION OF DAMAGE LOCATION USING A NOVEL SUBSTRUCTURE-BASED FREQUENCY RESPONSE FUNCTION APPROACH WITH A WIRELESS SENSING SYSTEM

2012 ◽  
Vol 12 (04) ◽  
pp. 1250029 ◽  
Author(s):  
T. K. LIN ◽  
S. L. HUNG ◽  
C. S. HUANG
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Dynamic Properties ◽  
Maintenance Cost ◽  
Structural Dynamic ◽  
Health Monitoring System ◽  
Structural Health ◽  
Damage Location ◽  
Structural Health Monitoring System

This paper intends to detect the damage locations for building structures under an earthquake excitation using a novel substructure-based FRF approach with a damage location index (SubFRFDI). An Imote2.NET-based wireless structural health monitoring system was developed and employed in the experimental studies for the sake of deployment flexibility, low maintenance cost, low power consumption, self-organization capability, and wireless communication capability. The feasibility of the proposed approach for damage detection was examined using the numerical response of a six-storey shear plane frame structure subjected to a base excitation. The results demonstrate that the SubFRFDI can be successfully used to identify the damage of different levels at a single site or multiple sites. The SubFRFDI is independent of the responses to various input earthquake excitations. Even with the addition of noises, the SubFRFDI still functions well. The feasibility and robustness of the proposed Imote2.NET-based wireless structural health monitoring system were assessed using a 1/8-scale three-storey steel-frame model. Following this, the proposed SubFRFDI was further applied to identifying the damage locations in a 1/4-scale six-storey steel structure with the proposed Imote2.NET-based wireless monitoring system. It was confirmed experimentally that good data transportation quality can be achieved via reliable data transmission and sensing protocol in identifying the structural dynamic properties, and the proposed SubFRFDI can be used to identify the damage locations effectively.

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