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Structural Health Monitoring Damage Classification and System Models
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Structural health monitoring (SHM) is a field of science that focuses its efforts on evaluating and monitoring the integrity of a structure of interest.

structural health monitoring machine learning physics-based model
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Subjects: Engineering, Civil
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Revisions: 2 times (View History)
Update Time: 07 Nov 2022
Table of Contents

    1. Introduction

    Structural health monitoring (SHM) is a field of science that focuses its efforts on evaluating and monitoring the integrity of a structure of interest [1]. Structural health monitoring systems are based on the design of sensing systems and structural models to evaluate machines and structures.
    Although SHM systems are not a new field of research, computational advances in sensing hardware and the computational power of embedded devices drive the generation of reliable data for developing models based on classification and prediction data, including machine-learning algorithms in SHM systems. Moreover, sensors, such as accelerometers, are inexpensive compared to other sensors and can be effectively deployed in a sensing system to implement vibration-based SHM systems [2]. Accelerometers, or when combining them with other sensors, are the dominant sensing approaches for these SHM applications [3]. Because vibration-based systems date back to the late 1970s [4], technological advances represent a field of opportunities to improve existing solutions in the field of damage identification.

    2. Damage Classification in SHM

    SHM covers several application areas and the assets monitored range from small components to huge civil structures and complex machines. Building SHM systems focus on measuring changes in the physical parameters to assess the current state of the structure and, in some cases, predict the building’s response to future seismic excitations. To make these predictions, it is necessary to identify the natural frequencies of the buildings [5]. In the case of buildings, the structure is subjected to the effects of static and dynamic loads, so the complexity of the analysis presents a challenge in giving an accurate model that includes all these known and unknown effects.
    SHM systems are composed of several hardware and software elements. An overview of the main components of SHM systems as defined by Farrar and Worden [6] are:
    • Operational assessment: The aspect related to damage conceptualization and operational conditions.
    • Data acquisition: The sensor system design and data preprocessing.
    • Feature extraction: The selection of sensitive damage features according to the damage identification capabilities of the desired SHM system.
    • Statistical model development: The design and implementation of the physics-based or data-based model.
    Yuan et al. [1] explored the data acquisition aspect (1) of recent proposals for SHM systems, showing several features of accelerometer sensing systems that are attractive for the structural monitoring and evaluation of SHM systems. This research focuses on areas three and four of SHM systems, analyzing proposals of the physics-based models and the data-based models presented in the literature that belong to these areas. Figure 1 shows these areas in SHM systems, and the methods, techniques and algorithms involved.
    Figure 1. Diagram of structural health monitoring systems areas.
    The core of SHM systems is their ability to perform damage identification. Damage is the change in the material’s physical properties due to progressive deterioration or as a result of a single event on a structure. This change can detract from the behavior or integrity of a structure. Damage characterization can be conceived in several ways depending on the objectives of the SHM system, and damage states can be defined in terms of extent, severity, remaining operational time, thresholds, and damage index standards. Rytter [7] also presents a damage identification classification in SHM systems, as shown in Table 1.
    Table 1. Damage characterization levels.
    Damage Characterization Level Description
    I: Detection The SHM system can decide if there is any damage to the structure of interest.
    II: Localization The SHM system can determine the existence and location of damage in the structure of interest.
    III: Assessment The SHM system can estimate the extent of damage in the structure of interest.
    IV: Prediction The SHM system can estimate the remaining lifetime of the structure.

    3. SHM System Models

    3.1. Physics-Based SHM Systems

    In the case of physical asset monitoring, there are two main branches of modeling: physics-based modeling, also known as physical-law modeling, and data-driven modeling. Physics-based modeling aims to describe phenomena by formulating mathematical models that integrate interdisciplinary knowledge to generate models that replicate observed behavior. Models are commonly presented in differential equations whose complexity increases as more factors become involved. Several terms and parameters must be defined to fully describe the system phenomena. Initial and boundary conditions must be identified to obtain physics equation solutions, and the computational cost associated with this operation can be very time-consuming for complex phenomena.
    In SHM systems, these models are implemented to assess the condition of an asset under operating conditions to monitor changes that may indicate the presence of damage and shorten the remaining useful life of the asset. Finite element modeling (FEM) software implements well-known analyses, such as modal analysis, and allows the simulation of different structures and initial and boundary conditions straightforwardly. FEM software includes physical law models integrated into software libraries to perform damage analysis on a virtualized model of structures efficiently.
    The complexity of modeling building structures under seismic excitation is caused by the intervening factors that can modify the behavior of these structures and by the difficulty of correctly defining their physical properties. Several works focus on estimating model parameters and uncertainties to improve the model of the structure. For example, Xu et al. [8] estimated the parameters of the structures based on linear and nonlinear regression analyses. These structures’ linear and nonlinear parameters, such as elastic stiffness and yield displacement, are obtained for a three-story structure. Gomes et al. [9] addressed an inverse identification problem using numerical models and a genetic algorithm.
    Model parameters obtained from experimental and recorded data can increase the model’s accuracy compared to the actual measured results. However, environmental and operating conditions do not remain constant throughout the life of the structure. In addition, physical law models have drawbacks that limit their applications in some SHM systems. The time to solve the equations in a real-time SHM system impacts the response time to ensure safety and the reduction of economic losses in response systems for seismic protocols and evacuation procedures. In order to reduce the time costs of performing calculations, optimization algorithms applied to the problem of SHM are encouraged and proposed in the literature [10].
    Table 2 lists physics-based proposals for SHM systems in buildings under vibration excitation for multi-story structures. In this table, two types of proposal contributions are shown: the identification of system parameters and damage. According to Farrar’s classification, the level of damage identification for the practical proposals is also presented.
    Table 2. N-story building structure health monitoring systems based on physical model techniques.

