Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Condition Prediction of Existing Concrete Bridges as a Combination of Visual Inspection and Analytical Models of Deterioration

(2019) Ivan Zambon et al.   Applied Sciences-Basel

A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges

(2019) Marco Carnevale et al.   Applied Sciences-Basel

Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

(2018) A. Khosravi et al.   APPLIED ENERGY

Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

(2018) Jie Chen et al.   ENERGY CONVERSION AND MANAGEMENT

On switching response surface models, with applications to the structural health monitoring of bridges

(2018) K. Worden et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Structural Health Monitoring and Time-Dependent Effects Analysis of Self-Anchored Suspension Bridge with Extra-Wide Concrete Girder

(2018) Guangpan Zhou et al.   Applied Sciences-Basel

In-Service Condition Assessment of a Long-Span Suspension Bridge Using Temperature-Induced Strain Data

(2017) Qi Xia et al.   Journal of Bridge Engineering

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

(2016) Haobo Lyu et al.   Remote Sensing

Ambient vibration testing and seismic behavior of historical arch bridges under near and far fault ground motions

(2015) Barış Sevim et al.   Bulletin of Earthquake Engineering

Environmental effects on the dynamic characteristics of the Gülburnu Highway Bridge

(2014) Alemdar Bayraktar et al.   CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS

Prediction of Bridge Monitoring Information Chaotic Using Time Series Theory by Multi-step BP and RBF Neural Networks

(2013) Jianxi Yang et al.   INTELLIGENT AUTOMATION AND SOFT COMPUTING

Analysis and assessment of bridge health monitoring mass data—progress in research/development of “Structural Health Monitoring”

(2012) AiQun Li et al.   Science China-Technological Sciences