Structural health monitoring using optimising algorithms based on flexibility matrix approach and combination of natural frequencies and mode shapes

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.

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Seismic and Structural Health Monitoring Systems For Large Dams: Theoretical, Computational and Practical Innovations

10.21203/rs.3.rs-597616/v1 ◽

2021 ◽

Author(s):

Sérgio Oliveira ◽

André Alegre ◽

Ezequiel Carvalho ◽

Paulo Mendes ◽

Jorge Proença

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Natural Frequencies ◽

Dynamic Monitoring ◽

Mode Shapes ◽

Seismic Events ◽

Arch Dams ◽

Structural Health ◽

Continuous Dynamic Monitoring ◽

Continuous Dynamic

Abstract Over the past decade, monitoring systems for Seismic and Structural Health Monitoring (SSHM) have been assuming a greater role in the safety control of large concrete dams. In this article, the dynamic behavior of two large arch dams equipped with SSHM systems is analyzed, in order to present some of the main theoretical, computational and practical innovations developed recently for the improvement of large dams’ continuous dynamic monitoring using SSHM systems. The case studies are two large arch dams that have been under continuous dynamic monitoring over the last ten years: Cabril dam (132 m high), the highest dam in Portugal, and Cahora Bassa dam (170 m high), located in Mozambique, one of the highest dams in Africa. The Seismic and Structural Health Monitoring (SSHM) systems installed in both dams have similar schemes and were designed to continuously record acceleration time series in several locations at the upper part of the dam body and near the dam-foundation interface, using uniaxial and triaxial accelerometers. Specific software was developed for monitoring data analysis, including automatic modal identification, to obtain natural frequencies and mode shapes, for automatic detection of vibrations induced by seismic events, to be distinguished by those caused by other operational sources, and for comparison between results retrieved from measured vibrations and numerical results obtained from computational 3DFE models. The numerical analyses are carried out using a 3DFE program for linear and non-linear dynamic analysis of concrete dams, based on a solid-fluid coupled formulation to simulate the dam-reservoir-foundation system, considering the dam-water dynamic interaction and the propagation of pressure waves throughout the reservoir. The most significant experimental results from continuous dynamic monitoring are presented for Cabril dam and Cahora Bassa dam and compared with numerical results, with emphasis on the evolution of natural frequencies over time, on vibration mode shapes for various water levels, and, finally, on the measured accelerations during seismic events. Furthermore, the main results of non-linear seismic response simulations, considering joint movements and concrete damage, are also presented for both dams in order to assess their seismic performance, using an intensifying seismic accelerogram prepared for Endurance Time Analysis.

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

Advances in Structural Engineering ◽

10.1177/13694332211038444 ◽

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.

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

Applied Sciences ◽

10.3390/app10217710 ◽

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.

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The role of dynamic response parameters in damage prediction

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406219841083 ◽

2019 ◽

Vol 233 (13) ◽

pp. 4620-4636 ◽

Cited By ~ 5

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.

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

Applied Sciences ◽

10.3390/app9214600 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4600 ◽

Cited By ~ 4

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.

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

Structural Health Monitoring ◽

10.1177/1475921719881237 ◽

2019 ◽

Vol 19 (4) ◽

pp. 1188-1201 ◽

Cited By ~ 4

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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Sensor Optimal Placement for Structural Health Monitoring Based on Stabilization Diagram

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.540.47 ◽

2013 ◽

Vol 540 ◽

pp. 47-54 ◽

Cited By ~ 1

Author(s):

Chun Li Wu ◽

Han Bing Liu ◽

Yan Li

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Sensor Placement ◽

Energy Criterion ◽

Optimal Placement ◽

Mode Shapes ◽

Diagram Method ◽

Structural Health ◽

Fitness Functions ◽

Stabilization Diagram

A novel stabilization diagram method was presented for sensor placement in structural health monitoring of bridges. The aim of the method is to select the optimal locations which can achieve the best identification of modal frequencies and mode shapes. A single parents genetic algorithm was adopted to optimize the sensor locations from a set of coordinate positions. Five fitness functions taken as the objective function are proposed based on effective independence, modal assurance and modal energy criterion, in which the combined fitness functions can obtain more comprehensive properties to reduce the noise interference. The proposed method puts forward a universal way for sensor placement of the civil engineering structure. The effectiveness of the method was proved by a simply supported beam and a continuous beam bridge in the An Longquan interchange overpass.

