Table of Contents
Overview
Definition and Purpose
Structural health monitoring (SHM) is defined as the process of implementing a damage identification strategy for aerospace, civil, and mechanical engineering infrastructure. This process involves the observation of a structure or mechanical system over time using periodically spaced measurements, which are essential for extracting appropriate damage-sensitive features from these measurements.[1.1] SHM encompasses the observation and analysis of a system over time, utilizing periodically sampled response measurements to monitor changes in the material and geometric properties of engineering structures, such as bridges and buildings. To effectively monitor the state of a system, it is crucial to identify features in the acquired data that can distinguish between undamaged and damaged structures.[2.1] The field of SHM has garnered significant attention from both academia and industry, particularly in the area of damage detection. This approach facilitates continuous monitoring of the structural integrity of systems and structures throughout their operational lifespan, which can lead to a reduced reliance on periodic inspections and lower maintenance costs.[3.1]Importance in Civil Engineering
Structural health monitoring (SHM) is increasingly recognized as a critical component in civil engineering, primarily due to its ability to enhance the reliability and safety of infrastructure. By continuously evaluating and monitoring the health of critical structures, SHM systems can respond to detrimental structural changes, thereby improving life cycle management and overall structural reliability.[4.1] This capability is particularly vital in the context of aging infrastructure, where traditional maintenance approaches may not adequately address the actual condition of structures. The integration of smart materials into SHM systems is crucial due to their sensitivity to environmental variables such as temperature fluctuations, humidity levels, and exposure to corrosive elements, which can significantly affect their performance in real-world applications.[5.1] Understanding the physico-mechanical and chemical properties of these materials is essential for developing effective monitoring techniques.[6.1] Furthermore, the role of specialized sensors, including crack propagation (CP) and fatigue damage (FD) sensors, is vital for reliable SHM, as they enable the detection of crack initiation and propagation, which is a prerequisite for effective monitoring.[7.1] Recent advancements in materials science focus on creating computational frameworks that enhance fatigue damage estimation in structural systems, thereby improving the overall reliability of SHM methods.[8.1] SHM is also recognized as a viable solution for reducing operational costs in civil engineering. By enabling a maintenance strategy based on the actual condition of structures rather than relying solely on scheduled inspections, SHM systems facilitate a significant reduction in inspection times and contribute to improved service life cost of structural components.[15.1] This novel maintenance approach mitigates the financial burden associated with routine maintenance practices, as it allows for timely interventions based on real-time data, thereby addressing the impact of maintenance costs reduction from a direct operating cost perspective.[14.1] Furthermore, SHM represents an evolution of traditional methods for designing and maintaining mechanical structures, emphasizing the necessity for continuous monitoring to ensure structural integrity and optimize both safety and cost efficiency.[15.1]In this section:
History
Early Developments
The early developments of Structural Health Monitoring (SHM) are organized into three proposed ages, with the first age delineated by the prehistory of SHM and the period that laid the groundwork for the discipline as it is known today.[40.1] This initial phase involved the recognition of the need to monitor changes in engineering structures, which has been increasingly addressed through the lens of statistical pattern recognition (SPR).[39.1] Recent research highlights that viewing SHM challenges as problems of statistical pattern recognition provides a productive approach to the discipline.[39.1] Structural health monitoring (SHM) is a critical field that involves the observation and analysis of engineering structures over time, utilizing periodically sampled response measurements to monitor changes in material and geometric properties. This approach is essential for distinguishing between undamaged and damaged structures, as it allows for the identification of features in the acquired data that indicate structural integrity.[42.1] The state of Oregon's Department of Transportation Bridge Engineering Department has made significant contributions to this field by developing and implementing a comprehensive SHM program. This program is noted for its innovative use of technologies, including fiber optic sensors, which enhance the monitoring capabilities of bridges.[42.1] The history of Structural Health Monitoring (SHM) encompasses significant developments, with particular attention given to stagnation points where progress faced various barriers. These barriers have been addressed over time, leading to advancements in methodologies and the capabilities of monitoring systems.[41.1] However, despite these efforts, there remain unresolved challenges in health monitoring, indicating that further evolution is necessary to effectively tackle these ongoing issues in the field.[43.1]Milestones in SHM Technology
The evolution of Structural Health Monitoring (SHM) technology has been significantly influenced by various milestones that highlight advancements in the field. The term "Structural Health Monitoring" first appeared in a paper title in 1990, marking a pivotal moment in the formal recognition of the discipline and its relevance to infrastructure safety.[45.1] Additionally, it is important to note that modern non-destructive evaluation (NDE) techniques began to emerge in the late 1800s, laying the groundwork for the development of more advanced monitoring methods.[45.1] In the early days, structural assessments relied heavily on manual inspections and basic tools, which limited the accuracy and efficiency of evaluations.[50.1] As technology progressed, the introduction of electronic sensors revolutionized the monitoring of structural deflections, allowing for more precise and continuous data collection.[48.1] This shift was further enhanced by advancements in sensor technology, which led to the development of more sensitive, durable, and multifunctional sensors.[47.1] The period from 1993 to 2015 saw a notable increase in the implementation of SHM platforms, driven by improvements in data processing systems and numerical models that focused on structural safety assessments.[46.1] The integration of artificial intelligence (AI) and machine learning (ML) into SHM has also been transformative, enhancing capabilities in data acquisition, anomaly detection, and predictive maintenance.[51.1] These technologies have enabled real-time risk analysis and improved the reliability of structural evaluations, marking a significant leap forward in the field.[51.1] Recent advancements have ushered in the era of big data in SHM, where data-driven methods based on statistical pattern recognition have become increasingly popular.[56.1] The combination of high-speed internet and cloud-based computation has facilitated the use of deep learning techniques, further enhancing the accuracy and reliability of SHM systems compared to traditional methods.[57.1] Overall, the milestones in SHM technology illustrate a continuous trajectory of innovation aimed at ensuring the safety and longevity of critical infrastructure.In this section:
Recent Advancements
Integration of IoT and Smart Technologies
