Damage Detection and Structural Health Monitoring of Concrete and Masonry Structures

Table of Contents

1. Frontmatter

2. Artificial Intelligence-Driven Structural Health Monitoring

   1. Frontmatter

   2. Chapter 1. A Comprehensive Synopsis of Artificial Intelligence-Driven Structural Health Monitoring of Concrete Structures: A Novel Approach Towards Sustainability

      Navdeep Mor, Pawan Kumar, Daniel Chukwuemeka, Madhu

      Abstract

      Civil engineering structures including masonry and concrete structures, seen as buildings, bridges, and even in other applications like monuments are prone to deterioration revealed as cracks, spalling, efflorescence, etc. The amount of money invested in building, time taken to build, quality of the original works, and most importantly the safety and life of the people inhabiting and using the structures, warrant such structures to be monitored and constantly assessed to confirm their utility safety to life. In this direction, a new technique called Damage detection enables us identification and recognition of defects in the structure, quantify them, and even propose measures by which these defects may be managed including predictions of the effects on the remaining structural life. Artificial intelligence (AI) uses data to machine and ensure human-like efficient results have been achieved. In the present study, the detailed study uncovers the present applications and uses of AI, and ML in the identification and recognition of damage present in masonry and concrete structures in the field of structural health monitoring and the utility of AI in damage detection from a different point of view. The findings from the study could help civil engineers about the different aspects of how AI and ML might help in the detection and monitoring of problems by monitoring structural health using simulations of real-life situations. It will be like a detailed theoretical solution to a practical problem. After thorough study, it can be deduced that AI is a powerful tool applied to the detection of damage in concrete structures, extending also to historical civil works and monuments, yielding quick solutions and reliable results with heightened accuracy.

   3. Chapter 2. Artificial Intelligence in Damage Detection of Concrete Structures: Techniques, Integration and Future Directions

      Salim Barbhuiya, Bibhuti Bhusan Das

      Abstract

      The chapter thoroughly explores the pivotal role played by Artificial Intelligence (AI) in the identification of damages in concrete structures. It delves into conventional methods, their limitations, and how AI can effectively complement these approaches. The basics of AI, encompassing machine learning and deep learning, are elucidated within the specific context of damage detection. Additionally, the chapter examines data acquisition and pre-processing techniques tailored for AI models. It sheds light on AI-driven damage detection methodologies, such as the utilization of convolutional neural networks for image analysis, vibration analysis, and AI-enhanced non-destructive testing methods, highlighting their precision in identifying structural issues. Moreover, the chapter investigates the integration of AI into structural health monitoring systems, providing in-depth discussions on data fusion and real-time monitoring. Emphasis is placed on the significance of performance assessment and model validation to ensure the reliability of AI algorithms. The chapter also addresses future trends, including the integration of AI with the Internet of Things (IoT), and delves into ethical considerations in the sphere of infrastructure development. In summary, the chapter underscores AI's transformative potential in revolutionizing damage detection and structural health assessment, contributing to the creation of more resilient and sustainable concrete structures.

   4. Chapter 3. Deep Learning-Enabled Health Assessment for Sustainable Maintenance of Existing Concrete Structures: A Review

      Punita Panwar, Khushi Goyal, Jatin Kumar Shandilya

      Abstract

      With growing emphasis on sustainable infrastructure, the maintenance and durability of existing concrete structures are critical factors. This paper proposes a novel approach leveraging Deep Learning (DL) Techniques for finding and estimating of the health of concrete buildings. The offered approach integrates advanced image processing and machine learning algorithms to analyze visual data obtained through non-destructive testing methods. Our framework focuses on first capturing and then interpreting the refined indicators of structural degradation, such as cracks, spalling, and surface irregularities, through the application of convolutional neural networks (CNNs). By training the model on a varied dataset of pictures of buildings under several environmental conditions, the system learns to identify and classify different levels of damage with high accuracy. The proposed DL-based approach offers several advantages over traditional inspection methods, including real-time analysis, cost-effectiveness, and reduced dependence on human interpretation. Having said that, with the collaboration of Artificial Intelligence (AI), the identification, detection and characterization of degradation and damage in all types of engineering structures consumes less time. Computational techniques (CT) are playing progressively important role in structural health monitoring, allowing for more accurate and efficient analysis of data from sensors and providing valuable insights into the health of structures. Machine Learning (ML) and DL models are taught from data without being specifically programmed. In other words, they take the help of algorithms to automatically learn patterns and make predictions based on the data is fed into the system. In this chapter, the researchers discuss how AI can be useful for civil engineers and mechanical engineers to monitor the health of buildings and day-by-day implementation of advanced deep learning methods is profitable to civil engineers in terms of cost as well as reduced human-efforts and people in terms of their safety.

