A majority of existing permeable pavement installations are related to parking lots and commercial areas with low speeds and light traffic loads. During the past two decades, a tremendous amount of progress has been made with regard to the application of permeable pavements regarding mix design, hydrologic design, water quality assessment and maintenance requirements. Review of the literature revealed, however, that there are still many important questions that must be answered before permeable pavements can be fully integrated in urban roads, especially in highways with high speeds and loads. This paper summarises the major research progress in the above listed areas and identifies research needs. Research needs are summarised under four topics: (1) structural and materials property, (2) hydrologic performance, (3) water quality and (4) maintenance. Research needs are further organised based on their immediate, short-term and long-term impact.

The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.

Layered elastic theory (LET) has been used successfully for many years in the design and analysis of asphalt pavements. An attractive feature of this approach is its ability to represent the layered-pavement structure with variable interface conditions between consecutive layers. Several computer programs (BISAR, WESLEA, and JULEA) for the analysis of pavement systems were developed on the basis of LET. A new Mechanistic–Empirical Pavement Design Guide (MEPDG) procedure was recently developed by NCHRP under AASHTO sponsorship. It requires that layered elastic analysis be performed more than 1,000 times for a single design simulation of a flexible (asphalt) pavement, which makes the flexible design process extremely time consuming. An important step toward reducing the time needed to use the MEPDG software is improvement in the computational efficiency of the layered elastic analysis. In this paper, a new layered elastic analysis program, MnLayer, is presented. Several improvements in the numerical evaluation of the inverse Hankel transform of the layered elastic solutions were implemented into the program. A comprehensive comparison of MnLayer with two widely used layered elastic analysis programs demonstrated that MnLayer's accuracy matches or exceeds the accuracy of these other programs while its computational time is as little as 1/20 of theirs.

The structural response of jointed plain concrete pavement slabs was evaluated using data obtained from instrumented slabs. The instrumented slabs were a part of newly constructed jointed plain concrete overlay that was constructed on existing asphalt concrete pavement on I–70 in Colorado, near the Kansas–Colorado border. The instrumentation consisted of dial gauges for measuring curling deflections at the slab corner and longitudinal edge and surface-mounted strain gauges for measuring load strains at the longitudinal edge at midslab. The through-thickness temperature profiles in the pavement slabs were also measured at 30-min intervals during the field test. Analysis of the field data showed that the instrumented slabs had a considerable amount of built-in upward curling and that concrete slabs on a stiff base can act completely independent of the base or monolithically with the base, depending on the loading condition. The built-in upward curling of the slabs has the same effect as negative temperature gradients. These findings suggest that the effects of temperature gradients on the critical edge stresses may not be as great as previously thought and that the corner loading, in some cases, may produce more critical conditions for slab cracking. Another important finding of this study is that a physical bond between pavement layers is not required to obtain a bonded response from concrete pavements.

Ultrasonic tomography is an emerging technology that shows promise for quality assurance/quality control (QA/QC) during construction or for rehabilitation decisions about concrete pavements. However, the benefits of this emerging technology have not yet been fully captured for widespread use in highway infrastructure management. Verification of a state-of-the-art ultrasonic tomography device, MIRA, is presented through multiple field trials involving typical pavement constructability and rehabilitation issues. Field trials indicate that although the device is a useful portable pavement diagnostic alternative capable of consistent thickness measurement, reinforcement location, and distress evaluation, significant efforts and user expertise are required for measurement and data interpretation of large-scale application. Software was developed for a more productive, objective signal interpretation method with auto-mated analysis of reinforcement location in continuously reinforced concrete pavement. This type of automation for multiple applications shows promise for the use of ultrasonic tomography to improve large-scale pavement QA/QC and rehabilitation projects in the future. Nevertheless, the research in the paper shows ultrasonic tomography to be an accurate, reliable, and convenient alternative or supplement to traditional techniques that can be used for a wide variety of small-scale pavement diagnostics applications.