The integration of Internet of Things (IoT) technologies into Structural Health Monitoring (SHM) has significantly advanced the field by enabling real-time data collection and analysis. IoT sensors facilitate continuous monitoring of structures, which is crucial for maintaining their functional utility, optimal performance, and security. This continuous supervision allows for more focused maintenance scheduling and complements existing diagnostic methods.[101.1] Recent developments have introduced cost-effective, "do-it-yourself" wireless sensor nodes equipped with IoT functionality, which enhance the capabilities of SHM systems. These innovations allow for real-time damage prediction and localization, making them particularly beneficial for monitoring civil infrastructures, including historical buildings and bridges.[98.1] The application of IoT technologies in SHM is gaining traction due to their ease of installation and ability to provide immediate insights into the structural health of engineering assets.[98.1] Moreover, the integration of smart sensor technologies within SHM systems has emerged as a contemporary trend, enhancing safety and efficiency in infrastructure management. These smart sensors are designed to collect and transmit data effectively, thereby improving the overall monitoring process.[99.1] The combination of IoT and smart technologies not only optimizes resource use but also supports sustainable development goals by reducing waste and ensuring the longevity of infrastructure assets.[94.1]Machine Learning and Data Analysis Techniques
The integration of machine learning (ML) techniques into structural health monitoring (SHM) has emerged as a transformative approach, significantly enhancing predictive maintenance and real-time data analysis capabilities. The amalgamation of artificial intelligence (AI) and ML within SHM is poised to redefine infrastructure management practices, marking a revolutionary shift in the field.[88.1] Since the introduction of the machine learning paradigm for SHM, the use of ML methods to analyze monitoring data, identify structural health status, and evaluate damage has become increasingly prominent.[92.1] Furthermore, the comparative application of various ML models, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and support vector machines (SVMs), is essential, as it considers factors like input data, feature selection techniques, and the specific structural configurations involved.[90.1] This comparative analysis not only aids in selecting appropriate ML techniques but also serves as a foundation for future SHM solutions that aim to achieve optimal performance in structural damage identification.[90.1] Recent studies emphasize the significance of comparing various machine learning (ML) techniques, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and support vector machines (SVMs), in the context of structural health monitoring (SHM) systems. This comparison takes into account factors like input data, feature selection techniques, structural configuration, data size, the level of damage identification, and the accuracy of the ML model.[90.1] Such analyses can serve as a foundational step for selecting appropriate ML and signal processing techniques for future SHM solutions, particularly when structural configurations or data features align with those from previous studies that demonstrated effective damage or system identification performance.[90.1] Additionally, the shift towards near real-time and online damage assessment in SHM systems represents a promising advancement that aims to address past inefficiencies while leveraging emerging technologies.[91.1] The integration of artificial intelligence (AI) and machine learning (ML) in structural health monitoring (SHM) is emerging as a revolutionary approach that is poised to redefine infrastructure management practices. This transformative role of AI and ML is particularly significant in the realms of predictive maintenance and real-time data analysis, as highlighted in recent studies.[88.1] A systematic review emphasizes the advances, challenges, and future directions of AI in SHM, underscoring the importance of ongoing research to address these challenges and leverage the opportunities presented by data-driven methodologies.[89.1] Furthermore, the shift towards near real-time and online damage assessment in SHM systems represents a promising transition aimed at bridging the gaps between past inefficiencies and future technological advancements.[89.1] This evolution is essential for improving damage detection processes and fostering the development of more proactive maintenance strategies within infrastructure systems.[89.1]In this section:
Applications
Types of Structures Monitored
Structural Health Monitoring (SHM) is a critical aspect of structural analysis in engineering, focusing on the continuous assessment of the integrity and performance of various structures. This methodology has gained significant importance in civil engineering due to the necessity of ensuring safety, reliability, and longevity, particularly for critical structures such as buildings and bridges.[126.1] The SHM system serves as a method for evaluating and monitoring the health of these structures, responding effectively to detrimental structural changes and thereby enhancing structural reliability and improving life cycle management.[4.1] In addition to buildings and bridges, SHM is increasingly employed in tunnels, power plants, and dams. These structures face unique environmental and operational stresses that necessitate specialized monitoring techniques. For instance, the use of fiber optic sensors has become prevalent due to their high sensitivity and ability to detect minute changes in structural parameters, which is crucial for proactive maintenance.[155.1] Structural Health Monitoring (SHM) has emerged as a vital technology in the aerospace industry, addressing the unique challenges posed by operational and environmental loads that can lead to structural deformation and fractures in aircraft structures.[134.1] This discipline emphasizes the continuous monitoring, diagnosis, and prediction of the health of aircraft structural systems, which is crucial for ensuring safety and reliability.[135.1] By capturing and analyzing data from a wide array of sensors and monitoring systems, SHM systems facilitate real-time evaluation of the aircraft's structural integrity, enabling timely interventions when necessary.[135.1]Case Studies and Real-World Examples
Structural Health Monitoring (SHM) is increasingly applied in the aerospace sector, where the design and implementation of these systems are heavily influenced by specific environmental conditions and operational stresses unique to this industry. Factors such as material composition, environmental conditions, and the type and direction of stress applied can significantly impact how composite materials fail, making it intricate to anticipate the exact type of failure.[136.1] Consequently, the development of SHM systems in aerospace necessitates the use of specialized sensors that can effectively address these complexities, ensuring the safety and reliability of aerospace structures.[136.1] Structural Health Monitoring (SHM) provides an in-depth overview of its principles, technologies, applications, and benefits, highlighting its significance in engineering.[130.1] This paper emphasizes the integration of SHM systems, which enhances the monitoring capabilities of various structures.[130.1]Methodologies
Sensor Technologies
Recent advancements in sensor technologies have significantly transformed the methodologies employed in Structural Health Monitoring (SHM). One of the most notable developments is the rise of wireless sensor networks (WSNs), which have become integral to SHM systems. These networks facilitate efficient data acquisition by replacing traditional wired connections with wireless links, thereby drastically reducing installation costs and enhancing the flexibility of sensor deployment.[202.1] The importance of WSNs continues to grow, driven by increasing demands for safety and security in urban environments, which has led to the integration of advanced wireless technologies into structural monitoring systems.[203.1] In addition to WSNs, the adoption of data-driven approaches has gained momentum, particularly with the advent of advanced sensors and Internet of Things (IoT) technologies. These innovations enable the collection of vast amounts of data, which can be processed and analyzed to assess the health of structures in real-time.[185.1] Data-driven SHM techniques, particularly those based on deep learning (DL), offer several advantages over conventional methods. Unlike traditional approaches that require expert knowledge for feature design and involve cumbersome training procedures, DL-based SHM allows for end-to-end training with automatic feature extraction, making it more efficient for large-scale structures.[180.1] Moreover, the integration of various sensing technologies—including electrical, magnetic, photonic, acoustic, and thermal sensors—has enhanced the capability of SHM systems to monitor multiple physical variables simultaneously. This multi-faceted approach enables structures to possess self-sensing and self-diagnostic abilities, thereby improving the accuracy and reliability of damage detection.[207.1] The development of intelligent data-driven strategies and innovative diagnostic methodologies further supports the evolution of SHM, allowing for more sophisticated predictive modeling and real-time monitoring.[205.1]Data Acquisition and Analysis
