Search Results (3,814)

The prevention and control of coupled disasters caused by strong mining pressure and high gas is currently the main challenge during coal seam deep mining in the southeastern mining areas of Shanxi Province. This paper takes the 1310 working face of Hudi Coal Mine as the engineering background, analyzing its on-site strong mining pressure event and triggering factors. A reasonable hydraulic fracturing scheme (including layer selection, drilling parameter design, etc.) is proposed based on theoretical analysis of the principles and advantages of hydraulic fracturing technology. Then, the physical analog modeling (PAM) method was used to study the movement law and fracture development of the overlying strata during coal seam mining after hydraulic fracturing. The weakening effect of mining pressure was analyzed through the evolution law of roof stress. The deformation of the surrounding rock in the roadway, coal drilling cuttings, support working resistance, and roof fracture development of the in situ measurement results show that hydraulic fracturing has a good effect on weakening mining pressure. It has achieved safe and efficient mining of coal seams while providing a reference for coal mines with similar conditions. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

Studies of microbial degradation of historic woods are essential to help protect and preserve these important cultural properties. The USS Cairo is a historic Civil War gunboat and one of the first steam-powered and ironclad ships used in the American Civil War. Built in 1861, the ship sank in the Yazoo River of Mississippi in 1862 after a mine detonated and tore a hole in the port bow. The ship remained on the river bottom and was gradually buried with sediments for over 98 years. After recovery of the ship, it remained exposed to the environment before the first roofed structure was completed in 1980, and it has been displayed under a tensile fabric canopy with open sides at the Vicksburg National Military Park in Vicksburg, Mississippi. Concerns over the long-term preservation of the ship initiated this investigation to document the current condition of the wooden timbers, identify the fungi that may be present, and determine the elemental composition resulting from past wood-preservative treatments. Micromorphological characteristics observed using scanning electron microscopy showed that many of the timbers were in advanced stages of degradation. Eroded secondary cell walls leaving a weak framework of middle lamella were commonly observed. Soft rot attack was prevalent, and evidence of white and brown rot degradation was found in some wood. DNA extraction and sequencing of the ITS region led to the identification of a large group of diverse fungi that were isolated from ship timbers. Soft rot fungi, including Alternaria, Chaetomium, Cladosporium, Curvularia, Xylaria and others, and white rot fungi, including Bjerkandera, Odontoefibula, Phanerodontia, Phlebiopsis, Trametes and others, were found. No brown rot fungi were isolated. Elemental analyses using induced coupled plasma spectroscopy revealed elevated levels of all elements as compared to sound modern types of wood. High concentrations of boron, copper, iron, lead, zinc and other elements were found, and viable fungi were isolated from this wood. Biodegradation issues are discussed to help long-term conservation efforts to preserve the historic ship for future generations. Full article

The indigenous community of Quisquinchir, in Saraguro (Loja, Ecuador), is facing a process of transformation of the rural Andean landscape associated with internal and external migration, as well as the influence of foreign architectural models. The new buildings symbolize, in the collective imagination, modernity and progress; however, they are alien to the natural environment characterized by the practice of agricultural and livestock activities. Although previous studies have described the loss of Andean vernacular architecture, its recent evolution in clear typologies has not been systematized. The objective of this study is to assess the current state of traditional dwellings and understand how migration reconfigures the landscape, collective memory, building traditions, and cultural identity of their inhabitants. Based on direct observation, photographic and stratigraphic analysis, and secondary sources, five typologies were identified: traditional one-story, traditional two-story, hybrid one-story, hybrid two-story, and eclectic. This classification indicates the replacement of earthen walls with cement blocks in 37% of the dwellings and of tile roofs with zinc roofs in 29%. However, 35% of the houses retain their traditional morphology and materials. These results and their classification are fundamental contributions to the design of local public policies that generate adequate interventions respectful of the environment. Full article

