Concrete is a complex heterogeneous material, and thus, it is important to develop numerical modeling methods to enhance the prediction accuracy of the fracture mechanism. In this study, a two-dimensional mesoscale model is developed using a non-ordinary state-based peridynamic (NOSBPD) method. Fracture in a concrete cube specimen subjected to pure tension is studied. The presence of heterogeneous materials consisting of coarse aggregates, interfacial transition zones, air voids and cementitious matrix is characterized as particle points in a two-dimensional mesoscale model. Coarse aggregates and voids are generated using uniform probability distributions, while a statistical study is provided to comprise the effect of random distributions of constituent materials. In obtaining the steady-state response, an incremental and iterativesolverisadopted for the dynamic relaxation method. Load-displacement curves and damage patterns are compared with available experimental and finite element analysis (FEA) results.Although the proposed model uses much simpler material damage models and discretization schemes, the load-displacementcurvesshownodifferencefromtheFEAresults. Furthermore,nomeshrefinement is necessary, as fracture is inherently characterized by bond breakages. Finally, a sensitivity study is conducted to understand the effect of aggregate volume fraction and porosity on the load capacity of the proposed mesoscale model

Aggregate type and mineralogy are critical factors that influence the engineering properties of concrete. Temperature variations result in internal volume changes could potentially cause a network of micro-cracks leading to a reduction in the concrete’s compressive strength. The study specifically studied the effect of the type and mineralogy of fine and coarse aggregates in the normal strength concrete properties. As performance measures, the coefficient of thermal expansion (CTE) and compressive strength were tested with concrete specimens containing different types of fine aggregates (manufactured and natural sands) and coarse aggregates (dolomite and granite). Petrographic examinations were then performed to determine the mineralogical characteristics of the aggregate and to examine the aggregate and concrete microstructure. The test results indicate the concrete CTE increases with the silicon (Si) volume content in the aggregate. For the concrete specimens with higher CTE, the micro-crack density in the interfacial transition zone (ITZ) tended to be higher. The width of ITZ in one of the concrete specimens with a high CTE displayed the widest core ITZ (approx. 11 µm) while the concrete specimens with a low CTE showed the narrowest core ITZ (approx. 3.5 µm). This was attributed to early-age thermal cracking. Specimens with higher CTE are more susceptible to thermal stress.

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm³) or high (0.752 g/cm³ to 1.893 g/cm³) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Modern machine learning methods, such as tree ensembles, have recently become extremely popular due to their versatility and scalability in handling heterogeneous data and have been successfully applied across a wide range of domains. In this study, two widely applied tree ensemble methods, i.e., random forest (parallel ensemble) and gradient boosting (sequential ensemble), were investigated to predict resilient modulus, using routinely collected soil properties. Laboratory test data on sandy soils from nine borrow pits in Georgia were used for model training and testing. For comparison purposes, the two tree ensemble methods were evaluated against a regression tree model and a multiple linear regression model, demonstrating their superior performance. The results revealed that a single tree model generally suffers from high variance, while providing a similar performance to the traditional multiple linear regression model. By leveraging a collection of trees, both tree ensemble methods, Random Forest and eXtreme Gradient Boosting, significantly reduced variance and improved prediction accuracy, with the eXtreme Gradient Boosting being the best model, with an R² of 0.95 on the test dataset.

The prediction of fatigue cracking is one of the critical items to assess the service life of flexible pavements. Accordingly, researchers have developed several test methods and index parameters to predict pavement performance based on fatigue damage. Recently, researchers at North Carolina State University developed a new index parameter, referred to as apparent damage capacity ($S_{\mathrm{app}}$ ), which is based on the simplified viscoelastic continuum damage (S-VECD) model. This index parameter is obtained by performing cyclic tension tests and using the simplified viscoelastic continuum damage model with a dissipated pseudoenergy concept (the so-called $D^R$ failure criterion). This paper presents $D^R$ and $S_{\mathrm{app}}$ values for 10 different asphalt mixtures commonly used in Georgia. The relationships among $D^R$ , $S_{\mathrm{app}}$ , and mix design factors such as nominal maximum aggregate size (NMAS), asphalt binder type, and binder content were investigated to determine ways that these factors affect $D^R$ and $S_{\mathrm{app}}$ . The $S_{\mathrm{app}}$ parameter was found to have a strong relationship with the polymer modification and NMAS of the asphalt mixtures. Based on the experimental data and the Georgia Department of Transportation’s practical guidelines for specific mixtures, this study developed $S_{\mathrm{app}}$ threshold values for different traffic levels. Finally, the experimental data were used to run pavement performance analyses using AASHTOWare Pavement ME Design software and FlexPAVE; the latter uses the S-VECD model for cracking evaluation. Top-down and bottom-up cracking performance was determined from a 10.2 cm (4 in.) thick single-layer pavement and a 15.2 cm (6 in.) thick 2-layer pavement. The results are used to discuss the different ways the two programs evaluate cracking.

This paper examines the effects of reclaimed asphalt pavement (RAP) content and binder grade on the fatigue resistance of Georgia asphalt concrete mixtures. The asphalt concrete mixtures were prepared based on two job mix formulas used in northern and southern Georgia and were composed of 12.5-mm nominal maximum aggregate size aggregate and three asphalt binders (PG 64-22, PG 67-22, and PG 76-22). Dynamic modulus and controlled crosshead cyclic tension fatigue tests were performed using the laboratory-prepared specimens. A simplified viscoelastic continuum damage (S-VECD) model was characterized for each mixture using the dynamic modulus and fatigue test results. The layered viscoelastic pavement analysis for critical distresses (LVECD) program was used to investigate the effects of RAP content and binder grade on the fatigue performance of pavements. The S-VECD model and LVECD program analyses showed that the addition of RAP up to 30% using corrected optimum asphalt content (COAC) method significantly improved the mixtures’ fatigue resistance, especially for the mixtures with PG 64-22 and PG 67-22 binders.