Data acquisition and analysis are critical components of structural health monitoring (SHM), enabling the effective observation and assessment of the condition of engineering structures over time. The process of SHM involves the systematic collection of data through various sensing technologies, which are essential for identifying changes in the material and geometric properties of structures such as bridges and buildings.[176.1] Recent advancements in sensing and data acquisition systems have led to the increased popularity of data-driven methods among civil engineers and researchers, primarily due to their simplicity, robustness, and computational efficiency.[170.1] The methodologies employed in SHM encompass a range of techniques for data acquisition, including the use of embedded sensors, optical fiber sensors, and advanced imaging techniques such as digital image correlation and tomography.[184.1] These technologies facilitate the real-time monitoring of structural conditions, allowing for the extraction of damage-sensitive features from the collected data.[176.1] Furthermore, the integration of wireless transmission technologies enhances the efficiency of data collection, enabling seamless communication between sensors and data processing units.[171.1] In the realm of Structural Health Monitoring (SHM), the integration of artificial intelligence (AI) and machine learning (ML) has significantly enhanced data acquisition and analysis capabilities. These technologies improve data processing and signal analysis, particularly in feature extraction and noise reduction, which are essential for accurate damage identification.[177.1] For example, One-Dimensional Convolutional Neural Networks (1D-CNN) have been effectively utilized to process one-dimensional data for various damage detection tasks, including crack and corrosion detection.[178.1] Furthermore, AI plays a crucial role in predictive maintenance by optimizing maintenance scheduling and enabling proactive management of infrastructure, thereby enhancing the overall reliability and safety of structures.[177.1] The future of SHM is promising, with advancements in IoT and smart materials facilitating real-time data collection and analysis, which can predict issues such as concrete deterioration and improve resource allocation.[182.1] This transformative approach is poised to redefine infrastructure management, ensuring the long-term stability of critical structures.[183.1]In this section:
Challenges And Limitations
Data Management and Interpretation
Data management and interpretation in structural health monitoring (SHM) face significant challenges primarily due to the sensitivity of these systems to environmental and operational conditions, which increases their susceptibility to noise and outliers in the acquired data. This noise can adversely affect the reliability of damage detection in structures, leading to a proliferation of unreliable signals that complicate data analysis.[226.1] Furthermore, the impact of noise is particularly pronounced in scenarios with lower signal-to-noise ratios (SNR), which can severely hinder the accuracy of damage identification algorithms.[226.1] To address these challenges, recent research has proposed effective model-updating-based optimization algorithms designed to alleviate the effects of outliers associated with field and operational fluctuations.[227.1] These advancements aim to enhance the overall reliability of SHM systems by improving their resilience against the inherent noise and outliers present in the data. To address these challenges, effective data preprocessing techniques are essential. This phase involves cleaning, transforming, and organizing the data to enhance the quality of input for subsequent analysis. Techniques such as anomaly detection and data cleaning methods are critical for mitigating the impact of noise and ensuring that the data used for damage identification is as accurate as possible.[225.1] Moreover, the integration of advanced sensor technologies and data analytics can provide the necessary intelligence to predict and mitigate potential structural issues, thereby improving the overall effectiveness of SHM systems.[224.1] The challenges associated with structural health monitoring (SHM) are multifaceted, particularly in high-rate systems that require real-time assessment and decision-making under conditions of uncertainty and risk. These systems must address complex issues such as multi-timescale problems and the need for adequate sensor networks that can respond effectively to detected anomalies.[215.1] Furthermore, while SHM technologies are crucial for ensuring the safety management of bridges and offer significant benefits in enhancing safety, extending service life, and reducing maintenance costs, they still face limitations in terms of accuracy, cost-effectiveness, and data processing capabilities.[220.1] To overcome these challenges, advancements in monitoring technologies, the integration of intelligent systems, and the application of big data analytics are essential. Such innovations are expected to enhance the precision of monitoring systems, thereby improving their ability to meet the complex monitoring needs of bridge structures and contributing to greater safety assurance and economic benefits in the future.[220.1]Environmental Factors and Their Impact
Structural health monitoring (SHM) systems are significantly influenced by environmental factors, which pose challenges to the reliability of wireless data transmission. These factors necessitate continuous monitoring of structural responses to ensure accurate health assessments. Key issues include maintaining network longevity, ensuring stability, and enhancing the reliability of damage detection. Additionally, optimizing model order is crucial to balance computational demands with system capabilities.[238.1] Temperature is a particularly influential environmental factor in SHM applications, affecting sensor performance and data accuracy. It is often used for compensation and estimation to mitigate environmental variations' impact on data transmission and analysis, which is vital for early damage prediction.[239.1] Addressing these environmental challenges is essential for improving the effectiveness and reliability of wireless sensor networks in SHM, distinct from data management issues that focus on noise and data interpretation.Future Directions
Emerging Technologies
Emerging technologies are significantly shaping the future of Structural Health Monitoring (SHM), particularly through the integration of artificial intelligence (AI) and machine learning (ML). These advancements are poised to enhance the capabilities of sensor technologies, leading to improved accuracy and efficiency in monitoring infrastructure. The amalgamation of AI and ML with SHM represents a revolutionary approach that redefines infrastructure management, enabling more effective damage classification and predictive maintenance strategies.[260.1] Recent developments in SHM technologies have transitioned from conventional damage detection techniques to state-of-the-art smart monitoring solutions. This shift facilitates near real-time and online damage assessment, bridging the gaps between past inefficiencies and future technological capabilities.[250.1] The integration of AI with sensor output data is particularly noteworthy, as it enhances the results of SHM campaigns and improves the prediction of bridge deterioration, thereby supporting reliable performance assessments.[258.1] Moreover, the analysis and interpretation of large volumes of data collected from sensor networks necessitate advanced computational models. These models are essential for predicting the behavior of infrastructure systems under complex loading environments and for identifying potential sources of damage in real time.[262.1] The interdisciplinary nature of this field encourages contributions from various domains, focusing on topics such as artificial neural networks, deep learning, and Big Data applications in infrastructure systems.[262.1] The benefits of implementing these emerging technologies in SHM are substantial. They include enhanced public safety, early risk detection, improved lifespan of structures, and reduced capital expenditures associated with maintenance.[253.1] As SHM continues to evolve, the integration of AI and ML will play a crucial role in ensuring the safety, reliability, and longevity of infrastructure assets.[251.1]Potential for Enhanced Safety and Maintenance
