Development for Mixed Lifespan Prediction Model of Expansion Joint Using Field Data

2019 ◽  
Vol 19 (1) ◽  
pp. 48-54
Author(s):  
Jungsoo Oh ◽  
Bongsoo Lee
Keyword(s):  
Prediction Model ◽  
Field Data ◽  
Expansion Joint

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 DIRECTIONAL PREDICTION MODEL OF WAVES WITH VARIABLE WIND

1988 ◽  
Vol 1 (21) ◽  
pp. 51
Author(s):  
Juan R. Acinas
Keyword(s):  
Prediction Model ◽  
Field Data ◽  
Numerical Results ◽  
Wave Component ◽  
Energy Propagation ◽  
Variable Wind ◽  
The Waves ◽  
Theoretical Results

A model of the response of the direction of the waves to changing winds is presented. First the theoretical results that associate an average direction of energy propagation to every wave component is introduced. From this model, numerical results are presented both by simulations of ideal wind veering situations and compared with field data taken from pitch and roll buoys.

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.

Prediction of Aeroacoustic Noise Levels in Cross-Flow Fans for Air Conditioners

Volume 1 ◽  
2004 ◽  
Author(s):  
Shigehisa Funabashi ◽  
Yasushi Shigenaga ◽  
Masatoshi Watanabe ◽  
Yoshihiro Takada
Keyword(s):  
Fluid Dynamics ◽  
Prediction Model ◽  
Field Data ◽  
Cross Flow ◽  
Close Relation ◽  
Flow Coefficient ◽  
Noise Levels ◽  
Air Conditioners ◽  
Aeroacoustic Noise ◽  
Good Agreement

We have developed a prediction model for the overall level of aeroacoustic noise generated by cross-flow fans. It is based on two assumptions: the velocities along the outside edge of the impeller have an especially close relation to the noise levels, and the noise levels are in proportion to the sixth power of the relative velocities at the edge of the blade. A computational fluid dynamics is used to obtain the necessary velocity-field data for this prediction model. The predicted noise levels of a test impeller for different flow coefficients are in good agreement with the measured results. This means that variations in noise levels with the flow coefficient can be described accurately by the prediction model, which should prove to be a useful tool for speeding up the development of silent cross-flow fans.

Field Validation of a TBC Life-Prediction Model for Land-Based Gas Turbines

2010 ◽  
Author(s):  
K. S. Chan ◽  
N. S. Cheruvu
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Bond Coat ◽  
Gas Turbines ◽  
Life Prediction ◽  
Field Data ◽  
Degradation Mechanisms ◽  
Curvature Effects ◽  
Model Predictions ◽  
Life Prediction Model

The coating life-prediction model, COATLIFE, was previously developed for estimating the lifetimes of first-stage blades and vanes in land-based power-generation gas turbines on the basis of degradation mechanisms observed in laboratory and field data. For first-stage blades with thermal barrier coatings (TBCs), degradation mechanisms treated in COATLIFE include thermo-mechanical fatigue (TMF), Al depletion due to bond coat oxidation, sintering of voids and microcracks in TBC, and curvature effects. Material constants in COATLIFE were evaluated using laboratory data and subsequently utilized with the model to predict the remaining life of first-stage blades in the field. In the present study, the predictive capabilities of COATLIFE were evaluated against field data obtained from first-stage blades with TBC extracted from land-based power generation gas turbines. The ex-service blades were sectioned to characterize the conditions of the TBC and bond coat after various times of service. For coating characterization, the Al content and volume fraction of the β phase in the bond coat, as well as the extent of oxidation and microcracking in the TBCs and along the TBC/bond coat interface at various locations of the blade were determined. These results were compared against model predictions generated by COATLIFE. Good agreement between the field data and model predictions validates the predictive capabilities of COATLIFE for estimating the oxidation lives for first-stage blades with TBCs.

scholarly journals Field data–driven online prediction model for icing load on power transmission lines

2019 ◽  
Vol 53 (1-2) ◽  
pp. 126-140
Author(s):  
Yong Chen ◽  
Peng Li ◽  
Wenping Ren ◽  
Xin Shen ◽  
Min Cao
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Transmission Lines ◽  
Prediction Accuracy ◽  
Field Data ◽  
Power Transmission ◽  
Data Driven ◽  
Support Vector ◽  
Model Parameters ◽  
Online Prediction

