Eng

In medium-voltage distribution systems, switch states determine how sections are grouped and supplied by feeders, defining the sectionalized operating configuration. Changes in load demand and distributed generation output can increase losses and cause voltage violations, making an existing configuration unsuitable for the new conditions. Switching corrections can redistribute power flows to reduce losses and voltage violations, but their benefits must be balanced against switching costs. Variations in loss costs also change return scales, which can impair reinforcement learning (RL) methods that use a fixed value-learning scale. To address these challenges, we propose an adaptive soft actor-critic (A-SAC) algorithm to correct sectionalized operating configurations and minimize cumulative operating cost. The dynamic adaptation mechanism adjusts the value-learning scale as operating conditions change, guiding the agent to learn lower-cost configuration correction policies while preserving the original cost objective. A masked softmax output layer excludes infeasible switching actions. Tests on a 37-bus test system under normal, high photovoltaic (PV) output, and changing source/load conditions show cost reductions of 3.2–6.2% relative to retaining the initial configuration and lower costs than other advanced RL algorithms. In the changing source/load scenario, the mean cost is 3.2% lower than that of soft actor-critic (SAC).

Autoclaved aerated concrete (AAC) masonry offers low self-weight and favorable thermal insulation, but its porous, relatively brittle matrix creates challenges for structural reliability and environmental durability. This study presents a targeted narrative critical review of experimental and numerical research on AAC manufacturing, pore structure, block–mortar interaction, seismic and blast behavior, hygrothermal performance, durability, and sustainability. Findings are critically compared in relation to material density, joint configuration, loading history, and moisture exposure, with emphasis on mechanisms connecting material-scale characteristics to wall-scale response. The synthesis indicates that stronger mortar adhesion can redirect cracking from joints into AAC units, while prior in-plane seismic damage can diminish subsequent out-of-plane resistance through cracking and loss of boundary restraint. Moisture accumulation increases thermal conductivity and can weaken masonry bonds, whereas freeze–thaw deterioration depends strongly on saturation and material properties. Strengthening benefits vary with reinforcement, anchorage, and test configuration and cannot be treated as universal improvement factors. Likewise, potential operational and structural environmental savings must be weighed against autoclaving energy and maintenance demands. The review identifies priorities for matched-condition testing, coupled hygrothermal–mechanical assessment, sequential seismic evaluation, and assembly-specific performance-based design guidance.

The strength of permeable concrete pavement (PCP) decreases as porosity increases; its water permeability depends on this porosity. Using the PRISMA 2020 guidelines, 28 open-access studies (337 mixtures) were selected that reported measured porosity, strength at 28 days, and filtration coefficient. Strength was predicted by a neural network, whose loss function incorporated the relationship between strength and porosity, its monotonically decreasing trend, and physical constraints. In the test studies, R² was 0.74; when excluding one study at a time, it was 0.31 (the network without physical terms yielded 0.43 and 0.25, and linear regression yielded 0.49 and 0.45); the differences between the models are not statistically significant. The contribution of porosity to the SHAP prediction (4.9 MPa) is an order of magnitude higher than that of the water-to-cement ratio and aggregate parameters. The filtration coefficient cannot be extrapolated to new studies; it was estimated by calculating the Boltzmann lattice equations for 500 virtual samples with a correction factor of β = 0.020. Using the NSGA-II method, mixtures with strengths of 10.0–22.1 MPa, porosity of 15–26.5%, and a carbon footprint of 311–397 kg CO₂-eq/m³ were obtained. In the EPA SWMM model for a 5-hectare watershed, a surface treatment using any of these mixtures reduces peak discharge by 96–100% during storm events with a return period of 2–50 years; permeability affects the result only when k < 0.01 cm/s. The composition should be selected based on strength and carbon footprint, while hydraulic efficiency should be ensured by the surface area of the pavement and the storage capacity of the subgrade. The results were obtained through calculation and require experimental verification.