The role of traffic data in the Mechanistic-Empirical (M-E) pavement design is significantly crucial in assessment of pavement performance throughout the pavement design life. Providing a well-qualified traffic data for the M-E pavement design requires a huge effort with respect to collecting and analyzing the data. The Texas Department of Transportation (TxDOT) manages 30 Weighin-Motion (WIM) stations and provided classification and weight data used for this study. Evaluating the classification data first, vehicle classification distribution along with the percentage of trucks, average annual daily truck traffic, and monthly adjustment factor were established. Processing axle load data provided the number of axles per truck and axle load spectra for each of the traditional 13 vehicle classes. To develop statewide axle load spectra data, the cluster analysis was conducted using vehicle classification distribution and the Class 9 tandem axle load spectra data to provide traffic input data for the Texas M-E flexible pavement design program where the load spectra data are not available due to the absence of WIM station. As a result, six clusters for both variables were identified and a guideline was successfully established to use those clusters to generate axle load spectra inputs for a given set of truck traffic classification data.

Increasing overweight truck traffic, as evident by a rapid increase of permits issued for such traffic in Texas, tends to accelerate the deterioration of pavement structural integrity. However, it is less known to what degree buried utility facilities along and across the right-of-way are affected by these overweight loads, especially if these facilities are aged or placed under an exception to the Texas Utility Accommodation Rules (UAR). In this study, researchers evaluated the damage potential of buried pipe subjected to repeat overweight traffic loads. Based on a review of technical design and engineering requirements for utility accommodation in Texas, a large-scale laboratory test was conducted to investigate fatigue behavior of polyvinyl chloride (PVC) and concrete pipe. The result of both static and fatigue load tests indicated that pipe deformation in terms of vertical diameter reduction was less than 5% of pipe diameter, the maximum allowable value. However, fatigue damage was observed fo...

Ground subsidence occurrences have drastically increased in the Seoul area of the Republic of Korea. The structural defects of underground utilities were found to be the primary cause of ground subsidence based on several field investigations. This paper presents a risk model that assesses the probability of occurrence of ground subsidence along railways. In this study, support vector machine (SVM) and multi-layer perceptron (MLP) approaches were successfully employed to develop an artificial neural network (ANN)-based risk model. The risk model, in conjunction with a database composed of underground utilities and geological boring data along urban railway networks, was utilized to develop a hazard map system. A limited field experimental program was conducted for the purpose of verification, resulting in a promising tool to effectively maintain railway networks.

Porous asphalt pavement is a part of the permeable pavement system, which can be used to mitigate the negative impacts of urbanisation on the water hydrological cycle and environment. This study aims to assess the mechanical and hydrologic characteristics of porous asphalt pavements, with and without geocell composites, using a plate load test, falling weight deflectometer test, and rainfall simulation test. The corresponding results indicate that the elastic modulus of the unreinforced pavement is lower than that of the reinforced pavement. The analysis demonstrates that the use of geocell composites effectively increases the load-bearing capacity of the pavement. When the base layer is reinforced with geocells, its load-bearing capacity increases. Observation of the rainfall simulation tests on the reinforced pavement indicates that the reinforced pavement effectively handles the surface runoff.

Construction of underground box structures that underpass existing infrastructures or facilities at shallow depths is being widely conducted in urban areas in order to avoid the interference of existing structures without the rerouting of traffic. Consequently, it is crucial to ensure safety during and after construction by monitoring the ground settlement induced by underground box installations that can be influential for the existing various structures. This paper provides a method to assess the risk potential around underground box structures with artificial neural networks (ANN), taking into account input variables that can be monitored in the field. By introducing the numerical methods and ANN, the probability of failures considering the variability of design parameters such as ground conditions, structure sizes and shapes, traffic loads, and presence of existing structures could be assessed and utilized for the safe construction of underground box structures at shallow depths. Experimental programs were also performed to investigate the effect of the umbrella method, which contributes to the decrease of risk potential in a practice. A limited field test evaluation using ground-penetrating radar (GPR) along with a pneumatic dynamic cone penetrometer (PDCP) was found to be promising in the assessment of risk potential at shallow depths.