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The global COVID-19 pandemic had an unprecedented impact on transportation worldwide. Significant decreases in transportation across all modes were evident and sustained as governments worldwide implemented various countrywide closures and quarantine restrictions to slow the spread of the virus. This paper quantifies and assesses daily vehicle counts by Federal Highway Administration (FHWA) vehicle classifications during the COVID-19 pandemic in New York and Florida throughout 2020. The study found that duringMarch and April of 2020, traffic among all investigated FHWA categories was significantly reduced in both Florida and New York. However, commodity carriers in both states were able to recover faster and remained more consistent than passenger vehicles. This pattern was also observed in both urban and rural communities in Florida. The findings of this work demonstrate how commodity carrier movements, assessed through FHWA vehicle category counts, were less impacted by the governmental restrictions during the pandemic than passenger transportation. While overall traffic volume dropped by more than half in most places at the height of the pandemic, larger commodity-carrying vehicles remained nearly unchanged from the prior year by June of 2020. This was likely because of the critical need to maintain trucking movements to sustain populations. Understanding how truck traffic and freight movements more broadly were impacted during the COVID-19 pandemic is critical in preserving the continuity of service and preventing supply shortages in the event of future outbreaks. [ABSTRACT FROM AUTHOR]

Since the Ronan Point collapse in the UK in 1968, the progressive collapse analysis of residential buildings has gradually drawn the attention of civil engineers and the scientific community. Recent advances in computer science and the development of new numerical methodologies allow us to perform high-fidelity collapse simulations. This paper assesses different scenarios that could have hypothetically caused the collapse of the Champlain Tower South Condo in Surfside, Florida, in 2021, one of the most catastrophic progressive collapse events that has ever occurred. The collapse analysis was performed using the latest developments in the Applied Element Method (AEM). A high-fidelity numerical model of the building was developed according to the actual structural drawings. Several different collapse hypotheses were examined, considering both column failures and degradation scenarios. The analyses showed that the failure of deep beams at the pool deck level, directly connected to the perimeter columns of the building, could have led to the columns' failure and subsequent collapse of the eastern wing of the building. The simulated scenario highlights the different stages of the collapse sequence and appears to be consistent with what can be observed in the footage of the actual collapse. To improve the performance of the structure against progressive collapse, two modifications to the original design of the building were introduced. From the analyses, it was found that disconnecting the pool deck beam from the perimeter columns could have been effective in preventing the local collapse of the pool deck slab from propagating to the rest of the building. Moreover, these analyses indicate that enhancing the torsional strength and stiffness of the core could have prevented the collapse of the eastern part of the building, given the assumptions and initiation scenarios considered. Building catastrophic collapses can cause significant lives and economic losses. Poor design and maintenance, in combination with aging, will more likely increase, in the next years, the number of buildings potentially vulnerable to the risk of collapse, due to either seismic, accidental, or degradation actions. This research focuses on the analysis of the Champlain Tower South condo collapse, which occurred in Surfside, Florida, in 2021. Different hypothetical collapse scenarios were simulated, comparing the analysis results with the actual evidence of the collapse. The analyses have shown that the degradation of the pool deck slab, due to corrosion, may have contributed to the collapse of the building. Finally, two different minor revisions of the original design of the building were analyzed to reduce the risk of failure and understand how the collapse of similar residential buildings could be prevented. [ABSTRACT FROM AUTHOR]

