Application of Artificial Intelligence in Predicting Structural Health of Civil Infrastructure

Thesis Abstract

Abstract
The advancement of technology in recent years has provided new opportunities for enhancing the monitoring and maintenance of civil infrastructure. This research project focuses on the application of Artificial Intelligence (AI) in predicting the structural health of civil infrastructure. The integration of AI techniques with structural health monitoring systems has the potential to revolutionize the way infrastructure assets are managed, leading to improved safety, reliability, and cost-effectiveness. Chapter One provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the foundation for the research by highlighting the importance of predicting structural health in civil infrastructure using AI. Chapter Two consists of a comprehensive literature review that examines existing studies, methodologies, and technologies related to AI applications in structural health prediction. The review covers various aspects such as machine learning algorithms, sensor technologies, data collection methods, and case studies of AI implementation in civil infrastructure monitoring. Chapter Three details the research methodology employed in this study. It outlines the research design, data collection methods, AI algorithms selected for analysis, sensor technologies used for data acquisition, data processing techniques, model validation processes, and other relevant aspects of the research methodology. Chapter Four presents a detailed discussion of the findings obtained through the application of AI in predicting the structural health of civil infrastructure. The chapter analyzes the performance of AI models in predicting structural health parameters, compares the results with traditional methods, identifies key factors influencing prediction accuracy, and discusses the implications of the findings for practical implementation. Chapter Five serves as the conclusion and summary of the research thesis. It synthesizes the key findings, discusses the implications for the field of civil engineering, highlights the contributions of the study, identifies areas for future research, and provides recommendations for the effective integration of AI in structural health prediction practices. Overall, this research project contributes to the growing body of knowledge on the application of AI in predicting the structural health of civil infrastructure. By leveraging AI technologies for predictive maintenance, civil engineers can proactively address structural issues, optimize asset management strategies, and ensure the long-term sustainability of critical infrastructure systems.