Geology of formations cropping out along State Highway 25, Marengo County, Alabama

1960 ◽  
Author(s):  
J.G. Newton
Keyword(s):  
State Highway

Evaluation of Selected Pier-Scour Equations Using Field Data

1996 ◽  
Vol 1523 (1) ◽  
pp. 186-195 ◽  
Author(s):  
Mark N. Landers ◽  
David S. Mueller
Keyword(s):  
Field Measurements ◽  
Bridge Piers ◽  
Critical Ratio ◽  
Scour Depth ◽  
Flow Depth ◽  
Clear Water ◽  
Pier Scour ◽  
Logarithmic Space ◽  
State Highway ◽  
Federal Highway

Field measurements of channel scour at bridges are needed to improve the understanding of scour processes and the ability to accurately predict scour depths. An extensive data base of pier-scour measurements has been developed over the last several years in cooperative studies between state highway departments, the Federal Highway Administration, and the U.S. Geological Survey. Selected scour processes and scour design equations are evaluated using 139 measurements of local scour in live-bed and clear-water conditions. Pier-scour measurements were made at 44 bridges around 90 bridge piers in 12 states. The influence of pier width on scour depth is linear in logarithmic space. The maximum observed ratio of pier width to scour depth is 2.1 for piers aligned to the flow. Flow depth and scour depth were found to have a relation that is linear in logarithmic space and that is not bounded by some critical ratio of flow depth to pier width. Comparisons of computed and observed scour depths indicate that none of the selected equations accurately estimate the depth of scour for all of the measured conditions. Some of the equations performed well as conservative design equations; however, they overpredict many observed scour depths by large amounts. Some equations fit the data well for observed scour depths less than about 3 m (9.8 ft), but significantly underpredict larger observed scour depths.

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

2021 ◽  
pp. 036119812110237
Author(s):  
Laura Camarena
Keyword(s):  
Machine Learning ◽  
Material Properties ◽  
Resilient Modulus ◽  
Nonlinear Behavior ◽  
Machine Learning Techniques ◽  
Design Parameters ◽  
Nonlinear Parameters ◽  
State Highway ◽  
Learning Techniques ◽  
State Highway Agencies

The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials.

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 Structural Condition Index to Support Pavement Maintenance and Rehabilitation Decisions at Network Level

2003 ◽  
Vol 1827 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Zhanmin Zhang ◽  
German Claros ◽  
Lance Manuel ◽  
Ivan Damnjanovic
Keyword(s):  
Evaluation Method ◽  
Surface Condition ◽  
Condition Index ◽  
Structural Condition ◽  
Pavement Management ◽  
Structural Deformation ◽  
Pavement Maintenance ◽  
State Highway ◽  
Structural Reinforcement ◽  
Maintenance And Rehabilitation

Every year, state highway agencies apply large amounts of seal coats and thin overlays to pavements to improve the surface condition, but these measures do not successfully address the problem. Overall pavement condition continues to deteriorate because of the structural deformation of pavement layers and the subgrade. To make effective decisions about the type of treatment needed, one should take into consideration the structural condition of a pavement. Several different structural estimators can be calculated by using falling weight deflectometer data and information stored in the Pavement Management Information System (PMIS) at the Texas Department of Transportation. The analysis considers pavement modulus and structural number as the structural estimators of a pavement. The evaluation method is based on the sensitivity of the structural estimators to deterioration descriptors. The deterioration per equivalent single-axle load of all major scores stored in the Texas PMIS is proposed as the primary indicator of pavement deterioration. In addition, the use of the structural condition index is recommended as a screening tool to discriminate between pavements that need structural reinforcement and those that do not. This index is calibrated for use in maintenance and rehabilitation analysis at the network level.

Determining Minimum Contract Time for Highway Projects

2000 ◽  
Vol 1712 (1) ◽  
pp. 196-201 ◽  
Author(s):  
Jin-Fang Shr ◽  
Benjamin P. Thompson ◽  
Jeffrey S. Russell ◽  
Bin Ran ◽  
H. Ping Tserng
Keyword(s):  
Low Cost ◽  
Road User ◽  
User Cost ◽  
Time Curve ◽  
Highway Projects ◽  
The Road ◽  
State Highway ◽  
Florida Department ◽  
The Cost ◽  
State Highway Agencies

An increasing number of state highway agencies (SHAs) are using A (cost) + B (time cost) bidding ( A + B bidding) for highway construction. The A + B bidding concept is designed to shorten the total contract time by allowing each contractor to bid the number of days in which the work can be accomplished, in addition to the traditional cost bid. The SHA is then presented with the problem of determining a reasonable range of contract time submitted by the bidders. Most SHAs do not currently restrict the range of B. However, several problems may arise from an unrestricted range of B. First, if no minimum is set for B, a bidder may inflate the cost bid and submit an unreasonably low B, using the excess cost bid to cover the disincentives charged for exceeding the time bid. Second, if no maximum is set for B, then a bidder with a high B and a low-cost bid may be awarded the job and make an unreasonable amount of money from incentive payments. This study develops a quantified model of the price-time bidding contract. A construction cost-versus-time curve is developed from Florida Department of Transportation (DOT) data. The contractor’s price-versus-time curve is then combined with the road-user cost to determine the optimum lower limit to be set on B. Finally, several projects completed by the Florida DOT will be used to illustrate this model.

Pay Factor Analysis for Highway Pavements through Percentage of Material within Specification Limits and Monte Carlo Simulation

2021 ◽  
pp. 036119812110255
Author(s):  
Yunpeng Zhao ◽  
Dimitrios Goulias
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Operating Characteristic ◽  
Production Quality ◽  
Specification Limits ◽  
State Highway ◽  
Long Run ◽  
Similar Materials ◽  
Pay Factor ◽  
State Highway Agencies

Many state highway agencies (SHAs) have adopted pay adjustment provisions in their acceptance plans for construction and materials. In these payment adjustment acceptance plans, the percentage of material within specification limits (PWL) has been selected as the quality measure to relate production quality to pay factors, and pay equations are used to determine a pay factor for a lot based on PWL. Various pay equations have been proposed by the highway community for adoption in SHAs’ specifications. However, the effectiveness of these pay equations has not been fully evaluated. Another issue concerning the pay adjustment acceptance plans is the risk associated with single and multiple pay factors. The purpose of this study was to evaluate the effects of different pay equations commonly used by SHAs and the risks associated with pay adjustment acceptance plans. This was achieved by developing operating characteristic curves associated with various pay factors and expected pay curves and Monte Carlo simulation for assessing the effects in the long run. The methodology suggested in this paper is transferable elsewhere where similar materials and specifications are used for the acceptance of pavements.

Evaluation of Network-Level Data Collection Variability and its Influence on Pavement Evaluation Utilizing Random Forest Method

2021 ◽  
pp. 036119812098043
Author(s):  
Xiaoyang Jia ◽  
Mark Woods ◽  
Hongren Gong ◽  
Di Zhu ◽  
Wei Hu ◽  
...  
Keyword(s):  
Data Collection ◽  
High Variability ◽  
Pavement Management ◽  
Data Sets ◽  
State Highway ◽  
Condition Evaluation ◽  
Level Data ◽  
Pavement Evaluation ◽  
Data Variability ◽  
Parallel Tests

The use of pavement condition data to support maintenance and resurfacing strategies and justify budget needs becomes more crucial as more data-driven approaches are being used by the state highway agencies (SHAs). Therefore, it is important to understand and thus evaluate the influence of data variability on pavement management activities. However, owing to a huge amount of data collected annually, it is a challenge for SHAs to evaluate the influence of data collection variability on network-level pavement evaluation. In this paper, network-level parallel tests were employed to evaluate data collection variability. Based on the data sets from the parallel tests, classification models were constructed to identify the segments that were subject to inconsistent rating resulting from data collection variability. These models were then used to evaluate the influence of data variability on pavement evaluation. The results indicated that the variability of longitudinal cracks was influenced by longitudinal lane joints, lateral wandering, and lane measurement zones. The influence of data variability on condition evaluation for state routes was more significant than that for interstates. However, high variability of individual metrics may not necessarily lead to high variability of combined metrics.

The Rakaia Gorge bridge deck replacement and seismic strengthening

2021 ◽  
Author(s):  
Peter Routledge ◽  
Andrew Bradfield
Keyword(s):  
Traffic Management ◽  
Load Capacity ◽  
Heritage Preservation ◽  
Unusual Structure ◽  
Wrought Iron ◽  
State Highway ◽  
Conflicting Objectives ◽  
Innovative Solutions ◽  
Seismic Resilience ◽  
Important Structure

<p>The Rakaia Gorge No. 1 Bridge on SH77 is a Category 1 Historic Place constructed circa 1882. The 55m span ‘Bollman-like’ truss is recognised as unique in the world and is also one of the oldest wrought iron bridges in New Zealand. To ensure that this important structure continued to provide a safe and resilient transport route, the deteriorating timber deck required replacement. Some innovative solutions were required to resolve the numerous challenges presented by this unusual structure related to load capacity, heritage effects, operational safety, seismic resilience, constructability and traffic management. The project sensitively balanced the conflicting objectives of state highway operations and heritage preservation – refurbishing the deck and providing seismic resilience to this strategically important structure, whilst respecting the heritage significance and successfully preserving the exceptional landmark appearance of the bridge.</p>

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