Search Results (416)

Urban infrastructure, such as water supply networks, sewage systems, and electricity networks, is essential for the functioning of cities and, consequently, for the well-being of citizens. Despite its essentiality, the distribution of infrastructure in urban areas is not homogeneous, especially in cities in developing countries. Socially vulnerable areas often face significant deficiencies in sewage and road paving, exacerbating urban inequalities. In this regard, urban planners must consider the multiple elements of urban infrastructure and assess the compensation levels between them to reduce inequality effectively. In particular, the complexity of the problem necessitates considering the multidimensionality and heterogeneity of urban infrastructure. This complexity qualifies the operational framework of composite indicators as the natural solution to the problem. This study develops a new weighting system for the balanced expansion of urban infrastructures through composite indicators constructed by the Ordered Weighted Average operator. Implementing these weighting systems provides an opportunity to analyze urban infrastructure from different perspectives, offering transparency regarding the weaknesses and strengths of each perspective. This prevents unreliable representations from being used in decision-making and provides a solid basis for allocating investments in urban infrastructure. In particular, the study suggests that adopting weighting systems that prioritize intermediate values and avoid extreme values can lead to better resource allocation, helping to identify areas with deficient infrastructure and promoting more equitable urban development. Full article

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

This work focuses on the use of municipal waste incineration bottom ash (MSWI) for the development and production of products suitable for use as construction products. The generation of these ashes is increasing every year due to the incineration of municipal waste. There are currently three incineration plants operating in major cities in Lithuania. The non-hazardous bottom ash remaining from the incineration process is stored in dedicated sorting and aging sites until it is used as an inert form of aggregate for the installation of road foundations. However, it has been observed that these ashes have a tendency to bind and cement when exposed to atmospheric precipitation at the storage site. Based on this characteristic, it was decided in this study to use alkaline activation of the ash to accelerate the bonding process and to create a dense, non-porous composite concrete structure. This activation method is known to create another problem during ash bonding, where the presence of metallic aluminum particles in the ash leads to the release of hydrogen gas and makes the structure of the cured samples porous. For the purposes of the study, it was decided to create a completely different mixture structure and not to use additional water in the mixtures tested. A very low water/solids ratio (W/S) of <0.08 was used for the alkaline activation of the mixtures. All the water required for ash activation was obtained from sodium silicate and sodium hydroxide solution. Metakaolin waste (MKW) was used to adjust the SiO₂/Na₂O/Al₂O₃ ratio of the mixtures. Vibro-pressing was used to form and increase the density of the samples. And for the formation of the concrete structure, 0/4 fraction sand was used as aggregate. The final alkali-activated sample obtained had properties similar to those of the very widely used vibro-pressed cementitious paving tiles and did not exhibit hydrogen evolution during alkali activation due to the very low W/S ratio. The best results were achieved by samples with a highest compressive strength of 40.0 MPa and a tensile strength of 5.60 MPa, as well as a density of 1950 kg/m³. It is believed that this alkaline activation and vibro-pressing method can expand the use of MSWI ash in the development of building products. Full article

The detection of voids in pavement infrastructure is essential for road safety and efficient maintenance. Traditional methods of analyzing ground-penetrating radar (GPR) data are labor-intensive and error-prone. This study presents a novel approach using the EfficientDet-D3 deep learning model for automated void detection in GPR images. The model combines advanced feature extraction and compound scaling to balance accuracy and computational efficiency, making it suitable for real-time applications. A diverse GPR image dataset, including various pavement types and environmental conditions, was curated and preprocessed to improve model generalization. The model was fine-tuned through hyperparameter optimization, achieving a precision of 91.2%, a recall of 87.5%, and an F1-score of 89.3%. It also attained mean Average Precision (mAP) values of 89.7% at IoU 0.5 and 84.3% at IoU 0.75, demonstrating strong localization performance. Comparative analysis with models such as YOLOv8 and Mask R-CNN shows that EfficientDet-D3 offers a superior balance between accuracy and inference speed, with an inference time of 68 ms. This research provides a scalable, efficient solution for pavement void detection, paving the way for integrating deep learning models into pavement management systems to enhance infrastructure sustainability. Future work will focus on model optimization and expanding dataset diversity. Full article

