Pavement surface cracking has long been considered an important criterion for maintenance intervention because of its detrimental effects on pavement performance. Once initiated, cracking increases in severity and extent and allows water to penetrate the pavement. The water weakens the unbound layers and consequently accelerates the rate of pavement deterioration. Cracking prediction and its control are thus key components in determining the timing and cost of pavement maintenance. A neural network-based model is presented in this paper for predicting flexible pavement cracking. One-, two-, and three-hidden-layer backpropagation neural network (BPNN) topologies are investigated and their cracking-prediction performances compared. Based on the analysis, it is concluded that for the same optimal number of processing elements, a one-hidden-layer BPNN topology may be sufficient in achieving satisfactory results in cracking prediction; increasing the number of layers may not add any significant benefit to the performance of the model.

Knowledge of truck axle weights and distribution is critical for assessing the damaging effects of loading on pavements. A study was initiated in Wisconsin to investigate the impact of a new law that allows vehicle combinations up to 98,000 lb on six axles to transport raw forest products during the spring thaw suspension period. Analysis of truck weight data collected at two logging industrial facilities was done to determine whether certain logging trucks were more damaging than others. The data collected included truck axle weight and spacing from platform scales at two logging industrial facilities, plus scale operators' monthly supplied log truck reports covering the period of December 2011 to May 2012. Analysis revealed that a considerable proportion of trucks were overloaded at the two facilities, with overload averaging 31% and 24% in winter and 25% and 33% in spring. The magnitude of the overload, however, averaged less than 5% of the 98,000-lb limit permitted under the law for gross vehicle weight. FHWA Class 9 loaded trucks produced a higher damage factor compared with that of Class 10 loaded trucks. In addition, Class 9 loaded trucks resulted in a lower average relative log carrying efficiency of 15,400 lb per equivalent single-axle load compared with 22,000 lb per equivalent single-axle load for Class 10 loaded trucks. Better truck configuration choices can benefit both the industry and the highway agency by allowing the industry to haul more logs while inflicting less damage to the pavement.

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From the 1990s to now, transportation maintenance quality assurance (MQA) programs have been developed to ensure that maintenance quality is being achieved. MQA programs must be capable of detecting insufficient maintenance efforts, poor material performance, and incorrect procedures when evaluating end-product performance. At the Maintenance Quality Assurance Peer Exchange held at Madison, Wisconsin, in October 2004, participants expressed interest in exploring how statistical tools might be more effectively applied in MQA programs. The purpose of this paper is to provide maintenance practitioners with knowledge of how to understand and use statistics in MQA programs. Literature pertaining to recent efforts in this area was reviewed and synthesized. In addition, hazardous debris data from Wisconsin and level of service data from North Carolina were analyzed to demonstrate how an agency could apply traditional statistical methods such as analysis of variance, confidence limits, means comparison, data stratification, and sample size determination to an MQA program.