The objective of this paper was to examine the effect of the nominal maximum aggregate size (NMAS) on the rutting and stripping performance of Georgia asphalt mixtures containing reclaimed asphalt pavement (RAP) mixtures using the Hamburg wheel-tracking device (HWTD). Five NMASs of 25, 19, 12.5, 9.5, and 4.75 mm of Superpave mixtures were investigated at three test temperatures of 50, 64, and 70°C. The test results were analyzed in terms of five indexes of rutting and stripping resistance: (1) number of loading passes at failure ($N_F$ ), (2) rut depth at 20,000 wheel passes, (3) stripping inflection point (SIP), (4) creep slope (CS), and (5) stripping slope (SS). The results indicated that Superpave mixtures containing RAP with large NMAS performed better than those with small NMAS in both rut and antistripping resistance. The relationships between the NMAS and SIP showed high correlation coefficients of more than than 0.86 at 64°C. The test temperatures of 64 and 70°C are appropriate for distinguishing size effect for the mixtures with regard to the three parameters of the number of wheel pass at selected rut depth, SIP, and CS.

This study explores new technologies for detecting structural failures in temporary structures by applying image processing techniques for the automated detection of structural failures. Temporary structures provide support for permanent structures during construction and serve as work platforms for construction workers, but most standards for temporary structures are voluntary. Design and construction considerations, therefore, are thus left to individual engineering judgement. The new approach utilizes a series of chronological digital images to detect incipient structural instability during the construction process. Any structural deformation becomes a measurable variable through image analysis and can serve as a measurable harbinger of structural failure. The proposed technology matches images in the series with their predecessors to highlight and locate differences in the structure. The small-scale experiment conducted and validated that the proposed methodology shows great potential for detecting micro changes that are too small to be detected by eye. The chronological images are expected to provide a coherent and persistent structural deficiency data-set that can be applied to diagnose physical forms of failure at an early stage of deformation, defining a clear starting point for rehabilitation and enabling structural engineers to draft and implement a comprehensive plan to address incipient problems in temporary structures.

Combined sewer overflow (CSO) is a significant environmental concern and public health risk (e.g., water contamination, eutrophication, and beach closure). The Environmental Protection Agency (EPA) has introduced the National Pollutant Discharge Elimination System (NPDES) permitting program to regulate and address this matter. This program mandates the control of CSOs for more than 700 municipalities obligated to devise Long-term Control Plans (LTCPs) to curb combined sewer overflows and reduce them to safe levels. The LTCP involves diverse strategies, including sewer separation, green infrastructure improvements, and conventional gray infrastructure upgrades. This study investigates several municipalities’ solutions for CSO problems that use conventional methods and wireless sensor technology as real-time control, mainly focusing on a comparative analysis of two cities, Richmond, Virginia, and South Bend, Indiana, such as their average rainfall, the frequency of overflows, and the capacity of treatment plants. The findings indicate that integrating sensor technology could significantly enhance modeling endeavors, bolster the capacity of existing structures, and substantially enhance preparedness for storm events. The EPA’s Storm Water Management Modeling (SWMM) software is utilized. Through an analysis of SWMM data, the study suggests the potential for leveraging wireless sensor technology to achieve more robust control over CSOs and significant cost savings as a part of LTCPs.

This study, conducted in Georgia, analyzes the impact of rejuvenators on foamed asphalt mixtures containing 100% reclaimed asphalt pavement (RAP) and assesses the feasibility of using the mixture as a structural layer in a pavement system. Three laboratory tests, the Marshall stability (MS), indirect tensile strength (ITS), and semi-circular bending (SCB), were conducted for the structural evaluation. Results indicate that the MS and ITS of the mixtures without rejuvenator are sufficiently high for use as a subsurface structural layer. However, dosing the mixture with ≥6% rejuvenator significantly decreases the MS and ITS. SCB tests for fracture resistance at an intermediate temperature indicate that mixtures without rejuvenators are more brittle than conventional hot mix asphalt (HMA). The flexibility index indicates that the mixtures with ≥6% rejuvenator have higher crack resistance than HMA and no-rejuvenator 100% RAP foamed asphalt mixtures. In conclusion, addition of rejuvenators significantly affects the structural integrity of 100% RAP foamed asphalt mixtures by increasing the cracking resistance but considerably decreasing the stability and indirect strength. Therefore, for practical purposes, the use of a rejuvenator in foamed asphalt mixtures is not recommended.

Combined sewer overflow (CSO) is a significant environmental concern and public health (e.g., water contamination, eutrophication, and beach closure). The Environmental Protection Agency (EPA) has introduced the National Pollutant Discharge Elimination System (NPDES) permitting program to regulate and address this matter. This program mandates the control of CSOs for more than 700 municipalities obligated to devise Long-term Control Plans (LTCPs) to curb combined sewer overflows and bring them down to safe levels. LTCP involves diverse strategies, including sewer separation, green infrastructure improvements, and conventional gray infrastructure upgrades. This study investigates several municipalities’ solutions for CSO problems in conventional methods and wireless sensor technology as real-time control. The investigation mainly focuses on a comparative analysis of two cities, Richmond, Virginia, and South Bend, Indiana, such as average rainfall, the frequency of overflows, and the capacity of treatment plants. The findings indicate that integrating sensor technology could significantly enhance modeling endeavors, bolster the capacity of existing structures, and substantially enhance preparedness for storm events. The EPA’s Storm Water Management Modeling (SWMM) software is utilized. Through an analysis of SWMM data, the study suggests the potential for leveraging wireless sensor technology to achieve more robust control over CSOs as a part of LTCPs.