Study of Backcalculated Pavement Layer Moduli from the LTPP Database

[[abstract]]The main objective of this study was to investigate the fundamental principles of flexible and rigid pavement backcalculation methodologies and their potential limitations. The two-layer backcalculation approach proposed by the 1993 AASHTO Design Guide for the structural evaluation of existing pavements was also adopted. The laboratory tested (or static) layer moduli were compared with the backcalculated (or dynamic) moduli using the Long-Term Pavement Performance (LTPP) database. Relatively high variability between the relationships of the static and the dynamic moduli was observed indicating that further research study is needed to improve the current state-of-the-art backcalculation approach. In addition, it was also found that slab thickness did have significant effects on the relationship of the backcalculated subgrade elastic modulus and the backcalculated modulus of subgrade reaction. Subsequently, a revised regression model was proposed for future practical applications.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Development of Prediction Models for Joint Faulting Using Long-Term Pavement Performance Database

[[abstract]]The main objective of this study is to develop improved faulting prediction models for jointed concrete pavements using the Long-Term Pavement Performance (LTPP) database. The retrieval, preparation, and cleaning of the database were carefully handled in a systematic and automatic approach. The prediction accuracy of the existing prediction models implemented in the recommended Mechanistic-Empirical Pavement Design Guide (NCHRP Project 1-37A) was found to be inadequate. Exploratory data analysis of the response variables indicated that the normality assumption with random errors and constant variance using conventional regression techniques might not be appropriate for prediction modeling. Therefore, without assuming the error distribution of the response variable, several modern regression techniques including generalized linear model (GLM) and generalized additive model (GAM) along with quasi-likelihood estimation method and Poisson distribution were adopted in the subsequent analysis. Box-Cox power transformation and visual graphical techniques were frequently adopted during the prediction modeling process. By keeping only those parameters with significant effects and reasonable physical interpretations in the model, various tentative performance prediction models were developed. The resulting mechanistic-empirical model included several variables such as pavement age, yearly ESALs, bearing stress, annual precipitation, base type, subgrade type, annual temperature range, joint spacing, modulus of subgrade reaction, and freeze-thaw cycle for the prediction of joint faulting. The goodness of fit was further examined through the significant testing and various sensitivity analyses of pertinent explanatory parameters. The tentatively proposed predictive models appeared to reasonably agree with the pavement performance data although their further enhancements are possible and recommended.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]TW

Preliminary Analysis of AASHO Road Test Flexible Pavement Data Using Linear Mix Effects Models

[[conferencetype]]國際[[conferencelocation]]London, U. K

Preliminary Analysis of Flexible Pavement Serviceability Index Data Using Linear Mixed Effects Models

[[conferencetype]]國際[[conferencedate]]20110123~20110127[[conferencelocation]]Washington, D.C., US

Development of a Robust Approach for Evaluation of Airport Pavement Bearing Capacity

[[conferencetype]]國際[[conferencedate]]20090111~20090115[[conferencelocation]]Washington, D.C., US

[[alternative]]創新式結構透水鋪面的應用與挑戰

[[abstract]]Pervious pavements are generally not structurally sound, weak in material strength, and can be easily clogged. Thus, they might only be used in sidewalks, squares, and parking lots, but not for vehicular loads. An innovative eco-technology recently developed in Taiwan uses air-circulated aqueduct frames and impervious Portland cement concrete on top of an aggregate (crushed stone or gravel) base layer to form a structured permeable concrete pavement (called JW eco-technology pavement) can alleviate such problems. The main functions and special features include: (1) The aggregate layer may serve as a detention reservoir to increase flood control capabilities; (2) The stored water may lower pavement surface temperature in summer and help to reduce heat island effects; (3) This eco-technology can prevent water accumulation on pavement surface to improve pedestrian and driving safety; and (4) Exhausts from vehicles may be absorbed by the pavement system and become the nutrients of an underground ecological system. Through proper designs, the structured JW pavement can possess adequate load bearing capacities as conventional concrete pavements do. Traditional concrete pavement thickness design approaches are proposed to be used as the basic guidelines for determining the required slab thickness. Since the aggregate base course serves as structural bearing layer and reservoir layer, its thickness can be determined as the thicker of the two results based on these design controls. The surface conditions of several JW pavements remain fairly good even after 10 years of service in Taiwan. Evidences indicated that the JW pavement can be successfully used in roadways subject to light traffic under normal geological conditions. However, the subgrade soil underneath the aggregate (or crushed stone) layer warrants further investigations, especially when the soils are suspicious to settlement or may lose shear strength due to wetting. There are still challenges ahead such as to better understand its failure mechanisms, structural capabilities in terms of allowable load repetitions, and other long term performance indices if the structured JW pavement should be used for normal or heavier traffic loading conditions.[[conferencetype]]國際[[conferencedate]]20150111[[booktype]]其他[[iscallforpapers]]N[[conferencelocation]]Washington, D.C., US