In order to shorten the curing time of the cement-treated aggregate base, provide a stable paving base for an asphalt mixture, and finally, achieve rapid traffic reopening during the maintenance of the pavement (milling and resurfacing of the base layer), sulphoaluminate cement (SAC) was used to prepare cement-treated aggregate with high early strength. As a result, the SAC was first optimized by adding several cement admixtures (i.e., polycarboxylic water reducer, borax, lithium carbonate, and calcium formate) based on hydration kinetics, setting time, compressive strength, and morphology tests. Then, the optimized SAC was used to prepare the sulphoaluminate cement-treated aggregate (SACTA). The test results show that the addition of compound retarder and compound early strength agent in SAC could delay the hydration, reduce microcracks, and ensure required setting time and high early strength. Compared with ordinary Portland cement-treated aggregates (OPCTAs) with the same cement content, the 1 d unconfined compressive strength and indirect tension strength of SACTAs increased by 87.7–184.6% and 133.8–263.6% respectively. The SACTA had smaller total drying shrinkage strain and better anti-scouring performance than OPCTA when using the same cement content. Besides, the 1 d interfacial bonding strength between SACTA and OPCTA was 0.18 MPa, which was higher than the indirect tension strength of OPCTA. The findings in this study indicate that the prepared SACTA could be used for rapid traffic opening during road maintenance. Full article

Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the roads require a lot of equipment, technicians, and time to obtain the nature and indices of the damage to estimate the roadway’s quality level. This study proposes the use of pavement distress detection and classification models based on Convolutional Neural Networks, starting from videos taken of any asphalt road. To carry out this work, various routes were filmed to list the degradations concerned. Images were extracted from these videos and then resized and annotated. Then, these images were used to constitute several databases of road damage, such as longitudinal cracks, alligator cracks, small potholes, and patching. Within an appropriate development environment, three Convolutional Neural Networks were developed and trained on the databases. The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. This method would make it possible to considerably reduce the financial resources used for each road data campaign. Full article

Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in large-scale searches. This study presents an innovative application of RAG technology for processing road traffic-related laws and regulations, particularly in the context of unmanned systems like autonomous driving. Our approach integrates embedding generation using a LoRA-enhanced BERT-based uncased model and an optimized retrieval strategy that combines maximal marginal similarity score thresholding with contextual compression retrieval. The proposed system enhances and achieves improved retrieval accuracy while reducing processing overhead. Leveraging road traffic-related regulatory datasets, the LoRA-enhanced model demonstrated remarkable performance gains over traditional RAG methods. Specifically, our model reduced the number of trainable parameters by 13.6% and lowered computational costs by 18.7%. Performance evaluations using BLEU, CIDEr, and SPICE scores revealed a 4.36% increase in BLEU-4, a 6.83% improvement in CIDEr, and a 5.46% improved in SPICE, confirming greater structural accuracy in regulatory text generation. Additionally, our method achieved an 8.5% improvement in retrieval accuracy across key metrics, outperforming baseline RAG systems. These contributions pave the way for more efficient and reliable traffic regulation processing, enabling better decision making in autonomous systems. Full article

Urban noise pollution significantly degrades people’s health and well-being and, furthermore, traditional noise reduction strategies often overlook individual perception differences. This study proposed to explore the role of eco-acoustic indices in capturing the interplay between biophony, geophony, and anthrophony, and their relationship with classical acoustic metrics and the perceived soundscapes within a University Campus (University of “Mila-no-Bicocca”, Italy). The study area is divided in to eight different sites in “Piazza della Scienza” square. Sound measurements and surveys conducted in June 2023 across four paved sites and adjacent courtyards involved 398 participants (51.7% female, 45.6% male, 2.7% other). The main noise sources included road traffic, technical installations, and human activity, where traffic noise was more prominent at street-level sites (Sites 1–4) and technical installations dominated underground courtyards (6–8). Human activity was most noticeable at Sites 4–8, especially at Site 5, which showed the highest activity levels. A circumplex model revealed that street-level sites were less pleasant and eventful than courtyards. Pairwise comparisons of noise variability showed significant differences among sites, with underground locations offering quieter environments. Eco-acoustic analysis identified two site groups: one linked to noisiness and spectral features, the other to intensity distribution metrics. Technical installations, people, and traffic noises showed distinct correlations with acoustic indices, influencing emotional responses like stimulation and liveliness. These findings emphasize the need to integrate subjective perceptions with objective noise metrics in soundscape descriptions. Full article

The overall goal of this study is to synthesize the existing literature on mix design approaches and to develop recommendations for the best practices for the design of asphalt mixtures specific to LVRs. The synthesis of best practices encompasses material characterization, performance evaluation techniques, and recommendations for construction and maintenance practices. This review suggests the need for further laboratory and field testing to enhance performance measures, explore sustainable materials and construction practices, and develop standardized specifications for the diverse needs of low-volume road networks. The recommended changes (or guidelines) include, but are not limited to, updated recycled asphalt pavement (RAP) percentages as per current law requirements, the addition of performance tests (IDEAL-CT and IDEAL-RT), RAP content, design methodology, volumetrics, and design gyrations. The review suggests the need for further laboratory and field testing, including performance testing, long-term performance assessments in various conditions, and improved methodologies for evaluating testing parameters. These enhancements aim to ensure more reliable performance predictions and the better implementation of LVR technologies. Overall, this study will help agencies and the paving industry to understand the updates made to the current LVR specifications and evaluate the mix design considerations for low-volume roads. Full article

