Abstract Lowndes County, Alabama, has struggled with inadequate wastewater management exacerbated by low population density, high rates of poverty, and suboptimal soil conditions prevalent throughout the Black Belt region. Steps are being taken to develop solutions, but a challenge in wastewater infrastructure planning is identifying the optimal number, location, and type of wastewater treatment systems (centralized, cluster, or onsite). Thus, the objectives of this research were to utilize a spatial optimization model, i.e., the sustainable infrastructure network planning model (SNIP), to determine the cost-optimal layout of wastewater treatment infrastructure for Lowndes County, Alabama, and assess how model results compare to existing technical assistance and proposed infrastructure improvement efforts in the town of White Hall (located in Lowndes County, AL). SNIP, created by Eggimann et al. (2015), minimizes the total wastewater system costs and requires only three input datasets: buildings, topography, and the street network. Overall, more centralized infrastructure, such as wastewater treatment plants or cluster wastewater treatment systems, is more cost optimal compared to the current heavy reliance on onsite wastewater treatment systems. Output geospatial data showing the location and capacity of wastewater treatment plants, sewer pipes, and pumps are particularly useful in communicating with stakeholders (e.g., residents, engineers, governing officials, and funders). Model outputs can guide household-level decision-making for the optimal wastewater treatment system (e.g., centralized, cluster, or onsite) not provided by current technical assistance. Ultimately, wastewater infrastructure planning requires consideration of local social, environmental, political, and economic dynamics, and this accessible tool can help communities quickly develop cost-optimal design options.
This study evaluates the ability of the modified Rankine vortex model, which is widely used to represent tornado-like vortices, to approximate a realistic tornado-like vortex generated within a high-resolution CM1 simulation. Horizontal wind fields at 10 m AGL were extracted from the CM1 simulation and averaged over multiple time windows to examine the influence of temporal averaging on peak wind speeds. The results show that 3-s averaging, although commonly used in engineering practice, can smooth short-lived transient flow structures and may underestimate instantaneous peak wind speeds. The CM1 wind field was then examined to determine the best-matched parameters of the Rankine vortex model. Separate decay exponents were applied inside and outside the vortex core, and a radially decaying translational velocity was incorporated. These modifications improved the near-surface wind field representation, particularly near the vortex core. Larger discrepancies were observed on the left side of the vortex, where complex asymmetric flow structures were not fully captured by the idealized Rankine vortex. Treefall patterns were simulated using different critical wind speeds, and the modified Rankine vortex produced results broadly comparable to those from the CM1 vortex. The findings of this study suggest that the Rankine vortex model, when applied with carefully selected best-matched parameters and combined with treefall modeling, could provide a practical framework for estimating near-surface tornado wind characteristics in engineering applications. Future work will examine additional cases to further assess the performance of the model under a wider range of tornado-like flow conditions.
Rapid and accurate detection of Salmonella serotypes associated with foodborne illness is critical for maintaining food safety. This study developed and evaluated the capability of a machine learning-enabled paper chromogenic array sensor (ML-PCA) approach for non-destructive, continuous, and simultaneous detection and differentiation of Salmonella Typhimurium (ST) and Salmonella Enteritidis (SE) in chicken breast in the presence of background microflora during storage at room temperature and refrigerated temperature (4 degrees C). At room temperature, the ML-PCA approach achieved accuracies of 80-88% for detecting ST and SE in mono-serotype, dual-serotype, and serotype cocktail as early as 6 h. At 4 degrees C, ST and SE could be detected as early as 1 d, with testing accuracy exceeding 80%. These results demonstrated that the ML-PCA approach enabled non-destructive, continuous, and simultaneous detection and differentiation of ST and SE on poultry meat. This methodology offers a potential tool for continuous Salmonella monitoring across the food system.
Abstract As-constructed air void is a critical control parameter in road construction, which affects the fatigue resistance of asphalt pavement. This study extracted and rigorously cleaned the data from the long-term pavement performance database to develop an artificial neural network (ANN) model for predicting fatigue cracking (FC). In addition, a series of benchmark experiments were conducted to investigate the effects of principal component analysis (PCA) cumulative variance thresholds and ANN architectures on model prediction accuracy and stability. An ANN architecture of 10-25-5-1 combined with a PCA cumulative variance of 90% showed the highest prediction accuracy for the long-term evolution of fatigue performance of asphalt pavements ( R 2 = 0.95 ). The SHAP-based global explanation found that the permutation of as-constructed air void resulted in significant changes in the loss function of the FC model, confirming that the as-constructed air void was a highly influential predictor for FC of asphalt pavement. Based on the developed ANN model, the parameter sensitivity analysis concluded that reducing the as-constructed air void content from 9% to 3% enhanced the fatigue resistance of asphalt pavement by 46.8%. The high precision ANN model developed in this study can provide quantitative support for maintenance planning and resource optimization.
Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability. Unlike continuous-fiber systems, the mechanical response of SFTs is governed by complex mesoscale interactions among fiber orientation, spatial clustering, and manufacturing-induced porosity. These microstructural features exhibit significant spatial variability in additively manufactured components and strongly influence stiffness, damage initiation, and nonlinear deformation. Although mesoscale finite element (FE) models can explicitly resolve such heterogeneity, their application to realistic three-dimensional microstructures containing thousands of fibers remains computationally intractable. In this study, a data-driven surrogate modeling framework is proposed to efficiently predict the mechanical behavior of additively manufactured, compression-molded (AM-CM) SFTs. Three-dimensional microstructures were reconstructed from high-resolution micro-computed tomography (mu-CT) data and discretized into localized Voronoi-based cells representing statistically distinct fiber-interaction neighborhoods. Each cell was homogenized via nonlinear FE simulations incorporating matrix damage. The resulting stress-strain responses were used to train a hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) architecture capable of simultaneously encoding microstructural topology and capturing nonlinear, history-dependent mechanical evolution. The trained surrogate accurately predicts the stiffness and stress-strain behavior of unseen microstructures, with a coefficient of determination R2 approximate to 0.98 relative to high-fidelity FE simulations, while achieving more than two-order-of-magnitude reduction in computational cost. Coupling the framework with experimentally calibrated static damage laws demonstrates that fiber orientation, clustering, and porosity collectively govern the local effective stiffness matrix. The proposed approach provides a physics-informed, data-efficient pathway to identify mechanically weak microstructural cells and accelerate digital-twin development for SFT components.