3. Methods and Techniques for Damage Identification

   1. Frontmatter

   2. Chapter 4. Methods of Assessing Concrete Structures and Identifying Damages Using Modal Data Processing

      Hashem Jahangir, Farid Fazel Mojtahedi, Ali Golaghaei Darzi, Moncef L. Nehdi

      Abstract

      This study explores how to evaluate structures and detect damage using modal data processing methods. It introduces modal testing and the factors derived from it. The practical applications of modal tests for concrete structures are examined, along with the advantages and disadvantages of research on the vibration behavior of reinforced concrete beams. The study concludes that the results of modal testing can be used to identify damage patterns in reinforced concrete beams. Additionally, the main goals and categories of modal data processing methods for damage assessment in structures are reviewed, along with their advantages and disadvantages. Damage detection methods using modal data are divided into two categories based on changes in primary modal factors or modal calculation quantities. The study proposes a new algorithm based on the local energy of modal strains to identify the location and severity of damages in beam structures. The proposed solution can reveal single and multiple fictitious damages in the numerical model. Numerical analysis results show that the proposed method can determine the damage location and severity with a limited number of vibration modes. Applying the proposed algorithm to the modal strains can help identify the damage early and prevent the sudden failure of the structure.

   3. Chapter 5. Cutting-Edge Network Based Concrete Crack Detection and Analysis for Structural Health Monitoring

      S. Gandhimathi Alias Usha

      Abstract

      Structural Health Monitoring (SHM) plays a crucial role in ensuring the safety and longevity of infrastructure, with concrete structures being integral components of our built environment. This chapter introduces DeepCrack, an innovative approach utilizing deep learning techniques for the detection and analysis of concrete cracks in the context of structural health monitoring. DeepCrack influences state-of-the-art Convolutional Neural Networks (CNNs) to automatically identify and characterize cracks in concrete surfaces. The proposed system exhibits high accuracy and efficiency in detecting both visible and subtle cracks, providing a comprehensive solution for structural integrity assessment. By employing a large dataset of annotated concrete images, DeepCrack learns intricate patterns and features associated with different types and stages of cracks. The detection process is complemented by a detailed analysis module that evaluates cross entropy, training and validation accuracy. This analysis not only aids in quantifying the severity of cracks but also contributes valuable insights into potential structural issues. DeepCrack's adaptability enables it to handle various environmental conditions, lighting scenarios, and surface textures commonly encountered in real-world applications. Furthermore, the integration of DeepCrack into existing structural health monitoring systems enhances the overall efficiency of maintenance strategies. Real-time monitoring and continuous analysis of concrete surfaces enable timely identification of structural vulnerabilities, facilitating proactive interventions and preventing potential disasters. The results of extensive experiments demonstrate the superior performance of DeepCrack compared to traditional methods, showcasing its potential as a reliable and accurate tool for concrete crack detection and analysis. The scalability and versatility of DeepCrack make it suitable for a wide range of applications, from routine inspections of buildings and bridges to large-scale infrastructure projects.

   4. Chapter 6. Short-Term Health Monitoring and Damage Assessment of a Concrete Cable-Stayed Bridge by an Integrated Unsupervised Learning Approach

