scholarly journals Sensitivity Analysis of 2002 Design Guide Distress Prediction Models for Jointed Plain Concrete Pavement

2006 ◽  
Vol 1947 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Venkata Kannekanti ◽  
John Harvey
Keyword(s):  
Sensitivity Analysis ◽  
Prediction Models ◽  
Plain Concrete ◽  
Concrete Pavement ◽  
Distress Prediction ◽  
Design Guide

Mechanistic Evaluation of Test Data from Long-Term Pavement Performance Jointed Plain Concrete Pavement Test Sections

1998 ◽  
Vol 1629 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Y. Jane Jiang ◽  
Shiraz D. Tayabji
Keyword(s):  
Test Data ◽  
Additional Data ◽  
Fatigue Cracking ◽  
Plain Concrete ◽  
Concrete Pavement ◽  
Pavement Performance ◽  
Distress Prediction ◽  
Program Data ◽  
Insight Into

Over the years, pavement engineers have attempted to develop rational mechanistic-empirical (M-E) methods for predicting pavement performance. In fact, the next version of AASHTO’s Guide for Design of Pavements is planned to be mechanistically based. Many M-E procedures have been developed on the basis of a combination of laboratory test data, theory, and limited field verification. Therefore, it is important to validate and calibrate these procedures using additional data from in-service pavements. The Long-Term Pavement Performance (LTPP) program data provide the means to evaluate and improve these models. A study was conducted to assess the performance of some of the existing concrete pavement M-E-based distress prediction procedures when used in conjunction with the data being collected as part of the LTPP program. Fatigue cracking damage was estimated using the NCHRP 1–26 approach and compared with observed fatigue damage at 52 GPS-3 test sections. It was shown that the LTPP data can be used successfully to develop better insight into pavement behavior and to improve pavement performance.

Adaptation and Calibration of the Faulting Model for Thin Concrete Pavements

2021 ◽  
Author(s):  
Carlos Echevarría ◽  
Juan Pablo Covarrubias
Keyword(s):  
High Speed ◽  
Field Data ◽  
Plain Concrete ◽  
Concrete Pavement ◽  
Comfort Level ◽  
Concrete Pavements ◽  
Differential Model ◽  
Pavement Distress ◽  
Design Guide ◽  
Design Software

Joint faulting is a pavement distress that affects the comfort level of jointed plain concrete pavements. The appearance of joint faulting usually occurs in areas of high traffic of trucks at high speed. Variables such as level of rainfall and the erodibility of the subbase increases the magnitude of this phenomenon. To predict joint faulting in Thin Concrete Pavements, the design software OptiPave2, launched in 2012, used the same model developed for the Mechanistic Empirical Pavement Design Guide (MEPDG), which uses an energy differential model. After 6 years of the release of the software and after 10 years since the construction of some thin concrete pavement projects, there are pavements with clear signs of joint faulting and others without. For this reason, the OptiPave2 model was reviewed and compared with field data, concluding that the faulting model needed to be adjusted This new model was calibrated with the data from existing concrete pavement projects.

Distress Prediction Models for Rigid Pavements for Texas Pavement Management Information System

1996 ◽  
Vol 1524 (1) ◽  
pp. 145-151 ◽  
Author(s):  
Cristopher Robinson ◽  
Muhammad Arif Beg ◽  
Terry Dossey ◽  
W. Ronald Hudson
Keyword(s):  
Management Information System ◽  
Transverse Crack ◽  
Prediction Models ◽  
Concrete Pavement ◽  
Pavement Management ◽  
Portland Cement Concrete ◽  
Cement Concrete ◽  
Management Information ◽  
Rigid Pavements ◽  
Distress Prediction

