Application of Artificial Intelligence in Structural Health Monitoring of Civil Infrastructure

Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) in structural health monitoring has emerged as a promising approach to enhance the safety and efficiency of civil infrastructure systems. This thesis explores the application of AI techniques in monitoring the health and performance of various civil structures such as buildings, bridges, and dams. The research investigates how AI algorithms can be utilized to analyze data collected from sensors embedded in structures to detect anomalies, predict potential failures, and optimize maintenance schedules. The first part of the thesis provides an overview of the current challenges in traditional structural health monitoring methods and the potential benefits of integrating AI technologies. The background study highlights the importance of ensuring the structural integrity of civil infrastructure to prevent catastrophic failures and minimize maintenance costs. The problem statement identifies the limitations of existing monitoring techniques and emphasizes the need for more advanced and efficient solutions using AI. The objectives of the study include developing AI models for real-time monitoring of structural health, identifying critical parameters for predictive maintenance, and evaluating the performance of AI algorithms in detecting structural anomalies. The scope of the research covers a wide range of civil infrastructure types and explores the applicability of different AI approaches, such as machine learning, deep learning, and neural networks. The methodology chapter details the research design, data collection methods, and AI algorithms used for analyzing structural health data. It outlines the process of training and validating AI models with sensor data to predict structural behavior and assess potential risks. The research methodology also includes a comparative analysis of different AI techniques to determine their effectiveness in structural health monitoring applications. The findings chapter presents the results of the research, including the performance metrics of AI models in detecting anomalies, predicting structural failures, and optimizing maintenance strategies. The discussion section elaborates on the implications of the findings and their significance in improving the safety and reliability of civil infrastructure systems. It also highlights the potential challenges and limitations of implementing AI-based monitoring systems in real-world applications. In conclusion, this thesis demonstrates the feasibility and effectiveness of applying AI in structural health monitoring to enhance the resilience and longevity of civil infrastructure. The study contributes to the growing body of knowledge on AI applications in engineering and provides valuable insights for future research and development in the field of structural health monitoring. The findings of this research have practical implications for engineers, policymakers, and stakeholders involved in maintaining and managing civil infrastructure assets.