Inflammatory bowel diseases (IBDs), comprising Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy—and potentially also in histology and imaging in the context of IBD—is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs. Full article

For centuries, earthen materials have regained popularity because of the high carbon emissions caused by the construction sector. Although earth-based materials possess superior properties, such as recyclability, easy accessibility, affordability, and high thermal conductivity, they are not without drawbacks. They are, for instance, relatively weak and sensitive to water, and their physical and chemical properties can vary considerably depending on the source from which they are obtained. Stabilisation is often used to overcome these drawbacks. In this study, natural earth-based materials were stabilised with biopolymers of organic origin, such as alginate, Arabic gum, xanthan gum, and locust bean gum, to preserve their natural properties. To produce the samples, the earth material used in the road sub-base layer was mixed with kaolin clay and silica sand, and the mixtures were prepared by substituting biopolymer materials with clay at a ratio of 0.1%. After determining the fresh unit volume weights, spreading diameters (flow table test), penetration depths (fall cone test), and air content of the mixtures, the flexural and compressive strengths of the cured specimens were measured. In addition, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses were performed to determine the microstructural characteristics. According to the 28-day compressive strength results, the mix with xanthan gum was found to be almost twice as strong as the other mixes. It has been concluded that biopolymer-stabilised earth mixtures can be used as a fill material in buildings where high strength is not required, or as a paving material on low-traffic roads. Full article

This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s I, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 °C between grassland (20.57 ± 5.13 °C) and water bodies (7.10 ± 1.25 °C) during afternoon periods. The Moran’s I analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 °C) and midnight (2.25 ± 1.51 °C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 °C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 °C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence ($R^2$≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices. Full article

Comprehensive datasets are crucial for developing advanced AI solutions in road infrastructure, yet most existing resources focus narrowly on vehicles or a limited set of object categories. To address this gap, we introduce the Korean Road Infrastructure Dataset (KRID), a large-scale dataset designed for real-world road maintenance and safety applications. Our dataset covers highways, national roads, and local roads in both city and non-city areas, comprising 34 distinct types of road infrastructure—from common elements (e.g., traffic signals, gaze-directed poles) to specialized structures (e.g., tunnels, guardrails). Each instance is annotated with either bounding boxes or polygon segmentation masks under stringent quality control and privacy protocols. To demonstrate the utility of this resource, we conducted object detection and segmentation experiments using YOLO-based models, focusing on guardrail damage detection and traffic sign recognition. Preliminary results confirm its suitability for complex, safety-critical scenarios in intelligent transportation systems. Our main contributions include: (1) a broader range of infrastructure classes than conventional “driving perception” datasets, (2) high-resolution, privacy-compliant annotations across diverse road conditions, and (3) open-access availability through AI Hub and GitHub. By highlighting critical yet often overlooked infrastructure elements, this dataset paves the way for AI-driven maintenance workflows, hazard detection, and further innovations in road safety. Full article

The accumulation of plastic waste poses a significant environmental challenge, necessitating sustainable solutions. This study investigates the potential of recycling waste plastics for use in the construction industry, emphasizing their integration into building materials and components. Earlier waste plastic recycling was excessively studied as an ingredient in concrete composites, roads, and other use in research. However, in this study, recycled plastic is assessed for use as a sole material for structural products. Raw plastics, including high-density polyethylene, Low-Density Polyethylene, polypropylene, polyolefin, samicanite, and virgin polyethylene, were analyzed for recycling through mechanical extrusion, and their mechanical properties were analyzed to determine their feasibility for construction applications. In this study, the extrusion process, combined with engineered dyes, was investigated with comprehensive material testing as per the ASTM standards to obtain the properties desired for construction. Advanced characterization techniques, including SEM, FTIR, and TGA, were employed to evaluate the chemical composition, thermal stability, and impurities of these waste plastics collected from municipal waste. A gas emission analysis during extrusion confirmed a minimal environmental impact, validating the sustainability of the recycling process. Municipal waste plastic has a considerable quantum of HDPE, PP, and LDPE, which was considered in this research for recycling for construction products. A total of 140 samples were recycled through extrusion and tested across shear, flexural, tensile, and compression categories: 35 samples each. The results showed that rHDPE and PP had good tensile strength and shear resistance. The findings pave the way for developing cost-effective, durable, and eco-friendly building materials, such as rebars, corrugated sheet, blocks, and other products, contributing to environmental conservation and resource efficiency for the construction Industry. Full article