The integration of advanced Structural Health Monitoring (SHM) technologies is poised to significantly enhance safety and maintenance practices in infrastructure management. Over the past five years, there has been a concerted effort among government agencies, industry, and academia to develop sophisticated health management technologies, which are essential for ensuring the safety of critical infrastructure such as airframes used by the United States Air Force (USAF).[254.1] This emphasis on innovation is driven by the increasing awareness of the economic and social impacts of aging infrastructure, which necessitates the adoption of advanced SHM and damage detection tools, particularly through the application of artificial intelligence (AI).[256.1] The convergence of Structural Health Monitoring (SHM) and Lifecycle Monitoring (LCM) signifies a pivotal advancement in civil infrastructure management, heralding a new era characterized by resilience and sustainability. This integration is not merely a technical enhancement; it serves as a foundational element for the future of sustainable infrastructure, emphasizing the importance of these technologies in monitoring and maintenance practices.[257.1] The integration of SHM and LCM into infrastructure management represents a significant leap towards achieving sustainable development goals, underscoring their role as essential components in the evolution of our built environment.[257.1] AI technologies have emerged as a game-changer in SHM, enabling real-time monitoring and advanced predictive analytics. The use of IoT-enabled sensors allows for continuous monitoring of structural parameters, which are then analyzed by AI systems to provide timely insights into the condition of infrastructure.[272.1] However, the implementation of these capabilities is not without challenges. Large SHM systems generate vast amounts of data, leading to issues related to data storage, transmission, and processing speed.[271.1] Addressing these challenges through solutions such as edge computing and efficient algorithms is essential for the successful deployment of AI-driven SHM systems. The integration of artificial intelligence (AI) and machine learning (ML) in structural health monitoring (SHM) systems has significantly advanced the field, particularly through vibration-based monitoring, which is one of the earliest applications of AI in this domain. ML algorithms are employed to analyze sensor data, enabling the identification of anomalies in the vibrational responses of structures and facilitating the estimation of potential damage or degradation.[263.1] A comparative study of various ML techniques, including artificial neural networks (ANNs), convolutional neural networks (CNNs), and support vector machines (SVMs), highlights the importance of considering factors such as input data, feature selection techniques, and the specific structure of interest to enhance the accuracy of damage identification.[264.1] As conventional damage detection methods are gradually supplanted by these advanced solutions, the potential for near real-time and online damage assessment becomes increasingly viable, marking a promising transition toward bridging the gaps between past inefficiencies and future technological advancements in SHM.[265.1]In this section:
References
[1] Structural Health Monitoring - an overview | ScienceDirect Topics — Structural health monitoring (SHM) is defined as "the process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure." 10 This process involves the observation of a structure or mechanical system over time using periodically spaced measurements, the extraction of appropriate damage-sensitive features from these measurements and the
[2] Structural health monitoring - Wikipedia — Structural health monitoring (SHM) involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges and buildings. To directly monitor the state of a system it is necessary to identify features in the acquired data that allows one to distinguish between the undamaged and damaged structure. The state of Oregon in the United States, Department of Transportation Bridge Engineering Department has developed and implemented a Structural Health Monitoring (SHM) program as referenced in this technical paper by Steven Lovejoy, Senior Engineer. References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges.
[3] A review on structural health monitoring: past to present — The field of structural health monitoring (SHM) has gained significant attention from academia and industry, particularly in the realm of damage detection. This approach allows continuous monitoring of the structural integrity of systems and structures throughout their operational lifespan, leading to reduced dependence on periodic inspections and lower maintenance costs. Importantly, this
[4] What is Structural Health Monitoring in Civil Engineering? — Structural health monitoring system is a method of evaluating and monitoring the health of critical structures. It has been employed for many important projects because of its ability to respond to detrimental structural changes, enhancing structural reliability, and improving life cycle management.
[5] PDF — Introduction Application of Smart Materials in Structural Health Monitoring: Introduction Additionally, environmental variables such as temperature fluctuations, humidity levels, and exposure to corrosive elements can significantly influence the performance of smart materials in real-world applications.
[6] A Dynamic Analysis of Smart and Nanomaterials for New Approaches to ... — Integrating structural control and health monitoring. The aim of this Special Issue is to gain new, unique knowledge about the relationships between the structures and physico-mechanical and chemical properties of new materials, including finding ways to structure the control and development of new methods for structural healthy monitoring.
[7] PDF — Abstract Fatigue damage sensing and crack propagation monitoring of any structure is a prerequisite for reliable and effective structural health monitoring. This paper, discusses the role of two different sensors, i.e., crack propagation (CP) and fatigue damage (FD) sensors in structural health monitoring. The CP sensor is capable of detecting crack initiation and subsequent propagation within
[8] Structural health monitoring and fatigue damage estimation using ... — In this work, a computational framework is proposed for fatigue damage estimation in structural systems by integrating operational experimental measurements in a high-fidelity, large-scale finite element model. The proposed method is applied in a linear steel substructure of a lignite grinder assembly at a Public Power Corporation power plant.
[14] Potential Benefit of Structural Health Monitoring System on Civil Jet ... — Moreover, the capital costs are usually the main part of direct operating costs, therefore the impact of maintenance costs reduction due to SHM couldn't emerge from DOC perspective. In the following, a schematic resume of the equation for the DOC calculation. ... Cusati V., Corcione S., Memmolo V. Impact of Structural Health Monitoring on
[15] Aircraft Maintenance: Structural Health Monitoring Influence on Costs ... — SHM systems allow to continuously (or discretely) monitor the structural integrity of an asset, not waiting for the planned check. This result implies an important reduction of inspection time and guarantees an improvement of service life cost of the structural components, but how and how much this cost can be influenced is still an open issue.
[39] An Introduction to Structural Health Monitoring — This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book.
[40] The Past, Present and Future of Structural Health Monitoring: An ... — This paper presents an overview of the discipline of structural health monitoring (SHM), organised in terms of three proposed ages. The first age is delineated by the prehistory of SHM and the period
[41] The Past, Present and Future of Structural Health Monitoring: An ... — torical developments in structural health monitoring (SHM), paying close attention to stagnation points in that history and explaining how the relevant barriers were overcome. There will be discussion of some of the challenges that remain and a suggestion of how SHM needs to further evolve in order to meet those challenges.