The development of distress prediction models for nonoverlaid portland cement concrete (rigid) pavements in Texas for the Texas Department of Transportation's pavement management information system is described. The regression models presented quantitatively predict distress level versus pavement age and are based on pavement condition data maintained by the Center for Transportation Research at The University of Texas at Austin. Models are available for the following distress types in continuously reinforced concrete pavement (CRCP): punchouts, portland cement concrete patches, asphalt patches, serviceability loss as measured by loss of ride score, transverse crack spacing, and crack spalling. Preliminary models are available for the following distresses in jointed concrete pavement and jointed reinforced concrete pavement: patches, corner breaks, faulted joints and cracks, spalled joints and cracks, transverse crack spacing, and slabs with longitudinal cracks. A sigmoidal regression equation was used for all distress types. Modifying factors to CRCP models, which are intended to capture the effects of structural, environmental, and traffic loading variables, are included. The models for CRCP represent a significant improvement from preliminary estimates made in 1993. The improvements to the models were made possible primarily by data collection efforts undertaken in the summer of 1994.

Local Calibration of Pavement Mechanistic-Empirical Faulting Reliability using Pavement Management Data

2021 ◽  
pp. 036119812110013
Author(s):  
Lucio Salles de Salles ◽  
Lev Khazanovich
Keyword(s):  
Model Calibration ◽  
Prediction Models ◽  
Plain Concrete ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Design ◽  
Reliability Model ◽  
Transverse Joint ◽  
Design Characteristics ◽  
And Performance

The Pavement ME transverse joint faulting model incorporates mechanistic theories that predict development of joint faulting in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance database. However, the Mechanistic-Empirical Pavement Design Guide (MEPDG) encourages transportation agencies, such as state departments of transportation, to perform local calibrations of the faulting model included in Pavement ME. Model calibration is a complicated and effort-intensive process that requires high-quality pavement design and performance data. Pavement management data—which is collected regularly and in large amounts—may present higher variability than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50% reliability. JPCP are usually designed for high levels of faulting reliability to reduce likelihood of excessive faulting. For design, improving the faulting reliability model is as important as improving the faulting prediction model. This paper proposes a calibration of the Pavement ME reliability model using pavement management system (PMS) data. It illustrates the proposed approach using PMS data from Pennsylvania Department of Transportation. Results show an increase in accuracy for faulting predictions using the new reliability model with various design characteristics. Moreover, the new reliability model allows design of JPCP considering higher levels of traffic because of the less conservative predictions.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

New Long-Life Concrete Pavements in the Czech Republic

2021 ◽  
Author(s):  
Bohuslav Slánský ◽  
Vit Šmilauer ◽  
Jiří Hlavatý ◽  
Richard Dvořák
Keyword(s):  
Isothermal Calorimetry ◽  
Coupled Model ◽  
Plain Concrete ◽  
Pilot Project ◽  
Economic Benefits ◽  
Concrete Pavement ◽  
Technical Solution ◽  
Concrete Pavements ◽  
Hydration Kinetics

A jointed plain concrete pavement represents a reliable, historically proven technical solution for highly loaded roads, highways, airports and other industrial surfaces. Excellent resistance to permanent deformations (rutting) and also durability and maintenance costs play key roles in assessing the economic benefits, rehabilitation plans, traffic closures, consumption and recycling of materials. In the history of concrete pavement construction, slow-to-normal hardening Portland cement was used in Czechoslovakia during the 1970s-1980s. The pavements are being replaced after 40-50 years of service, mostly due to vertical slab displacements due to missing dowel bars. However, pavements built after 1996 used rapid hardening cements, resulting in long-term surface cracking and decreased durability. In order to build durable concrete pavements, slower hardening slag-blended binders were designed and tested in the restrained ring shrinkage test and in isothermal calorimetry. Corresponding concretes were tested mainly for the compressive/tensile strength evolution and deicing salt-frost scaling to meet current specifications. The pilot project was executed on a 14 km highway, where a unique temperature-strain monitoring system was installed to provide long-term data from the concrete pavement. A thermo-mechanical coupled model served for data validation, showing a beneficial role of slower hydration kinetics. Continuous monitoring interim results at 24 months have revealed small curling induced by drying and the overall small differential shrinkage of the slab.

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