Over the past decades, extensive studies on municipal solid waste incineration (MSWI) ashes have been performed to develop more effective recycling and waste management programs. Despite the large amount of research activities and the resulting improvements to MSWI ashes, the recycling programs for MSWI ashes are limited. For instance, although the U.S. generates more MSWI ashes than any other country in the world, its reuse/recycle programs are limited; bottom ash and fly ash are combined and disposed of in landfills. Reuse of MSWI ashes in the construction sectors (i.e., geomaterials, asphalt paving, and concrete products) as replacements for raw materials is one of most promising options because of the large consumption and relatively lenient environmental criteria. The main objective of this study was to comprehensively review MSWI ashes with regard to specific engineering properties and their performance as construction materials. The focus was on (1) the current practices of MSWI ash management (in particular, a comparison between European countries and the U.S.), (2) the engineering properties and performance of ashes when they are used as substitutes of construction materials and for field applications, and (3) the environmental properties and criteria for the use of MSWI ashes. Overall, the asphalt and concrete applications are the most promising, from both the mechanical and leachate viewpoints. However, cons were also observed: high absorption of MSWI ash requires a high asphalt binder content in hot-mix asphalt, and metallic elements in the ash may generate H₂ gas in the high-pH environment of the concrete. These side effects can be predicted via material characterization (i.e., chemical and physical), and accordingly, proper treatment and/or modified mix proportioning can be performed prior to use.

With the continuous research efforts, sophisticated predictive molecular dynamics (MD) models for C-S-H have been developed, and the application of MD simulation has been expanded from fundamental understanding of C-S-H to nano-engineered cement composites. This paper comprehensively reviewed the current state of MD simulation on calcium-silicate-hydrate (C-S-H) and its diverse applications to nano-engineered cement composites, including carbon-based nanomaterials (i.e., carbon nanotube, graphene, graphene oxide), reinforced cement, cement–polymer nanocomposites (with an application on 3D printing concrete), and chemical additives for improving environmental resistance. In conclusion, the MD method could not only compute but also visualize the nanoscale behaviors of cement hydrates and other ingredients in the cement matrix; thus, fundamental properties of C-S-H structure and its interaction with nanoparticles can be well understood. As a result, the MD enabled us to identify and evaluate the performance of new advanced nano-engineered cement composites.

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.

Depressions due to sinkhole formation cause significant structural damages to buildings and civil infrastructure. Traditionally, visual inspection has been used to detect sinkholes, which is a subjective way and time- and labor-consuming. Remote sensing techniques have been introduced for morphometric studies of karst landscapes. This study presents a methodology for the probabilistic detection of sinkholes using LiDAR-derived digital elevation model (DEM) data. The proposed study provides benefits associated with: (1) Detection of unreported sinkholes in rural and/or inaccessible areas, (2) automatic delineation of sinkhole boundaries, and (3) quantification of the geometric characteristics of those identified sinkholes. Among sixteen morphometric parameters, nine parameters were chosen for logistic regression, which was then employed to compute the probability of sinkhole detection; a cutoff value was back-calculated such that the sinkhole susceptibility map well predicted the reported sinkhole boundaries. According to the results of the LR model, the optimal cutoff value was calculated to be 0.13, and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.90, indicating the model is reliable for the study area. For those identified sinkholes, the geometric characteristics (e.g., depth, length, area, and volume) were computed.

The magnitude of the overall settlement depends on several variables such as the compression index, $C_c$ , and recompression index, $C_r$ , which are determined by a consolidation test; however, the test is time consuming and labor intensive. Correlations have been developed to approximate these compressibility indexes. In this study, a data driven approach has been employed to estimate $C_c$ and $C_r$ . Support vector machines classification is used to determine the number of distinct models to be developed. Classification accuracy is used for detecting the existence of separability between different soil classes that in turn is indicative of the number of models needed. The statistical models are built through a forward selection stepwise regression procedure. Seven variables were used, including the moisture content (w), initial void ratio ($e_o$ ), dry unit weight (${\gamma }_{\text{dry}}$ ), wet unit weight (${\gamma }_{\text{wet}}$ ), automatic hammer SPT blow count (N), overburden stress ($\sigma$ ), and fines content ($-200$ ). The results confirm the need for separate models for three out of four soil types, these being coarse grained, fine grained, and organic peat. The models for each classification have varying degrees of accuracy. The model for the fine grained classification performs on par with existing correlations, with respect to $C_c$ , whereas the models for coarse grained and organic peat classifications perform considerably better than that of existing correlations. The models generated also incorporate several factors not utilized in correlations from previous literature. These factors include the fines content ($-200$ ), automatic hammer blow count (N), and the interactions between the wet and dry density (${\gamma }_{\text{wet}}$ and ${\gamma }_{\text{dry}}$ ).

The object of this study is to investigate the feasibility of ball-milled graphene oxide nanoflakes (GONFs) produced by a mechanochemical process as an additive in Ordinary Portland Cement (OPC)-based paste and concrete. Different percentages of GONFs (0.01–1 wt. %) were added in OPC paste mix to find the optimum content of GONF in GONF-combined cement composites. To investigate the effect of the changes in the mix design on OPC paste, two mix design methods were employed: (1) Dry-mix, where GONFs and OPC powders are mixed prior to paste formation and (2) Wet-mix, where sonicated GONFs solution is used as the pour water with OPC paste mix. Data from mechanical tests (compressive and flexural tests) showed that OPC paste incorporating 0.05% GONF resulted in the highest strength for OPC paste. The results of strength tests indicated that Dry-mix is competitive as compared with Wet-mix. Scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS) was used to identify GONFs inside products of cement hydration. To explore the practicality of using GONF in concrete, concrete specimens were made, cured and tested with different percentages of dry-mixed GONFs (0.01–0.1 wt. %). The result of strength test showed that GONF improves the compressive and flexural strengths of concrete around 15% with 0.01 wt. % GONF.