    3.2. Data-Based SHM Systems

    Machine-learning techniques are a subset of the field of artificial intelligence. Due to their statistical nature, their vision is to address problems of interest in pattern recognition identification and classification tasks. SHM from the ML point of view is a classification problem in which at least two states are compared in SHM systems employing ML techniques: damaged and undamaged states.
    A machine-learning model extracts information in the form of features from a given data set and classifies those data features. These data-driven models require large amounts of information to train the model and avoid the overfitting problem. The generalization problem depends on the amount of available data and significant diversity of this training data to avoid overfitting and ensure a reasonable level of generalization. In an idealized situation, the data set should include the samples of the possible range of excitation that can be applied to the structure. In addition, data quality improvement using signal processing techniques, such as normalization and noise filtering, is desirable for the generation of the data set. ML algorithms are applied in the damage identification process and in analyzing the anomalous data obtained from the sensors [18][19], thereby improving data quality. In addition, signal processing techniques, such as WT and HHT also improve data quality and are applied in SHM systems [20][21].
    ML techniques can be divided into supervised learning (for regression and classification tasks), unsupervised learning (anomaly detection and clustering) and reinforcement learning. The most popular ML techniques implemented in the construction of SHM solutions are support vector machines (SVMs) and convolutional neural networks (CNNs) [22][23]. In the case of neural network techniques, damage-sensitive feature selection plays a crucial role in the performance of the SHM system. SVM optimally classifies features in linear and nonlinear problems. CNN, a subset of neural network (NN) methods, include convolution operations in the hidden layer of neural networks to classify data, usually in image format. Other techniques, such as PCA, improve the features of the training data set by making them uncorrelated.
    The selection of a ML technique is guided by the limitations of each technique and the requirements of SHM in terms of damage identification level and operating conditions. Identifying the modal systems of building structures can also be performed using deep neuronal networks (DNNs) [24][25]. Table 3 summarizes the ML-based proposals for SHM systems in buildings under vibration excitation for multi-story structures.
    Table 3. N-story building structure health monitoring systems based on machine-learning techniques.
    Publication Structure Data
    Type Used for Training
    Machine-Learning
    Technique
    Damage Identification and Level Year
    [26] A four-story physical building prototype under vibration table Acceleration
    response data from a physical prototype
    ANN Damage existence and localization II 2016
    [27] An eight-story physical building mathematical model Artificially generated dataset from an algorithm FCN Damage existence and localization II 2020
    [28] A three-story physical building simulated model Simulation-generated dataset from OpenSeesMD software ANN Damage detection and localization II 2017
    [29] 30 buildings including 3, 5 and 7 stories with different structural parameters Simulation-generated dataset from Raumoko3D software ANN Damage detection and localization II 2017
    [30] An instrumented main steel frame Experimental simulation from a physical prototype with modal shaker excitation CNN Damage detection and localization II 2016
    [31] A three-story physical building-simulated model Simulation-generated dataset from OpenSeesMD software SVM Damage existence, localization and
    severity
    III 2019
    [32] A three-story steel frame structure Intensity-based features SVM Damage detection I 2019
    [33] A seven-story steel structure Simulation-generated dataset ANN Damage existence, localization and
    severity
    III 2018
    [34] A five-story steel structure Simulation-generated dataset ANN Damage detection and localization II 2008

    3.3. Model Type Comparative in Building SHM Systems

    Table 4 analyzes the advantages and disadvantages of applying the physics-based model and the data-based model based on reviewing the proposals mentioned above.
    Table 4. Advantages and disadvantages of a physics-based model and data-based models in SHM building applications.
    Recent proposals implement hybrid approaches that improve SHM damage identification models and integrate physics-based and data-driven modeling solutions. One of the most common strategies is to build artificial datasets used to train ML models from FEM-generated data. Conversely, FEM parameters can be estimated from the output of an ML regression model and improve a structural virtual model.

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    Subjects: Engineering, Civil
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    Update Time: 07 Nov 2022
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      Gomez-Cabrera, A.; Escamilla-Ambrosio, P.J. Structural Health Monitoring Damage Classification and System Models. Encyclopedia. Available online: https://encyclopedia.pub/entry/33105 (accessed on 27 November 2022).
      Gomez-Cabrera A, Escamilla-Ambrosio PJ. Structural Health Monitoring Damage Classification and System Models. Encyclopedia. Available at: https://encyclopedia.pub/entry/33105. Accessed November 27, 2022.
      Gomez-Cabrera, Alain, Ponciano Jorge Escamilla-Ambrosio. "Structural Health Monitoring Damage Classification and System Models," Encyclopedia, https://encyclopedia.pub/entry/33105 (accessed November 27, 2022).
      Gomez-Cabrera, A., & Escamilla-Ambrosio, P.J. (2022, November 04). Structural Health Monitoring Damage Classification and System Models. In Encyclopedia. https://encyclopedia.pub/entry/33105
      Gomez-Cabrera, Alain and Ponciano Jorge Escamilla-Ambrosio. ''Structural Health Monitoring Damage Classification and System Models.'' Encyclopedia. Web. 04 November, 2022.
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