Methods for the accurate prediction of icing loads in overhead transmission lines have become an important research topic for electrical power systems as they are necessary for ensuring the safety and stability of power-grid operations. Current machine learning models for the prediction of icing loads on transmission lines are afflicted by the following issues: insufficient prediction accuracy, high randomity in the selection of kernel functions and model parameters, and a lack of generalizability. To address these issues, we propose a field data–driven online prediction model for icing loads on transmission lines. First, the effects of the type of kernel function used in the support vector regression algorithm on the prediction accuracy of the model were analyzed using micrometeorological data and icing data collected by on-site monitoring systems. The particle swarm optimization algorithm was then used to optimize and determine the model parameters such as penalty coefficients. An offline support vector regression prediction model was thus constructed. Using the accurate online support vector regression algorithm, the weighting coefficients of the samples were dynamically adjusted to satisfy the Karush–Kuhn–Tucker conditions, which allowed online updates to be made to the regression function and prediction model. Finally, a simulation analysis was performed using actual icing incidents that occurred in a transmission line of the Yunnan Power Grid, which demonstrated that our model can make online predictions for the icing load on transmission lines in actual applications. Our model proved to be superior to conventional icing-load prediction models with regard to the single-step and multi-step prediction accuracies and generalizability. Hence, our prediction model will improve the decision-making processes regarding the deicing and maintenance of power transmission and transformation systems.

scholarly journals Combined Experimental and Field Data Sources in a Prediction Model for Corrosion Rate under Insulation

2019 ◽  
Vol 11 (23) ◽  
pp. 6853 ◽  
Author(s):  
Nurul Rawaida Ain Burhani ◽  
Masdi Muhammad ◽  
Nurfatihah Syalwiah Rosli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Model ◽  
Field Data ◽  
Industrial Applications ◽  
Data Sources ◽  
Uniform Corrosion ◽  
Prediction Modeling ◽  
Artificial Neural

Corrosion under insulation (CUI) is one of the increasing industrial problems, especially in chemical plants that have been running for an extended time. Prediction modeling, which is one of the solutions for this issue, has attracted increasing attention and has been considered for several industrial applications. The main objective of this work was to investigate the effect of combined data input in prediction modeling, which could be applied to improve the existing CUI rate prediction model. Experimental data and field historical data were gathered and simulated using an artificial neural network separately. To analyze the effect of data sources on the final corrosion rate under the insulation prediction model, both sources of data from experiment and field data were then combined and simulated again using an artificial neural network. Results exhibited the advantages of combined input data type from the experiment and field in the final prediction model. The model developed clearly shows the occurrence of corrosion by phases, which are uniform corrosion at the early phases and pitting corrosion at the later phases. The prediction model will enable better mitigation actions in preventing loss of containment due to CUI, which in turn will improve overall sustainability of the plant.

Sand Production Forecasts for Unconsolidated Gas Reservoirs

2009 ◽  
Vol 62-64 ◽  
pp. 466-473
Author(s):  
Babs Mufutau Oyeneyin ◽  
Babatunde Moriwawon
Keyword(s):  
Prediction Model ◽  
Field Data ◽  
Integrated Approach ◽  
Technology Selection ◽  
Thick Wall ◽  
Sand Production ◽  
Gas Reservoirs ◽  
Production Forecast ◽  
Transient Phenomena ◽  
Independent Field

Development of appropriate strategy for the management of reservoirs with sanding problems is rather complex and requires an integrated approach to finding the optimum solution to solving the problem. This requires integration of key aspects of reservoir characterisation, drilling, completion and production technologies including sand tolerances (Seabed wellhead/flow lines, topside facilities. Providing an accurate forecast of the tolerance depends on accurate prediction of sand failure and the corresponding volume of produced sand. This is a transient phenomena further complicated by gas reservoir fluid flow. In this paper the results of a comprehensive Thick Wall Cylinder[TWC] experimental sand production studies carried out on synthetic sandstones are presented. The sand production prediction model for liquid flow are further calibrated and upscaled with field data for gas reservoirs. The prediction model developed is further validated with independent field data with good results. The results represent a first for sand production forecast for gas reservoirs. Mitigation of sanding requires reliable sanding prediction, precise well design, accurate technology selection as well as optimum completion strategy.

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