Anshu Manik

Neural networks surrogate models for simulating payment risk in pavement construction

A common provision in quality control/quality assurance (QC/QA) highway pavement construction contracts is the adjustment of the pay that a contractor receives on the basis of the quality of the construction. It is important to both the contractor and the contracting agency to examine the amount of pay that the contractor can expect to receive for a given level of construction quality. Previous studies have shown that computer simulations can provide a better, more de- tailed examination of the pay schedule than is possible by simply determining the expected pay. In particular, the simula- tion process can provide an indication of the variability of pay at various quality levels and can identify the factors most responsible for pay adjustments. Stochastic simulation models are very useful in estimating and analyzing payment risk in highway pavement construction. However, such models are constrained by their computational requirements, and it is of- ten necessary to couple them with simpler models to speed up the process of decision-making. This paper investigates the use of Neural Networks (NN) to build surrogate models for a pavement construction payment-risk prediction model. The results show that although the average error associated with the NN predictions are acceptable; in some particular cases the errors may be unacceptably high.

Multi-Depth Deflectometer Dynamic Response Measurements under Simulated New Generation Aircraft Gear Loading

In this paper, the comparative effect of dual-tridem (Boeing 777) and dual-tandem (Boeing 747) gear trafficking is studied on the Multi-Depth Deflectometer (MDD) responses of two medium-strength subgrade flexible test sections at the National Airport Pavement Test Facility (NAPTF). Using the MDD response-time histories and in-depth displacements obtained under repeated traffic loading, the results were investigated in terms of rebound (recovered) and residual (unrecovered) displacements in the pavement and in the subgrade. The resilient behavior of the NAPTF flexible pavement sections were characterized by analyzing the MDD rebound response data. The results showed that the rebound surface displacements were significantly influenced by changes in asphalt concrete surface layer temperature. Analysis of transverse distributions of MDD rebound surface displacements revealed multiple-wheel load interaction effects. In general both the rebound and residual (and hence the maximum total) displacements are approximately equivalent for both the dual-tridem and dual-tandem gears.

Relating Flexible Pavement Acceptance Criteria to Performance Using Mepdg Simulations and Piecewise Cubic Spline Interpolation

Abstract The performance of the highway system is inevitably linked to its quality of design and construction. To control the quality of construction, elaborate Quality Assurance (QA) programs have been developed by highway agencies based on statistical sampling and acceptance procedures to ensure that the work is in accordance with the plans and specifications. The QA procedure is intended to ensure that the constructed pavement would perform well during its design life. However, numerous field investigations and research studies over the years have identified a gap in our understanding of the relationship between the QA test results and pavement performance (fatigue cracking, rutting, etc.). This paper is intended to present the framework for developing an understanding of the relationship between QA tests and flexible pavement performance, which is essential for developing Performance Related Specifications (PRS). In the first part of the paper, the Michigan Department of Transportations (MDOT) QA pro...