scholarly journals Highway Performance Evaluation Index in Semiarid Climate Region Based on Fuzzy Mathematics

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Sanqiang Yang ◽  
Meng Guo ◽  
Xinlei Liu ◽  
Pidong Wang ◽  
Qian Li ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Pavement Design ◽  
Pavement Performance ◽  
Fuzzy Mathematics ◽  
Accurate Evaluation ◽  
Multi Index ◽  
Climate Region ◽  
Design Scheme ◽  
Semiarid Climate ◽  
Highway Pavement

Accurate evaluation and analysis of expressway pavement performance is a prerequisite for determining the pavement design scheme and maintenance scheme. Due to the fuzziness and randomness of many factors affecting the pavement performance, this paper relies on the reconstruction and expansion project of Xinglin section of the Taihang mountain expressway, a method of highway pavement performance evaluation based on fuzzy mathematics is proposed. The results show the following: ① the study uses the factor domain, the comment level domain, the fuzzy relationship matrix, the evaluation factor full vector, and the fuzzy comprehensive evaluation result vector five-step method. The method can be effectively combined with the multi-index comprehensive detection index used in the specification. ② Based on the multi-index comprehensive test and evaluation adopted in the specification, the performance grade of the old road surface was quantitatively evaluated by the iterative calculation of fuzzy mathematics that broke through the evaluation mode which was based on the traditional detection methods. The research results provide innovative theoretical methods for the accurate evaluation and analysis of highway pavement performance in the semiarid climate region and also play a technical supporting role for the pavement design scheme and maintenance scheme decision-making in the semiarid climate region.

Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

Development of a Driving Cycle for Amman City With Performance Evaluation for ICE Vehicle

2014 ◽  
Author(s):  
M. Abu Mallouh ◽  
B. W. Surgenor ◽  
E. Abdelhafez ◽  
M. Salah ◽  
M. Hamdan
Keyword(s):  
Performance Evaluation ◽  
Fuel Cell ◽  
Fuel Economy ◽  
Performance Comparison ◽  
Driving Cycle ◽  
Accurate Estimation ◽  
Accurate Evaluation ◽  
Driving Cycles ◽  
Considerable Research ◽  
Future Work

A good driving cycle is needed for accurate evaluation of a vehicle’s performance in terms of emission and fuel consumption. Driving cycles obtained for certain cities or countries are not usually applicable to other cities or countries. Therefore, considerable research has been conducted on developing driving cycles for certain cities and regions. In this paper, a driving cycle for a taxi in Amman city, the capital of Jordan, is developed. Significant differences are noted when comparing the Amman driving cycle with other driving cycles. A model of a gasoline powered vehicle is used to conduct a performance comparison in terms of fuel economy and emissions utilizing the developed Amman driving cycle and six other worldwide driving cycles. The developed Amman driving cycle is very useful in obtaining accurate estimation of fuel economy and emissions for vehicles running on Amman roads and will be used in future work to study the performance of hybrid fuel cell/ battery vehicles.

Evaluation of Seasonal Effects on Subgrade Soils

2003 ◽  
Vol 1821 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Andrew G. Heydinger
Keyword(s):  
Seasonal Variations ◽  
Resilient Modulus ◽  
Monitoring Program ◽  
Test Site ◽  
Pavement Design ◽  
Seasonal Effects ◽  
Pavement Performance ◽  
Climatic Effects ◽  
Subgrade Soils ◽  
Frost Penetration

One objective of the FHWA’s Long-Term Pavement Performance (LTPP) program is to determine climatic effects on pavement performance. The LTPP instrumentation program includes seasonal monitoring program (SMP) instrumentation to monitor the seasonal variations of moisture, temperature, and frost penetration. Findings from the SMP instrumentation are to be incorporated into future pavement design procedures. Data from SMP instrumentation at the Ohio Strategic Highway Research Program Test Road (US-23, Delaware County, Ohio) and other reported results were analyzed to develop empirical equations. General expressions for the seasonal variations of average daily air temperature and variations of temperature and moisture in the fine-grained subgrade soil at the test site are presented. An expression for the seasonal variation of resilient modulus was derived. Average monthly weighting factors that can be used for pavement design were computed. Other factors such as frost penetration, depth of water table, and drainage conditions are discussed.

scholarly journals Variability in Pavement Design

2015 ◽  
Vol 16 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Paola Dalla Valle ◽  
Nick Thom
Keyword(s):  
Pavement Design ◽  
Pavement Performance ◽  
Design Parameters ◽  
Probability Density Distribution ◽  
Analysis And Design ◽  
The Subject ◽  
The Mean ◽  
Subgrade Modulus ◽  
Input Variables ◽  
The Uk

