Composite Multiscale Cross-Sample Entropy Analysis for Long-Term Structural Health Monitoring of Residential Buildings.

Tzu-Kang Lin,Dong-You Lee

doi:10.3390/e23010060

Abstract

This study proposesd a novel, entropy-based structural health monitoring (SHM) system for measuring microvibration signals generated by actual buildings. A structural health diagnosis interface was established for demonstration purposes. To enhance the reliability and accuracy of entropy evaluation at various scales, composite multiscale cross-sample entropy (CMSCE) was adopted to increase the number of coarse-grained time series. The degree of similarity and asynchrony between ambient vibration signals measured on adjacent floors was used as an in-dicator for structural health assessment. A residential building that has been monitored since 1994 was selected for long-term monitoring. The accumulated database, including both the earthquake and ambient vibrations in each seismic event, provided the possibility to evaluate the practicability of the CMSCE-based method. Entropy curves obtained for each of the years, as well as the stable trend of the corresponding damage index (DI) graphs, demonstrated the relia-bility of the proposed SHM system. Moreover, two large earthquake events that occurred near the monitoring site were analyzed. The results revealed that the entropy values may have been slightly increased after the earthquakes. Positive DI values were obtained for higher floors, which could provide an early warning of structural instability. The proposed SHM system is highly stable and practical.

Highlights

Infrastructures, including bridges, dams, and buildings have been widely set up to protect and facilitate our daily life
As the structural performance of a building which is frequently subjected to natural hazards or human use is likely to lose its original strength over time, how to evaluate whether the building is safe or not has become an important issue
In 2010, inspired by the field of bio-medical engineering, an structural health monitoring (SHM) system was implemented by integrating the naïve Bayesian (NB) classification and DNA-like expression data, taking advantage of a double-tier re-gression process to extract the expression array from the recorded structural time history [5]

Summary

Introduction

Infrastructures, including bridges, dams, and buildings have been widely set up to protect and facilitate our daily life. In 1991, Pincus improved the Kolmogorov entropy with approximate entropy (ApEn), which could measure the complexity and regularity of time series in real life without the need for coarse-grained processes [9]. Richman published sample entropy (SampEn) in 2000, improving the shortcomings of ApEn. The advantage of sample entropy is that the length of time series has no effect on the analysis results and the error of ApEn can be reduced [10,11]. The aim of this study was to develop an entropy-based SHM system to effectively monitor long-term structural safety. Based on previous research and experimental results, composite multiscale cross-sample entropy (CMSCE), which had good performance in accuracy and reliability, was adopted as the core SHM algorithm.

Discussion

DI Measure

Introduction of the Dexin Residential Building

Conclusions