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Structural Interpretation of Data from Static and Dynamic Structural Health Monitoring of Monumental Buildings

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.747.431 ◽

2017 ◽

Vol 747 ◽

pp. 431-439 ◽

Cited By ~ 5

Author(s):

Simonetta Baraccani ◽

Michele Palermo ◽

Riccardo M. Azzara ◽

Giada Gasparini ◽

Stefano Silvestri ◽

...

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Dynamic Properties ◽

Data Interpretation ◽

Mode Shapes ◽

Evolutionary Trends ◽

Historical Buildings ◽

Structural Behaviour ◽

Structural Health ◽

Daily Variations

Structural Health Monitoring (SHM) has a crucial role in the diagnosis and conservation of historical buildings, which are typically characterized by articulated fabrics, constructed over decades using different materials and construction techniques. All these issues lead to very complex structural behaviour whose reliable assessment cannot disregard from a sound interpretation of data from SHM systems. SHM systems can be classified into (i) static systems, monitoring the long term time evolutions of specific quantities (such as amplitude of cracks, inclination of walls, relative distances, etc.) and (ii) dynamic systems, continuously monitoring the dynamic response (velocities, accelerations) in order to gather information upon overall dynamic properties such as natural frequencies, mode shapes and damping ratios. The recorded raw data need to be processed in order to distinguish eventual evolutionary trends from the seasonal and daily variations related to thermal effects. In the present work, a simple unified approach for data interpretation acquired from both static and dynamic SHM systems installed in historical buildings is presented. The approach is aimed at: (i) introducing reference quantities for interpretation of seasonal and daily variations, (ii) providing order of magnitudes of reference quantities and (iii) identifying eventual evolutionary trends which could be related to the presence of potential structural criticalities. The approach is illustrated referring to the “Two Towers” of Bologna.

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Multi-rate data fusion for dynamic displacement measurement of beam-like supertall structures using acceleration and strain sensors

Structural Health Monitoring ◽

10.1177/1475921719857043 ◽

2019 ◽

Vol 19 (2) ◽

pp. 520-536 ◽

Cited By ~ 2

Author(s):

Hongping Zhu ◽

Ke Gao ◽

Yong Xia ◽

Fei Gao ◽

Shun Weng ◽

...

Keyword(s):

Structural Health Monitoring ◽

Data Fusion ◽

Health Monitoring ◽

Optimization Technique ◽

Strain Sensors ◽

Mode Shapes ◽

Dynamic Displacement ◽

Structural Health ◽

Set Up ◽

Longitudinal Strains

Accurate measurement of dynamic displacement is important for the structural health monitoring and safety assessment of supertall structures. However, the displacement of a supertall structure is difficult to be accurately measured using the conventional methods because they are either inaccurate or inconvenient to be set up in practice. This study provides an accurate and economical method to measure dynamic displacement of supertall structures accurately by fusing acceleration and strain data, which are generally available in the structural health monitoring system. Dynamic displacement is first derived from the measured longitudinal strains based on geometric deformation without requiring mode shapes. An optimization technique is utilized to optimize the deployment of strain sensors for achieving more accurate strain-derived displacement. The strain-derived displacement is then combined with measured acceleration via a multi-rate Kalman filtering approach. Applications to a numerical supertall structure and a laboratory cantilever beam verify that the proposed method accurately estimates displacement including both high-frequency and pseudo-static components, under different noise cases and sampling rates. A full-scale field test on the 600 m-high Canton Tower is implemented to validate the applicability of the proposed method to real supertall structures. Error analysis demonstrates that the data fusion displacement is more accurate than the global position system-measured displacement in the time and frequency domains.

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