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.

During concrete pavement construction, comprehensive information on concrete strength development is necessary for knowledgeable scheduling decisions. To improve in situ strength estimation, nondestructive tests can be combined to maximize available information and increase collection efficiency. Ultrasonic testing has a higher accuracy in strength estimation for early age concrete and the external device allows for more data to be easily collected, while maturity testing can use temperature models to predict strength development. A procedure was created that combines these methods and their strength models in the laboratory for improved and increased field strength data. Using the procedure allows more data to be collected with greater accuracy and provides an adjustable, predicted strength development. This improves the efficiency of fast-track construction projects without resorting to costly alternatives by providing more information on strength development of in situ concrete than traditional strength testing or nondestructive methods individually.

Four continuously reinforced concrete pavement (CRCP) sections were built at the University of São Paulo campus in order to analyze the pavement performance in a tropical environment. The sections short length coupled with particular project aspects made the experimental CRCP cracking be different from the traditional CRCP one. After three years of construction, a series of nondestructive testing were performed - Falling Weight Deflectometer (FWD) loadings - to verify and to parameterize the pavement structural condition based on two main properties: the elasticity modulus of concrete (E) and the modulus of subgrade reaction (k). These properties estimation was obtained through the matching process between real and EverFE simulated basins with the load at the slab center, between two consecutive cracks. The backcalculation results show that the lack of anchorage at the sections end decreases the E and k values and that the longitudinal reinforcement percentage provides additional stiffness to the pavement. Additionally, FWD loadings tangential to the cracks allowed the load transfer efficiency (LTE) estimation determination across cracks. The LTE resulted in values above 90 % for all cracks

Road construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones—transient and in restricted areas—increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors with the ability to conduct activities from a safer distance. This paper aims to scan and evaluate several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning highlighted over twenty technologies in several levels of development that met this goal. Each technology was briefly evaluated not only based on safety features but also on productivity, data processing, and requirements for implementation. Finally, recommendations for implementation of selected technologies were provided. This consolidated review provides a unique and timely resource for researchers and practitioners.

Conventional approaches to the local calibration of mechanistic-empirical (ME) pavement performance prediction models require the use of field data with low variability. However, these strict data requirements often lead to datasets with a limited number of observations. Local calibrations using such datasets permit the elimination of biases in predictions with 50% reliability, but might not provide an accurate evaluation of predictions of higher design reliability, which can exceed 90%. In this paper, we propose a novel approach to faulting model evaluation for concrete pavements that specifically focuses on high levels of reliability. To address the issue of limited dataset size, our approach leverages pavement management system (PMS) data, which are collected regularly and in large quantities at a local level. We also account for the presence of censored data from out-of-service or modified pavement sections. This permits the local calibration of performance prediction models, with a particular emphasis on the accurate prediction of pavement distress with high reliability levels, which is critical for the design of high-volume roads. To validate our methodology, we applied it to evaluate, modify, and calibrate the Pavement ME faulting model using Pennsylvania PMS data. The proposed methodology can be applied for the local calibration of other Pavement ME models.