[42] Structural health monitoring - Wikipedia — Structural health monitoring (SHM) involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges and buildings. To directly monitor the state of a system it is necessary to identify features in the acquired data that allows one to distinguish between the undamaged and damaged structure. The state of Oregon in the United States, Department of Transportation Bridge Engineering Department has developed and implemented a Structural Health Monitoring (SHM) program as referenced in this technical paper by Steven Lovejoy, Senior Engineer. References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges.
[43] Structural Health Monitoring, History, Applications and Future. A ... — 14 Structural Health Monitoring: History, Applications and Future health monitoring which remain unsolved yet. List of references is given in the bibliography at the end of this book.
[45] The Past, Present and Future of Structural Health Monitoring: An ... — "Structural Health Monitoring " first appeared in a paper title in 1990 . However, it is important to note that there are many ... detailed insight into the evolution of various aspects of SHM technology . However, some of these reviews are quite ... Modern NDE techniques arguably started to emerge in the late 1800s; they saw more
[46] Global overview on advances in structural health monitoring ... - Springer — Advances in the development of sensors, data processing systems, and numerical models have motivated the implementation of structural health monitoring (SHM) specially focused on the assessment of structural safety. Thus, this work presents a literature review about SHM platforms, especially from 1993 to 2015. In this way, a short history review about the recent advances on SHM, mainly related
[47] How Emerging Technologies are Transforming Structural Health Monitoring ... — Structural health monitoring (SHM) is crucial for maintaining the safety and integrity of structures like bridges, buildings and others. ... Some of the key advantages are: ... Advancements in Sensor Technology; Advances in sensor technology will continue, leading to more sensitive, durable and multi-functional sensors which will increase the
[48] The Role of Technology in Monitoring Structural Deflection — Advancements in Technology. With the advent of new technologies, monitoring structural deflection has become more efficient and accurate. Here are some key technological advancements that have transformed the field: 1. Electronic Sensors. Electronic sensors have revolutionized the way engineers monitor structural deflections.
[50] Innovations in Structural Health Monitoring (SHM) — The path of SHM technologies has been one of constant evolution and innovation. From the early days of manual inspections and basic monitoring to today's sophisticated systems, SHM has grown in leaps and bounds. Initially, structural health was assessed through periodic visual inspections and the use of simple tools.
[51] AI in Structural Health Monitoring for Infrastructure ... - MDPI — We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions.
[56] Big Data Analytics and Structural Health Monitoring: A ... - MDPI — Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional
[57] Data-Driven Structural Health Monitoring and Damage Detection ... - MDPI — Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper
[88] PDF — Published by International Scientific Indexing & Institute for Scientific Information Advancements in Structural Health Monitoring through Artificial Intelligence and Machine Learning Jelita Usamah Department of Computer Science and Information System, Pathumwan Institute of Technology, Thailand ABSTRACT This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in the realm of structural health monitoring (SHM). In this context, the amalgamation of artificial intelligence (AI) and machine learning (ML) with structural health monitoring (SHM) emerges as a revolutionary approach, poised to redefine the paradigm of infrastructure management. "Machine learning process evaluating damage classification of composites." International Journal of Science and Advanced Technology 9.12 (2023): 240-250 Amini, Mahyar, Koosha Sharifani, and Ali Rahmani.
[89] Integrating AI in Structural Health Monitoring (SHM): A ... - SSRN — Integrating AI in Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future Directions by Girmay Mengesha :: SSRN Top Papers Top Papers Integrating AI in Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future Directions Keywords: Artificial Intelligence, Structural Health Monitoring, Machine Learning, Deep Learning, Predictive Maintenance, Anomaly Detection, Infrastructure Management, Data-Driven Approaches, Challenges and Opportunities and Future Research Directions Mengesha, Girmay, Integrating AI in Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future Directions (January 10, 2025). Do you have a job opening that you would like to promote on SSRN? PAPERS Feedback to SSRN SSRN Rankings Top Papers About SSRN
[90] Review of Machine-Learning Techniques Applied to Structural Health ... — However, the importance of this study is to compare the application of artificial neural networks (ANNs), convolutional neural networks (CNNs) and support vector machine (SVM) techniques considering the input data, feature selection techniques, the structure of interest, data size, the level of damage identification and the accuracy of the ML model. In addition, this study could provide a starting point for the selection of ML techniques and signal processing techniques for future SHM ML-based solutions where structural configuration or data features have similarity with previous studies that achieved good structural damag or system identification performance. Gomez-Cabrera, A.; Escamilla-Ambrosio, P.J. Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures. Gomez-Cabrera, A.; Escamilla-Ambrosio, P.J. Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures.
[91] Machine learning and structural health monitoring overview with ... — Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past's applicative inefficiencies and the emerging technologies of the future.
[92] Recent advances in structural health diagnosis: a machine learning ... — Since the introduction of machine learning paradigm for SHM, using machine learning methods to analyze the monitoring data, identify, and evaluate structural health status has become a prominent
[94] Future-Proofing Infrastructure: The Impact of SHM and LCM — The integration of Structural Health Monitoring (SHM) and Lifecycle Monitoring (LCM) into infrastructure management practices represents a significant leap towards achieving sustainable development goals. By leveraging these methodologies, we can significantly reduce waste, optimize the use of resources, and ensure that infrastructure assets
[98] Use of IoT for structural health monitoring of civil engineering ... — Extensive structural health evaluation can be conducted using real-time test data collected from various IoT sensors on civil infrastructures. An IoT-based structural seismic monitoring system has been developed by Dang et al. (2024) introduces a real-time damage prediction and localization method utilizing a cost-effective, "do-it-yourself" wireless sensor node equipped with IoT functionality for structural health monitoring (SHM). This summary outlines the use of IoT technologies and various sensors for structural health monitoring (SHM) of historical buildings. The application of IoT technologies for bridge structural health monitoring (SHM) is gaining traction due to their cost-effectiveness, ease of installation, and real-time monitoring capabilities. IoT technologies can collect real-time SHM data from various sensors to assess the structural health of civil engineering infrastructure.
[99] Emerging Trends in the Integration of Smart Sensor Technologies in ... — All Journals Emerging Trends in the Integration of Smart Sensor Technologies in Structural Health Monitoring: A Contemporary Perspective This analysis identifies emerging trends and applications in smart sensor integration in civil and structural health monitoring, enhancing safety and efficiency in infrastructure management. Illustration of the nations where the corresponding author researches integrating smart sensor technologies in SHM. Emerging Trends in the Integration of Smart Sensor Technologies in Structural Health Monitoring: A Contemporary Perspective. Emerging Trends in the Integration of Smart Sensor Technologies in Structural Health Monitoring: A Contemporary Perspective. Emerging Trends in the Integration of Smart Sensor Technologies in Structural Health Monitoring: A Contemporary Perspective. International Journal of Environmental Research and Public Health Journal of Marine Science and Engineering
[101] How IoT Is Enhancing Structural Health Monitoring (SHM) — Continuous Structural Monitoring. IoT sensors enable the supervision of a structure on a continuous basis in real-time. This is important in order to maintain functional utility, optimal performance and security. Maintenance scheduling becomes more focused. Monitoring is complementary to already existing methods to test and diagnose e.g.