      Hassan Sarmadi, Alireza Entezami

      Abstract

      Civil structures are valuable assets of every society with significant influences on human life, economics, transportation networks, and energy supply. These assets are always susceptible to natural and man-induced hazards, aging, and material deterioration. The adverse consequences of these events are the occurrence and growth of different patterns of structural damage, failure, and even collapse. Vibration-based structural health monitoring (SHM) supported by sensory data and artificial intelligence is an emerging and innovative technology for assessing the safety and functionality of various civil structures, especially bridges. Although this technology can be implemented in short- and long-term programs, some limitations do not allow civil engineers to benefit from the Dunn long-term SHM, in which case an automated short-term monitoring program is an effective and practical alternative. However, some challenges make this program problematic. The most significant challenge relates to encountering limited vibration data during a short period of monitoring. The other important issue is profound effects of operational and environmental variability on vibration data such as modal frequencies, which cause false alarm and mis-detection errors. To address these challenges, this chapter proposes an innovative SHM approach suitable for short-term monitoring programs based on the concept of integrated unsupervised learning. Using limited modal frequencies as the main vibration features, this approach comprises three steps of local feature segmentation, unsupervised feature selection, and unsupervised anomaly detection. First, agglomerative hierarchical clustering in conjunction with the Dunn’s index is employed to segment the limited set of modal frequencies into local subsets (clusters). Second, a filter-based unsupervised feature selector is proposed to find the most appropriate cluster with the features insensitive to environmental/operational conditions. Third, these features are used to develop an anomaly detector based on the local outlier factor and determine anomaly indices for damage assessment. A concrete cable-stayed bridge is considered to testify the proposed approach. It is observed that this approach succeeds in alarming damage with limited data under a short-term monitoring program.

   5. Chapter 7. Utilizing Rayleigh–Ritz Technique to Identify Damages of Multi Span Beams

      Mohammad Hassan Daneshvar, Ali Reza Gharighoran, Seyed Reza Zareei, Hashem Jahangir, Maria Rashidi

      Abstract

      In this paper, a novel technique for identifying the location and the severity of damage in multi-spans beams and “beam and column” is proposed, which has an ability to reveal the structural damage with any type of support conditions. The proposed method is an expanded version of the finite element method (FE) by the assistance of the Ritz method which is called Ritz Damage Detection Method (RDDM). The innovation of the proposed method in this paper is the omission of some limitations of the RDDM method, including unique support conditions, and applying the effects of “beam and column”. In this paper, by using the shape function, the definition of support conditions as a spring, and entering the interaction effect of “beam and column”, the equations of RDDM method are developed to provide possible damage detection of the bridges with all support conditions. Also, singular value decomposition (SVD) method is used to determine the quantity and the severity of the damage, which is sensitive to dynamic characteristics changes, originated from the damage. The efficiency and capability of the proposed method for damage detection are evaluated by a numerical sample of “a multi-span beam” and “beam to column connections. The investigation results showed that the proposed damage detection method has the ability to identify the location and the severity of the damages.

   6. Chapter 8. Identification of Damages in Concrete and Steel Structures: A Comprehensive Review

      Abolfazl Yosefi, Farid Fazel Mojtahedi, Michael Bahrami

      Abstract

      This chapter examines various methods and techniques for detecting, assessing, and monitoring damage in concrete and steel structures. It covers a wide range of damage types, including corrosion, fatigue, impact, fire, earthquake, burst, cracks, fractures, anomalous shape change, abnormal deflections, abnormal bending, abnormal vibration, and unbalance. The importance of identifying and addressing structural damage to ensure safety and integrity is emphasized. The non-destructive testing (NDT) methods such as visual inspection, ultrasonic inspection, magnetic particle inspection, radiographic testing, and eddy current inspection is discussed. It also explores imaging techniques, quasi-static loading, structural monitoring, and artificial intelligence (AI) techniques for damage detection. Additionally, structural health monitoring (SHM) methods, including Acoustic Emission Testing (AET), the PZT-Based Active Wave Method, noise emission control, vibration-based methods, strain-based methods, and the utilization of machine learning algorithms, were investigated. The advantages, limitations, and applications of each method are highlighted, emphasizing the need for a comprehensive approach that combines multiple techniques for effective structural health assessment. The conclusion included discussing data collection and analysis methods, such as sensor data, seismic observations, visual data, and satellite data, along with imaging techniques like thermography, magnetic imaging, radar imaging, and electrical imaging. By utilizing these methods and techniques, engineers can enhance their understanding of structural health and make informed decisions to ensure the safety and longevity of critical infrastructure.