This research introduces a novel dual CO₂ storage (DCS) approach by simultaneously storing CO₂ gas in abandoned mines and securing it within mineralized backfill. For this method, CO₂ mineralized backfill materials (CMBM) are pumped into CO₂ mineralized storage segments (CMSSs) to support the roof while gaseous CO₂ is injected into gaseous CO₂ storage segments (GCSSs) to maximize storage amounts. This study focuses on the Yu-Shen coal area in Yulin City, Shaanxi Province, China. A three-level evaluation model was constructed to predict DCS feasibility based on the analytic hierarchy process (AHP) and fuzzy comprehensive assessment method. The model was generalized and applied to the whole coal area. Each indicator affecting adaptability is plotted on a thematic map to determine the corresponding membership degree. The aptness for 400 boreholes distributed in the entire area was derived and a zoning map which divides the whole area into different suitability was drawn. This paper puts forward a mathematical model for predicting DCS suitability. The findings establish an engineering paradigm that simultaneously addresses CO₂ sequestration, industrial waste recycling, and ecological water table preservation. The research results can provide references for determining the site of DCS, contributing to the generalization of DCS in a larger range. Full article

As drone-based Light Detection and Ranging (LiDAR) becomes more accessible, it presents new opportunities for automated, geometry-driven classification. This study investigates the use of LiDAR point cloud data and Machine Learning (ML) to classify rooftop solar panels from building surfaces. While rooftop solar detection has been explored using satellite and aerial imagery, LiDAR offers geometric and reflectance-based attributes for classification. Two datasets were used: the University of Southern Queensland (UniSQ) campus, with commercial-sized panels, both elevated and flat, and a suburban area in Newcastle, Australia, with residential-sized panels sitting flush with the roof surface. UniSQ was classified using RANSAC (Random Sample Consensus), while Newcastle’s dataset was processed based on reflectance values. Geometric features were selected based on histogram overlap and Kullback–Leibler (KL) divergence, and models were trained using a Multilayer Perceptron (MLP) classifier implemented in both PyTorch and Scikit-learn libraries. Classification achieved F1 scores of 99% for UniSQ and 95–96% for the Newcastle dataset. These findings support the potential for ML-based classification to be applied to unlabelled datasets for rooftop solar analysis. Future work could expand the model to detect additional rooftop features and estimate panel counts across urban areas. Full article

Under extreme snowfall conditions, wind-induced snow drifting can lead to the redistribution of snow accumulation on roofs, resulting in localized overloads that pose a serious threat to building structural safety. Notably, morphological differences in snow particles significantly alter their aerodynamic characteristics, causing variations in their motion trajectories and increasing the uncertainty in determining roof snow loads. Therefore, this study develops a numerical simulation method that accounts for snow morphologies based on the drag coefficients of typical snow crystals, and further investigates the accumulation characteristics of differently shaped snow particles on typical roofs. Analysis results demonstrate that the observed variations in snow particle motion characteristics primarily originate from differences in their respective drag coefficients. The drag coefficient exerts a direct influence on particle settling velocity, which subsequently governs spatial distribution patterns of snow concentration and final accumulation patterns. Under identical inflow snow concentration conditions, particles with higher drag coefficients exhibit reduced depositional accumulation on roof surfaces. Notably, this shape-dependent effect diminishes with increasing roof span and slope. Full article