[126] Structural Health Monitoring - discoverengineering.org — Structural Health Monitoring (SHM) is a critical aspect of structural analysis in engineering, focusing on the continuous assessment of the integrity and performance of structures. This field has gained significant importance due to the need for ensuring the safety, reliability, and longevity of various structures, including buildings, bridges
[130] Structural Health Monitoring (Shm): Advances, Methods and Applications — This paper provides an in-depth overview of the principles, technologies, applications, and benefits of Structural Health Monitoring. Additionally, it emphasizes the integration of SHM
[134] Structural health monitoring in aviation: a comprehensive review and ... — Structural Health Monitoring (SHM) stands out as an emerging technology in today's aircraft industry. ... Aircraft structures are exposed to a variety of operational and environmental loads that can cause structural deformation and fractures. Structural Health Monitoring (SHM) has emerged as a promising solution for in-situ monitoring of
[135] A Comprehensive Review of Emerging Trends in Aircraft Structural ... — Structural prognostics and health management (SPHM), a vital discipline in aerospace engineering, emphasizes the importance of continuous monitoring, diagnosis, and prediction of the health of aircraft structural systems .By capturing and analyzing data from a wide array of sensors and monitoring systems, SPHM systems play an instrumental role in facilitating the real-time evaluation of
[136] Advanced Sensors and Sensing Systems for Structural Health Monitoring ... — Factors such as material composition, environmental conditions, and the type and direction of stress applied can also significantly impact how composites fail, making it intricate to anticipate the exact type of failure. ... 3 Sensors for Structural Health Monitoring of Aerospace Composite Structure. Generally, sensors for SHM systems need to
[155] Structural Health Monitoring: Techniques for Infrastructure ... - Collegenp — Advanced Techniques for Structural Health Monitoring | SHM for Infrastructure Structural health monitoring (SHM) offers a proactive solution to this challenge by using sensor-based systems to assess, monitor, and detect potential issues before they escalate into serious problems. Structural health monitoring (SHM) refers to the use of advanced sensors, data collection systems, and analytical tools to monitor the condition of structures over time. With SHM, infrastructure managers gain access to real-time data that allows them to monitor a structure's condition continuously. SHM systems, including fiber-optic sensors and acoustic emission testing, provide continuous monitoring to detect any signs of wear or damage. By utilizing advanced techniques such as vibration-based monitoring, fiber-optic sensors, acoustic emission testing, and strain gauges, engineers can detect damage early and take preventive measures before issues escalate.
[170] An Introduction to Structural Health Monitoring — This chapter of the book introduces the background and motivation of this process, the main levels and methods of structural health monitoring. Due to advances in sensing and data acquisition systems, data-driven methods have become increasingly popular among civil engineers and researchers owing to simplicity, robustness, and computational
[171] A review of methods and applications in structural health monitoring ... — This paper provides a comprehensive summary of the various key methods, practical applications and future trends of bridge SHM, emphasizing their current research status in the past five years, which includes an overview of sensing technologies, wireless transmission of data, damage identification methods, prediction and warning models. To address this gap and enhance the comprehensive understanding of bridge SHM system applications, this paper summarizes key approaches to bridge SHM in recent years as well as the challenges associated with practical applications across five dimensions: sensing technologies, wireless transmission technologies, data preprocessing methods, damage identification methods, and early warning systems. Bridge SHM facilitates real-time monitoring of the structural condition of bridges through several modules, including sensing technology, data transmission, data preprocessing, damage identification, and an early warning system, thereby providing essential support for the safety and long-term stability of bridge operations.
[176] Structural health monitoring - Wikipedia — Structural health monitoring (SHM) involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges and buildings. To directly monitor the state of a system it is necessary to identify features in the acquired data that allows one to distinguish between the undamaged and damaged structure. The state of Oregon in the United States, Department of Transportation Bridge Engineering Department has developed and implemented a Structural Health Monitoring (SHM) program as referenced in this technical paper by Steven Lovejoy, Senior Engineer. References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges.
[177] AI in Structural Health Monitoring for Infrastructure ... - MDPI — We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions.
[178] Deep Learning for Structural Health Monitoring: Data, Algorithms ... — Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends - PMC Keywords: structural health monitoring, deep learning algorithms, damage detection, data acquisition, facilities On this basis, One-Dimensional Convolutional Neural Networks (1D-CNN) with simple architecture and low computational complexity are applied to SHM to directly process 1D data for crack detection , corrosion detection , multi-type damage identification , abnormal data detection , etc. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. 224.Wang Z.W., Li A.D., Zhang W.M., Zhang Y.F. Long-term missing wind data recovery using free access databases and deep learning for bridge health monitoring. 231.Abdeljaber O., Avci O., Kiranyaz M.S., Boashash B., Sodano H., Inman D.J. 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data.
[180] Data-Driven Structural Health Monitoring and Damage Detection through ... — Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. In conventional data-driven SHM techniques: (1) expert knowledge is required to design the features; (2) a step-by-step training procedure is required; and, (3) trained models are generally less efficient for large-scale structures and may not be suitable for vision-based SHM applications. On the other hand, for the DL-based SHM: (1) training procedure has an end-to-end structure and features are automatically extracted; (2) hyper-parameters are trained simultaneously; and, (3) trained models are suitable for large-scale structures and efficient for vision- and vibration-based SHM while dealing with compressed or big data.
