Correlation Analysis between Temperature Indices and Flexible Pavement Distress Predictions Using Mechanistic-Empirical Design

2017 ◽  
Vol 31 (4) ◽  
pp. 04017009 ◽  
Author(s):  
Xu Yang ◽  
Zhanping You ◽  
Jacob Hiller ◽  
David Watkins
Keyword(s):  
Correlation Analysis ◽  
Flexible Pavement ◽  
Pavement Distress ◽  
Temperature Indices ◽  
Empirical Design

Pavement performance zone based on mechanistic-empirical design and temperature indices

2018 ◽  
Vol 15 (1) ◽  
pp. 91-113 ◽  
Author(s):  
Xu Yang ◽  
Zhanping You ◽  
Jacob Hiller ◽  
David Watkins
Keyword(s):  
Pavement Performance ◽  
Temperature Indices ◽  
Empirical Design

Using Fuzzy Logic and Expert System Approaches in Evaluating Flexible Pavement Distress: Case Study

2010 ◽  
Vol 136 (2) ◽  
pp. 149-157 ◽  
Author(s):  
Hari Krishan Koduru ◽  
Feipeng Xiao ◽  
Serji N. Amirkhanian ◽  
C. Hsein Juang
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Flexible Pavement ◽  
Pavement Distress ◽  
System Approaches

scholarly journals Selecting the Affected Factors on Pavement Distress Problems Using Analytical Hierarchy Process [AHP]

2018 ◽  
Vol 7 (2.29) ◽  
pp. 716
Author(s):  
Abdalrhman Milad ◽  
Noor Ezlin Ahmed Basri ◽  
Mohammad K.Younes ◽  
Hassan. M.Abdelsalam ◽  
Riza Atiq Abdullah Bin O.K Rahmat
Keyword(s):  
Analytical Hierarchy Process ◽  
Surface Defects ◽  
Flexible Pavement ◽  
Decision Makers ◽  
Tropical Region ◽  
Pairwise Comparisons ◽  
Pavement Maintenance ◽  
Pavement Distress ◽  
Surface Deformations ◽  
Hierarchy Process

This study describes the implementation of analytical hierarchy process [AHP] in pavement multi-criteria selection problem solving. The practice of expressing flexible pavement distress priority is widely accepted. However, an insistent demand exists for a technique that allows decision makers to determine their priorities, rational weights of the importance of pavement distress priority and the ranking of these factors. In this study, AHP is adopted in selecting the best level of distress in flexible pavements in Malaysia as an example of a tropical region. Knowledgeable and experienced experts in flexible pavement maintenance at jabatan kerja raya [JKR] and Kumpulan Ikram Sdn Bhd [IKRAM] were interviewed; as pairwise comparisons, their inputs were structured. Four criteria are set as follows: cracking, surface defects, surface deformations and patching and potholes. These criteria developed into a few other sub-criteria. Results show that cracking is the most significant factor [0.5500], followed by surface deformations [0.2300], patching and potholes [0.1600] and surface defects [0.0600]. Thus, cracking has the most significant distress among the four factors.

Research of Steel Deck Pavement Suitable Paving Materials

2011 ◽  
Vol 243-249 ◽  
pp. 4092-4096 ◽  
Author(s):  
Yi Wei Weng ◽  
Jyh Dong Lin ◽  
Wei Hsing Huang ◽  
Ming Chin Yeh
Keyword(s):  
Mechanical Response ◽  
Compressive Strain ◽  
Flexible Pavement ◽  
Traditional Theory ◽  
Tire Pressure ◽  
Wheel Load ◽  
Suitable Material ◽  
Pavement Distress ◽  
Asphalt Mix ◽  
Steel Deck

This study utilized mechanical calculation method and finite element method ABAQUS software to analyze the mechanical response of different flexible pavement material combinations on steel deck. Heavy vehicle load with high axle load and high tire pressure were considered, so as to know the reasons for steel deck pavement distress, and to define the arrangement principle for steel deck pavement and the combination of materials suitable for flexible pavement. The results show that the fatigue damage of steel deck pavement coincides with traditional theory, the maximum tensile strain at the bottom of surface course is still the determination index, and the fatigue crack is presented mainly in four types; the maximum compressive strain on the top of steel plate is the determination index of rutting damage, the major cause for rut is the surface course under compressive strain in the wheel load position. The suitable material for steel deck one-course pavement is full depth Guss asphalt mix; the suitable combination for steel deck two-course pavement is modified asphalt mix on top and Guss asphalt mix at bottom.

Estimating the Sensitivity of Design Input Variables for Rigid Pavement Analysis with a Mechanistic–Empirical Design Guide

2005 ◽  
Vol 1919 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Kevin D. Hall ◽  
Steven Beam
Keyword(s):  
Plain Concrete ◽  
Relative Sensitivity ◽  
Pavement Performance ◽  
Portland Cement Concrete ◽  
Traffic Loading ◽  
Pavement Distress ◽  
Design Guide ◽  
Pavement Structures ◽  
Input Variables ◽  
Empirical Design

Many highway agencies use AASHTO methods for the design of pavement structures. Current AASHTO methods are based on empirical relationships between traffic loading, materials, and pavement performance developed from the AASHO Road Test (1958–1961). The applicability of these methods to modern-day conditions has been questioned; in addition, the lack of realistic inputs regarding environmental and other factors in pavement design has caused concern. Research sponsored by the NCHRP has resulted in the development of a mechanistic–empirical design guide (M-E design guide) for pavement structural analysis. The new M-E design guide requires more than 100 inputs to model traffic, environmental, material, and pavement performance to provide estimates of pavement distress over the design life of the pavement. Many designers may lack specific knowledge of the data required. A study was performed to assess the relative sensitivity of the models used in the M-E design guide to inputs relating to portland cement concrete materials in the analysis of jointed plain concrete pavements. Twenty-nine inputs were evaluated by analysis of a standard pavement section and change of the value of each input individually. The three pavement distress models (cracking, faulting, and roughness) were not sensitive to 17 of the 29 inputs. All three models were sensitive to six of the 29 inputs. Combinations of only one or two of the distress models were sensitive to six of the 29 inputs. These data may aid designers in focusing on inputs that have the most effect on desired pavement performance.

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