A rectangular rapid flashing beacon (RRFB) is a pedestrian crosswalk countermeasure system that cautions drivers by providing them with real-time warning about the presence of pedestrians in an upcoming crosswalk. This paper investigated the safety effectiveness of existing RRFBs installed in the state of Florida between 2013 and 2018. Additionally, traffic volume, roadway characteristics, and land-use mix were included as explanatory variables. The study evaluated 154 treated sites with RRFB installations and 158 control sites with similar characteristics. Safety performance functions (SPF) were developed, and crash modification factors (CMF) were calculated using the empirical Bayes (EB) methodology for various crash types. A CMF of 0.31 for total pedestrian crashes was estimated, showing that RRFBs have the potential to reduce 69% of total pedestrian-involved crashes. CMF for total vehicular crashes and rear-end crashes were also investigated to ascertain whether RRFBs have adverse effects on rear-end crashes. CMF for total vehicular crashes was found to be 1.14, showing that RRFBs do not have positive effect in reducing total vehicular crashes. Similarly, it was observed that CMF for the rear-end crashes was 1.11, indicating RRFBs might have adverse effect on rear-end crashes because vehicles needs to slow down or stop due to the presence of pedestrians. CMFunctions for significant variables for pedestrian, rear-end, and total crash models were also estimated in this study. An economic analysis indicated that the implementation of RRFBs will yield a benefit to cost ratio (calculated over 3 years) of approximately 85.5 with a 4% discount rate. [ABSTRACT FROM AUTHOR]

Small wastewater systems serving less than 10,000 population equivalents (p.e.) face a wide range of challenges due to technical and managerial capacity issues, limited resources, and the need to reduce environmental impacts. Based on the wide range of challenges of small systems, the authors of this manuscript developed the social, environmental, and economic wastewater decision support tool (SEE WWDST) to aid decision makers in comparing the sustainability of treatment alternatives. The tool uses multi-criteria decision analysis to produce a single SEE impact score that assesses 12 sustainability metrics across three larger categories: economic performance (e.g., net present worth of costs, affordability), environmental performance (e.g., carbon footprint, eutrophication, water reclaimed), and social performance (e.g., maintenance time, employee training, degree of automation, operator certification requirement, system intrusiveness, daily, and seasonal resilience). This paper compares the performance of three onsite wastewater treatment systems in Florida. The three systems evaluated include: (1) a passive septic tank effluent biofilter (STEB) system, (2) a mechanized aerobic treatment unit (ATU) system, and (3) a passive clay, sulfur, and shell biofilter (CSSB) system. The passive CSSB system was found to be the preferred system due to its lower present worth of cost, lower carbon footprint and eutrophication potential, and better performance for various social factors (e.g., maintenance time, degree of automation, system intrusiveness). The passive STEB system was the second preferred alternative, despite having a worse social performance and the highest present worth of cost. The systems were then assessed using even, social, environmental, and economic weighting preferences and a sensitivity analysis was conducted. In spite of shifts in weighting preference and changes to input data parameters to assess sensitivity, the passive CSSB system was found to be the preferred alternative. This paper highlights the importance of using triple bottom line sustainability metrics to assess small wastewater treatment alternatives using a novel decision support tool. [ABSTRACT FROM AUTHOR]

Even with the continuous development of coating technologies, coating systems are susceptible to corrosion-induced premature failures and unable to meet the anticipated service life. Coating premature failures will negatively impact the safety, integrity, and longevity of steel bridge structural elements. The advancement of the data analytics approach (e.g., machine learning) offers an opportunity to leverage the wealth of historical bridge inspection and maintenance data collected by Departments of Transportation to evaluate coating conditions of steel bridges effectively and efficiently. This paper focuses on presenting a data-driven study that analyzes the conditions of coating systems of steel bridges in the state of Florida. Three machine learning algorithms were used in developing models that can be used to analyze steel bridge coating conditions based on data collected from the Bridge Management System of the Florida Department of Transportation. The result showed that the machine learning–based models were able to effectively predict steel bridge coating conditions. The k -nearest neighbor (KNN) regression algorithm offered the best performance. Although the approach was currently applied to the steel bridges of Florida, it can be easily replicable for similar data sets from other states. The research contributes to the body of knowledge by offering a data-driven understanding of coating performance of steel bridge elements. The research has the potential to offer a valuable decision-making tool for transportation agencies to effectively and easily analyze or predict steel bridge coating conditions. [ABSTRACT FROM AUTHOR]