[182] The Future of Structural Health Monitoring Emerging Trends and Technologies — The Future of Structural Health Monitoring Emerging Trends and Technologies The Future of Structural Health Monitoring: Emerging Trends and Technologies Structural Health Monitoring (SHM) is rapidly evolving, driven by advances in technology and the growing need to maintain the integrity of critical infrastructure. At Pulse IoT Technologies, we are at the forefront of this transformation, leveraging cutting-edge IoT solutions to monitor and safeguard structural health. IoT is revolutionizing structural health monitoring by enabling real-time data collection and analysis. For example, by analyzing data from structure monitoring systems, machine learning can predict the onset of concrete deterioration, enabling proactive maintenance strategies and more efficient allocation of resources. The future of structural health monitoring is bright, with IoT, smart materials, and advanced analytics
[183] PDF — Published by International Scientific Indexing & Institute for Scientific Information Advancements in Structural Health Monitoring through Artificial Intelligence and Machine Learning Jelita Usamah Department of Computer Science and Information System, Pathumwan Institute of Technology, Thailand ABSTRACT This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in the realm of structural health monitoring (SHM). In this context, the amalgamation of artificial intelligence (AI) and machine learning (ML) with structural health monitoring (SHM) emerges as a revolutionary approach, poised to redefine the paradigm of infrastructure management. "Machine learning process evaluating damage classification of composites." International Journal of Science and Advanced Technology 9.12 (2023): 240-250 Amini, Mahyar, Koosha Sharifani, and Ali Rahmani.
[184] Recent Advances in Structural Health Monitoring - MDPI — Journals Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. Journals Find a Journal Journal All Journals Among others, the methodologies that involve the use of embedded N/MEMS sensors for local damage detection, corrosion sensors, optical fiber sensors, sonic-ultrasonic tests, digital image correlation, tomography techniques, Raman and terahertz spectroscopy, and electromagnetic analysis, which allow evaluating the level of structural damage and its evolution over time, will find space in this Topical Collection. Journals International Journal of Environmental Research and Public Health International Journal of Molecular Sciences Journal of Marine Science and Engineering
[185] Emerging Trends and Innovations in Structural Health Monitoring — Emerging Trends in SHM. Emerging trends in Structural Health Monitoring are shaping the field, offering innovative solutions and advancements in monitoring the health and integrity of structures. Data-Driven SHM: The adoption of data-driven approaches is on the rise. Advanced sensors and IoT technologies collect vast amounts of data, which is
[202] PDF — The adoption of wireless sensor networks (WSN)s for structural health monitoring promises to lower installation costs drastically. This is achieved by replacing the cables between the sensors and the data logger with wireless links.
[203] Wireless sensor network for structural health monitoring: A ... — The importance of wireless sensor networks in structural health monitoring is unceasingly growing, because of the increasing demand for both safety and security in the cities. The speedy growth of wireless technologies has considerably developed the progress of structural monitoring systems with the combination of wireless sensor network technology. Wireless sensor network-based structural
[205] Structural Health Monitoring: Advanced Sensing, Diagnostics and ... - MDPI — In recent years, different advanced sensing technologies, intelligent data-driven strategies, and innovative diagnostic and prognostic methodologies have witnessed revolutionary advancement. ... This Special Issue of Sensors aims to gather recent research findings and present the latest advancements in Structural Health Monitoring (SHM) in
[207] Structural Health Monitoring: From Sensing Technology Stepping to ... — Structural health monitoring (SHM) takes a breakthrough of civil engineering by integrating electrical, magnetic, photic, acoustic, thermal and other physical variables, chemical variables, information technology, computer science and technology as well as communication technology into a civil structure to make the structure have self-sensing and self-diagnostic abilities.
[215] High-Rate Structural Health Monitoring and Prognostics: An Overview — The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including multi-timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk.
[220] PDF — Innovation and application analysis of the health monitoring technology of the bridge structure Siyuan Cheng Hebei University of Engineering Science, Shijiazhuang, Hebei, China Keywords: Health monitoring of bridge structure; technology innovation; intelligence and big data Abstract: Bridge Structural Health Monitoring (SHM) technologies play a critical role in ensuring the safety management of bridges, offering significant value in enhancing safety, extending service life, and reducing maintenance costs. Nevertheless, current SHM technologies still face challenges in terms of accuracy, cost-effectiveness, and data processing, which limit their ability to fully meet the monitoring needs of complex bridge structures. By improving the precision of monitoring technologies, integrating intelligent systems and big data analytics, and developing low-cost, high-efficiency monitoring systems, bridge health monitoring will play a greater role in safety assurance, economic benefits, and environmental protection in the future.
[224] Innovations in Structural Health Monitoring (SHM) — Through advanced sensor technologies, data analytics, and predictive modeling, SHM provides the necessary intelligence to predict and mitigate potential structural issues. The integration of Structural Health Monitoring (SHM) within the broader framework of Lifecycle Monitoring (LCM) represents a holistic approach to infrastructure management. The I-395 Signature Bridge project, although still under construction, offers an insightful preview into the future capabilities of Structural Health Monitoring (SHM) in enhancing infrastructure resilience and sustainability. SHM Solutions Implemented: A network of sensors continuously collects data on environmental impacts and structural behavior, facilitating real-time monitoring and analysis. This exploration of Structural Health Monitoring (SHM) has highlighted its indispensable role in advancing Lifecycle Monitoring (LCM) and the creation of resilient, sustainable infrastructure.
[225] (PDF) Machine Learning Approach for Structural Health Monitoring and ... — Data preprocessing is a vital phase in the machine learning pipeline for structural health monitoring (SHM) and damage detection. This step involves cleaning, transforming, and organ izing the
[226] Structural Damage Detection Using Mutual Information and Improved ... — Structural health monitoring (SHM) faces a significant challenge in accurately detecting damage due to noise in acquired signals in composite plates, which can adversely affect reliability. ... causing a proliferation of noise and outliers in the data. ... As depicted in the figure, the impact of noise with lower SNR is more pronounced than at
[227] Enhanced damage detection for noisy input signals using improved ... — The sensitivity of structural health monitoring systems to environmental and operational conditions poses a significant challenge due to their inherent susceptibility to outliers. This paper proposes an effective model-updating-based optimization algorithm that can alleviate the impact of outliers associated with field and operational fluctuations.
[238] PDF — to provide continuous structural response data to quantitatively assess structural health, many important issues including network lifetime and stability, damage detection reliability, and trade-offs in model order to balance computational capabilities must be realistically addressed. Only then can wireless embedded sensor networks become a
[239] Towards the Structural Health Monitoring of Bridges Using Wireless ... — The particular focus is made on the data transmission and analysis for an early damage prediction. ... The temperature is commonly used in SHM applications for compensation and estimation purposes of environmental factors. ... Rosing T.S. Active Sensing Platform for Wireless Structural Health Monitoring; Proceedings of the 6th International
[250] Machine learning and structural health monitoring overview with ... — Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past's applicative inefficiencies and the emerging technologies of the future.
