Structural Health Monitoring Architecture Research Articles

Smart innovations in structural health monitoring through wireless sensing technologies

The article examines the current developments in structural health monitoring (SHM), emphasizing innovative inventions made possible by wireless sensing techniques. Wireless Sensing Technologies (WST) make use of detectors that do not require wiring to collect and communicate data wirelessly, allowing for automation, real-time information, and remote monitoring. SHM combined with WST enables continuous data collecting, cost-effective servicing, and quick detection of possible structural concerns. IoT is becoming an essential technique for monitoring systems as a consequence of the quick development of technologies for sensing like radio-frequency identification as well as sensors and the merging of computer technologies like internet and wireless communication. To provide smart and trustworthy monitoring, the present work discusses and presents an architecture for SHM utilizing IoT technologies. Particularly, technologies related to the creation of SHM systems and IoT environments are given, along with data routing strategies. Big data systems are being created to assist with the complicated and vast amount of data acquired from devices mounted on structures because sensors are generating data more quickly and voluminously than ever.

Vibration data‐driven machine learning architecture for structural health monitoring of steel frame structures

AbstractA vibration data‐based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag‐of‐Features algorithm that extracts the speeded‐up robust features (SURF) from the time‐frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K‐means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k‐nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.

Facilitating the monitoring and management of structural health in civil infrastructures with an Edge/Fog/Cloud architecture

Structural Health Monitoring (SHM) is nowadays a requirement for civil infrastructures like tunnels, bridges and viaducts. With the advances provided by the Internet of Things (IoT) in recent years in areas such as wireless communications, miniaturisation and application protocols, this paradigm is spreading to the wider society to ensure appropriate protection of critical infrastructures. Both fog and edge computing have also strongly contributed to SHM and the IoT by bringing computing as close as possible to where data is produced, thereby reducing the latency response with respect to traditional cloud integrations. In this paper, an Edge/Fog/Cloud architecture for SHM in civil infrastructures is presented. The main goal of this architecture is to create and provide a flexible framework involving all the required components for the management, monitoring and deployment of SHM solutions, enabling high availability and easy distribution of the components over the architecture. The architecture has been evaluated as an alternative to the real deployment in a tunnel of a cloud architecture showing the benefits of adopting an Edge/Fog/Cloud hierarchy.

Structural Health Monitoring and Prognostic of Industrial Plants and Civil Structures: A Sensor to Cloud Architecture

The deployment of Structural Health Monitoring (SHM) systems is a natively interdisciplinary task that involves joint research contributions from sensing technologies, data science and civil engineering. The capability to assess, also from remote stations, the working conditions of industrial plants or the structural integrity of civil buildings is widely requested in many application fields. The technological development aims to continuously provide innovative tools and approaches to satisfy these demands. As a first instance, reliable monitoring strategies are needed to detect structural damages while filtering out environmental noise. Ongoing solutions to tackle these topics are based on the exploitation of highly customized sensing technologies, such as shaped transducers for Acoustic Emission (AE) testing or Micro-Electro-Mechanical System (MEMS) accelerometers for Operational Modal Analysis (OMA) [1]. On the other hand, effective data acquisition and storage techniques must be employed to cope with the heterogeneity of the sensing devices and with the amount of data produced by collecting raw measured signals. Finally, damage detection and prediction tasks should be computed via data-driven algorithms that can complement the model-based alternatives traditionally used in civil engineering. Layered SHM architectures [2] represent straightforward approaches to address the system complexity originated by this interdisciplinary design; however, few real-world implementations have been presented so far in the literature. In this paper, we overcome these limitations by presenting an Internet of Things (IoT)-based SHM architecture for the predictive maintenance of industrial sites and civil engineering structures and infrastructures. The proposed cyber-physical system includes a monitoring layer, that consists of accelerometer-based sensor networks, a data acquisition layer, built on the recent W3C Web of Things standard [3], and a data storage and analytics layer, which leverages distributed database and Machine Learning tools. We extensively discuss the hardware/software components of the proposed SHM architecture, by stressing its advantages in terms of device versatility, data scalability and interoperability support. Finally, the effectiveness of the system is validated on a real-world use-case, i.e., the monitoring of a metallic frame structure located at the SHM research labs of the University of Bologna, Italy, within the MAC4PRO project [4].

Dynamic probability modeling-based aircraft structural health monitoring framework under time-varying conditions: Validation in an in-flight test simulated on ground

This paper proposes a dynamic probability modeling-based aircraft Structural Health Monitoring (SHM) framework which provides a modular hierarchical SHM architecture for the development of applicable aircraft SHM techniques. The problem of reliable damage monitoring under time-varying conditions, which is the main application obstacle, is fully considered in the framework by the double probability models combined with the short term and long term dynamic update of the models. To realize the SHM capability of the framework, an adaptive constructing method of Gaussian mixture model is proposed for stable and efficient probability modeling and the probability similarity between dynamically updated models is measured for normalized damage detection. The framework is realized by combining with the guided wave SHM technique and validated in a full-scale aircraft fatigue test which is an in-flight test simulated on ground. The cracks of the right landing gear spar and the left wing panel on the aircraft structure are monitored reliably under the fatigue load conditions.