Applications of Artificial Neural Networks to Pavement Prediction Modeling: A Case Study

[[abstract]]Artificial neural networks (ANN) have been used in many pavement prediction modeling analyses. However, the convergence characteristics and model selection guidelines are rarely studied duc to the requirement of extensive network training time. Thus, the techniques and applications of back propagation neural networks were briefly reviewed. Three ANN models were developed using deflection databases generated by factorial BISAR runs. A study of the convergence characteristics indicated that the resulting ANN model using all dominating dimensionless parameters was proved to have higher accuracy and require less network training time and data than the other counterpart using purely input parameters. Increasing the complexity of ANN models does not necessarily improve the modeling statistics. With the incorporation of subject-related engineering and statistical knowledge into the modeling process, reasonably good predictions may be achieved with more convincing generalization and explanation yet requiring minimal amount oftime and effort.[[sponsorship]]International Chinese Transportation Professionals Association; Beijing University of Technology; Transportation & Development Institute (T &DI) of the American Society of Civil Engineers[[conferencetype]]國際[[conferencedate]]20140525~20140527[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Beijing, Chin

Closure to “Development of Fatigue Cracking Prediction Models Using Long-Term Pavement Performance Database” by Hsiang-Wei Ker, Ying-Haur Lee, and Pei-Hwa Wu

[[abstract]]The article presents clarifications of the article "Development of Fatigue Cracking Prediction Models Using Long-Term Pavement Performance Database," by Hsiang-Wei Ker, Ying-Haur Lee and Pei-Hwa Wu, published in the 2008 issue. It explains several key points in the article, including the phenomenon of fatigue mechanism of asphalt pavement and the distribution of the actual fatigue cracking. The article also discusses the equations used in ascertaining the load repetition and pavement age.[[incitationindex]]SCI[[booktype]]紙

Development of Simplified Pavement Classification Number Approach for Reporting Rigid Airfield Pavement Bearing Strength

[[abstract]]The Aircraft Classification Number/Pavement Classification Number (ACN/PCN) method has been adopted as the standard method for reporting airfield pavement bearing strength since 1980's. Recently, the Federal Aviation Administration (FAA) developed an advisory circular to provide specific guidance on PCN determination. Even though the new airport pavement design approach using the FAARFIELD design software have been approved, "the pavement thickness requirements associated with the ACN-PCN procedures are based upon historical procedures identified in previous versions of AC 150/5320-6". The thickness mode for rigid pavements in the latest COMFAA 3.0 program still uses the FAA Westergaard method. The primary objectives of this study are to investigate its fundamental principles, the reasoning of the new revisions, and the effects on PCN determination. The original development of ACN/PCN methodology was first reviewed. The newly revised approach using cumulative damage factors for computing PCN based on equivalent traffic was also discussed. A Visual Basic Application (VBA) module was added to the conventional design spreadsheet (R805FAA) by executing the COMFAA 2.0 program iteratively to automatically determine the PCN value. The existing TKUAPAV program was recoded into an Excel spreadsheet with additional VBA modules to facilitate automatic PCN calculations using both R805FAA's bilinear fatigue relationship and PCA's fatigue equation. Revisions to the existing ACN/PCN calculation procedure have been made and implemented in the above spreadsheet based on four different failure models and two different critical stress models (edge or interior). A standard aircraft with a dual wheel main gear load of 90,000 lbs was introduced in this study to expedite critical aircraft conversions for PCN calculations. The potential problems of the existing procedures and the benefits of the proposed revisions are compared and illustrated through different case studies. Attempts to simplify ACN/PCN definitions are proposed and discussed.[[conferencetype]]國際[[conferencedate]]20140112~20140116[[iscallforpapers]]Y[[conferencelocation]]Washington, D.C., US

Preliminary Analysis of AASHO Road Test Rigid Pavement Data Using Modern Regression Techniques

NSC 101-2221-E-032-033[[abstract]]The normality assumptions with random errors and constant variance were often violated while analyzing multilevel pavement performance data using conventional regression techniques. Because of its hierarchical data structure, multilevel data are often analyzed using Linear Mixed-Effects (LME) models. The exploratory analysis, statistical modeling, and the examination of model-fit of LME models are more complicated than those of standard multiple regressions. A systematic modeling approach using visual-graphical techniques and LME models was proposed and demonstrated using the original AASHO road test rigid pavement data. The basic modeling approach includes: selecting a preliminary mean structure, selecting a random structure, selecting a residual covariance structure, model reduction, and examining the model fit. A goodness of fit plot indicates that the preliminary LME model provides better explanation to the data.[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[booktype]]紙本[[iscallforpapers]]