[251] (Pdf) Structural Health Monitoring (Shm): Advances, Methods and ... — (PDF) STRUCTURAL HEALTH MONITORING (SHM): ADVANCES, METHODS AND APPLICATIONS STRUCTURAL HEALTH MONITORING (SHM): ADVANCES, METHODS AND APPLICATIONS STRUCTURAL HEALTH MONITORING (SHM) TECHNOLOGIES Negi, P., Kromanis, R., Dorée, A.G. and Wijnberg, K.M., 2024. Advances in the development of sensors, data processing systems and numerical models have motivated the implementation of structural health monitoring (SHM) specially focused on the assessment of structural safety. Structural Health Monitoring (SHM) has emerged as a critical area of research and practice within the field of civil engineering, aiming to ensure the safety, reliability, and longevity of infrastructure assets. Vibration based Structural Health Monitoring (SHM) technologies have been the focus of significant development efforts for applications to a broad spectrum of transportation and civil structural systems.
[253] Recent advances and trends in structural health monitoring — Monitoring of structures using recent advances and trends in structural health monitoring has been reviewed and emphasized. In this chapter, benefits of implementation of SHM, such as enhancement of public safety, early risk detection, improvement in the life span of the structure, and decrease in the capital expenditures involved, are discussed.
[254] PDF — emphasis has been placed on the development of advanced health management technologies (for engines, structures, flight controls, etc.) within government agencies, industry, and academia over the past five years. The following section describes the current ASIP process used to insure safety of United States Air Force (USAF) airframes.
[256] SHMCEES, LLC - Google Sites — The Challenge: Increased awareness of the economic and social effects of aging, deterioration and extreme events on critical infrastructure has driven the need for advanced SHM and damage detection tools via leveraging AI. As an example, aging bridges, dams, and levees, and the shortage of funds needed to repair or replace them, are urgent
[257] Future-Proofing Infrastructure: The Impact of SHM and LCM — As we stand on the brink of a new era in civil infrastructure management, the convergence of Structural Health Monitoring (SHM) and Lifecycle Monitoring (LCM) emerges as a beacon of innovation, driving us towards a future where our built environment is not only resilient but inherently sustainable. As we reflect on this evolution, it’s clear that SHM and LCM are more than just techniques for monitoring and maintenance; they are the foundations upon which the future of sustainable, resilient infrastructure will be built. The integration of Structural Health Monitoring (SHM) and Lifecycle Monitoring (LCM) into infrastructure management practices represents a significant leap towards achieving sustainable development goals.
[258] Artificial intelligence in structural health management of existing ... — The second area examined the integration between AI and sensor output data to improve the results of structural health monitoring (SHM) campaigns. The third area of interest regards using AI to improve the prediction of bridge deterioration and support a reliable performance assessment process.
[260] PDF — Published by International Scientific Indexing & Institute for Scientific Information Advancements in Structural Health Monitoring through Artificial Intelligence and Machine Learning Jelita Usamah Department of Computer Science and Information System, Pathumwan Institute of Technology, Thailand ABSTRACT This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in the realm of structural health monitoring (SHM). In this context, the amalgamation of artificial intelligence (AI) and machine learning (ML) with structural health monitoring (SHM) emerges as a revolutionary approach, poised to redefine the paradigm of infrastructure management. "Machine learning process evaluating damage classification of composites." International Journal of Science and Advanced Technology 9.12 (2023): 240-250 Amini, Mahyar, Koosha Sharifani, and Ali Rahmani.
[262] Artificial-Intelligence-Based Methods for Structural Health Monitoring — The analysis and interpretation of a large volume of data (collected by the sensor network or digital images) and the extraction of critical information that can determine the state of health, reliability, and safety, as well as the life cycle assessment of these infrastructures (including feature extraction), require advanced and more realistic computational models to be developed, as well as analysis tools that can predict the behavior of these systems under complex and even hazardous loading environments and identify potential sources of damage and deterioration in real time. Given the interdisciplinary nature of this topic, the proposed Special Issue will be a collection of contributions from scholars in several fields, and will cover topics such as: artificial neural networks; deep learning neural networks; system identification; Big Data in infrastructure systems; optimization; probabilistic methods for SHM combined with AI methods; and dynamic response prediction via AI methodologies.
[263] Advances in artificial intelligence for structural health monitoring: A ... — Vibration-based monitoring is one of the earliest and most significant applications of AI in SHM. Machine learning (ML) algorithms have been employed to analyze sensor data, allowing anomalies in the vibrational responses of structures to be identified and potential damage or degradation estimated (Sony et al., 2021; Wang et al., 2022).By utilizing AI, patterns that were difficult to detect
[264] Review of Machine-Learning Techniques Applied to Structural Health ... — However, the importance of this study is to compare the application of artificial neural networks (ANNs), convolutional neural networks (CNNs) and support vector machine (SVM) techniques considering the input data, feature selection techniques, the structure of interest, data size, the level of damage identification and the accuracy of the ML model. In addition, this study could provide a starting point for the selection of ML techniques and signal processing techniques for future SHM ML-based solutions where structural configuration or data features have similarity with previous studies that achieved good structural damag or system identification performance. Gomez-Cabrera, A.; Escamilla-Ambrosio, P.J. Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures. Gomez-Cabrera, A.; Escamilla-Ambrosio, P.J. Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures.
[265] Machine learning and structural health monitoring overview with ... — Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past's applicative inefficiencies and the emerging technologies of the future.
[271] Current Challenges with BIGDATA Analytics in Structural Health Monitoring — 9 Current Challenges with BIGDATA Analytics in Structural Health Monitoring 81 9.2.3 Velocity One of the main challenges in BIGDATA management is for data transmission, storage, and processing to keep up with the high velocity of data generation. Processing these datasets can be difficult with increasing velocity (i.e., batch to real-time
[272] PDF — AI-Driven Structural Health Monitoring: Innovations, Challenges, and Future Directions Royana Anand1 and Surabhi Anand1 1Affiliation not available August 01, 2024 Abstract Structural Health Monitoring (SHM) is a critical aspect of ensuring the safety and longevity of infrastructure such as bridges, buildings, and dams. The advent of AI technologies offers promising solutions to address these limitations by automating data processing, enabling real-time monitoring, and providing advanced predictive analytics. IoT-enabled sensors continuously monitor structural parameters and trans-mit data to AI systems for real-time analysis. Integration with Emerging Technologies The integration of AI with emerging technologies, such as blockchain for data integrity and 5G for enhanced connectivity, could further improve the effectiveness of SHM systems. AI-driven Structural Health Monitoring represents a significant advancement in infrastructure management, offering improved efficiency, predictive capabilities, and real-time analysis.