scholarly journals The use of modal parameters in structural health monitoring

2018 ◽  
Vol 162 ◽  
pp. 04020
Author(s):  
Ali Al-Ghalib ◽  
Fouad Mohammad
Keyword(s):  
Structural Health Monitoring ◽  
Natural Frequency ◽  
Health Monitoring ◽  
Damping Ratio ◽  
Reinforced Concrete Beams ◽  
Modal Testing ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Structural Health ◽  
Vibration Parameters

The concrete is liable to damage due to various stresses which compensate its adequacy and safety. The estimation of remaining strength in reinforced concrete beams when subjected to increased loading action utilizing vibration parameters is investigated. For this reason, three beams are loaded statically close to failure in various increasing load steps and then repaired. The beams are all of same dimensions, but are different in strength and range of defects introduced to each sample. Following each loading step, the experimental modal testing is utilized to collect the vibration parameters (natural frequency, damping ratio and mode shapes) of each beam when tested under free support boundary conditions. The use of vibration parameters for the purpose of damage identification are known to be an elaborate and lengthy process. On the other hand, they are successful for the structural health monitoring given that they are able to provide global on-site automated continuous monitoring. The paper features post analysis procedures for experimental modal measurements of three concrete samples to obtain and correlate the basic modal parameters (natural frequency, modal damping and mode shapes). The results of the extracted modal parameters and their combination are exploited in this research as quantified identification parameters. This paper concludes that modal parameters are successful in determining the location and quantity of structural degradation, when holistic approach considered through a system.

Structural Health Monitoring of Steel Frame Structure by Experimental Modal Parameter Identification

2020 ◽  
Vol 37 ◽  
pp. 1-13
Author(s):  
Bulbul Ahmed ◽  
Florea Dinu ◽  
Ioan Marginean
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Steel Frame ◽  
Parameters Estimation ◽  
Operating Conditions ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Damage Initiation ◽  
Structural Health ◽  
Lower Beam

Structural health monitoring (SHM) is a modern technique f or damage identification in the e xis ting structure. The structural stiffness, frequency, damping, and dominant mode shapes represent the actual operating conditions of the structure. The main principle of structural health monitoring is to identif y the mod al parameters from experime ntal resu lts both damaged and undamaged conditions. Damage is much effective to decrease stiffness and strength of structural components and it changes dynamic behaviour and damping ratio of whole structures. Bruel & Kjaer experi mental modal analys is technique is r ecently used for civil engineering structures for modal parameters estimation. The paper describes the initial structural health monitoring of a steel frame . The modal parameters were estimated for undamaged condition s a nd these result s are verified and up dated by the numerical FEM tool SAP2000. For the undamaged structure , mode shapes and frequencies were calibrated properly. In the second step, damaged was initiated by dismantling one element from the lower part of the frame. The estimat ed m odal parameter s were compared to the initial one. The mode shapes and frequencies are quite different for some specific mode due to damage initiation . One extra mode was created for the damaged frame due to damage initiation. The 4 th mode was not found f or the initial m easurement because of presence of lower beam. Lower beam restraints the 4 th mode and the frame behaves more flexible. Keywords: SHM , Modal parameters, FEM modelling, Damage characterization, Experimental mo dal analysis (EMA) .

A Structural Health Monitoring System for Composite Beams With Coupled Bending-Torsion

2012 ◽  
Author(s):  
Zeaid Hasan ◽  
Ghassan Atmeh
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Natural Frequency ◽  
Health Monitoring ◽  
Composite Beam ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Current Time ◽  
Structural Health ◽  
Area Of Interest

