While cement treatment is a proven method for improving the strength and durability of soils and aggregates, cement hydration causes shrinkage strains in the cement-treated base (CTB) that can lead to reflection cracking in asphalt surfaces. Cracking may then cause increased pavement roughness and lead to poor ride quality. The overall purpose of this research was to utilize data collected through the Long-Term Pavement Performance (LTPP) program to investigate the use and classification of CTB layers and evaluate the relative impact of cement content on the development of roughness and cracking in asphalt concrete (AC) pavements constructed using CTB layers. The data included 52 LTPP test sites, which represented 13 different states and one Canadian province, with cement contents ranging from 3.0 to 9.5 percent by weight of dry aggregate. Statistical procedures were utilized to identify the factors that were most correlated to the observed pavement performance and to develop prediction equations that transportation agencies can use to estimate the amount of roughness for a given pavement at a given age and the amount of distress associated with a particular crack severity level for a given pavement. The data collected for this study suggest that wide ranges of cement contents are used to stabilize soils within individual American Association of State Highway and Transportation Officials soil classifications. The data also suggest that CTBs comprising flexible pavement structures are constructed mainly on rural facilities. A backward-selection model development technique was used to develop sets of prediction equations for roughness and cracking. Age, AC thickness, CTB thickness, and cement content were determined to be significant predictors of International Roughness Index, while age, air freezing index, AC thickness, CTB thickness, cement content, and traffic loads in thousands of equivalent single-axle loads were determined to be significant predictors of low-severity, medium-severity, and high-severity block, fatigue, longitudinal (wheel-path and non-wheel-path), and transverse cracking in AC pavements constructed using CTB layers. Investigation of the relationships between CTB modulus and the development of roughness and cracking is recommended for further study.

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abstract: The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy. It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings. / Dissertation/Thesis / Masters Thesis Civil Engineering 2015

In Sweden, there has been an long term effort to allow as heavy traffic as possible, provided thatthe road network can handle it. This is because heavy traffic offers a competitive advantage withsocio-economic gains. In July 2018, the Swedish Transport Administration made 12 percent ofthe Swedish road network avaliable for the new maximum vehicle weight of 74 tonnes, basedon a legislative change from 2017. It is known that heavy traffic has a negative effect on thedegradation of the road, but it prevails divided opinions on whether 74 tonnes have a greaterimpact on the degradation rate compared to previous maximum gross weights of 64 tonnes.The 74 tonne vehicles have the same allowed axle load, which means more axles per vehicle. Some argue that an increased total load and more axles affect the degradation associated withtime-dependent material properties, while others argue that 74 tonnes mean fewer heavy vehiclesoverall, and thus should have a positive impact on the road’s lifespan. The construction companySkanska therefore requests a statistical analysis that enables to nuance the effects that heavytraffic has on the Swedish state road network. Since there is very limited data on the effect of 74 tonne traffic, this Master thesis instead focuseson modeling heavy traffic in general in order to be able to draw conclusions on which variablesare significant for a road’s lifetime. The method used is survival analysis where the lifetimeof the road is defined as the time between two maintenance treatments. The model selectedis the semi-parametric ’Cox Proportional Hazard Model’. The model is fitted with data froman open source database called LTPP (Long Term Pavement Performance) which is providedby the National Road and Transport Research Institute (VTI). The result of the modeling ispresented with hazard ratios, which is the relative risk that a road will require maintance atthe next time stamp compared to a reference category. The covariates that turned out to besignificant for a road’s lifetime and thus are included in the model are; lane width, undergroundtype, speed limit, asphalt layer thickness, bearing layer thickness and proportion of heavy traffic. Survival curves estimated by the model are also presented. In addition, a sensitivity analysis ismade by exploring survival curves estimated for different scenarios, with different combinationsof covariate levels.The results is then compared with previous studies on the subject. The most interesting finding isa case study from Finland since Finland allow 76 tonne vehicles since 2013. In the comparison,the model’s significant variables are confirmed, but the significance of precipitation and thenumber of axes for a roads lifetime is also highlighted