   7. Chapter 9. Detection of Damages in Concrete Structures by Signal Processing and Image Processing Techniques: A Critical Review

      S. M. Priok Rashid, Atefeh Soleymani, Hashem Jahangir, Moncef L. Nehdi

      Abstract

      Concrete cracks are among the primary indications of the structure' failure, which is crucial for maintenance and the resulting exposure will damage the environment. To support vibration-based structural health monitoring (SHM), this chapter provides a review on the assessment of current studies on signal processing methods and image processing. Data may be collected, displayed, analyzed, and then utilized to inform maintenance choices using various tools and procedures. Civil construction, such as buildings, columns, beams, and slabs, has been the focus of attention. SHM based on vibration relies heavily on signal processing. In signal processing, the goal is to find, quantify, and locate the structure's damage by extracting subtle changes in the vibration signals. There remains a gap in the exact measurement of the width of cracks in millimeters, despite the fact that significant research has been done on crack identification utilizing image processing. By highlighting and analyzing some of the most influential papers from around the world, this review article illuminates research from all over the world.

4. Signal Processing and Analysis

   1. Frontmatter

   2. Chapter 10. Signal Processing in Real-Life Structural Health Monitoring: MATLAB and Python Implementation

      Chandrabhan Patel, Naman Garg, S. K. Panigrahi, Vikesh Kumar

      Abstract

      Signal and data processing are necessary for analyzing experimental data. Signal processing techniques are well-defined and handy for real-life applications, but applying those techniques to experimental data is challenging for the researcher. In structural health monitoring, post-processing helps remove noise and residuals from the sensor data. Various software and programming can help achieve the best signal-processing algorithms. Each package has its solver to solve the signal processing algorithms. Due to the countless options available for signal processing, choosing the best and most reliable package to solve the problem is foremost. Nowadays, MATLAB and Python are two packages that are reliable, popular, and accurate for multiple applications. This article evaluates the signal processing capabilities of MATLAB and Python in the context of structural health monitoring applications. File handling, simulating the signals, filter realization, filter response, and adding and removing noise from the signal are discussed and implemented for structural health monitoring. Signal processing is implemented, keeping structural health monitoring as the focus area. The real-life demonstration used a supported beam instrumented with Fiber Bragg Grating strain sensors. It has been determined that both MATLAB and Python can be utilized for structural health monitoring based on cross-correlation techniques, as the absolute difference between their results is approximately 0.0001, which is negligible.

   3. Chapter 11. A Review of Structural Health Monitoring and Damage Detection Techniques in Frames and Bridges

      Saeed Hamidi, Atefeh Soleymani, Maria Rashidi

      Abstract

      Structures can experience localized damages for various reasons, and if the location of these damages remains unknown, they can be exacerbated due to natural disasters such as earthquakes or artificial factors like improper excavations, leading to their destruction. Therefore, monitoring the health of structures and their components is considered one of the most important research topics in civil engineering, mechanical, and aerospace disciplines. Numerous research studies have been published so far in the field of structural health monitoring. Nowadays, with the use of structural health monitoring methods, it is possible to accurately identify structural damages in the early stages and prevent financial and life-threatening damages from occurring. Structural health monitoring is essential to ensure the safe performance of a structure throughout its operational lifespan. Due to the typically uncertain location and severity of damage in structures, significant efforts have been made to achieve an accurate, reliable, and cost-effective method for damage detection. The detection of damage in structures, especially in recent years, has received significant attention. A comprehensive review of published research studies has been conducted in this chapter and an overall description was provided of various methods and compared them in terms of accuracy and correctness.

   4. Chapter 12. Application of Image Processing Techniques for Analyzing Strength of Concrete Structures—A Novel Approach

      Navdeep Mor, Pawan Kumar, Madhu, Charity Aliyinza, Jamiu Ajibola Busari

      Abstract

      Generally, a myriad of reasons leads to the falling apart of concrete structures over time creating unsafe situations in the environment. The circumvention of this problem requires recurring and constant evaluation involving the utilization of simple and efficient tools for the surveillance of structural health allowing for the early detection of threatening changes that have occurred over time in these structures that may pose as unsafe to the environment using not only empirical data but also data collected in real time. Surveillance of structural health is dynamic for the efficient functioning and complete safety of significant civil structures throughout their stipulated time as well as thereafter. Visual inspection is traditionally way being used for the evaluation of structural wellness, performing a thorough inspection and evaluation, allows for the extraction of enough details that can be linked in an array of strategies when assessing structure integrity. In this study, an attempt has been made to present the applications of image processing techniques for analyzing the strength of concrete structures. The study explains in detail about the Complex, laborious and costly problems in civil engineering primarily related to the strength of concrete structures that could be solved through employment of various image processing methods. Towards the end, it has been concluded that the image processing technique is capable enough to solve complex problems related to the structural health monitoring and accordingly the prevention measures could be taken by various stakeholders associated with this field to improve the utility of the structures.

