To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …

Crack is common damage that can reduce the durability of concrete structures and
accelerate structural degradation. With intent to improve the accuracy and efficiency of …

In this paper, a novel enhanced Particle Swarm Optimization (PSO) algorithm is introduced
for solving damage identification problems. For the first time, the algorithm is applied to a …

Up to now, extensive literature on the usage of deep learning in manufacturing can be
found. Though, actual usage of deep learning in manufacturing sites is somehow restrained …

Many physics-based and surrogate models used in structural health monitoring are affected
by different sources of uncertainty such as model approximations and simplified …

There has been growing interest in applying the artificial neural network (ANN) approach in
structural system identification and health monitoring. The learning process of neural …

Due to the influence of various uncertain factors, there will inevitably be certain errors
between the prediction of finite-element (FE) model and observed data for a target structure …

The vibration response data measured from the structure contains uncertainties and
requires different techniques for its analysis. The present study involves the comprehensive …

Structural damage detection techniques are gaining widespread attention in construction
engineering and management. However, the scarcity of structural damage samples and the …

This work presents the ML model in which data collected from the open access repository
where experiments conducted on steel structure bridge data for 1-year duration are …