Incorporation of Reliability into Mechanistic-Empirical Pavement Design

2000 ◽  
Vol 1730 (1) ◽  
pp. 73-80 ◽  
Author(s):  
David H. Timm ◽  
David E. Newcomb ◽  
Theodore V. Galambos
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Design Procedure ◽  
Fatigue Cracking ◽  
Pavement Design ◽  
Pavement Performance ◽  
Type I ◽  
Standard Design ◽  
Input Parameters

Pavement thickness design traditionally has been based on empiricism. However, mechanistic-empirical (M-E) design procedures are becoming more prevalent, and there is a current effort by AASHTO to establish a nationwide M-E standard design practice. Concurrently, an M-E design procedure for flexible pavements tailored to conditions within Minnesota has been developed and is being implemented. Regardless of the design procedure type, inherent variability associated with the design input parameters will produce variable pavement performance predictions. Consequently, for a complete design procedure, the input variability must be addressed. To account for input variability, reliability analysis was incorporated into the M-E design procedure for Minnesota. Monte Carlo simulation was chosen for reliability analysis and was incorporated into the computer pavement design tool, ROADENT. A sensitivity analysis was conducted by using ROADENT in conjunction with data collected from the Minnesota Road Research Project and the literature. The analysis demonstrated the interactions between the input parameters and showed that traffic weight variability exerts the largest influence on predicted performance variability. The sensitivity analysis also established a minimum number of Monte Carlo cycles for design (5,000) and characterized the predicted pavement performance distribution by an extreme value Type I function. Finally, design comparisons made between ROADENT, the 1993 AASHTO pavement design guide, and the existing Minnesota design methods showed that ROADENT produced comparable designs for rutting performance but was somewhat conservative for fatigue cracking.

Reliability Analysis of Cracking and Faulting Prediction in the New Mechanistic–Empirical Pavement Design Procedure

2005 ◽  
Vol 1936 (1) ◽  
pp. 150-160 ◽  
Author(s):  
Michael Darter ◽  
Lev Khazanovich ◽  
Tom Yu ◽  
Jag Mallela
Keyword(s):  
Monte Carlo ◽  
Reliability Analysis ◽  
Design Procedure ◽  
Practical Method ◽  
Pavement Design ◽  
Rigid Pavement ◽  
Pavement Distress ◽  
Reliability Based Design ◽  
Definition Of ◽  
Reliability Performance

Reliability analysis is an important part of the mechanistic–empirical pavement design guide (M-E PDG). Even though mechanistic concepts provide a more accurate and realistic methodology for pavement design, a practical method to consider the uncertainties and variations in design and construction is needed so that a new or rehabilitated pavement can be designed for a desired level of reliability (performance as designed). Several methods, ranging from closed-form approaches to simulation-based methods, can be adopted to perform reliability-based design. However, some methods may be more suitable than others, given the complexities of the design procedure. A formal definition of reliability within the context of the M-E PDG, as well as two reliability analysis approaches considered for incorporation into the design procedure for evaluating the reliability of the rigid pavement design for cracking and faulting, was evaluated. A Monte Carlo–based simulation was combined with the damage accumulation procedure for rigid pavement distress prediction. This approach is recommended for future improvements of the procedure. The development of the reliability analysis procedure implemented into the M-E PDG also was documented. It was demonstrated that although the adopted approach is not as sophisticated as a Monte Carlo–based one, it still represents a step forward compared with AASHTO-93 reliability analysis.

scholarly journals Development of Smart Pavement Design Sensitivity Analysis Software for Asset Management System

2020 ◽  
Vol 5 (7) ◽  
pp. 56
Author(s):  
Byungkyu Moon ◽  
Jungyong “Joe” Kim ◽  
Hosin “David” Lee
Keyword(s):  
Sensitivity Analysis ◽  
Management System ◽  
Design Sensitivity ◽  
Pavement Management ◽  
Pavement Design ◽  
Design Sensitivity Analysis ◽  
Sensitivity Analyses ◽  
Analysis Software ◽  
Input Parameters ◽  
Pavement Thickness

There are a number of pavement management systems, but most of them are limited in providing pavement design and pavement design sensitivity information. This paper presents efforts towards the integrated pavement design and management system, by developing smart pavement design sensitivity analysis software. In this paper, the sensitivity analyses of critical design input parameters have been performed to identify input parameters which have the most significant impacts on the pavement thickness. Based on the existing pavement design procedures and their sensitivity analysis results, a smart pavement design sensitivity analysis (PDSA) software package was developed, to allow a user to retrieve the most appropriate pavement thickness and immediately perform pavement design sensitivity analysis. The PDSA software is a useful tool for managing pavements, by allowing a user to instantaneously retrieve a pavement design for a given condition from the database and perform a design sensitivity analysis without running actual pavement design programs. The proposed smart PDSA software would result in the most efficient pavement management system, by incorporating the optimum pavement thickness as part of the pavement management process.