Yangjiatang Village traces its origins to the late Ming and early Qing dynasties. It has evolved over more than 400 years of history. There are 78 rammed-earth buildings left, making it one of the most complete and largest rammed-earth building complexes in East China. This study investigated the traditional rammed-earth houses in Yangjiatang Village, Songyang County, Zhejiang Province. By combining field investigation, microscopic characterization, and experimental simulation, we systematically revealed the erosion resistance of rammed earth in a subtropical humid climate was systematically revealed. Using a combination of advanced techniques including drone aerial photography, X-ray diffraction (XRD), microbial community analysis, scanning electron microscopy (SEM), and soil leaching simulations, we systematically revealed the anti-erosion mechanisms of rammed-earth surfaces in Yangjiatang Village. The study found that (1) rammed-earth walls are primarily composed of Quartz, Mullite, lepidocrocite, and Nontronite, with quartz and lepidocrocite being the dominant minerals across all orientations. (2) Regulating the community structure of specific functional microorganisms enhanced the erosion resistance of rammed-earth buildings. (3) The surface degradation of rammed-earth walls is mainly caused by four factors: structural cracks, surface erosion, biological erosion and roof damage. These factors work together to cause surface cracking and peeling (depth up to 3–5 cm). (4) This study indicates that the microbial communities in rammed-earth building walls show significant differences in various orientations. Microorganisms play a dual role in the preservation and deterioration of rammed-earth buildings: they can slow down weathering by forming protective biofilms or accelerating erosion through acid production. Full article

Urban areas are increasingly challenged by the combined effects of climate change, rapid population growth, and high energy demand. The integration of renewable energy systems, such as photovoltaic (PV) panels, and nature-based solutions, such as green roofs, represents a key strategy for sustainable urban development. This study evaluates the spatial potential for PV and green roof implementation in Iași, Romania, using moderate to high-resolution geospatial datasets, including the ALOS AW3D30 Digital Surface Model (DSM) and the Copernicus Urban Atlas 2018, processed in ArcMap 10.8.1 and ArcGIS Pro 2.6.0. Solar radiation was computed using the Area Solar Radiation tool for the average year 2023, while roof typology (flat vs. pitched) was derived from slope analysis. Results show significant spatial heterogeneity. The Copou neighborhood has the highest PV-suitable roof share (73.6%) and also leads in green roof potential (46.6%). Integrating PV and green roofs can provide synergistic benefits, improving energy performance, mitigating urban heat islands, managing stormwater, and enhancing biodiversity. These findings provide actionable insights for urban planners and policymakers aiming to prioritize green infrastructure investments and accelerate the local energy transition. Full article

Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article

In recent years, the amount of electricity produced by photovoltaic systems in Poland has increased significantly. This paper presents an evaluation of commercial software (PVGIS 5.3, ENERAD, and PVGIS 24) used for simulating energy produced by four photovoltaic installations. The results of the simulation were compared with the real energy production. The installations differ in terms of panel orientation (S, SE, SE-NW), tilt angle (12°, 25°, 37°) and location (roof- or ground-mounted). The average annual electricity production per 1 kW of module power for each installation was as follows: PV1—1104 kWh·kW⁻¹, PV2—1169 kWh·kW⁻¹, PV3—927 kWh·kW⁻¹, and PV4—831 kWh·kW⁻¹. The highest values were recorded for ground-mounted installations facing south. Simulations carried out using computer programs show differences between simulated and real electricity production values of 35–41% for the ENERAD software, 3–13% for the PVGIS 5.3 software, and 3–32% for the PVGIS 24 software. The most accurate forecasts were obtained for the PV2 system in the PVGIS 24 software (MPE 3%, RMSE 12%), and the most unfavorable for the same installation in the ENERAD software (MPE 41%, RMSE 48%). Full article