«Лахта Центр»: автоматизированный мониторинг деформаций несущих конструкций и основания

Многофункциональный комплекс МФК «Лахта Центр» - масштабный проект общественно-деловой застройки Приморского района Санкт-Петербурга. В состав комплекса вошли три уникальных сооружения: сверхвысокая Башня, большепролётные Многофункциональное здание (МФЗ) и входная Арка[1], объединённые общей стилобатной частью. При возведении объектов комплекса было задействовано множество инновационных строительных технологий, применены нестандартные проектные решения.
 В соответствии с действующими нормативными документами для обеспечения безопасности возведения и эксплуатации уникальных зданий в рамках проведения научно-технического сопровождения (НТС) строительства был организован комплекс мероприятий по постоянному мониторингу напряжённо-деформированного состояния (НДС) конструкций МФК.
 Основная цель проведения мониторинга - минимизировать вероятность возникновения аварийных ситуаций, вызванных неопределённостями в работе несущих конструкций здания и грунтов основания.
 В статье приводится общая информация о проектных и организационных решениях по внедрению системы автоматизированного мониторинга НДС конструкций Башни МФК «Лахта Центр». Даны подробные сведения об архитектуре и топологии системы, описаны применяемое оборудование, методика проведения измерений, программное обеспечение и регистрируемые с его помощью параметры напряжённодеформированного состояния сооружений.
 Обобщён опыт проектирования, монтажа, наладки и эксплуатации системы мониторинга деформаций несущих конструкций Башни во время её возведения. Особое внимание уделено результатам сопоставления расчётов ряда ответственных конструкций с данными инструментального мониторинга. Отмечены проблемы, неизбежно возникающие при интерпретации результатов мониторинга, предложены пути их решения.
 Особо подчёркнута ценность данных, предоставляемых системами автоматизированного мониторинга, для повышения качества научно-технического сопровождения строительства и калибровки компьютерных моделей основания, фундамента и несущих конструкций зданий.
 
 [1] В статье приняты проектные написания названия МФК и составляющих его объектов.

An Efficient Approach To Civil Structures Health Monitoring Using Fog Computing As Clusters Through 5G Network Environment

Structural Health Monitoring (SHM) is a process to trace real time information about the physical health of a civil structures at any given point of time. SHM systems can also approximate the life expectancy of the structure. This constant monitoring can help during emergency situations such as earthquakes. The increasing need for SHM is the result of deterioration of several set of structures over time. A proper maintenance plan is beneficial to not just keep the structure in a favorable state throughout its lifecycle, but to additionally minimize the relative costs required by the structures through resource optimization. Various forms of SHM have been implemented over the past three decades like traditional wired systems. Recent development has resulted in Wireless Sensor Networks (WSN) being designed for the same purpose. Few publications have also included IoT framework along with WSNs. The introduction of WSN has led to easy deployment of SHM compared to the traditional wired network. But WSN's have led to significant challenges. The main challenge that has been focused in this paper is the constraint of power consumption. The power consumption is most critical factor for the sensors deployed in high-rise structures, high density traffic bridges etc., since it is difficult to reach the places to replace the battery drained sensors and also involved high installation cost. This paper proposes a concept of a framework for SHM with FOG computing which hasn't been explored in the field of SHM yet. In this model, the WSN is comprised of a piconet which transmits data through Bluetooth Low energy (LE) and the FOG transmits data to cloud by exploiting 5G network features in order to reduce the power consumption furthermore. Thus, saving power at every level possible in the SHM architecture. It also introduced a head sensor algorithm within clusters which maximizes the lifetime of the WSN. A mathematical model has been developed in support to evaluate how the power of the system can be minimized for the FOG node and cloud servers in accordance with the 5G network environment Experiments have been carried out to validate proposed framework and demonstrate the results.

Evaluation of Multi-agent Architecture for Structural Damage Detection and Location

In this paper the results of using a Multi-agent system (MAS) for Structural Health Monitoring (SHM) are detailed. A study between different MAS architectures reported in literature is presented in order to select and adapt the most adequate one for SHM tasks. Requirements are established according to recent solutions, where main parameters are type and number of sensors and communication protocols, among others. MAS technique uses several intelligent agents, that are algorithms able to act in a reactive or active way. Their action depends on surrounding environment or collected data. These agents can work in a decentralized way, searching the fulfillment of an individual goal or they can work with another system to achieve a common goal. Decision is based on their internal state (beliefs, goals and commitments). MAS’ effectiveness depends on the interconnection between the agents. Type of agents is defined according to its communication method and protocol, common and individual goals, among others. Decentralization and versatility are two important characteristics of MAS technique useful to solve SHM problem. This is one of the main motivations to consider this technique to be a good approach for the studied problem. A benchmark numerical model, which consists of a metallic framework, was used to validate and demonstrate the feasibility of the selected architecture for SHM

Implementation of a WSN-Based Structural Health Monitoring Architecture Using 3D and AR Mode

Recently wireless sensor networks (WSN) has risen as one of the advanced candidate technologies in order to provide more efficient structure health monitoring (SHM) solution in construction sites. In this paper, we proposed WSN monitoring framework in building sites based on 3D visualization and Augmented Reality (AR) in mobile devices. The proposed system applies 3D visualization and AR technology to camera-enabled mobile devices in WSN environment in order to gather much more information than before. Based on the proposed system, we made an experiment to validate the effectiveness of 3D and AR mode using collected data in IEEE 802.15.4-based WSN.

An Integrated Sensing/Communication Architecture for Structural Health Monitoring

In this paper, a novel sensor network architecture for structural health monitoring is presented. The system is based on the adoption of purposely developed contactless sensors that make use of near-field coupling to both sense the structure displacements and deploy a local communication network. The key features of the adopted technology are the low-realization costs and the lower power consumption that nodes feature as compared to traditional wireless communication. In addition, the use of a fully decentralized positioning algorithm, running on each node, is demonstrated as a viable mean for the local mapping of extended structures, thus crediting the architecture for the evaluation of structural in-plane deformations.