Open Access
Structural damage detection and classification based on machine learning algorithms
Jaime Vitola Oyaga,Diego Alexander Tibaduiza Burgos,Maribel Anaya Vejar,Francesc Pozo Montero +3 more
TLDR
This work presents a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms which are validated and compared by using data from real structures in order to determine its behavior.Abstract:
Structural Health Monitoring is a growing area of interest given the benefits obtained from its use This area includes different tasks in the damage identification process, among them, the most important is the damage detection at an early stage which enables to increase the security in mechanisms and systems, reducing risks and avoiding accidents As a contribution in this topic, this work presents a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms which are validated and compared by using data from real structures in order to determine its behavior In the methodology, PCA (Principal component analysis) and some pre-processing steps are used as the mechanisms to reduce data and build the features vector with relevant information about the different states of the structures under test This methodology is validated by using some aluminum plates which are instrumented and inspected by means of PZT transducers attached to them and working in in several actuation phases Results show a properly damage detection and classification of different simulated and real-damagesread more
Citations
More filters
Book ChapterDOI
Artificial Intelligence Techniques for Smart City Applications
TL;DR: An overview of ML algorithms used for smart monitoring is presented, providing an overview of categories ofML algorithms for smart Monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.
Book ChapterDOI
Machine Learning Techniques for Structural Health Monitoring
TL;DR: Light is shed on the methodologies to predict the structural damage on concrete structures with the help of sensor technology by effectively combining data science and ML strategies and good accuracy results are obtained with three well-known ML algorithms.
Journal ArticleDOI
Automatic damage type classification and severity quantification using signal based and nonlinear model based damage sensitive features
TL;DR: Results show that by introducing NMBF, classification performance is improved and dimension reduction of features vector using Principal Component Analysis (PCA) allows for high recognition rates while reducing features vector dimension.
Book ChapterDOI
Data-Driven Approach to Structural Health Monitoring Using Statistical Learning Algorithms
Debarshi Sen,Satish Nagarajaiah +1 more
TL;DR: This chapter provides a brief review of applications of statistical learning algorithms, both supervised and unsupervised, in SHM for real-time condition assessment of civil infrastructure systems.
References
More filters
Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI
An introduction to structural health monitoring
Charles R. Farrar,Keith Worden +1 more
TL;DR: Technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner and the historical overview and summarizing the SPR paradigm are provided.
Proceedings ArticleDOI
Health monitoring of civil infrastructures using wireless sensor networks
Sukun Kim,Shamim N. Pakzad,David E. Culler,James Demmel,Gregory L. Fenves,Steven D. Glaser,Turon Martin A +6 more
TL;DR: A Wireless Sensor Network for Structural Health Monitoring is designed, implemented, deployed and tested on the 4200 ft long main span and the south tower of the Golden Gate Bridge and the collected data agrees with theoretical models and previous studies of the bridge.