Structural health monitoring (SHM) is the process of damage identification in structural systems which have been an area of interest and a well-recognized field of technology in the past decade. Such systems involve the integration of smart materials, sensors and decision-making algorithms into the structure to detect damage, evaluate the structural integrity and predict the remaining life time. These systems have the potential to replace traditional non-destructive evaluation (NDE) of structures. This study focuses on presenting an automated structural health monitoring (SHM) system based on detecting shifts in natural frequencies of the structure. The damage detection technique is implemented on a cracked composite beam vibrating in coupled bending-torsion where the crack is assumed open. Modal analysis is conducted on the composite beam in order to predict the natural frequency and the associated mode shapes. Based on this analysis, a database of information related to the specific composite beam being analyzed such as layups and natural frequencies are stored. The natural frequency will be measured and compared to that database for damage detection. A finite element model is also presented and compared with the analytical results. It is observed that the variation of natural frequencies in the presence of a crack is affected by the crack ratio, crack location and fiber orientation. In particular, the variation pattern is different as the magnitude of bending-torsion coupling changes due to different fiber angles. A simple circuit containing a microcontroller is implemented to simulate the automated SHM concept. The microcontroller serves as the data storage device as well as the decision maker based on the instantaneous comparison between the healthy and the damaged structure. The proposed system may be implemented in many structural components such as aircraft frames and bridges. This SHM technology may help replace the current time-based maintenance scheme with a condition-based one. The condition-based maintenance scheme relies on the ability to monitor the condition of the system and supply information of damage detection to allow a corrective action to be taken.

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

2021 ◽  
pp. 136943322110384
Author(s):  
Xingyu Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Frequency ◽  
Data Driven ◽  
Mode Shapes ◽  
Monitoring Methods ◽  
Destructive Testing ◽  
Electromechanical Impedance ◽  
Structural Health

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

scholarly journals A Multiobjective Perspective to Optimal Sensor Placement by Using a Decomposition-Based Evolutionary Algorithm in Structural Health Monitoring

2020 ◽  
Vol 10 (21) ◽  
pp. 7710
Author(s):  
Tsung-Yueh Lin ◽  
Jin Tao ◽  
Hsin-Haou Huang
Keyword(s):  
Structural Health Monitoring ◽  
Evolutionary Algorithm ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Frame Structure ◽  
Mode Shapes ◽  
Beam Model ◽  
Optimal Sensor Placement ◽  
Modal Assurance Criterion ◽  
Structural Health

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.

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.

Reliability of reinforced concrete beams in serviceability limit state via microprestress-solidification theory, a structural health monitoring strategy

2022 ◽  
pp. 146442072110690
Author(s):  
Mohsen Ghabdian ◽  
Seyed BB Aval ◽  
Mohammad Noori ◽  
Wael A Altabey
Keyword(s):  
Reinforced Concrete ◽  
High Temperature ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Concrete Beams ◽  
Limit State ◽  
Reinforced Concrete Beams ◽  
Nonlinear Creep ◽  
Structural Health ◽  
Serviceability Limit State

An important and critical area within the broad domain of structural health monitoring, as related to reinforced civil and mechanical structures, is the assessment of creep, shrinkage, and high-temperature effects on reliability and serviceability. Unfortunately, the monitoring and impact of these inherent mechanical characteristics and behaviors, and subsequent impact on serviceability, have rarely been considered in the literature in structural health monitoring. In this paper, the microprestress-solidification creep theory for beams is generalized for the simultaneous effect of linear/nonlinear creep, shrinkage, and high temperature in a reliability framework. This study conducts a systematic time-dependent procedure for the reliability analysis of structures using a powerful nanoscale method. It must be noted that this paper aims to extend the previously developed microprestress-solidification method in a health monitoring reliability-based framework with a close look at a nonlinear creep, parameters affecting creep, and long-time high temperature. A finite element approach is proposed where creep, shrinkage, temperature, and cracking are considered using strain splitting theory. First, the model performance was evaluated by comparing the results with the experimental test available in the literature in the case of creep and shrinkage. Then, the simultaneous effect of creep, shrinkage, and temperature was compared with experimental results obtained by the authors. Reliability analysis was applied to reinforced concrete beams subjected to sustained gravity loading and uniform temperature history in order to calculate exceedance probability in the serviceability limit state. It was found that the exceedance probability of reinforced concrete beams was dependent on the shear span-to-depth ratio. In the serviceability limit state, exceedance probabilities of 0.012 and 0.157 were calculated for the span-to-depth ratios of 1 and 5, respectively. In addition, it was shown that temperature plays an important role in the reliability of reinforced concrete beams. A 4.27-fold increase was observed in the case of moderate to high temperature. Finally, for three different load levels of 40%, 70%, and 80%, the exceedance probabilities were 0.156, 0.328, and 0.527, respectively, suggesting that load level is another key parameter affecting the reliability of reinforced concrete beams. It is thus concluded these fundamental phenomenological studies should be further considered as part of the broad field of structural health monitoring.

scholarly journals Condition assessment of reinforced concrete beams – Comparing digital image analysis with optic fibre Bragg gratings