Study of the Effect of Process Induced Uncertainties on the Performance of a Micro-Comb Resonator

2009 ◽  
Vol 147-149 ◽  
pp. 716-725 ◽  
Author(s):  
Irina Codreanu ◽  
Adam Martowicz ◽  
A. Gallina ◽  
Łukasz Pieczonka ◽  
Tadeusz Uhl
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
Mode Shapes ◽  
Computational Techniques ◽  
Simulation Methods ◽  
Input Parameters ◽  
Mobile Mass ◽  
Study Case

This paper presents a modeling technique based on the integration in the classic deterministic simulation methods of probabilistic computational techniques such as uncertainty analysis and sensitivity analysis. As study case, it is presented a micro-comb resonator that is actuated electrostatically to vibrate in the plane parallel to the substrate. A deterministic Finite Element coupled electromechanical analysis is performed to evaluate the mode shapes and the corresponding eigenfrequencies of the mobile mass and afterwards a Monte Carlo simulation is used to determine the dispersion of the eigenfrequency of the mode shape of interest in function of the variations of the input parameters. The scatter of the results is analyzed and then it is presented a sensitivity analysis for establishing which of the input parameters have more influence on the variability of the microresonators performance.

Probability and Sensitivity Analysis of Plate

2014 ◽  
Vol 617 ◽  
pp. 193-196 ◽  
Author(s):  
Katarina Tvrdá
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Flat Plate ◽  
Concrete Structures ◽  
Probability Analysis ◽  
Triangular Distribution ◽  
High Quality ◽  
Input Parameters ◽  
Concrete Solution

This paper deals with some problems of the ceiling plate, made of the Cobiax-system. Cobiax provides a system to produce voided, biaxial, flat plate slabs as a high-quality concrete solution for large spans and slim slabs. Plastic voids in the shape of spheres or flattened spheres are contained in steel cages and put into concrete structures to create longer spans and reduce vertical loads. The presented plate is made of cobiax balls with a diameter of 27 cm located outside the area of columns. Probability analysis of Monte-Carlo method in Ansys is presented. Input parameters are changing according to Gauss or triangular distribution.

scholarly journals Inverse sensitivity analysis of mathematical models avoiding the curse of dimensionality

2018 ◽  
Author(s):  
Ben Lambert ◽  
David J. Gavaghan ◽  
Simon Tavener
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Mathematical Models ◽  
Biological Systems ◽  
Sensitivity Analyses ◽  
Deterministic Models ◽  
Deterministic Systems ◽  
Input Parameters ◽  
Insight Into

1AbstractBiological systems have evolved a degree of robustness with respect to perturbations in their environment and this capability is essential for their survival. In applications ranging from therapeutics to conservation, it is important to understand not only the sensitivity of biological systems to changes in their environments, but which features of these systems are necessary to achieve a given outcome. Mathematical models are increasingly employed to understand these mechanisms. Sensitivity analyses of such mathematical models provide insight into the responsiveness of the system when experimental manipulation is difficult. One common approach is to seek the probability distribution of the outputs of the system corresponding to a known distribution of inputs. By contrast, inverse sensitivity analysis determines the probability distribution of model inputs which produces a known distribution of outputs. The computational complexity of the methods used to conduct inverse sensitivity analyses for deterministic systems has limited their application to models with relatively few parameters. Here we describe a novel Markov Chain Monte Carlo method we call “Contour Monte Carlo”, which can be used to invert systems with a large number of parameters. We demonstrate the utility of this method by inverting a range of frequently-used deterministic models of biological systems, including the logistic growth equation, the Michaelis-Menten equation, and an SIR model of disease transmission with nine input parameters. We argue that the simplicity of our approach means it is amenable to a large class of problems of practical significance and, more generally, provides a probabilistic framework for understanding the inversion of deterministic models.2Author summaryMathematical models of complex systems are constructed to provide insight into their underlying functioning. Statistical inversion can probe the often unobserved processes underlying biological systems, by proceeding from a given distribution of a model’s outputs (the aggregate “effects”) to a distribution over input parameters (the constituent “causes”). The process of inversion is well-defined for systems involving randomness and can be described by Bayesian inference. The inversion of a deterministic system, however, cannot be performed by the standard Bayesian approach. We develop a conceptual framework that describes the inversion of deterministic systems with fewer outputs than input parameters. Like Bayesian inference, our approach uses probability distributions to describe the uncertainty over inputs and outputs, and requires a prior input distribution to ensure a unique “posterior” probability distribution over inputs. We describe a computational Monte Carlo method that allows efficient sampling from the posterior distribution even as the dimension of the input parameter space grows. This is a two-step process where we first estimate a “contour volume density” associated with each output value which is then used to define a sampling algorithm that yields the requisite input distribution asymptotically. Our approach is simple, broadly applicable and could be widely adopted.

