Green spaces are a significant component of urban infrastructure that helps increase the resilience of urban environments, reducing the undesirable consequences of fast urbanization. This study aims on Jaipur, a Tier-II city in india, to evaluate urban growth trends using geospatial technology and spatial metrics for Jaipur city. The study further identifies suitable land for green spaces development by combining the analytical hierarchy process (AHP) and multi-criteria evaluation techniques. There has been a notable increase in urbanization, by almost 43% during 2011–2023, a rapid urban growth for a Tier-II city, which contributes to the loss of green spaces and leads to a rise in land surface temperature LST). Spatial metrics revealed that the increase in urban compactness, combined with rapid urban growth (43%), significantly affected the availability of land for green space development. It is reflected in the land suitability analysis that limited space is available in the medium (670.82 ha) and high (615.85 ha) suitability categories for future green space development. Moreover, less than 2% of the non-built-up area is available for developing green spaces in the central part of the city. Hence, results highlight the importance of combining participatory planning techniques to promote successful urban green space development plans and overall sustainable development.

Despite mounting global attention toward sustainable construction, reverse logistics (RL) remains underutilized in the construction and demolition (C&D) waste management practices of developing economies. This study bridges a critical gap by empirically evaluating the effect of RL implementation intensity on sustainability outcomes within Pakistan’s construction sector. A comparative field experiment involving 36 construction projects was conducted, segmented into three groups based on RL maturity: none, moderate, and advanced. A Strategic Sustainability Scorecard (S³) was developed to quantify performance across six dimensions, waste recovery, cost avoidance, emissions reduction, RL integration, stakeholder alignment, and regulatory compliance. The results demonstrate that advanced RL projects achieved an average S³ score 108% higher than baseline projects, with material recovery rates exceeding 65%, and cost avoidance averaging PKR 4.3 million per project. Emissions reductions were also substantial, reaching up to 39.4 tons of CO₂ per project, primarily through steel and cement recovery. Stakeholder engagement scores showed strong positive correlation with sustainability outcomes (R² ≈ 0.73), underscoring the strategic role of institutional alignment. These findings establish RL as a high-leverage operational and policy tool for driving measurable sustainability performance, offering critical implications for procurement strategies, regulatory incentives, and circular economy integration in emerging construction markets.

The construction sector plays a pivotal role in advancing sustainable development but remains a major contributor to environmental degradation. In Rwanda, rapid urbanization and projected increases in building-related carbon emissions underscore the urgency of adopting sustainable building materials. This study investigates the drivers and barriers to adopting of Sustainable building materials among construction professionals in Kigali City, Rwanda. Using a qualitative research design with stratified purposive sampling, data were collected from architects, construction engineers, quantity surveyors, and building developers through questionnaires, semi-structured interview and document analysis. Data were analysed thematically using NVivo 15 software. The results reveal five major drivers, including regulatory frameworks, economic incentives, environmental awareness, social and cultural values, and technological advancements. Conversely, adoption of sustainable building materials is constrained by awareness and knowledge gaps, technical and training limitations, high upfront costs, supply chain weaknesses, regulatory and policy shortcomings, and cultural resistance to change. The findings indicate that regulatory and economic drivers are the most influential in shaping adoption, while awareness and financial constraints remain the most persistent obstacles. The study contributes to sustainability literature by highlighting the importance of context-specific dynamics, showing that in Kigali City, regulatory and economic drivers are critical drivers to adopting sustainable building materials. Policy implications include strengthening enforcement of green building regulations, expanding local manufacturing, establishing strong certification systems, and integrating sustainability into education and training. Addressing these barriers is critical to bridging the gap between Rwanda’s sustainability ambitions and on-the-ground practices, thereby accelerating the transition to a resilient and climate-conscious built environment.

Accurate spatial prediction of shallow foundation bearing capacity remains challenging in geologically complex terrains where conventional interpolation methods fail to adequately capture abrupt geological transitions. This study introduces an integrated KNN-GIS framework that combines predictive modeling with quantitative validation and systematic bias correction. Using data of 410 boreholes in the Pokhara Valley, Nepal, an optimized KNN model was developed and validated through comprehensive stratum-wise geological analysis. The framework establishes a validation system that categorizes geological formations into four classes (A-D) based on predictive accuracy, identifies systematic biases in karst-prone formations, and provides mathematically derived correction factors to address these limitations. A three-phase engineering decision system translates validation results into actionable investigation protocols, producing spatially explicit bearing capacity maps with quantified uncertainty bounds. The methodology successfully delineates high-risk karst zones from stable bedrock areas while offering a clear and reproducible workflow for preliminary site assessment. This integrated approach bridges machine learning predictions with engineering reliability, providing a robust, transferable tool for risk-informed urban development in geologically challenging environments worldwide.

Geological detection ahead of shield cutterhead is a challenge in the construction of large-diameter shield tunnels. This study proposed a framework for geological conditions identification based on stacking algorithm in large-diameter shield tunnel excavation of Chunfeng tunnel project. The relationships among shield parameters were analyzed using the Spearman rank correlation coefficient. Principal component analysis was employed to extract essential information from the parameters during shield tunneling. Then, the K-means+ +clustering algorithm was used to construct a correlation dataset between the principal components of shield parameters and the categories of geological conditions. Subsequently, the Stacking algorithm was applied to recognize the geological categories ahead of the shield cutterhead, and the results were compared with those of optimized random forest, support vector machine, and gradient boosting decision tree algorithms. The proposed method achieved superior performance, accurately classifying geological features ahead of the shield cutterhead with an accuracy of 0.984. Moreover, the stacking algorithm achieves higher accuracy in geological category recognition compared to the optimized single-machine learning algorithms. This research results can provide valuable geological conditions for adjusting shield parameters, gesture control, and risk reduction during the large-diameter shield tunneling.

Dhaka, the capital of Bangladesh, is one of the largest cities in the world. It is rapidly growing, and new housing areas are being developed by filling water bodies and flood-prone lowlands. Additionally, the city is in a highly vulnerable seismic hazard area in South Asia. Accessing liquefaction potential analysis in Dhaka has become an important task for the geotechnical engineering community, as most studies have relied on SPT (Standard Penetration Test) data in the LPI (Liquefaction Potential Index) method. A gap remains in probabilistic assessments that incorporate uncertainty, such as the Monte Carlo simulation (MCS), which can provide more precise and reliable risk evaluations and validation of the LPI method. This study assesses liquefaction potential in some reclaimed areas of Dhaka using the LPI, which is based on SPT data from 534 boreholes in 140 projects, and the MCS, yielding similar findings for both methods. In many areas, the shallow depths exhibited very high liquefaction susceptibility, with some locations showing even higher susceptibility within the first 15 to 20 m.