Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques

Gordan, Meisam; Sabbagh-Yazdi, Saeed-Reza; Ghaedi, Khaled; Thambiratnam, David; Ismail, Zubaidah

doi:10.5772/intechopen.104905

Cited by 5 publications

(3 citation statements)

References 73 publications

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“…This study shows how critical has been the study of SHM of infrastructures since the early 2000s. In study [11], the monitoring of bridge infrastructure using soft computing techniques is presented. As early as 2006 the contribution of radar interferometry to the assessment of landslide hazards was analyzed and presented in [12].…”

Section: Problem Definition and Literature Overviewmentioning

confidence: 99%

Soft computing algorithms for infrastructure monitoring: preliminary results of PROION project

Nikolakopoulos,

Mpelogianni,

Groumpos

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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Structural health monitoring of civil infrastructures is a complex engineering problem that requires the use of advanced soft computing algorithms. The rapid advances in soft computing have been a step forward in the direction of infrastructure monitoring. The field of computer science has given promising results in monitoring systems when applied along with engineering technologies. In this framework, Artificial Intelligence (AI) Deep and Machine Learning applications, Neural Networks, Fuzzy Logic, Fuzzy Cognitive Maps (FCM), Genetics Algorithms and Hybrid systems are growing exponentially in the field of structural health monitoring, including structural recognition, change detection, crack detection, damage identification, damage quantification and damage prediction. Specifically, some of the above-mentioned more sophisticated infrastructure soft computing monitoring algorithms are utilized to generate strategies and processing pipelines towards structural building damage mapping and assessment. In this paper remote sensing data, acquired by Global Navigation Satellite System (GNSS), Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAV) sensors will be processed and a state-of-theart unified platform imbedding Neural Networks, Fuzzy Cognitive Maps and Hybrid systems in the field of Structural Health Monitoring of civil structures is proposed.

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Section: Problem Definition and Literature Overviewmentioning

confidence: 99%

Soft computing algorithms for infrastructure monitoring: preliminary results of PROION project

Nikolakopoulos,

Mpelogianni,

Groumpos

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

View full text Add to dashboard Cite

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“…On one hand, development of deep learning is inseparable from exponential growth of data, which has spawned a lot of research works related to data mining [10][11][12]. On the other hand, artificial intelligence has been successfully applied to various fields, such as smart city [13,14] and intelligent transportation [15,16]. However, to our knowledge, most of these applications are on the land; underwater applications with artificial intelligence have not been fully explored yet.…”

Section: Introductionmentioning

confidence: 99%

Marine Robotics 4.0: Present and Future of Real-Time Detection Techniques for Underwater Objects

Er¹,

Chen²,

Zhang³

2023

Artificial Intelligence

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Underwater marine robots (UMRs), such as autonomous underwater vehicles, are promising alternatives for mankind to perform exploration tasks in the sea. These vehicles have the capability of exploring the underwater environment with onboard instruments and sensors. They are extensively used in civilian applications, scientific studies, and military missions. In recent years, the flourishing growth of deep learning has fueled tremendous theoretical breakthroughs and practical applications of computer-vision-based underwater object detection techniques. With the integration of deep-learning-based underwater object detection capability on board, the perception of underwater marine robots is expected to be enhanced greatly. Underwater object detection will play a key role in Marine Robotics 4.0, i.e., Industry 4.0 for Marine Robots. In this chapter, one of the key research challenges, i.e., real-time detection of underwater objects, which has prevented many real-world applications of object detection techniques onboard UMRs, is reviewed. In this context, state-of-the-art techniques for real-time detection of underwater objects are critically analyzed. Futuristic trends in real-time detection techniques of underwater objects are also discussed.

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“…Advanced, large, and expensive engineering assets such as high-rise buildings, long-span bridges, dams, oil platforms, hydraulic structures, wind turbines, ofshore structures, railways, and ports were designed to last long [1][2][3][4][5][6][7]. However, many of them were more than halfway through their intended service life, and some of them have already reached the end of it [8].…”

Section: Introductionmentioning

confidence: 99%

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

Gordan,

Sabbagh-Yazdi,

Ghaedi

et al. 2023

Advances in Civil Engineering

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Over the last decades, the emergence of new technologies has inspired a paradigm shift for the fourth industrial revolution. For example, circular economy, data mining, and artificial intelligence (AI), which are multidisciplinary topics, have recently attracted industrial and academic interests. Sustainable structural health monitoring (SHM) also concerns the continuous structural assessment of civil, mechanical, aerospace, and industrial structures to upgrade conventional SHM systems. A damage detection approach inspired by the principles of data mining with the adoption of circular-economic thinking is proposed in this study. In addition, vibration characteristics of a composite bridge deck structure are employed as inputs of AI algorithms. Likewise, an artificial neural network (ANN) integrated with a genetic algorithm (GA) was also developed for detecting the damage. GA was applied to define the initial weights of the neural network. To aid the aim, a range of damage scenarios was generated and the achieved outcomes confirm the feasibility of the developed method in the fault diagnosis procedure. Several data mining techniques were also employed to compare the performance of the developed model. It is concluded that the ANN integrated with GA presents a relatively fitting capacity in the detection of damage severity.

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Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques

Cited by 5 publications

References 73 publications

Soft computing algorithms for infrastructure monitoring: preliminary results of PROION project

Soft computing algorithms for infrastructure monitoring: preliminary results of PROION project

Marine Robotics 4.0: Present and Future of Real-Time Detection Techniques for Underwater Objects

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

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