Development of pavement permanent deformation prediction model by grey modelling method

2005 ◽  
Vol 22 (2) ◽  
pp. 109-121 ◽  
Author(s):  
Jia-chong Du ◽  
Der-hsien Shen
Keyword(s):  
Prediction Model ◽  
Permanent Deformation ◽  
Deformation Prediction ◽  
Modelling Method

Modeling the Development of Permanent Deformation in Asphalt Interlayers of Unbonded Concrete Overlays of Concrete Pavements

2020 ◽  
Vol 2674 (9) ◽  
pp. 489-499
Author(s):  
Nicole C. Souder ◽  
John W. DeSantis ◽  
Julie M. Vandenbossche ◽  
Steven G. Sachs
Keyword(s):  
Prediction Model ◽  
Permanent Deformation ◽  
Longitudinal Crack ◽  
Concrete Pavement ◽  
Field Analysis ◽  
Deformation Model ◽  
Concrete Pavements ◽  
Element Analysis ◽  
Deformation Prediction ◽  
State Highway

Unbonded concrete overlay of concrete pavement (UBOL) is an effective rehabilitation method involving the construction of a new concrete pavement over a deteriorated concrete pavement, separated by an interlayer. While UBOL is used in practice to improve the structural capacity of existing concrete pavements, the performance of the interlayer is not currently accounted for in the pavement mechanistic–empirical design process. Therefore, the objective of this research is to improve prediction of UBOL performance by accounting for the effects of asphalt interlayer consolidation on the development of longitudinal cracks in the wheelpath. First, a laboratory investigation was performed using beams cut from in-service pavements in Michigan, Minnesota, and Pennsylvania to assess the susceptibility of permanent deformation of asphalt interlayers. This data was utilized in conjunction with a finite element analysis to develop/calibrate a permanent deformation prediction model for dense graded asphalt interlayers. The framework of the model follows that of the permanent deformation prediction model for asphalt surface pavements incorporated into the American Association of State Highway and Transportation Officials (AASHTO) Mechanistic–Empirical Pavement Design Guide. In addition, a field analysis was conducted, using the Long-Term Pavement Performance (LTPP) database, to assess longitudinal cracking in the wheelpath caused by permanent deformation in asphalt interlayers. The laboratory-calibrated permanent deformation model was then validated using the performance data for UBOLs in the LTPP database and deformation thresholds for asphalt interlayers were established. This research resulted in the development of a framework for the prediction of longitudinal crack development in UBOLs because of permanent deformation in the asphalt interlayer.

Arch Dam Deformation Prediction Model Based on PSO-SVM

2013 ◽  
Vol 351-352 ◽  
pp. 1306-1311 ◽  
Author(s):  
Jing Yang Liu ◽  
He Zhi Liu
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Small Sample ◽  
Arch Dam ◽  
Support Vector ◽  
Model Parameters ◽  
Deformation Prediction ◽  
Dam Foundation ◽  
Model Based ◽  
Dam Deformation

Arch dam has gradually evolved as one of dam type as main large-scale hydraulic project, dam deformation prediction is an important part of dam safety monitoring, and it is difficult to forecast because of the complicated nonlinear characteristics of the monitoring data. Support Vector Machine (SVM) could solve the small sample, nonlinear high dimension problem due to the excellent generalization ability, and hence it has been widely used in the forecast of arch dam deformation. However, the forecast results considerably depend on the choice of SVM model parameters. In this paper, Particle Swarm Optimization (PSO), which has the characteristic of fast global optimization, was applied to optimize the parameters in SVM, and then the dam deformation prediction model based on PSO-SVM could be established. The model is applied to a certain arch dam foundation prediction. The accuracy of this employed approach was examined by comparing it with multiple regression method. In a word, the experimental results indicate that the proposed method based on PSO-SVM can be used in arch dam deformation prediction.

scholarly journals Deformation Modeling of Flexible Pavement in Expansive Subgrade in Texas

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 446
Author(s):  
Asif Ahmed ◽  
MD Sahadat Hossain ◽  
Pratibha Pandey ◽  
Anuja Sapkota ◽  
Boon Thian
Keyword(s):  
Prediction Model ◽  
Field Data ◽  
Temperature Sensors ◽  
Stress Concentrations ◽  
Deformation Prediction ◽  
Rainfall Events ◽  
Rain Gauges ◽  
State Highway ◽  
Subgrade Soil ◽  
Over Time

The tendency of expansive subgrade soil to undergo swelling and shrinkage with the change in moisture has a significant impact on the performance of the pavement. The repeated cycles of wet and dry periods throughout a year lead to considerable stress concentration in the pavement subgrade soil. Such stress concentrations leads to the formation of severe pavement cracks. The objective of the research is to develop a prediction model to estimate the deformation of pavement over expansive subgrade. Two pavement sites—one farm to market road and one state highway—were monitored regularly using moisture and temperature sensors along with rain gauges. Additionally, geophysical testing was performed to obtain a continuous profile of the subgrade soil over time. Topographical surveying and horizontal inclinometer readings were taken to determine pavement deformation. The field monitoring data resulted in a maximum movement up to 80 mm in the farm to market road, and almost 38 mm in the state highway. The field data were statistically evaluated to develop a deformation prediction model. The validation of the model indicated that only a fraction of the deformation was reflected by seasonal variation, while inclusion of rainfall events in the equation significantly improved the model. Furthermore, the prediction model also incorporated the effects of change in temperature and resistivity values. The generated model could find its application in predicting pavement deformation with respect to rainfall at any time of the year.

scholarly journals A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1288 ◽  
Author(s):  
Sunwen Du ◽  
Guorui Feng ◽  
Jianmin Wang ◽  
Shizhe Feng ◽  
Reza Malekian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Field Data ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Deformation Model ◽  
Machine Learning Method ◽  
Deformation Prediction ◽  
New Machine

Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners.

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