[HTML][HTML] Machine learning for bridge wind engineering
Z Zhang, S Li, H Feng, X Zhou, N Xu, H Li… - Advances in Wind …, 2024 - Elsevier
Modeling and control are primary domains in bridge wind engineering. The natural wind
field characteristics (eg, non-stationary, non-uniform, spatial-temporal changing …
field characteristics (eg, non-stationary, non-uniform, spatial-temporal changing …
Machine Learning for public transportation demand prediction: A Systematic Literature Review
Abstract Within the Intelligent Public Transportation Systems (IPTS) field, the prediction of
public transportation demand is a key point for enhancing the quality of the services. These …
public transportation demand is a key point for enhancing the quality of the services. These …
Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting
Wind energy is one of the most widely used and fastest-growing renewable energy. Wind
speed prediction is an efficient way to rationally dispatch wind power generation and ensure …
speed prediction is an efficient way to rationally dispatch wind power generation and ensure …
Adaboost algorithm in artificial intelligence for optimizing the IRI prediction accuracy of asphalt concrete pavement
C Wang, S Xu, J Yang - Sensors, 2021 - mdpi.com
The international roughness index (IRI) for roads is a crucial pavement design criterion in
the Mechanistic-Empirical Pavement Design Guide (MEPDG). However, studies have shown …
the Mechanistic-Empirical Pavement Design Guide (MEPDG). However, studies have shown …
Bayesian dynamic regression for reconstructing missing data in structural health monitoring
Massive data that provide valuable information regarding the structural behavior are
continuously collected by the structural health monitoring (SHM) system. The quality of …
continuously collected by the structural health monitoring (SHM) system. The quality of …
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 …
Sparse Gaussian process regression for multi-step ahead forecasting of wind gusts combining numerical weather predictions and on-site measurements
Accurate forecasts of wind gusts are crucially important for wind power generation, severe
weather warnings, and the regulation of vehicle speed. To improve the short-term and long …
weather warnings, and the regulation of vehicle speed. To improve the short-term and long …
Data set from wind, temperature, humidity and cable acceleration monitoring of the Jiashao bridge
Y Ding, XW Ye, Y Guo - Journal of Civil Structural Health Monitoring, 2023 - Springer
With the development of structural health monitoring (SHM) technology, digital of long-span
bridge construction has become the focus of intelligent construction in the future. Data have …
bridge construction has become the focus of intelligent construction in the future. Data have …
[HTML][HTML] Machine learning-enabled estimation of crosswind load effect on tall buildings
This paper presents an approach to predict crosswind force spectra and associated
response of tall buildings with rectangular cross-section based on machine learning (ML) …
response of tall buildings with rectangular cross-section based on machine learning (ML) …
[HTML][HTML] Prediction and early warning of wind-induced girder and tower vibration in cable-stayed bridges with machine learning-based approach
XW Ye, Z Sun, J Lu - Engineering Structures, 2023 - Elsevier
Long-span cable-stayed bridges are prone to significant vibrations under strong wind events
such as typhoons, which pose a risk to the bridge functioning and the driving safety of …
such as typhoons, which pose a risk to the bridge functioning and the driving safety of …