Novel Damage Assessment Framework for Dynamic Systems through Transfer Learning from Audio Domains

Nowadays, damage detection strategies built on the application of Artificial Neural Network tools to define models that mimic the dynamic behavior of structural systems are viral. However, a fundamental issue in developing these strategies for damage assessment is given by the unbalanced nature of the available databases for civil, mechanical, or aerospace applications, which commonly do not contain sufficient information from all the different classes that need to be identified. Unfortunately, when the aim is to classify between the healthy and damaged conditions in a structure or a generic dynamic system, it is extremely rare to have sufficient data for the unhealthy state since the system has already failed. At the same time, it is common to have plenty of data coming from the system under operational conditions. Consequently, the learning task, carried on with deep learning approaches, becomes case-dependent and tends to be specialized for a particular case and a very limited number of damage scenarios. This doctoral research presents a framework for damage classification in dynamic systems intended to overcome the limitations imposed by unbalanced datasets. In this methodology, the model's classification ability is enriched by using lower-level features derived through an improved extraction strategy that learns from a rich audio dataset how to characterize vibration traits starting from human voice recordings. This knowledge is then transferred to a target domain with much less data points, such as a structural system where the same discrimination approach is employed to classify and differentiate different health conditions. The goal is to enrich the model's ability to discriminate between classes on the audio records, presenting multiple different categories with more information to learn. The proposed methodology is validated both numerically and experimentally

Damage Sensitive Features. From Classic Parameters to New Indicators

Structural Health Monitoring (SHM) is the discipline that concerns about the health condition of engineering structures, mechanical systems, aerospace models, at every moment during their utility life. The primary object of SHM is to spot damage, if present, in the observed system and give a consequent diagnosis. Damage can be considered as a variation in the properties of the system that permanently affects the performance of the structure. This variation is meaningless unless contextualized as a comparison between two different states: damaged and healthy. It is precisely this deviation from normal conditions that approaches like vibration-based algorithms are looking for. Vibration-based SHM aims to implement a strategy to correctly detect damage through the assessment of changes in the identified vibration response of civil structures. The structural response is represented, employing a compact representation of its primary traits, called damage sensitive features (DSFs). It can be stated, therefore, that the effectiveness of vibration-based methodology in identifying damage depends on the robustness of the chosen DSFs. They need to be sensitive enough in spotting anomalies in the structural behavior, but at the same time, they need to be insensitive as much as possible towards temporary or seasonally variation of the structural properties that fall into the common behavior of structural systems. In this dissertation, two typologies of DSFs are investigated: the first type, well known in the SHM research community, is derived from the response of the system using user's dependent extraction algorithms, while the other is directly computed from the response of the system using digital signal processes alone. In both approaches, the effects of external conditions, like the seasonal variation of air temperature, are accounted for. Within the first kind of DSFs, an automated procedure is proposed to reduce the interdependency of the algorithm from the user's abilities, leading to a robust identification procedure, more suitable for long-term monitoring purposes. The second health indicator here proposed offers a very low-burden computation cost, with almost non-existing dependency from the user and its extraction process makes these features less sensible to external variation like temperature. The two DSFs and the associated extraction processes are investigated and validated both numerically and experimentally

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges

Recently, vibration-based monitoring technologies have become extremely popular, providing effective tools to assess the health condition and evaluate the structural integrity of civil structures and infrastructures in real-time. In this context, battery-operated wireless sensors allow us to stop using wired sensor networks, providing easy installation processes and low maintenance costs. Nevertheless, wireless transmission of high-rate data such as structural vibration consumes considerable power. Consequently, these wireless networks demand frequent battery replacement, which is problematic for large structures with poor accessibility, such as long-span bridges. This work proposes a low-power multi-hop wireless sensor network suitable for monitoring large-sized civil infrastructures to handle this problem. The proposed network employs low-power wireless devices that act in the sub-GHz band, permitting long-distance data transmission and communication surpassing 1 km. Data collection over vast areas is accomplished via multi-hop communication, in which the sensor data are acquired and re-transmitted by neighboring sensors. The communication and transmission times are synchronized, and time-division communication is executed, which depends on the wireless devices to sleep when the connection is not necessary to consume less power. An experimental field test is performed to evaluate the reliability and accuracy of the designed wireless sensor network to collect and capture the acceleration response of the long-span Manhattan Bridge. Thanks to the high-quality monitoring data collected with the developed low-power wireless sensor network, the natural frequencies and mode shapes were robustly recognized. The monitoring tests also showed the benefits of the presented wireless sensor system concerning the installation and measuring operations

