In the field of structural health monitoring (SHM), with mature development artificial intelligence, deep learning-based damage identification techniques have attracted wide attention. this paper, convolutional neural network (CNN) is used to extract feature simple supported steel beams. Firstly, transient dynamic analysis beam carried out by finite element software, and acceleration response s...

The time series analysis approach for damage detection employed by Los Alamos National Laboratory (LANL) is modified and applied to the benchmark building designed by ASCE task group for damage detection and localization. A two-tier prediction model, using the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX) is used for obtaining the damage sensitive feature. The da...

This paper provides an overview to detect, locate, and characterize fault in structural and mechanical systems using various methods. Since last few decades experimentally measured frequencies has been extensively used to crack detection. The relationships between frequency changes and structural damage are discussed. Various reverse techniques proposed for detecting fault using natural frequen...

We demonstrate that the unique characteristics of random lasing in bone can be used to assess nanoscale structural alterations as a mechanical or structural biosensor, given that bone is a partially disordered biological nanostructure. In this proof-of-concept study, we conduct photoluminescence experiments on cortical bone specimens that are loaded in tension under mechanical testing. The ultr...

The value of using permanently installed monitoring systems for managing the life an engineering asset is determined by confidence in its damage detection capabilities. A framework proposed that integrates data from with probabilistic structural integrity assessments. Probability (POD) curves are used combination particle filtering methods to recursively update a distribution postulated defect ...