Rock coring is an essential method of geotechnical site characterization for foundations supported by rock, in which three possible phases of operation exist: inefficient, optimized, and destructive. When operating within the optimized phase, core recoveries and the rock quality designation reflect the strata encountered. However, the optimized phase is rarely achieved in practice, leading to deficiencies in strength assessment and geotechnical site characterization. This paper identifies the operational limits that separate each phase of rock coring by using measuring while drilling (MWD) and quantifies the increased energy that results from an inadequate depth of cut (Phase 1), the effects of breaking rock particles into smaller sizes, and the damage that occurs to core specimens from increased frictional resistance (Phase 3), as well as the effect rock strength and bit geometry have on mechanical efficiency. Drilling parameter interdependencies are discussed and detailed guidelines to assess rock strength in situ via MWD are provided. Finally, recommended coring guidelines are established through MWD which optimize the quantity and quality of strength assessments collected to improve geotechnical site characterization. [ABSTRACT FROM AUTHOR]

Although there is extensive work related to design and implementation of model-driven optimal groundwater management, evaluation of the performance of those models is less reported. This hinders transferability of lessons learned and wide adoption of specific policies. This paper reports a study designed to evaluate the effectiveness of a real-time optimal groundwater management tool, optimized regional operations plan (OROP), used in the Tampa Bay region (located on the west coast of Florida in the United States) since 1998. The overarching question in this study is how different groundwater levels would have been at monitoring sites if such an operational decision-supporting tool was not implemented. The proposed framework is comprised of multiple steps, including generating a non-OROP groundwater pumping scenario that would have happened without the decision support tool in place, running a modified optimization model in retrospective mode, and comparing water level between OROP-based and non-OROP–based pumpage scenarios. The framework is applied to historical data in the past decade (2010–2019). In generating an alternative pumpage scenario, realistic model constraints (e.g., pump capacity, pipeline hydraulics, and network) were used to generate wellfield pumpage to meet demands. Wellfield pumpage is then disaggregated at individual wells. Differences in resulting water levels at monitoring wells, at multiple timescales, between the OROP and non-OROP pumpage scenarios were calculated. Results reveal that although at a weekly timescale the effectiveness of OROP seems minimal over the region, the cumulative effectiveness at the monthly and annual timescales is remarkable. It is also found that benefit of using OROP is even higher during dry seasons when groundwater levels are more critical in maintaining wetland health. The proposed framework presented in this study can be applied to other areas in evaluating groundwater management strategies. [ABSTRACT FROM AUTHOR]

With the increased emphasis on developing integrated models capable of simulating runoff through hydrologic systems that include hillslopes and wetlands, analytical solutions have become useful in analyzing and testing numerical models applied to both. An analytical solution in algebraic form is developed in this paper to describe a sinusoidal moisture profile migrating through a wetland or a hillslope, the magnitude of the peak runoff, and the phase lag. The solution is obtained for an instantaneous sinusoidal recharge signal, and results are expressed in terms of the basin geometry and flow resistance parameters. The analytical solution is used to verify the accuracy of a numerical model based on the implicit total variation diminishing Lax-Friedrichs (TVDLF) method. This method is used in the Regional Simulation Model (RSM) developed at the South Florida Water Management District (SFWMD) to simulate the complex hydrologic system of south Florida. The runoff peak, phase lag, propagation speed, and decay rate over a range of Péclet numbers are used in the comparison of analytical and numerical solutions. The numerical model using the TVDLF method is tested by applying to the Boggy Creek watershed in central Florida, and the wetland stormwater treatment area (STA)-3/4 in south Florida to simulate storm runoffs and water levels. Results show that the model using a general power-law equation to represent flow resistance, and a conceptual model consisting of a canallike drainage feature to represent rapid drainage can be used to simulate the hydrology in both the watershed and the wetland. [ABSTRACT FROM AUTHOR]