5. Advanced Technologies and Applications

   1. Frontmatter

   2. Chapter 13. Structural Health Monitoring: Glass Recycle Material

      Rakesh Choudhary, P. V. Ramana

      Abstract

      Structural Health Monitoring (SHM) is crucial for ensuring the safety and reliability of infrastructure by continuously assessing structural integrity and detecting potential damage. This chapter explores the application of SHM to concrete specimens reinforced with glass powder, a sustainable building material. Through comprehensive testing and analysis, the study aims to evaluate the effect of glass powder on concrete properties and assess its suitability for practical use. The research contributes to environmentally friendly construction practices and infrastructure development, aligning with principles of sustainability and responsible engineering. The study also discusses challenges associated with SHM, highlighting the need for accurate sensor placement, effective data management, and advanced data analysis techniques. Additionally, the chapter introduces Digital Image Correlation (DIC) as a cutting-edge technique within SHM for measuring and analysing full-field displacements and strains. DIC offers non-contact, high-precision measurements suitable for various materials and loading conditions. While DIC provides significant insights into material characteristics and structural performance, challenges such as calibration complexity and sensitivity to environmental factors must be addressed. Overall, the integration of SHM and DIC presents promising opportunities for enhancing infrastructure safety, sustainability, and resilience.

   3. Chapter 14. A Comprehensive Review of Utilizing Smart Bricks in Structural Health Monitoring and Damage Detection of Masonry Structures

      S. M. Priok Rashid, Atefeh Soleymani, Mohammadreza Mofidi

      Abstract

      Most of damage detection and structural health monitoring (SHM) methods need sensors which are attached to the structure surfaces or gather required data via remote sensing process. The principal objective of this article is to examine novel and recent concepts regarding smart bricks which can be classified as a robust structural health monitoring method in masonry structures. Moreover, recent advances in smart sensor technology are discussed, along with various field applications for monitoring strain in a full/small-scale masonry building prototype subjected to controlled damage. In recent years, innovative piezoresistive smart sensors that bear resemblance to traditional clay bricks in appearance, and are known as “smart bricks”, have been developed to monitor diffuse strain in masonry structures, thereby enabling the deployment of monitoring systems to be significantly facilitated for this type of building. The article further elaborates on the manufacturing process of smart bricks, their integration into masonry load-bearing structures, and the numerous experimental outcomes. In order to maximize such improvements to the structure, SHM functions may offer significant details on the structural stability of brick structures to use. These systems are designed at regaining or enhancing their structural integrity for timely preservation and ensuring the inhabitants' safety. Through laboratory experimentation utilizing a full or small-scale wall sample subjected to diverse loading scenarios and controlled impairment, it has been evidenced that smart bricks possess the potential for multifarious applications, including the capacity for identifying fractures within masonry walls. In essence, the findings demonstrate that a small number of strategically positioned smart bricks can effectively monitor the strain magnitude within a wall and generate data that can be utilized for identifying the presence of cracks.

   4. Chapter 15. Bridge Health Monitoring: A Review of Utilizing the Internet of Things, Digital Twin, and Advanced Technologies

      Hamed Hasani, Francesco Freddi

      Abstract

      Structural monitoring of bridges is commonly carried out using conventional methods. However, with the advancements in the internet of things (IoT) and digital twin (DT) technologies, this process has been revolutionized. The implementation of IoT enables enhanced sustainability by monitoring various parameters through interconnected devices and sensors. Simultaneously, DT technology creates a synchronized virtual representation of the physical bridges, improving safety, maintenance, and decision-making. This review paper explores foundational concepts of IoT and DT in bridge monitoring, discussing the synergy between DT and IoT for heightened precision in damage detection. It also covers innovative methodologies such as photogrammetry, terrestrial laser scanning, ground penetrating radar, building information modeling, and unmanned aerial vehicles/drones. These methodologies produce detailed 3D models, improving precision, practicality, and efficacy in the monitoring process. The paper serves as a guide for researchers and practitioners, illustrating a progression from foundational concepts to the state-of-the-art technologies. This envisions a future where bridges are monitored with unprecedented accuracy to ensure safer and more resilient infrastructure on a global scale.