This paper proposes a multi-sensor data-fusion-based method for real-scene 3D reconstruction and digital twin visualization of coal mine tunnels, aiming to address issues such as low accuracy in non-photorealistic modeling and difficulties in feature object recognition during traditional coal mine digitization processes. The research employs cubemap-based mapping technology to project acquired real-time tunnel images onto six faces of a cube, combined with navigation information, pose data, and synchronously acquired point cloud data to achieve spatial alignment and data fusion. On this basis, inner/outer corner detection algorithms are utilized for precise image segmentation, and a point cloud region growing algorithm integrated with information entropy optimization is proposed to realize complete recognition and segmentation of tunnel planes (e.g., roof, floor, left/right sidewalls) and high-curvature feature objects (e.g., ventilation ducts). Furthermore, geometric dimensions extracted from segmentation results are used to construct 3D models, and real-scene images are mapped onto model surfaces via UV (U and V axes of texture coordinate) texture mapping technology, generating digital twin models with authentic texture details. Experimental validation demonstrates that the method performs excellently in both simulated and real coal mine environments, with models capable of faithfully reproducing tunnel spatial layouts and detailed features while supporting multi-view visualization (e.g., bottom view, left/right rotated views, front view). This approach provides efficient and precise technical support for digital twin construction, fine-grained structural modeling, and safety monitoring of coal mine tunnels, significantly enhancing the accuracy and practicality of photorealistic 3D modeling in intelligent mining applications. Full article

Urban school environments often face significant thermal discomfort due to extensive paved surfaces, limited vegetation, and outdated building designs. This study examines how green spaces can mitigate temperature extremes and improve thermal comfort at two secondary schools in Coimbra, Portugal: Escola Secundária José Falcão (ESJF) and Escola Secundária D. Dinis (ESDD). Using a mixed-methods approach that combined school community surveys with on-site microclimatic measurements, we integrated user feedback on comfort with data on temperature and humidity variations across different indoor and outdoor spaces. Results revealed that tree-shaded areas consistently maintained lower air temperatures and higher relative humidity than unshaded zones, which experienced intense heat accumulation—up to a 5 °C difference. At ESJF, the older infrastructure and large asphalt surfaces led to severe heat retention, with east-facing classrooms recording the highest indoor temperatures. ESDD’s pavilion-style layout and existing green spaces provided comparatively better thermal conditions, although insufficient vegetation maintenance and limited shade reduced their effectiveness. The findings demonstrate a clear correspondence between the school community’s perceptions of thermal comfort and the measured microclimatic data. Vegetation—particularly deciduous trees—plays a critical role in cooling the school microclimate through shading and evapotranspiration. Strategic interventions such as expanding tree cover in high-exposure areas, installing green roofs and walls, and carefully selecting species can significantly reduce temperature extremes and improve outdoor usability. In addition, fostering environmental education and participatory co-design programs can encourage sustainable behaviors within the school community, underlining the importance of inclusive, nature-based solutions for climate adaptation. This research highlights that integrating green infrastructure in school design and management is a cost-effective strategy for thermal regulation. Green spaces, when co-designed with community involvement, not only enhance climate resilience and student well-being but also contribute to broader sustainable urban development goals. Full article

Rapid urbanization and frequent extreme events have made urban flooding a growing threat to residents. This issue is acute in old urban districts, where extremely limited land resources, outdated standards and poor infrastructure have led to inadequate drainage and uneven pipe settlement, heightening flood risk. This study applies InfoWorks ICM Ultimate (version 21.0.284) to simulate flooding in a typical old urban district for six return periods. A risk assessment was carried out, flood causes were analyzed, and mitigation strategies were evaluated to reduce inundation and cost. Results show that all combined schemes outperform single-measure solutions. Among them, the green roof combined with pipe optimization scheme eliminated high-risk and medium-risk areas, while reducing low-risk areas by over 78.23%. It also lowered the ponding depth at key waterlogging points by 70%, significantly improving the flood risk profile. The permeable pavement combined with pipe optimization scheme achieved similar results, reducing low-risk areas by 77.42% and completely eliminating ponding at key locations, although at a 50.8% higher cost. This study underscores the unique contribution of cost-considered gray-green infrastructure retrofitting in old urban areas characterized by land scarcity and aging pipeline networks. It provides a quantitative basis and optimization strategies for refined modeling and multi-strategy management of urban waterlogging in such regions, offering valuable references for other cities facing similar challenges. The findings hold significant implications for urban flood control planning and hydrological research, serving as an important resource for urban planners engaged in flood risk management and researchers in urban hydrology and stormwater management. Full article