A novel machine learning‐based algorithm to detect damage in high‐rise building structures

Summary A novel model is presented for global health monitoring of large structures such as high‐rise building structures through adroit integration of 2 signal processing techniques, synchrosqueezed wavelet transform and fast Fourier transform, an unsupervised machine learning technique, the restri...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The structural design of tall and special buildings Jg. 26; H. 18
Hauptverfasser: Rafiei, Mohammad Hossein, Adeli, Hojjat
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Wiley Subscription Services, Inc 25.12.2017
Schlagworte:
Algorithms
Artificial intelligence
Classification
Concrete
Concrete construction
Damage detection
Data processing
Fast Fourier transformations
Feature extraction
Fourier transforms
Global health
health monitoring
High rise buildings
high‐rise building
Information processing
Learning algorithms
Machine learning
neural dynamics model of Adeli and Park
Neural networks
Noise reduction
Reinforced concrete
Signal processing
Structural damage
tall building
Wavelet transforms
ISSN:1541-7794, 1541-7808
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Summary A novel model is presented for global health monitoring of large structures such as high‐rise building structures through adroit integration of 2 signal processing techniques, synchrosqueezed wavelet transform and fast Fourier transform, an unsupervised machine learning technique, the restricted Boltzmann machine, and a recently developed supervised classification algorithm called neural dynamics classification (NDC) algorithm. The model extracts hidden features in the frequency domain of the denoised measured response signals recorded by sensors on different elevations or floors of a structure. The extracted features are used as an input of the NDC to detect and classify the global health of the structure into categories such as healthy, light damage, moderate damage, severe damage, and near collapse. The proposed model is validated using the data obtained from a 3D 1:20 scaled 38‐story reinforced concrete building structure. The results are compared with 3 other supervised classification algorithms: k‐nearest neighbor (KNN), probabilistic neural networks (PNN), and enhanced PNN (EPNN). NDC, EPNN, PNN, and KNN yield maximum average accuracies of 96%, 94%, 92%, and 82%, respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1541-7794
1541-7808
DOI:10.1002/tal.1400