Читать книгу Computer Vision for Structural Dynamics and Health Monitoring - Dongming Feng - Страница 11

Over the past few decades, a significant number of studies have been conducted in the area of structural health monitoring (SHM), with the objective of detecting anomalies and quantitatively assessing structural integrity based on measurements using various types of sensors. Although these studies have produced SHM methods, frameworks, and algorithms that have been validated through numerical, laboratory, and field applications, their wide deployment in real‐world engineering structures is limited by the prohibitive requirement of installing dense on‐structure sensor networks and associated data‐acquisition systems. To address these practical limitations, the research and industrial communities have been actively exploring new sensing technologies that can advance the current state‐of‐the‐art in SHM.

Rapid advances in digital cameras and computer vision algorithms have made vision‐based sensing a promising next‐generation monitoring technology to complement conventional sensors. Significant advantages of the vision‐based sensor include its low cost, ease of setup and operation, and flexibility to extract displacements at multiple points on the structure from a single video measurement. In the past 10 years, the authors have been fortunate to lead, participate in, and witness the development of computer vision‐based sensing and its application to structural dynamics and SHM. In our activities, however, we have seen a gap between the significant potential offered by this emerging sensing technology and its practical applications. Many undergraduate and graduate students, researchers, and practicing engineers are interested in learning how this sensing technology works and what unique benefits it can offer.

This book is intended to provide a comprehensive introduction to vision‐based sensing technology, based primarily on the authors' research. Fundamental knowledge, important issues, and practical techniques critical to the successful development of the vision‐based sensor are presented and discussed in detail. A wide range of tests have been carried out in both laboratory and field environments to demonstrate its measurement accuracy and unique merits. The potential of the vision sensor as a fast and cost‐effective tool for solving SHM problems is explored. In addition to SHM, novel and practical solutions to other engineering problems are presented, such as estimating cable tension forces using vision‐based sensing. Finally, the book outlines the achievements and challenges of current vision‐based sensing technologies, as well as open research challenges, to assist both the structural engineering and computer science research communities in setting an agenda for future research.

The goal of this book is to help encourage the application of the emerging vision‐based sensing technology not only in scientific research but also in engineering practice, such as assessing the field condition of civil engineering structures and infrastructure systems. Although the book is conceived as an entity, its chapters are mostly self‐contained and can serve as tutorials and reference works on their respective topics. The book may also serve as a textbook for graduate students, researchers, and practicing engineers; thus, much emphasis has been placed on making the computer vision algorithms, structural dynamics, and SHM applications easily accessible and understandable. To achieve this goal, we provide MATLAB code for most of the problems discussed in the book. In addition, readers working in structural dynamics and health monitoring will find this book hands‐on and useful.

The authors would like to express their gratitude to the following individuals: Professors Shun’ichi Kaneko and Takayuki Tanaka at Hokkaido University, for inspiring the authors' work on computer vision more than a decade ago and for kindly providing the orientation code matching (OCM) MATLAB code included in Chapter 2; Dr. Yoshio Fukuda, former associate research scientist at Columbia University, for developing the OCM software package with the C++ language; Casey Megan Eckersley, PhD student of Columbia University, for her valuable assistance in editing the book; and last but not least, the authors' families for their strong support.