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Structural health monitoring of large civil engineering structures
A critical review of key developments and latest advances in Structural Health Monitoring technologies applied to civil engineering structures, covering all aspects required for practical application Structural Health Monitoring (SHM) provides the facilities for in-service monitoring of structural performance and damage assessment, and is a key element of condition based maintenance and damage prognosis. This comprehensive book brings readers up to date on the most important changes and advancements in the structural health monitoring technologies applied to civil engineering structures.
Book
Structural Health Monitoring of Large Civil Engineering Structures
Structural Health Monitoring (SHM) provides the facilities for in-service monitoring of structural performance and damage assessment, and is a key element of condition based maintenance and damage prognosis. This comprehensive book brings readers up to date on the most important changes and advancements in the structural health monitoring technologies applied to civil engineering structures. It covers all aspects required for such monitoring in the field, including sensors and networks, data acquisition and processing, damage detection techniques and damage prognostics techniques. The book also includes a number of case studies showing how the techniques can be applied in the development of sustainable and resilient civil infrastructure systems. This book offers in-depth chapter coverage of: Sensors and Sensing Technology for Structural Monitoring; Data Acquisition, Transmission, and Management; Structural Damage Identification Techniques; Modal Analysis of Civil Engineering Structures; Finite Element Model Updating; Vibration Based Damage Identification Methods; Model Based Damage Assessment Methods; Monitoring Based Reliability Analysis and Damage Prognosis; and Applications of SHM Strategies to Large Civil Structures.
eBook
The value of seismic structural health monitoring for post-earthquake building evacuation
In the aftermath of a seismic event, decision-makers have to decide quickly among alternative management actions with limited knowledge on the actual health condition of buildings. Each choice entails different direct and indirect consequences. For example, if a building sustains low damage in the mainshock but people are not evacuated, casualties may occur if aftershocks lead the structure to fail. On the other hand, the evacuation of a structurally sound building could lead to unnecessary financial losses due to business and occupancy interruption. A monitoring system can provide information about the condition of the building after an earthquake that can support the choice between several competing alternatives, targeting the minimization of consequences. This paper proposes a framework for quantifying the benefit of installing a permanent seismic structural health monitoring (S2HM) system to support building evacuation operations after a seismic event. Decision-makers can use this procedure to preventively evaluate the benefit of an SHM system and decide about the worthiness of its installation.
Journal Article
Design and evaluation of 5G-based architecture supporting data-driven digital twins updating and matching in seismic monitoring
Digital Twins (DT) models are gaining special attention in the management and maintenance of facilities. The quality of data contained in these models may be enhanced by the use of processed information coming from long-term Structural Health Monitoring (SHM). In this case real time processing and updating in systems using sensor networks for SHM need low latency and reliable communication. This paper presents a solution for exploiting DT models for SHM and early warning solutions improvement. The case study scenario resides within the 5G experimentation in the city of L’Aquila and it exploits a highly adaptable sensor board and a 5G Multi-Access Edge Computing architecture.
Journal Article
Implementation and application of a SHM system for tall buildings in Turkey
Number of tall buildings in metropolitan areas such as Istanbul increased dramatically in recent years. On the other hand, it is crucial to conduct condition assessment of such structures after an earthquake due to public safety and owner-need reasons. Structural health monitoring (SHM) enables condition assessment of structures before, during and after earthquake rapidly, remotely and objectively. SHM systems are mandatory to be installed on tall buildings in Turkey since 2019 because of their significant contribution to our understanding of dynamic behavior of tall buildings and our capability of condition monitoring. With the motivations and reasons mentioned above, three tall buildings in Istanbul have been monitored continuously by the research team led by the second author. In the first part of this paper, results from long-term monitoring of Building-3 are presented and also, identification of modal parameters before, during and after Mw 5.7 earthquake by different methods are discussed. Afterwards, a unique methodology on the development and updating of Timoshenko beam model of the building based on identification results is given. This methodology is able to estimate the accelerations of non-instrumented floors from instrumented floors; therefore, condition assessment of the building during an earthquake can be precisely performed by computing inter-story drift ratios. In addition to tracking modal values and calculating inter-story drifts, determination of wave propagation is explained as an alternative condition assessment tool. Lastly, in-house developed software platform for real-time monitoring of tall buildings is presented.
Journal Article
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.
Journal Article
Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes
Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. Therefore, it is necessary to monitor the structural response to detect the damage in an early stage, evaluate the health condition of structures, and ensure the operation safety of structures. In fact, the structure and the evaluation can be considered as a special symmetry. Among several SHM methods, vibration-based SHM techniques have been widely adopted recently. Hence, this paper reviews the vibration-based SHM methods in terms of the vibrational parameters used. In addition, the technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications. Several related ISO standards and national codes have been developed and implemented, while more specific technical codes are still required to provide more detailed guidelines in practice to maintain structure safety and natural symmetry.
Journal Article