Traditional economic forecasts had consistently underestimated the hypergrowth of South Florida and its need for freshwater from the 1970s through the 1990s. It was hypothesized that the continued rapid growth of urban and farm water use into the 2000–2030 period would undermine efforts to restore the Everglades. To test this hypothesis, the corresponding author had hybridized two widely used regional economic modeling tools: the static, single-period Impact Analysis for Planning (IMPLAN); and the dynamic multiperiod Regional Economic Modeling Inc. (REMI) to forecast population and water use for the period from 2010–2030. In the present paper, these early forecasts are compared to actual census and water use counts for 2010 and 2015. Some of these early forecasts are found to be surprisingly accurate while others seriously overshoot the mark. The on-target forecasts validate the basic hybrid model while the other "deviant" forecasts measure the success of the radical antidrought policies and the region's economic reaction to the financial collapse. This retrospective provides a new metric for measuring actual changes in the paradigm of water consumption. [ABSTRACT FROM AUTHOR]

A calibrated flow rating equation is often used to determine the flow rate through a hydraulic structure as a convenient and relatively inexpensive method to avoid the difficulties of direct discharge measurements in an open channel. In South Florida, there are hundreds of gated culverts, where accurate and quick estimation of discharge in real time is critical for effective water resource management. Flow through culverts under various conditions and geometries has been studied extensively; however, studies of stage-discharge relationships through gate-controlled culverts are rare and, to the authors' knowledge, not discussed in any open-channel hydraulics books, relevant journals, or design manuals. In this paper, two approaches to improve the stage-discharge relationships at gated culverts are presented, and case studies demonstrate their successful and cost-effective use: first, the application of computational fluid dynamics (CFD) for flow data simulation to supplement field measurements to resolve data limitations and, second, the application of dimensional analysis to improve gated-culvert rating equations. The proposed nondimensional flow rating approach is useful for data planning and consistency testing of hybrid data (field flow data and CFD results) and reduces the flow types occurring at a culvert, making the flow classification less complex while maintaining reasonable rating accuracy within 6% of field flow measurements. [ABSTRACT FROM AUTHOR]

The Everglades is a complex, multiwetland ecosystem that is heavily managed to meet often-competing flood control, water supply, and environmental demands. Using objective measures to balance these demands through operational protocols has always been a challenge in the multibillion-dollar restoration plans for the ecosystem. Physically based models have been the primary tools for planning efforts but for such a complex system, they are laborious and computationally intensive. Development of optimal operations based on iterative runs of these models is a great challenge. This paper presents an inverse modeling framework for formal optimization suited for wetland system operations that helps overcome such limitations. Labor-intensive and computation-intensive physically representative models are emulated in each individual wetland area using an autoregressive artificial neural network with exogenous variables. Using prescribed inflow, outflow, and meteorological input data, such hydrologic model emulators aided by a dimension-reduction technique provide targeted spatial and temporal predictions for water level (stage) within each area of the Everglades, while excluding computation processes that are intensive but insignificant to the predictions. This computer software uses the augmented Lagrangian genetic algorithm technique (subject to linear and nonlinear constraints) to steer predictions of stage spatial variability within individual wetlands towards corresponding desired goals (including restoration targets). In the augmented Lagrangian genetic algorithm, flow releases are coded as the decision variables to be optimized subject to budget, intrahydraulic conveyance, flow capacity, and upstream storage constraints. Optimization is performed by dividing and solving a sequence of subproblems using the genetic algorithm procedures of initialization, selection, elitism, crossover, and mutation. As part of the process, Lagrangian and penalty parameters are updated, and optimization terminates when certain stopping criteria are met. Applying the technique reported in this paper to a specific Everglades restoration plan (the River of Grass Project) showed a sound hydrologic model emulator prediction when compared to the physical model for all wetland areas. Feeding optimal releases predicted by the computer software into a physical model showed equal or better matching of the restoration target with different release patterns compared to that of the physical model base run scenario. Results show that hydraulic conveyance limitations play a significant role in Everglades restoration. Also, results show that employing an adversity tradeoff matrix presented multiple so-called optimal solutions with different optimization weights and a powerful negotiation matrix. [ABSTRACT FROM AUTHOR]