Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …
to increased exposure and vulnerability of structural and infrastructure systems to hazards …
Applications of machine learning to wind engineering
Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
Physics guided wavelet convolutional neural network for wind-induced vibration modeling with application to structural dynamic reliability analysis
Deep neural network (NN) has become one of the common choices of surrogate model for
reliability analysis of structural dynamic response under complex wind loads. However, the …
reliability analysis of structural dynamic response under complex wind loads. However, the …
Knowledge-enhanced generative adversarial networks for schematic design of framed tube structures
In the schematic design phase of framed tube structures, component sizing is a vital task that
requires expert experience and domain knowledge. Deep learning-based structural design …
requires expert experience and domain knowledge. Deep learning-based structural design …
A Bayesian deep learning approach for random vibration analysis of bridges subjected to vehicle dynamic interaction
Vehicle actions represent the main operational loading for various types of bridges. It is
essential to conduct random vibration analysis due to the unavoidable uncertainties arising …
essential to conduct random vibration analysis due to the unavoidable uncertainties arising …
A knowledge‐enhanced deep reinforcement learning‐based shape optimizer for aerodynamic mitigation of wind‐sensitive structures
Structural shape optimization plays an important role in the design of wind‐sensitive
structures. The numerical evaluation of aerodynamic performance for each shape search …
structures. The numerical evaluation of aerodynamic performance for each shape search …
Machine learning approach to model order reduction of nonlinear systems via autoencoder and LSTM networks
In analyzing and assessing the condition of dynamical systems, it is necessary to account for
nonlinearity. Recent advances in computation have rendered previously computationally …
nonlinearity. Recent advances in computation have rendered previously computationally …
[HTML][HTML] A comparative study of wind-induced dynamic response models of long-span bridges using artificial neural networks, support vector regression and buffeting …
Long-span cable-supported bridges are structures susceptible to high dynamic responses
due to the buffeting phenomenon. The current state-of-the-art method for buffeting response …
due to the buffeting phenomenon. The current state-of-the-art method for buffeting response …
Dynamic response prediction of vehicle-bridge interaction system using feedforward neural network and deep long short-term memory network
Vehicular loads represent one of the most critical external dynamic actions on the bridge
structures, especially the short-and medium-span bridges. The dynamic interactions …
structures, especially the short-and medium-span bridges. The dynamic interactions …
Physics-informed long short-term memory networks for response prediction of a wind-excited flexible structure
Slender and flexible infrastructures such as sign supports, cantilever traffic signal structures
and high mast lighting towers are sensitive to wind force and were reported to have fatigue …
and high mast lighting towers are sensitive to wind force and were reported to have fatigue …