Multifunctional Fiber-Reinforced Polymer Composites for Damage Detection and Memory

Self-structural health monitoring (SHM) functionalities for fiber-reinforced polymer composites have become highly sought after to ensure the structural safety of newly advancing components in the automotive, civil, mechanical, and aerospace industries. This paper introduces a self-damage detection and memory (SDDM) hybrid composite material, where the structural carbon fiber tow is transformed into a piezoresistive sensor network, and the structural glass fiber operates as electrical insulation. In this study, SDDM specimens were fabricated, and tensile and impact tests were performed. The tensile tests of SDDM specimens find two distinct loading peaks: first where the carbon fiber fails, and second where the glass fiber fails. A linear correlation was observed between the carbon fiber resistance and composite strain up to a threshold, beyond which a sharp nonlinear increase in resistance occurred. The resistance then approached infinity, coinciding with the first loading peak and failure of the carbon fiber elements. This demonstrates the potential for a damage early warning threshold. Additionally, the effect of stitching the sensor tow in a zig-zag pattern over a large area was investigated using tailored fiber placement (TFP) of 1-loop, 3-loop, and 5-loop specimens. Tensile testing found that increasing the number of loops improved the sensor’s accuracy for strain sensing. Furthermore, impact tests were conducted, and as the impact energy progressively increased, the sensor resistance permanently increased. This illustrates a capability for self-memory of microdamage throughout the life cycle of the structure, potentially useful for predicting the remaining life of the composite

Vibration-based structural health monitoring of a RC-masonry tower equipped with non-conventional TMD

This article presents the development and the results of three years of implementation of an automated vibration-based structural health monitoring system for modal tracking the dynamic characteristics of a concrete-masonry Civic Tower in Rieti (Italy) equipped with a passive vibration control system, a Non-Conventional Tuned Mass Damper. The system is based on the data recorded by a small number of high-sensitivity accelerometers set on remote automated modal parameters tracking. The analysis of monitoring data highlights the main characteristics of the response of the tower to operational vibrations and low-return period earthquakes. Despite the low levels of vibration in operational conditions, the system can track the evolution of the structural frequencies along time and successfully capture their dependence from temperature, both daily and seasonally. Moreover, the robustness of the modal identification procedure allowed the detection of anomalous variation from the validated reference dynamics of the structure.This article presents the development and the results of three years of implementation of an automated vibration-based structural health monitoring system for modal tracking the dynamic characteristics of a concrete-masonry Civic Tower in Rieti (Italy) equipped with a passive vibration control system, a Non-Conventional Tuned Mass Damper. The system is based on the data recorded by a small number of high-sensitivity accelerometers set on remote automated modal parameters tracking. The analysis of monitoring data highlights the main characteristics of the response of the tower to operational vibrations and low-return period earthquakes. Despite the low levels of vibration in operational conditions, the system can track the evolution of the structural frequencies along time and successfully capture their dependence from temperature, both daily and seasonally. Moreover, the robustness of the modal identification procedure allowed the detection of anomalous variation from the validated reference dynamics of the structure

Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms

In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure’s modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field. This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm’s parameters need to be constantly updated by the user

On the use of multivariate autoregressive models for vibration-based damage detection and localization

This paper proposes a novel method suitable for vibration-based damage identification of civil structures and infrastructures under ambient excitation. The damage-sensitive feature employed in the presented algorithm consists of a vector of multivariate autoregressive parameters estimated from the vibration responses collected at different locations of the analyzed structure. Outlier analysis and statistical pattern recognition are exploited for damage detection and localization. In particular, the Mahalanobis distance between a set of reference (i.e., \u201chealthy\u201d) and inspection parameters is evaluated. A threshold is then selected to determine whether the inspection vectors refer to damaged or undamaged conditions. The effectiveness of the proposed approach is proved using numerical simulations and experimental data from a benchmark test. The analysis results show that the largest values of Mahalanobis distance can be found in the proximity of those sensors closest to the damaged elements. Thus, the Mahalanobis distance applied to vectors of multivariate autoregressive parameters has proven to be a robust indicator for damage detection and localization