Significance: Spatial analysis of daily Covid-19 cases at the US county scale revealed a dynamic multifractal scaling of infections, spanning from 10 to 2,600 km and consistently trending toward that of the susceptible population. A susceptible–infected–recovered model was expanded to include spatial spread across counties using a spatial kernel. The reproduction number R _b (average number of persons infected by an infected person) decreased because of interventions (masks, social distancing). The model shows that reducing R _b in isolation is not sufficient to stem the spread of the disease and concomitant measures such as curfews and lockdowns may be needed. The R _b of 2.0 estimated here in July to October 2020 is large, hinting at super-spreaders and super-spreader events.

The use of fiber-reinforced self-consolidating concrete (FR-SCC) in repairing damaged concrete beams has been evaluated. An experimental program was conducted to design and test key fresh and hardened properties of SCC and FR-SCC mixtures. The designed FR-SCC mixtures included two types of supplementary cementitious materials (silica fume (SF) and slag (SL)) and two types of fibers (steel fiber (STF) and polypropylene fiber (PPF)) were used. To ensure good workability to repair congested areas, the optimized volume fractions of the STF were 0.25% and 0.50% compared with 0.10%, 0.15%, and 0.20% for the PPF. In addition, the flexural behavior of 10 beam specimens was investigated. The main reinforcement for the control beams consisted of #5 reinforcing bars, while the main reinforcement for the repaired beams was either #4 or #3 reinforcing bars that were introduced to simulate 35% and 65% reduction of the bar areas, respectively, due to corrosion. The results demonstrate that the optimized FR-SCC mixtures are effective repair materials and can develop adequate bond strength to existing concrete. The flexural test results showed that the repair mixtures were able to increase the cracking load for the repaired beams compared with the control beams. Such an increase is expected to contribute to extending the life of the damaged member or structure at the service load level. This paper also presents a comparison of the predicted values for the first-crack load strength using the ACI 544 code equation with the experimental data. Results showed that the code equation provides safe prediction.

Cracking due to restrained shrinkage is a recurring issue with concrete bridge decks, impacting durability and ultimately service life. Several scholars’ research has proven that the incorporation of fibers in concrete mitigates restrained shrinkage cracking when utilizing high (0.5–3%) fiber volumes. This often presents a mixing and placement issue when used for ready-mixed concretes, which discourages their use in bridge decks. This study aims to optimize the incorporation of fibers for their benefits while producing concrete that is conducive to ready-mix, jobsite use. A series of tests were performed on a high-performance concrete (HPC) mix which incorporated blended, multiple fiber types (steel crimped, macro polypropylene, and micro polypropylene) while maintaining low total fiber (0.19–0.37%) volume. These “hybrid” fiber mixes were tested for multiple mechanical properties and durability aspects, with a focus on the AASHTO T334 ring test, to evaluate fiber efficiency under restrained conditions. Promising results indicate the use of a low-volume hybrid fiber addition, incorporating a macro and micro polypropylene fiber (0.35% by volume) blend, reduced the cracking area by 16.6% when compared to HPC incorporating a single fiber type, and 39% when compared to nonfibrous HPC control mixture.

Live load distribution factor (LDF) equations are among the most important bridge design calculations because they provide the distributed moment and forces, which are needed for designing new or for evaluating existing bridges. The AASHTO load and resistance factor design specification is the current standard for bridge design. It uses a power function containing parameters that are often unknown when bridges are being designed and thereby allows the design procedure to be iterative. This iterative design procedure is perceived by many practicing engineers as impractical and has raised questions regarding the desired level of simplicity in bridge design calculations. Therefore, simpler but more accurate LDF equations are needed. An experimental program to evaluate the behavior of girder bridges was launched. The program consisted of live load testing of actual girder bridges. A parametric study was carried out by using a detailed three-dimensional finite element analysis to determine how sensitive various bridge parameters are to the LDF. On the basis of the parametric study, new simplified LDF equations are proposed for various types of girder bridges, including steel, prestressed, and spread box girder bridges. The equation format is simplified as a function of two bridge parameters only (girder spacing and span length) while an equal level of accuracy is maintained. Results are compared with those from other available equations and are shown to have excellent correlation with the more exact finite element method.

Highway agencies prioritize maintaining bridge infrastructure through bridge management systems amid budget constraints. The premature deterioration of reinforced concrete (RC) bridge decks caused by more frequent and increasingly heavy truck load spectra coupled with aggressive environmental conditions has become a critical concern. Despite the prevalence of conventional models and the emerging popularity of machine learning (ML) models in bridge deterioration predictions, they fall short in feature selection and handling of climate conditions, leading to suboptimal accuracy. To address these gaps, this study presents a data-driven framework utilizing ML-based techniques to predict the condition rating of RC bridge decks with a focus on identifying the influencing factors that affect the deck condition. The framework employs the XGBoost algorithm for model development, encompassing comprehensive datasets that include structural, geographical, and climate variables from across the U.S. Furthermore, the Shapley additive explanations approach is applied to identify the explanatory variables with the most impact. Age emerged as the most crucial factor, followed by freeze-thaw cycles and truck traffic, as indicated by the average daily truck traffic. Rainfall also plays a substantial role in deck deterioration. Based on feature importance and monotonicity, this study recommends a series of bridge classifications for transportation agencies to incorporate into their deterioration models. Overall, this research enhances understanding of the primary causes of bridge deck deterioration, enabling more informed decisions about funding allocation and bolstering bridge performance against environmental challenges.