Danny X. Xiao

The objective of this study is to analyze bridge deck condition deterioration and evaluate the impact of concrete sealer and salt usage on deck condition and life-cycle cost. To achieve this goal, machine learning models were built to predict the evolution of bridge deck rating. The deck maintenance history shows that the average bridge age at deck overlay and deck replacement is around 25 and 50 years, respectively. Deck overlay can improve deck condition from an average rating of 6.3 to 7.1, and deck replacement can efficiently recover deck condition from an average rating of 5.3 to 8.5. The effect of concrete sealer on bridge deck condition is only observable at the stage before the first overlay, indicating that concrete sealer may not be effective over the long term. More usages of prewet salt and salt brine in anti-icing result in slightly higher deck condition ratings, while more dry salt in deicing presents slightly lower deck condition ratings, indicating the benefits of salt brine over dry salt. When concrete sealer is applied every 2 or 4 years, it can help extend the service life of the bridge deck by around 1~2 years. If concrete sealer is applied every 12 years, a 6% reduction in life cycle cost could be achieved.

Penetrating sealers have been applied to some concrete pavement joints to reduce moisture movement and provide additional durability in Wisconsin since 1999. In these projects, contractors were asked to first pressure-wash all saw slurry from sawed joints and allow them to dry thoroughly, then apply a silane sealer directly to the interior of the sawed joint. The objective of this study was to evaluate the effectiveness of penetrating sealer in protecting pavement joints through field investigation of three projects in Wisconsin. Site visit surveys along with sample evaluation of field cores were completed. Although there was no visual detection of the presence of sealers in in-service pavements, laboratory tests proved the presence and functionality through contact angle, absorption, and penetration depth. The depth of penetration ranged from 1.46 to 11.75 mm, with an average of 5.14 mm. Penetration was correlated with concrete strength where less penetration was associated with high-performance concrete. When compared with the samples without sealer, more than half of the joints with sealer were still performing better in terms of contact angle and absorption after 8.2 years of service.

The current mechanistic-empirical pavement design guide does not directly consider longitudinal cracking in concrete pavement design. However, longitudinal cracking has been widely observed on joint plain concrete pavements, sometimes even more significant than transverse cracking, which adversely affect the performance and service life of concrete pavements. The objective of this study was to determine the possible causes for the longitudinal cracking problem in joint plain concrete pavements so that effective actions can be taken in future design and construction. Both field project survey of severe longitudinal cracking and numerical simulations were conducted. Results confirmed that construction problems such as inadequate longitudinal joint forming and inadequate base support are among the contribution factors, in particular for premature and localized longitudinal cracks. However, field survey indicated that the amounts of longitudinal cracking increased with widened slabs and tied concrete shoulders. Results from numerical simulation further demonstrated that the geometry of a slab could greatly influence the potential of longitudinal cracking, especially when the traffic is composed of more tandem and tridem axles. The field data led to a development of an empirical model for the prediction of longitudinal cracking. As the geometry including slab length, width and thickness is generally a design issue, this study warrants the necessity and possibility of developing a mechanistic-empirical model for longitudinal cracking in future mechanistic-empirical pavement design.

Pavement condition data usually consist of multiple performance indicators, which poses difficulties in maintenance and fund allocation decision making. In conventional practice, empirical-based scoring methods have been used to assess multidimensional pavement condition attributes. However, if performance data on a roadway network are available, data-driven approaches can be applied for such multiattribute decision-making problems. In this study, an unsupervised cluster method called normalized cuts (NCut) is developed to group pavement sections into clusters with homogenous conditions. Geometric centers of the clusters are used to determine the performance ranking of each cluster. The proposed methodology is demonstrated with a case study in Louisiana. A total of 35 pavement sections with eight performance parameters are grouped into five clusters indicating conditions ranging from very good to very poor. It is validated with current practice that the methodology presented in this study is effective in supporting pavement prioritization decision making.

Cracking characterization is one of the most important tasks in automated pavement data analysis. Although cracking detection and segmentation algorithms have become more reliable in recent years, accurate cracking classification remains a constant challenge to pavement engineers. Conventionally, manual recognition uses cracking orientation and topological features to classify cracking into different types such as alligator cracking and transverse cracking. However, the rules to classify cracking are often complicated and subjective, which compromises the reliability of computerized implementation. This study develops a support vector machine (SVM)–based method to intelligently identify cracking types in an automated manner. Pavement cracks are grouped using a minimum rectangular cover (MRC) model. Using the relative location, orientation, and size of the MRC, as well as the cracking characteristics such as cracking density and cracking connectivity, three SVM models are compared in this study. It is found that an 88.07% accuracy is achieved for 10,134 MRCs collected from four highway sections using the two-phase SVM model. The proposed methodological framework would improve overall accuracy in cracking classification.