Implementation Initiatives of the Mechanistic–Empirical Pavement Design Guides in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Tommy Nantung ◽  
Ghassan Chehab ◽  
Scott Newbolds ◽  
Khaled Galal ◽  
Shuo Li ◽  
...  
Keyword(s):  
Pavement Design ◽  
Sensitivity Analyses ◽  
Pavement Performance ◽  
Design Parameters ◽  
State Highway ◽  
Design Guide ◽  
Input Parameters ◽  
Pavement Structures ◽  
State Highway Agencies ◽  
Empirical Design

The release of the Mechanistic–Empirical Design Guide for New and Rehabilitated Pavement Structures (M-E design guide) generated a new paradigm for designing and analyzing pavement structures. It is expected to replace the commonly used empirical design methodologies. The M-E design guide uses a comprehensive suite of input parameters deemed necessary to design pavements with high reliability and to predict pavement performance and distresses realistically. However, the considerable amount of input needed and the selection of the corresponding reliability level for each might present state highway agencies with complexities and challenges in its implementation. An overview is presented of ongoing investigative studies, sensitivity analyses, and preimplementation initiatives conducted by the Indiana Department of Transportation (INDOT) in an effort to accelerate the adoption of the new pavement design guide by efficiently using existing design parameters and determining those parameters that influence the predicted performance the most. Once the sensitive inputs are identified, the large amount of other required design input parameters can be significantly reduced to a manageable level for implementation purposes. A matrix of trial runs conducted with the M-E design guide software suggests that a higher design level input does not necessarily guarantee a higher accuracy in predicting pavement performance. The software runs also confirmed the need to use input values obtained from local rather than national calibration. Such findings are important for state highway agencies such as INDOT in drafting initiatives for implementing the M-E design guide.

Updating Pavement Design Procedures for New York State

1996 ◽  
Vol 1539 (1) ◽  
pp. 51-57
Author(s):  
Hong-Jer Chen ◽  
Luis Julian Bendaña ◽  
Dan E. McAuliffe ◽  
Raymond L. Gemme
Keyword(s):  
New York ◽  
Sensitivity Analysis ◽  
Life Cycle ◽  
New York State ◽  
Design Procedure ◽  
Pavement Design ◽  
Life Cycle Costs ◽  
Past Performance ◽  
Design Variables ◽  
Pavement Thickness

New York's effort in adapting concepts from AASHTO's pavement design guide as a basis for a revised state design procedure for thickness of new and reconstructed pavements is summarized. The rationale for this revised procedure was to design more durable pavements and reduce life-cycle costs. New York's past pavement design practice and the background for the revisions are briefly described. A sensitivity analysis was conducted to identify how AASHTO design variables affect pavement thickness. Past performance of selected New York pavements was also studied. The rationale is discussed for determination of appropriate design variables, based on the sensitivity analysis, performance studies, and reviews of past and current practice. Also described is the justification of other design features, such as 50-year design life, granular subgrade, permeable base, edge drains, shorter slabs, maximum and minimum pavement thicknesses, and new dowel and tie-bar designs. Development and implementation of New York's new AASHTO-based thickness design procedure are major steps toward accomplishing the goals of building longer-lasting pavements and reducing life-cycle costs.

Stone Interlayer Pavement Design

2000 ◽  
Vol 1709 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Masood Rasoulian ◽  
Byron Becnel ◽  
Gary Keel
Keyword(s):  
Pavement Design ◽  
Superior Performance ◽  
Base Layer ◽  
Pavement Performance ◽  
Reflective Cracking ◽  
Evaluation Period ◽  
Standard Design ◽  
Pavement Testing ◽  
Transportation Research ◽  
Cement Stabilized Base

A project was initiated to evaluate the pavement performance of an alternative pavement design, referred to here as stone interlayer or inverted pavement. The test section consisted of a 4-in. (102-mm) layer of stone base on top of 6 in. (152 mm) of in-place cement stabilized base. The control section consisted of 8.5 in. (216 mm) of cement-stabilized base layer on top of prepared subgrade (standard design). A 3.5-in. (99-mm) layer of flexible pavement was placed over both sections. The objective of the study was to evaluate reflective cracking reduction through the asphaltic pavement as well as overall pavement performance. The project took place in Acadia Parish, Route LA-97, near Jennings, Louisiana. LA-97 is considered a low-volume rural highway, with average daily traffic of 2,000 vehicles. The pavement was monitored for 7 years after construction. During the evaluation period, annual crack survey, ride, and deflection measurements were collected. Additionally, as part of the Louisiana Transportation Research Center accelerated pavement testing research program, the same pavement design was tested to failure using the Accelerated Loading Facility device. Results of the investigation showed that the stone interlayer had significantly reduced the amount of reflective cracking. The ride characteristics and structural capacity of both sections were similar during the evaluation period. Accelerated testing results also verified the superior performance of the stone interlayer pavement system. The cost analysis showed initial construction costs for the stone interlayer system may be as high as 20 percent more than standard design. However, the life of the stone interlayer pavement system is increased to almost five times that of the standard soil cement pavement as tested under accelerated loading.

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