Relationship Between Weights Measured by Permanent Truck Scales and Golden River Weigh-In-Motion Scales

The Division of Planning purchased a Golden River Weigh-in-Motion system. The first assignment was to determine the optimum calibration setting for operating each weigh mat. The second assignment was to determine the sensitivity of the weigh data. The third assignment was to develop appropriate relationships to adjust the dynamic data to equivalent static data. A series of correlation efforts established the appropriate calibration factor for each weigh mat. Then the mats were installed at a site on I 64 in Shelby County and data were collected for over 1,600 trucks. From these data, it became evident that equations to adjust dynamic loads to equivalent static loads should be developed for individual axle locations on the truck rather than the gross weight as recommended by the manufacturer. The primary reason was that the steering axle\u27s dynamic load was approximately 70 percent of the static axleload. The discrepancy is due to the torque transmitted from the engine to the drive axles which partially lifts the steering axle off the pavement. Therefore, equations were developed for: Steering Axle Single Drive Axle Single Axle on Trailer Drive Tandem Axles Trailer Tandem Axles Drive Tridem Axles Trailer Tridem Axles Observations of truck dynamics combined with literature review indicated that dynamic axleloads are affected by: Engine torque, Temperature of rigid pavements, Location of axle on truck, Pavement roughness rather than pavement type, and Suspension system between truck frame and axles

Hybrid Information Flow Analysis for Programs with Arrays

Information flow analysis checks whether certain pieces of (confidential) data may affect the results of computations in unwanted ways and thus leak information. Dynamic information flow analysis adds instrumentation code to the target software to track flows at run time and raise alarms if a flow policy is violated; hybrid analyses combine this with preliminary static analysis. Using a subset of C as the target language, we extend previous work on hybrid information flow analysis that handled pointers to scalars. Our extended formulation handles arrays, pointers to array elements, and pointer arithmetic. Information flow through arrays of pointers is tracked precisely while arrays of non-pointer types are summarized efficiently. A prototype of our approach is implemented using the Frama-C program analysis and transformation framework. Work on a full machine-checked proof of the correctness of our approach using Isabelle/HOL is well underway; we present the existing parts and sketch the rest of the correctness argument.Comment: In Proceedings VPT 2016, arXiv:1607.0183

Parameterized Construction of Program Representations for Sparse Dataflow Analyses

Data-flow analyses usually associate information with control flow regions. Informally, if these regions are too small, like a point between two consecutive statements, we call the analysis dense. On the other hand, if these regions include many such points, then we call it sparse. This paper presents a systematic method to build program representations that support sparse analyses. To pave the way to this framework we clarify the bibliography about well-known intermediate program representations. We show that our approach, up to parameter choice, subsumes many of these representations, such as the SSA, SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more frugal than the previous techniques used to construct SSI - Static Single Information - form programs. We produce intermediate representations isomorphic to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow problems that can be partitioned per variables. However, contrary to SEGs, we can handle - sparsely - problems that are not in this family