Sensing Techniques and Data Analytics

Welcome to this book on sensing techniques and data analytics for structural health monitoring (SHM). The aim is straightforward: to help you understand how sensors, signal processing, and data-driven methods work together so that you can plan instrumentation, process time-series data, and make informed engineering decisions for bridges and similar civil infrastructure.

The material follows the full pathway from sensing to decisions. We begin with the role of sensors and data in SHM, then move through sensor technologies, time- and frequency-domain analysis, feature engineering and statistical detection, optimization for FE model updating and optimal sensor placement, and finally selected machine-learning methods for subtle damage detection. Throughout, the emphasis is on clear concepts, practical workflows, and examples that can be adapted to other engineering systems where sensors are central.

1About the Author and Course Context¶

I am Mohammad Talebi-Kalaleh, and I prepared this book as the main teaching resource for a graduate-level course in the Department of Civil and Environmental Engineering at University of Alberta in Fall 2025. Within that course, the book is integrated with a carefully designed, four-phase project that runs across the term. The project asks students to plan sensing strategies, work with real or realistic data, implement analysis pipelines, and interpret results in an engineering context.

Although the examples are rooted in bridge engineering, the ideas extend to many areas of civil and broader engineering practice. My goal is to keep the exposition straightforward, honest about limitations, and focused on what you can actually implement.

If you would like access to the lecture notes or the complete project package that accompanies this book, feel free to email me at talebika@ualberta.ca. I am always happy to share resources and connect with colleagues and students who are interested in sensing, data analytics, and SHM.

2How to Use This Book¶

The early chapters introduce the motivation for SHM, what sensors measure, and how monitoring fits into the broader lifecycle of infrastructure. Subsequent chapters discuss sensing technologies, signal processing in time and frequency domains, feature engineering and statistical detection, and more advanced modeling and machine-learning topics. You can follow the chapters in order as a complete course, or dip into specific sections when you need a focused reference for a project or research task.

Some familiarity with basic Python, calculus, and linear algebra is helpful. Prior exposure to structural dynamics is useful but not required; when needed, we try to recall the essential ideas rather than assume a specialized background.

Despite careful checking, the material may contain mistakes. If you see anything that looks incorrect, unclear, or misleading—whether in the text, code, or visualizations—please let me know. Your feedback will directly help improve future versions of this book for the community. You can always reach me at talebika@ualberta.ca.

3Citation¶

If this material is useful in your teaching or research, please cite it:

@misc{talebi2025SHMbook,
  author       = {Talebi-Kalaleh, M. and Mei, Q.},
  year         = {2025},
  title        = {Sensing Techniques and Time Series Analysis in Python (v0.1) [e-book]},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17094969},
  url          = {https://doi.org/10.5281/zenodo.17094969}
}