2018 ◽  
Vol 199 ◽  
pp. 06011
Author(s):  
Elsabe Kearsley ◽  
SW Jacobsz
Keyword(s):  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Construction Material ◽  
Bragg Gratings ◽  
Condition Assessment ◽  
Reinforced Concrete Beams ◽  
Large Strains ◽  
Structural Health ◽  
Concrete Elements

Reinforced concrete is the most widely used construction material and thus effective condition assessment of reinforced concrete elements forms a significant part of structural health monitoring. An effective structural health monitoring system should be able to give the owner prior warning that structural elements are reaching conditions approaching either serviceability or ultimate limit states. The aim of this investigation is to compare strain data recorded during load testing of a reinforced concrete beam using Fibre optic Bragg Gratings (FBG) and a photographic technique to determine circumstances most suitable for the use of each of the techniques. The test results indicate that FBG sensors can be used to detect small strains as well as large strains in uncracked concrete elements, while optical images can be used to accurately map crack development over the surface area of the structure.

scholarly journals The role of dynamic response parameters in damage prediction

2019 ◽  
Vol 233 (13) ◽  
pp. 4620-4636 ◽  
Author(s):  
Behzad Ahmed Zai ◽  
MA Khan ◽  
Kamran A Khan ◽  
Asif Mansoor ◽  
Aqueel Shah ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Dynamic Response ◽  
Health Monitoring ◽  
Structural Damage ◽  
Mode Shapes ◽  
Future Research ◽  
Modal Parameter ◽  
Structural Health ◽  
Structural Applications

This article presents a literature review of published methods for damage identification and prediction in mechanical structures. It discusses ways which can identify and predict structural damage from dynamic response parameters such as natural frequencies, mode shapes, and vibration amplitudes. There are many structural applications in which dynamic loads are coupled with thermal loads. Hence, a review on those methods, which have discussed structural damage under coupled loads, is also presented. Structural health monitoring with other techniques such as elastic wave propagation, wavelet transform, modal parameter, and artificial intelligence are also discussed. The published research is critically analyzed and the role of dynamic response parameters in structural health monitoring is discussed. The conclusion highlights the research gaps and future research direction.

scholarly journals Analysis of Guided Wave Propagation in a Multi-Layered Structure in View of Structural Health Monitoring

2019 ◽  
Vol 9 (21) ◽  
pp. 4600 ◽  
Author(s):  
Yevgeniya Lugovtsova ◽  
Jannis Bulling ◽  
Christian Boller ◽  
Jens Prager
Keyword(s):  
Wave Propagation ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Guided Waves ◽  
Oil And Gas ◽  
Numerical Approach ◽  
Guided Wave ◽  
Mode Shapes ◽  
Engineering Structures ◽  
Structural Health

Guided waves (GW) are of great interest for non-destructive testing (NDT) and structural health monitoring (SHM) of engineering structures such as for oil and gas pipelines, rails, aircraft components, adhesive bonds and possibly much more. Development of a technique based on GWs requires careful understanding obtained through modelling and analysis of wave propagation and mode-damage interaction due to the dispersion and multimodal character of GWs. The Scaled Boundary Finite Element Method (SBFEM) is a suitable numerical approach for this purpose allowing calculation of dispersion curves, mode shapes and GW propagation analysis. In this article, the SBFEM is used to analyse wave propagation in a plate consisting of an isotropic aluminium layer bonded as a hybrid to an anisotropic carbon fibre reinforced plastics layer. This hybrid composite corresponds to one of those considered in a Type III composite pressure vessel used for storing gases, e.g., hydrogen in automotive and aerospace applications. The results show that most of the wave energy can be concentrated in a certain layer depending on the mode used, and by that damage present in this layer can be detected. The results obtained help to understand the wave propagation in multi-layered structures and are important for further development of NDT and SHM for engineering structures consisting of multiple layers.

scholarly journals SHMnet: Condition assessment of bolted connection with beyond human-level performance

2019 ◽  
Vol 19 (4) ◽  
pp. 1188-1201 ◽  
Author(s):  
Tong Zhang ◽  
Suryakanta Biswal ◽  
Ying Wang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Steel Frame ◽  
Mode Shapes ◽  
Repeated Impact ◽  
Traditional Methods ◽  
Bolted Connection ◽  
Structural Health ◽  
Sensor Locations

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet .

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