The introduction of functional groups, such as graphene oxide, can improve the reactivity between molecules, increasing the potential for their use in many fields such as gas sensing and adsorption. It was reported that that graphene materials are actively utilized in toxic gas sensor materials by modifying the surface with their chemical and structural stability. In order to understand the mechanisms of graphene and graphene oxides for adsorbing the hazardous gases, we classified the four gases (H₂S, NH₃, HF and COS) with their phases (two asymmetric and two linear), and conducted density functional theory calculations to determine the adsorption affinity, which represents the binding energy, bond distance, energy charge (Mulliken and Hirshfeld methods) and band gap between the HOMO (Highest Occupied Molecular Orbital) and the LUMO (Lowest Unoccupied Molecular Orbital). The results showed that introducing a functional group enhanced the binding energy with a narrowed band gap in asymmetric gas adsorption (H₂S and NH₃), while the results of the linear gases (HF and COS) showed lowered binding energy with a narrowed band gap. It is judged that the oxygen functional groups can narrow the band gap by introducing localized states between the valence and conduction bands or by forming new hybrid states through interactions with all the gases. However, from the differences in the phases, the linear gases stably interacted with a defect-free, porous and flat structure like with π–π interactions. In short, the theoretical findings confirm that the oxidation functional groups narrowed the band gap with a local interaction; however, linear gases showed enhanced binding energies with pristine graphene, which highlights the importance of surface material selection dependent on the target gases.

Rapid urbanization and climate change have increased the extent of impervious surfaces, intensifying urban flooding and disrupting natural water circulation. To address these challenges, Low-Impact-Development techniques have been adopted, with permeable pavements serving as effective decentralized stormwater systems. This study evaluates the runoff reduction performance of permeable pavements featuring different upper block structures under identical subbase conditions. Two types of blocks were tested: Block A, an interlocking joint-type block that allows infiltration through its joints, and Block B, a monolithic permeable block that enables direct infiltration through the block surface. Artificial rainfall tests were conducted for 60 min at rainfall intensities of 42 mm/h (2-year) and 66 mm/h (10-year). Both blocks reduced runoff and delayed the equilibrium time compared with those of the impervious pavement. Block B exhibited a lower equilibrium flow rate and a longer recession period owing to uniform infiltration and gradual drainage through the joint filler. These findings indicate that the block structure and joint material properties significantly affect runoff reduction performance.

Reliable prediction of abutment scour requires accurate point estimates and quantification of predictive uncertainty. This study developed a NGBoost (Natural Gradient Boosting) framework using 297 laboratory observations under clear-water conditions across three abutment geometries. Hyperparameters were optimized using Optuna, with separate training, validation, calibration, and test sets. The model achieved a test-set R² of 0.980 and a root mean square error of 0.094. At the 95% nominal level, prediction-interval coverage was 96.67%, and the mean interval width was 26.69% smaller than that of split conformal prediction. Feature analyses identified the abutment Froude number and abutment geometry as important predictors. Monte Carlo simulation was used for a distribution-level reliability analysis based on independent sampling from measured and predictive scour-depth distributions. A design correction factor of 2.04 corresponded to a target reliability index of 1.5. The results demonstrate the potential of NGBoost for probabilistic abutment-scour assessment, while indicating the need for field validation and reliability analyses that account for dependence between measurements and predictions.