The paper presents a systematic review about the use of artificial intelligence (AI) in the field
of structural health management of existing bridges. Using the PRISMA protocol, 81 journal …

Railway systems are time-varying and complex systems with nonlinear behaviors that
require effective optimization techniques to achieve optimal performance. Evolutionary …

This work proposes a supervised Deep Learning approach for damage identification in
bridge structures. We employ a hybrid methodology that incorporates Finite Element …

Structural health monitoring (SHM) is a substantial research direction in structural
engineering being scrutinized in recent years due to its significance in ensuring structural …

High-frequency train loads and complex environmental factors over a long period will
inevitably cause subgrade settlement, an issue that adversely affects line smoothness. The …

Existing studies have utilized highly efficient partial least-squares regression (PLSR) to
estimate nodal loads of the entire bridge using a small number of bridge sensors, and when …

Bridge damage detection methods based on moving load induced response and deep
learning are popular, and they are effective when the training data generated by a finite …

Structural health monitoring is an important research field being investigated around the
globe. In recent years, meta-heuristics are being used to solve the complex inverse problem …

Many damage detection strategies have been developed within the field of structural health
monitoring, showing promising results in real‐world applications. Most of them rely on the …

As the precision of model updating based on modal parameters usually depends on the
accuracy of mode shapes, which are frequently affected by measurement noise and spatial …