Louisiana utilized performance data from the pavement management system (PMS) to evaluate and calibrate the AASHTO Pavement Mechanistic–Empirical (ME) Design. Analysis of the PMS faulting data revealed that there were no records between 0 and 0.2 in. (5 mm); others over 0.2 in. (5 mm) appeared to be much greater than would be expected based on engineering experience. Therefore, several tasks were completed to validate the PMS faulting data and prepare them for local calibration. This paper presents details of the problem, approach, results, and lessons learned. First, faulting data from the PMS and Long-Term Pavement Performance database were analyzed to have an overview of the common range of joint faulting. To validate the PMS faulting data, 43 representative projects across Louisiana were selected for further analysis. Longitudinal profiles were collected with high-speed profilers and analyzed with the AASHTO R36 automated faulting measurement (AFM) algorithms. Manual measurements were also conducted during site visits. The comparison of faulting from different methods showed that the PMS data extremely overestimated faulting compared with the AFM estimation or the manual measurement. Results from the AFM algorithm were much closer (in the same magnitude) to the manual measurements. Therefore, faulting data from the AFM algorithm were used, and the faulting model was successfully calibrated. It is recommended to evaluate PMS faulting data carefully before applying them to calibrate the AASHTO Pavement ME Design software. Automated faulting measurement based on high-speed profiles is a feasible approach.

The application of truck axle load spectra in the Mechanistic Empirical Pavement Design Guide (MEPDG) has brought great advancement in pavement design through quantifying pavement accumulated damage due to individual axle loads. However, how to relate the truck axle load volume and spectra directly to pavement performance remains a practical challenge for pavement engineers. This paper presents a systematic index approach to this issue that characterizes three aspects of traffic loading to pavement performance: volume, load, and damage. Four summary indices were investigated in this study: cumulative truck volume (CTV), cumulative truck load (CTL), equivalent single axle load (ESAL), and relative pavement performance impact (RPPI). The involved concepts and calculation procedures were first introduced, followed by a numerical evaluation analysis of 30 axle load spectra, 18 vehicle class distributions, 2 truck configurations, and 2 pavement types. To demonstrate how these summary indices could be used, a case study was presented. Overall results suggested that the systematic indices introduced in this study had a clear relationship with pavement performance, so it could be used to assist engineers in many ways such as comparing different load spectra, communicating between engineers, and understanding the relationship between traffic and pavement performance for a specific design at any point in time

State highway agencies need pavement-condition data to select candidates for pavement maintenance and rehabilitation. However, it is a challenge for pavement engineers to simultaneously assess a number of attributes that represent different aspects of pavement condition. In conventional practice, empirical comprehensive evaluation methodologies, such as fuzzy set theory and analytical hierarchy process, have been used to aggregate multiple distresses into integrated pavement-performance indices. These methodologies, however, are mostly based on experts’ or engineers’ judgment rather than data-driven approaches. In this paper, a framework of applying a data-driven approach to conduct comprehensive pavement evaluation and ranking is presented. The method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is introduced and applied to rank pavement sections with various evaluation attributes. Principal component analysis (PCA) is employed to combine distresses with high correla...

Since the release of the first research version of Mechanistic–Empirical Pavement Design Guide software, the State of Louisiana has conducted a series of studies in preparation for adopting the new AASHTO design guide. The objective of this study was to develop an implementation guideline for the full implementation of the AASHTOWare Pavement ME Design program (Pavement ME) in Louisiana. For this purpose, a Louisiana default input strategy that reflected state and local conditions and practices was first developed. Design reliability and performance criteria were established on the basis of a consensus survey and pavement management system (PMS) distress triggers. A total of 162 projects were selected from the existing Louisiana highway network, including flexible pavements with five types of base, rigid pavements with three types of base, and rehabilitation with asphalt overlays. Performance data were retrieved from the PMS database and validated through additional field performance surveys. Through comparison of the performance predicted by the Pavement ME with the measured performance recorded in the PMS database, distress models were evaluated and calibrated according to local Louisiana conditions. Subsequently, a Louisiana Pavement ME Design Guideline that contains all necessary design input information and calibration coefficients was developed. Finally, the practical and economic benefits of the full implementation of the Pavement ME for Louisiana pavement design were demonstrated by a direct comparison with the current 1993 AASHTO design method through a group of thickness design examples and life-cycle cost analyses.