Xufeng Niu

Area of research

Artificial Intelligence · Global and Planetary Change

Research interest

Research topics from publications: Prediction of the crack condition of highway pavements using machine learning models; Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors; Effects of error covariance structure on estimation of model averaging weights and predictive performance; Assessment of Deterioration of Highway Pavement using Bayesian Survival Model; Boundary Detection Using a Bayesian Hierarchical Model for Multiscale Spatial Data; Time-varying coefficient models with ARMA–GARCH structures for longitudinal data analysis; Tests and classification methods in adaptive designs with applications; Transformation Models for Survival Data Analysis with Applications. Representative work: Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functiona Deteriorating highway pavement conditions have largely been evaluated through visual inspection, nondestructive evaluations, smart sensing technologies, and image analysis techniques. These techniques have been successful in rating the conditions of roadway segments, but have also been faced with the challenge of subjective uncertainties and errors, signal noise, electrical and electromagnetic interference, and other effects on a large-scale implementation for the forecasting of the future condition of pavement sections. The goal of this paper is to implement some machine learning methodologies in predicting the condition of highway pavements based on previous pavement condition ratings and

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