This study presents an integrated civil engineering validation framework for district heating infrastructure systems based on residual energy conversion from municipal solid waste. The proposed methodology combines energy conversion analysis, thermo-hydraulic transport modeling, and infrastructure-dependent performance evaluation.Using real operational data from the city of Novi Pazar (2022–2024), the system demonstrates a total energy potential of 11.8–14.8 MW, with thermal losses ranging from 10–30% and strong nonlinear hydraulic constraints. The results reveal that increasing mass flow rate reduces temperature decay but leads to a rapid increase in pumping power, resulting in a coupled thermo-hydraulic trade-off.An optimal operating range of 1.3–1.6 kg/s is identified, representing a transition between thermally and hydraulically controlled regimes. To extend the analysis, an economic and environmental assessment is introduced, including net present value (NPV), levelized cost of heat (LCOH), and CO₂ reduction.The findings demonstrate that effective system performance is governed by infrastructure constraints rather than theoretical energy potential, providing a robust framework for the design and optimization of sustainable district heating systems.This finding is consistent with recent studies in district heating optimization, where increased capital investment is justified by improved operational efficiency and reduced lifecycle costs.

BIM–AI construction management studies increasingly use multivariate models in which theoretically important relationships may be weaker than dominant technology adoption paths. This study develops a pre-empirical Monte Carlo design audit framework for testing whether such relationships are recoverable before field data are collected. A five-construct BIM–AI architecture with 28 ordinal indicators was evaluated through the original 33-scenario design and three additional stress tests, yielding 48,000 Monte Carlo replications. The extension introduces a finer sample size grid, missing completely at random (MCAR) block missingness conditions, and score construction sensitivity. For β = 0.15, empirical power increased from 0.292 at N = 100 to 0.824 at N = 400 and 0.906 at N = 500. With 20% block missingness, power was 0.604 at N = 286 and 0.808 at N = 500. Principal component analysis (PCA)-based scoring produced only modest differences relative to equal-weight composites. The findings show that sample size, measurement quality, data completeness, and effect magnitude must be audited jointly. The empirical power recoverability map is proposed as a transparent model-specific planning tool rather than a universal minimum sample rule.

Road-embankment maintenance decisions are often based on fragmented design, analytical, and monitoring information, limiting consistent condition assessment and timely intervention prioritization. This study develops a Digital-Twin-ready conceptual framework by integrating a structured database, analytical condition assessment, and fuzzy decision support. The road-embankment condition index (RECI) combines normalized settlement, static-stability, and seismic-stability indicators, while the IPI integrates the RECI with condition trends and data reliability. The framework was numerically demonstrated using documented computational data for three embankments along the E-863 Lučani–Požega motorway section. Because monitoring time series were unavailable, the trend was treated as a controlled scenario input. Changing trends from stable to rapidly deteriorating increased the intervention priority by two classes in all cases when the RECI and reliability were held constant. Reduced reliability also increased the IPI under less favorable condition-trend combinations. Variations of ±10% in RECI weights and coordinated ±5% variations in fuzzy membership parameters and IPI boundaries produced no classification changes, demonstrating local robustness within the tested ranges. The framework provides a transparent basis for condition-based maintenance planning and future integration of monitoring data, automated trend determination, and empirical calibration, thereby supporting more consistent, sustainable, and resilient road-infrastructure management.

Thermal insulation is a key factor in energy-efficient and sustainable construction. It reduces heat loss, improves comfort, prevents condensation and mold, and contributes to lowering CO₂ emissions. Insulating materials are classified based on their technical, environmental, and health-related properties, with the thermal conductivity coefficient being the most important criterion. Mineral wool, expanded polystyrene (EPS), and extruded polystyrene (XPS) are the most commonly used materials, while natural materials offer better environmental characteristics. Innovations such as vacuum insulation panels (VIP), aerogels, and transparent insulation enable high performance with thin layers. This paper analyzes legal regulations and U-value (thermal transmittance coefficient) standards in Serbia, Montenegro, Bosnia and Herzegovina, Turkey, and the EU, aiming to improve insulation standards and reduce energy consumption.