Post-Earthquake Damage Identification of Buildings with LMSST

Copyright © 2023 by the authors.. The structure is said to be damaged if there is a permanent shift in the post-event natural frequency of a structure as compared with the pre-event frequency. To assess the damage to the structure, a time-frequency approach that can capture the pre-event and post-event frequency of the structure is required. In this study, to determine these frequencies, a local maximum synchrosqueezing transform (LMSST) method is employed. Through the simulation results, we have shown that the traditional methods such as the Wigner distribution, Wigner–Ville distributions, pseudo-Wigner–Ville distributions, smoothed pseudo-Wigner–Ville distribution, and synchrosqueezing transforms are not capable of capturing the pre-event and post-event frequency of the structure. The amplitude of the signal captured by sensors during those events is very small compared with the signal captured during the seismic event. Thus, traditional methods cannot capture the frequency of pre-event and post-event, whereas LMSST employed in this work can easily identify these frequencies. This attribute of LMSST makes it a very attractive method for post-earthquake damage detection. In this study, these claims are qualitatively and quantitatively substantiated by comprehensive numerical analysis.The APC was funded by Manipal Academy of Higher Education

Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements

Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process. The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model. To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge

The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained

Indirect structural health monitoring (iSHM) of transport infrastructure in the digital age

Workshop reportCopyright © Joint Research Centre (European Commission). The existing European motorway infrastructure network is prone to ageing and subject to natural events (e.g. climate change) and hazards (e.g. earthquakes), necessitating immediate actions for its maintenance and safety. Within this context, the structural health monitoring (SHM) framework allows a quantitative assessment of the structural integrity, serviceability and performance, facilitating better-informed decisions for the management of the existing infrastructure. The European Commission Joint Research Centre (JRC) established the exploratory research project MITICA (Monitoring Transport Infrastructures with Connected and Automated vehicles) to investigate the opportunity to use novel methods for infrastructure motoring, aiming at the efficient maintenance of the European aging road infrastructure. This report summarizes the discussion and the outcomes of a workshop held at the JRC in Ispra (Italy) on June 6-7 2022, as part of the MITICA project. Considering the EU priority “A Europe fit for the digital age”, the workshop was dedicated to SHM and its application to civil infrastructure, focusing on innovative indirect structural health monitoring (iSHM) approaches that rely on the vehicle-bridge interaction and the deployment of sensor-equipped vehicles for the monitoring of the existing bridge infrastructure. The report aims to become a reference document in the area of iSHM using passing vehicles, for both scholars and policy makers

Adaptive data analysis for damage detection and system identification in civil infrastructure

Time-varying structural systems are often encountered in civil engineering. As extreme events occur more frequently and severely in recent years, more structures are loaded beyond their elastic conditions and may thus experience damage in the years to come. Even if structures remain elastic, energy dissipation devices installed on structures often reveal hysteretic behaviors under earthquake loads. Therefore, it is imperative to develop and implement novel technologies that enable the identification and damage detection of time-varying systems. In this dissertation, adaptive wavelet transform (AWT) and multiple analytical mode decomposition (M-AMD) are proposed and applied to identify system properties and detect damage in structures. AWT is an optimized time-frequency representation of dynamic responses for the extraction of features. It is defined as an average of overlapped short-time wavelet transforms with time-varying wavelet parameters in order to extract time-dependent frequencies. The effectiveness of AWT is demonstrated by various analytical signals, acoustic emission and impact echo responses. M-AMD is a response decomposition method for the identification of weakly to moderately nonlinear oscillators based on vibration responses. It can be used to accurately separate the low and high frequency components of time-varying stiffness and damping coefficients in dynamic systems. The efficiency and accuracy of the proposed M-AMD are evaluated with three characteristic nonlinear oscillators and a 1/4-scale 3-story building model with frictional damping under seismic excitations. Finally, AWT-based M-AMD is applied to decompose the measured dynamic responses of a 1/20-scale cable-stayed bridge model tested on four shake tables and evaluate the progression of damage under increasing earthquake loads --Abstract, page iii

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity

The Public Service Media and Public Service Internet Manifesto

This book presents the collectively authored Public Service Media and Public Service Internet Manifesto and accompanying materials.The Internet and the media landscape are broken. The dominant commercial Internet platforms endanger democracy. They have created a communications landscape overwhelmed by surveillance, advertising, fake news, hate speech, conspiracy theories, and algorithmic politics. Commercial Internet platforms have harmed citizens, users, everyday life, and society. Democracy and digital democracy require Public Service Media. A democracy-enhancing Internet requires Public Service Media becoming Public Service Internet platforms – an Internet of the public, by the public, and for the public; an Internet that advances instead of threatens democracy and the public sphere. The Public Service Internet is based on Internet platforms operated by a variety of Public Service Media, taking the public service remit into the digital age. The Public Service Internet provides opportunities for public debate, participation, and the advancement of social cohesion. Accompanying the Manifesto are materials that informed its creation: Christian Fuchs’ report of the results of the Public Service Media/Internet Survey, the written version of Graham Murdock’s online talk on public service media today, and a summary of an ecomitee.com discussion of the Manifesto’s foundations

Structural Health Monitoring Damage Detection Systems for Aerospace

This open access book presents established methods of structural health monitoring (SHM) and discusses their technological merit in the current aerospace environment. While the aerospace industry aims for weight reduction to improve fuel efficiency, reduce environmental impact, and to decrease maintenance time and operating costs, aircraft structures are often designed and built heavier than required in order to accommodate unpredictable failure. A way to overcome this approach is the use of SHM systems to detect the presence of defects. This book covers all major contemporary aerospace-relevant SHM methods, from the basics of each method to the various defect types that SHM is required to detect to discussion of signal processing developments alongside considerations of aerospace safety requirements. It will be of interest to professionals in industry and academic researchers alike, as well as engineering students. This article/publication is based upon work from COST Action CA18203 (ODIN - http://odin-cost.com/), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications