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

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.

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

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.

Database Design and Integration Framework for Risk Management for State Highway Agencies

Risk-based transportation asset management program (TAMP) gives transportation agencies the ability to have a mechanism for documenting and measuring risks to their operations, as this will help drive the potential mitigation activities. The monitoring and updating of the risk management process is essential to TAMP as it will ensure that the financial plan and investment strategy components of the TAMP are suitable to ensure that agencies continue to fulfill their primary responsibilities. However, a review of the initial TAMP documents submitted by U.S. transportation agencies showed that although risks are acknowledged, there does not exist a clear line of sight between risk management and agencies’ programming. This is the result of a lack of cross-asset risk integration. As a result, the paper proposes a data integration framework for developing a cross-asset comprehensive database for risk management that integrates many of the common risks that state highway agencies have identified in the initial TAMP documents. In addition, the paper proposes modifications to the risk identification methodology that leverage the collaborative aspects of risk management to quantify risk in monetary terms.

Evaluation of Acceptance Risks through Percent within Limit for Highway Materials and Pavement Construction

The majority of State Highway Agencies now employ statistical-based specifications for the acceptance of highway materials and pavement construction. The parameters of these statistical acceptance plans are specified based on engineering judgment and may result in a high level of risk to both the agency and contractor. To appropriately apply such specifications to the pavement construction industry, the associated production quality (i.e., materials and construction variability) must be well understood by all parties involved, and its potential effects need to be assessed. To address this, the objective of this study is to quantify the risks to the agencies and contractors (i.e., Type I and Type II errors) associated with the use of both single and multiple acceptance quality characteristics through constructing operating characteristic curves. It is also the purpose of this study to provide guidelines to properly implement the key components of an acceptance plan and its associated statistics. The methodology and findings identified in this study can be applied elsewhere to evaluate the acceptance plans and the associated risks related to highway materials and pavement construction.

Developing an Automated Technique to Calibrate the AASHTOWare Pavement ME Design Software

Many state highway agencies are in the process of implementing the AASHTOWare Pavement ME Design (PMED) software for routine pavement design. However, a recurring implementation challenge has been the need to locally calibrate the software to reflect an agency’s design and construction practices, materials, and climate. This study introduced a framework to automate the calibration processes of the PMED performance models. This automated technique can search PMED output files and identify relevant damages/distresses for a project on a particular date. After obtaining this damage/distress information, the technique conducts model verification with the global calibration factors. Transfer function coefficients are then automatically derived following an optimization technique and numerical measures of goodness-of-fit. An equivalence statistical testing approach is conducted to ensure predicted performance results are in agreement with the measured data. The automated technique allows users to select one of three sampling approaches: split sampling, jackknifing, or bootstrapping. Based on the sampling approach chosen, the automated technique provides the calibration coefficients or suitable ranges for the coefficients and shows the results graphically. Model bias, standard error, sum squared error, and p-value from the paired t-test are also reported to assess efficacy of the calibration process.

Road User Costs for Highway Construction Projects Involving a Lane Closure

While for years most American State Highway Agencies (SHAs) have performed Road User Cost (RUC) calculations, no uniformity from state to state has been established. There is scant research available that documents the testing and validation of existing RUC calculation methods for highway rehabilitation projects. Especially scarce are studies addressing the unique problem of accurately calculating RUC in the event of lane closures. This research addresses this problem by describing and comparing two methods of making such calculations: A manual method developed by the Texas Transportation Institute (TTI), and adopted by many other state agencies, such as the Florida Department of Transportation (FDOT), and a commercial software package.

Optimizing In-Place Density Through Improved Density Specifications

The objective of this research was to (1) determine critical requirement(s) for in-place density based on a review of the literature; (2) analyze density test results shared by state highway agencies (SHAs) across the country to identify state specifications that minimize density results failing the identified critical requirement; and (3) document specification parameters that are important to achieve the critical requirement to share with SHAs that are interested in improving their density specifications. Based on prior research, the minimum density of an asphalt mixture should be 92.0% of the theoretical maximum specific gravity, as density below this critical level would have a detrimental effect on the long-term performance of the mix. Twelve SHAs identified thus far in this research have successfully adopted density specifications that minimize the number of test results below the 92.0% threshold. The statewide density results below the threshold in these states ranged from 3.1 to 11.0%. The density specifications in the 12 states play an important role in achieving these results as discussed in the paper. The case study presented in this paper showed that the density results below the identified threshold for a state in the Federal Highway Administration (FHWA) Demonstration Project decreased from 20.0% to only 5.7% with an improved density specification. There are likely more states with test results like those identified, and they will be added as they are identified in the future. In addition, more states will be added as they make improvements to their density specifications through this effort.

Ontology-Based Knowledge Management System for Digital Highway Construction Inspection

Inspection is critical to ensuring the quality of infrastructure construction. In recent years, state highway agencies (SHAs) have been facing the challenge of a shortage of experienced inspectors due to retirements, workforce downsizing, and resignations to take jobs in the private sector. There is a critical need to retain and manage the accumulated construction inspection knowledge (what, when, and how to inspect) and integrate this knowledge into the construction business process. This paper presents an ontological approach to managing inspection knowledge and the development of a risk-based digital inspection system integrated with the construction process. The developed system consists of five knowledge compartments: inspection activities prioritized based on risk; activity-centered pay items; inspection check items; detailed checks that include priority, frequency, object, attribute, and acceptance criteria; and training materials (optional). This risk-based system is designed to align with the construction process by connecting an activity to a pay item and that pay item to a check item, resulting in an automatically generated comprehensive inspection checklist for the activity. The optional training materials are designed to train inspectors in real time on an as-needed basis. Since risk is embedded in every knowledge compartment, this system not only automates and retains the inspection knowledge but also enables SHAs to allocate their limited resources to the most critical items. This system is currently being implemented by the Indiana Department of Transportation as a digital inspection protocol integrated in its e-construction process.

Risk Management and Data Needs: A State of the Practice Survey of State Highway Agencies

Risk management analysis is one of the new requirements under MAP-21 that separates transportation asset management programs from business as usual for the state departments of transportation (DOTs). Based on this requirement, each agency will discuss the concept of risk and how it should be incorporated into its transportation asset management program as well as how it informs maintenance practices, asset replacement or rehabilitation, and emergency management and response planning. This will require an agency to provide a list of risk exposures and document its system-wide risk management strategy. This paper presents the results of a state of the practice survey of how agencies are developing their risk-based asset management plan and discusses recommendations for future research. The results show that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data, and as a result most agencies do not have a data driven way of tracking risk and updating their risk exposures. The significance of the results highlights the need for further research on data driven risk management and to synthesize methodologies for integrating risk assessment into an agency’s decision-making process.

Examining Curing Efficiency using Neutron Radiography

Many state highway agencies use prescriptive specifications for the curing of concrete bridge decks, pavements, flatwork, or structural elements. For example, concrete pavements are frequently specified to have a curing compound applied shortly after placement and bridge decks typically require seven days of wet curing. These specifications are often based on historical practices that have developed over the last century as opposed to quantitative measurements of performance. New approaches to curing are being introduced which include advanced curing compound formulations or internal curing, for example. However, clear information is not always available as to how this may affect curing requirements. This paper demonstrates the potential to use neutron radiography to quantify the degree of hydration at various distances from the finished surface. It describes how different curing approaches affect cement hydration in terms of both time and distance from the surface. The results show that in a sample exposed to drying after one day the top 12.5 mm (1/2 in) of the mortar was dramatically affected by evaporation, and the degree of hydration in this region was 32% lower than in a 14-day moist-cured sample. Also, the use of superabsorbent polymers increased degree of hydration by about 3.7–7.8% for sealed samples and samples exposed to drying. While these results are preliminary, it is believed by the authors that neutron radiography provides a powerful approach that could be used to determine equivalent curing requirements for new materials.