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Machine learning
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Machine learning approaches for improving condition-based maintenance of naval propulsion plants
Scour Depth Evaluation of a Bridge with a Complex Pier Foundation
Vibration-based Damage Detection in Bridges via Machine Learning
A reinforcement learning model for personalized driving policies identification
Flood Hazard Rating Prediction for Urban Areas Using Random Forest and LSTM
Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction
Predicting the traction power of metropolitan railway lines using different machine learning models
Pavement Surface Condition Index Prediction Based on Random Forest Algorithm
Tree-Based Ensemble Methods: Predicting Asphalt Mixture Dynamic Modulus for Flexible Pavement Design
An optimized metamodel for predicting damage and oil outflow in tanker collision accidents
Truck industry classification from anonymous mobile sensor data using machine learning
Data-driven model for ship encounter intention inference in intersection waters based on AIS data
Transportation Mode Detection by Using Smartphones and Smartwatches with Machine Learning
Predicting Hydrological Drought Alert Levels Using Supervised Machine-Learning Classifiers
Using supervised machine learning algorithms in pavement degradation monitoring
Automatic clustering-based approach for train wheels condition monitoring
Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification
The impact of the combination equilibrium of horizontal and sag-vertical curves on safety
Representative truck activity patterns from anonymous mobile sensor data
Inferring Trip Destination Purposes for Trip Records Collected through Smartphone Apps
Seismic Acceleration Estimation Method at Arbitrary Position Using Observations and Machine Learning
Do Perceptions of Hydrogen Energy Effect on Vehicle Preference? A Learning-Based Model Approach
Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting
A Machine Learning Tool for Pavement Design and Analysis
Large-Scale Freeway Traffic Flow Estimation Using Crowdsourced Data: A Case Study in Arizona