A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network

TL;DR

This paper introduces a new method combining Stockwell transform spectrograms and deep CNNs to accurately detect deterioration and damage in building structures using ambient vibration data.

Contribution

It presents a novel damage and deterioration identification procedure that integrates Stockwell transform analysis with deep convolutional neural networks for the first time in building health monitoring.

Findings

High accuracy in damage detection achieved

Effective analysis of ambient vibrations with noise

First combined use of ST and CNN for building assessment

Abstract

In this paper, a novel deterioration and damage identification procedure (DIP) is presented and applied to building models. The challenge associated with applications on these types of structures is related to the strong correlation of responses, which gets further complicated when coping with real ambient vibrations with high levels of noise. Thus, a DIP is designed utilizing low-cost ambient vibrations to analyze the acceleration responses using the Stockwell transform (ST) to generate spectrograms. Subsequently, the ST outputs become the input of two series of Convolutional Neural Networks (CNNs) established for identifying deterioration and damage to the building models. To the best of our knowledge, this is the first time that both damage and deterioration are evaluated on building models through a combination of ST and CNN with high accuracy.