Abstract This paper presents the results of a review on variability of key pavement design input variables (asphalt modulus and thickness, subgrade modulus) and assesses effects on pavement performance (fatigue and deformation life). Variability is described by statistical terms such as mean and standard deviation and by its probability density distribution. The subject of reliability in pavement design has pushed many highway organisations around the world to review their design methodologies, mainly empirical, to move towards mechanistic-empirical analysis and design which provide the tools for the designer to evaluate the effect of variations in materials on pavement performance. This research has reinforced this need for understanding how the variability of design parameters affects the pavement performance. This study has only considered flexible pavements. The sites considered for the analysis, all in the UK (including Northern Ireland), were mainly motorways or major trunk roads. Pavement survey data analysed were for Lane 1, the most heavily trafficked lane. Sections 1km long were considered wherever possible. Statistical characterisation of the variation of layer thickness, asphalt stiffness and subgrade stiffness is addressed. A sensitivity analysis is then carried out to assess which parameter(s) have the greater influence on the pavement life. The research shows that, combining the effect of all the parameters considered, the maximum range of 15th and 85th percentiles (as percentages of the mean) was found to be 64% to 558% for the fatigue life and 94% to 808% for the deformation life.

Multi-index design method of asphalt overlay based on pavement performance

2016 ◽  
pp. 615-624
Author(s):  
Xiangchen Hou ◽  
Yiyi Ren ◽  
Liping Cao ◽  
Song Yang
Keyword(s):  
Design Method ◽  
Pavement Performance ◽  
Multi Index ◽  
Asphalt Overlay

Rational Selection of Factors of Safety in Reliability-Based Design of Flexible Pavements in Saudi Arabia

1996 ◽  
Vol 1540 (1) ◽  
pp. 39-47 ◽  
Author(s):  
A. Samy Noureldin ◽  
Essam Sharaf ◽  
Abdulrahim Arafah ◽  
Faisal Al-Sugair
Keyword(s):  
Saudi Arabia ◽  
Performance Prediction ◽  
Flexible Pavement ◽  
Pavement Design ◽  
Pavement Performance ◽  
Design Performance ◽  
Reliability Based Design ◽  
Pavement Engineering ◽  
Pavement Performance Prediction ◽  
Selection Of

Explicit applications of reliability in pavement engineering have been of interest to pavement engineers for the last 10 years. Variabilities in parameters affecting pavement design performance result in variability in pavement performance prediction and thus affect the reliability of how long the pavement will last. Rational quantification of those variabilities is essential for incorporating reliability and selecting the proper factors of safety in the pavement design performance process. The prevailing methodology in Saudi Arabia of quantifying the variability in pavement performance due to the variabilities of the parameters affecting that performance is demonstrated. Factors of safety for flexible pavement design at various reliability levels and based on those prevailing variabilities are presented. These factors of safety are recommended for flexible pavement design in Saudi Arabia.

Use of Long Term Pavement Performance-Seasonal Monitoring Program Data to Develop and Validate a Generalized Regression Model to Predict the In-Situ Resilient Modulus of Subgrade Soils for Pavement Design and Evaluation

2020 ◽  
Vol 2674 (5) ◽  
pp. 673-684
Author(s):  
Mohamed Elshaer ◽  
Christopher DeCarlo ◽  
Wade Lein ◽  
Harshdutta Pandya ◽  
Ayman Ali ◽  
...  
Keyword(s):  
Prediction Model ◽  
Resilient Modulus ◽  
Monitoring Program ◽  
Pavement Design ◽  
Pavement Performance ◽  
Regression Approach ◽  
Seasonal Monitoring ◽  
Generalized Regression

Resilient modulus (Mr) is a critical input for pavement design as it is the main property used to evaluate the contribution of subgrade to the overall pavement structure. Considering this, practitioners need simple and accurate ways to determine the Mr of in-situ subgrade without the need for expensive and time-consuming testing. The objective of this study is to develop a generalized regression prediction model for in-situ Mr of subgrades, compare it with established prediction models, and assess the model’s predictions on pavement performance using the Mechanistic-Empirical Pavement Design Guide (Pavement ME). The prediction model was built using field data from 30 pavement sections studied in the Long Term Pavement Performance (LTPP) Seasonal Monitoring Program where backcalculated modulus from falling weight deflectometer testing, in-situ moisture contents, and subgrade material properties were considered in the model. Based on the results, it was found that liquid limit, plasticity index, WPI (the product of percent passing #200 and plasticity index), percent coarse sand, percent fine sand, percent silt, percent clay, moisture content, and their respective interactions were significant predictors of in-situ Mr values. The findings showed that the generalized regression approach was able to predict Mr more accurately than predictions from the Witczak model. To assess the application of the predictive model on pavement performance, three LTPP sections located in New York, South Dakota, and Texas were analyzed to predict the rutting performance based on Mr values obtained from the developed generalized prediction model and those obtained from the current Pavement ME model and then compared with rut depths measured in the field. The findings showed that, for coarse-grained subgrades that have a low degree of plasticity, the generalized regression model predicted rutting performance similar to the embedded Pavement ME model. For fine-grained subgrades, the developed model tends to predict lower rut depths which were closer to the field measured rut depths. Overall, the generalized regression approach was successfully applied to create a simple, practical, cost-effective and accurate Mr prediction model that can be used to estimate the stiffness of subgrades when designing and evaluating pavements.

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