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Xianhua Chen

Bio: Xianhua Chen is an academic researcher from Southeast University. The author has contributed to research in topics: Asphalt & Asphalt concrete. The author has an hindex of 14, co-authored 72 publications receiving 652 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the effects of electrically conductive additives (steel fiber and graphite) on the laboratory-measured electrical and mechanical properties of asphalt concrete were investigated, and the results from this study indicate that the critical embedded steel fiber length is 9.6mm to maximize the fiber's potential to bridge across the crack from single fiber tensile test.

84 citations

Journal ArticleDOI
TL;DR: Compared with the back propagation neural network model and the support vector machine model, the proposed GBDT model can produce more accurate prediction results, especially in multi-step prediction, indicating that GBDT is a promising method in travel time prediction.
Abstract: To improve the prediction accuracy of traffic flow, a travel time prediction model based on gradient boosting decision tree (GBDT) is proposed. In order to test the applicability of GBDT, models with different prediction horizons (5 min ahead, 10 min ahead, and 15 min ahead) are established. The 11 variables are viewed as candidates in this paper. Different from other machine learning algorithms as black boxes, GBDT can provide interpretable results through variable importance. In the proposed model, the variable importance shows that for different prediction horizons, the most important influence variable is uniform, which is travel time in the current period. Traffic conditions in the current period have the greatest influence on the predicted travel time. Compared with the back propagation neural network model and the support vector machine model, the proposed GBDT model can produce more accurate prediction results, especially in multi-step prediction, indicating that GBDT is a promising method in travel time prediction.

76 citations

Journal ArticleDOI
30 Nov 2013-Wear
TL;DR: In this paper, the development of skid resistance of asphalt road surfaces was simulated in a laboratory and the Aachen Polishing Machine (APM) with real tires under various conditions and their skid resistances were determined using the Wehner/Schulze machine.

69 citations

Journal ArticleDOI
15 Oct 2014-Wear
TL;DR: The contribution of micro-texture to the skid resistance can be described with a function of the two texture parameters as mentioned in this paper, and the results showed that microtexture changes of the slabs are related to the applied polishing agent, initial roughness and mineralogical compositions of the granite slabs.

62 citations

Journal ArticleDOI
Xing Cai1, Jiayun Zhang, Gang Xu1, Gong Minghui, Xianhua Chen1, Jun Yang1 
TL;DR: In this paper, the aging behavior of bio-rejuvenated Pen70 and SBS modified asphalts using internal aging indexes that characterize the physical property was evaluated, and two potential aging indexes related with activation energy and viscosity of dashpot separately were found linearly correlated with related chemical aging functional groups.

62 citations


Cited by
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Journal ArticleDOI
Ali Behnood1
TL;DR: A review of the literature on the applications of various types of rejuvenators in paving industry and their effects on the properties of the aged binders is presented in this paper, where the techniques for rejuvenating the aged asphalt binders and the mechanism of rejuvenation are also reviewed.

178 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a model that incorporates different methods to achieve effective prediction of heart disease, which used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model.
Abstract: Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

169 citations

Journal ArticleDOI
TL;DR: The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.
Abstract: With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability, and quality of smart grids have become increasingly important. The renewable energy output forecasting applications have also been developing rapidly in recent years, and such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, and the model is able to generate a 24-h probabilistic and deterministic forecasting of PV power output based on meteorological information, such as temperature, solar radiation, and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. The results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.

152 citations

Journal ArticleDOI
TL;DR: In this paper, a review of the existing rutting solutions and test methods for asphalt pavement is presented, which is expected to provide an overall insight on the existing solutions and recommend future studying areas relevant to the problem of permanent deformation of asphalt pavement.

144 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a review of the mechanical impact of fibers in hot mix asphalt by analyzing their reinforcement effect in a qualitative and quantitative manner, and the results of mechanical improvement are displayed.

125 citations