ANN-Based Fatigue and Rutting Prediction Models Versus Regression-Based Models for Flexible Pavements

2019 ◽  
pp. 117-133
Author(s):  
Mostafa M. Radwan ◽  
Mostafa A. Abo-Hashema ◽  
Hamdy P. Faheem ◽  
Mostafa D. Hashem
Keyword(s):  
Prediction Models ◽  
Flexible Pavements

Evaluation of Mechanistic-Empirical Performance Prediction Models for Flexible Pavements

1998 ◽  
Vol 1629 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Hesham A. Ali ◽  
Shiraz D. Tayabji
Keyword(s):  
Prediction Models ◽  
Flexible Pavements ◽  
Fatigue Cracking ◽  
Continuous Functions ◽  
Poor Agreement ◽  
Empirical Performance ◽  
Pavement Structures ◽  
Using Data ◽  
Program Data

In recognition of the potential of mechanistic-empirical (M-E) methods in analyzing pavements and predicting their performance, pavement engineers around the country have been advocating the movement toward M-E design methods. In fact, the next AASHTO Guide for Design of Pavement Structures is planned to be mechanistically based. Since many of the performance models used in the M-E methods are laboratory-derived, it is important to validate these models using data from in-service pavements. The Long-Term Pavement Performance (LTPP) program data provide the means to evaluate and improve these models. The fatigue and rutting performances of LTPP flexible pavements were predicted using some well-known M-E models, given the loading and environmental conditions of these pavements. The predicted performances were then compared with actual fatigue cracking and rutting observed in these pavements. Although more data are required to arrive at a more conclusive evaluation, fatigue cracking models appeared to be consistent with observations, whereas rutting models showed poor agreement with the observed rutting. Continuous functions that relate fatigue cracking to fatigue damage were developed.

Multi-input performance prediction models for flexible pavements using LTPP database

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
M. A. Younos ◽  
R. T. Abd El-Hakim ◽  
S. M. El-Badawy ◽  
H. A. Afify
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Flexible Pavements

Mechanistic-Empirical Rut Prediction Model for In-Service Pavements

2000 ◽  
Vol 1730 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Hyung Bae Kim ◽  
Neeraj Buch ◽  
Dong-Yeob Park
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Flexible Pavements ◽  
Proposed Model ◽  
Major Mode ◽  
Global Positioning ◽  
Pavement Engineering ◽  
Using Data ◽  
Consistency Properties

Rutting is a major mode of failure in flexible pavements. Development of accurate predictive rut performance models is an ongoing pursuit of the pavement engineering community. This has resulted in a plethora of rut prediction models ranging from purely mechanistic to empirical. Presented is the development of a mechanistic-empirical rut prediction model that uses data from 39 in-service flexible pavements from Michigan. The proposed model accounts for the rut contribution of the subgrade, subbase, base, and asphalt concrete layers. The model addresses inventory-type variables like pavement cross section, ambient temperature, and asphalt consistency properties. The applicability of the model was validated by using data from 24 Long-Term Pavement Performance–Global Positioning System (GPS) sites. For 19 of the 24 GPS sites, the predicted rut depth was within 5 mm of the measured rut depth.

scholarly journals Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
C. Makendran ◽  
R. Murugasan ◽  
S. Velmurugan
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Models ◽  
Flexible Pavements ◽  
Linear Regression Analysis ◽  
Road Construction ◽  
Multiple Linear Regression Analysis ◽  
Low Volume Roads ◽  
Low Volume

Prediction models for low volume village roads in India are developed to evaluate the progression of different types of distress such as roughness, cracking, and potholes. Even though the Government of India is investing huge quantum of money on road construction every year, poor control over the quality of road construction and its subsequent maintenance is leading to the faster road deterioration. In this regard, it is essential that scientific maintenance procedures are to be evolved on the basis of performance of low volume flexible pavements. Considering the above, an attempt has been made in this research endeavor to develop prediction models to understand the progression of roughness, cracking, and potholes in flexible pavements exposed to least or nil routine maintenance. Distress data were collected from the low volume rural roads covering about 173 stretches spread across Tamil Nadu state in India. Based on the above collected data, distress prediction models have been developed using multiple linear regression analysis. Further, the models have been validated using independent field data. It can be concluded that the models developed in this study can serve as useful tools for the practicing engineers maintaining flexible pavements on low volume roads.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

Sign in / Sign up

Export Citation Format

Share Document