Search Results (316)

We developed a microscale airflow modeling system with detailed building and terrain data to better understand the urban microclimate. Building shapes and heights, and terrain elevation data were integrated to construct a high-resolution urban surface geometry. The system, based on computational fluid dynamics using OpenFOAM, can resolve complex flow structures around built environments. Inflow boundary conditions were generated using logarithmic wind profiles derived from Automatic Weather System (AWS) observations under neutral stability. After validation with wind-tunnel data for a single block, the system was applied to airflow modeling around a university campus in Seoul using AWS data from four nearby stations. The results demonstrated that the system captured key flow characteristics such as channeling, wake, and recirculation induced by complex terrain and building configurations. In particular, easterly inflow cases with high-rise buildings on the leeward side of a mountain exhibited intensified wakes and internal recirculations, with elevated centers influenced by tall structures. This modeling framework, with further development, could support diverse urban applications for microclimate and air quality, facilitating urban resilience. Full article

This article presents an analysis on natural gas heating in residential areas, focusing on two primary systems: (1) local heating, where piped gas is delivered directly to individual dwellings equipped with autonomous gas boilers, and (2) district heating, where gas or an alternative fuel powers a central heating plant, and the generated heat is distributed to buildings via a thermal network. The choice between these systems should first consider safety and environmental factors, followed by the urban characteristics of the settlement. In particular, building typology—such as size, function, and spatial configuration—and urban topology, referring to the relative positioning of buildings, play a crucial role. For example, very tall buildings often exclude the use of piped gas due to safety concerns, whereas in other cases, economic efficiency becomes the determining factor. To support decision-making, a comparative cost analysis is conducted, assessing the required infrastructure for both systems, including pipelines, boilers, and associated components. The study identifies representative residential building types in selected urban areas of Serbia and Czechia that are suitable for either heating approach. Additionally, the article examines the broader energy context in both countries, with emphasis on recent developments in the natural gas sector and their implications for urban heating strategies. Full article

Coastal climate processes that affect landscape hydrology and beach dynamics are studied using local and remote data sets near Cape Vidal (28.12° S, 32.55° E). The sporadic intra-seasonal pulsing of coastal runoff, vegetation, and winds is analyzed to understand sediment inputs and transport by near-shore wind-waves and currents. River-borne sediments, eroded coral substrates, and reworked beach sand are mobilized by frequent storms. Surf-zone currents ~0.4 m/s instill the northward transport of ~6 10⁵ kg/yr/m. An analysis of the mean annual cycle over the period of 1997–2024 indicates a crest of rainfall over the Umfolozi catchment during summer (Oct–Mar), whereas coastal suspended sediment, based on satellite red-band reflectivity, rises in winter (Apr–Sep) due to a deeper mixed layer and larger northward wave heights. Sediment input to the beaches near Cape Vidal exhibit a 3–6-year cycle of southeasterly waves and rainy weather associated with cool La Nina tropical sea temperatures. Beachfront sand dunes are wind-swept and release sediment at ~10³ m³/yr/m, which builds tall back-dunes and helps replenish the shoreline, especially during anticyclonic dry spells. A wind event in Nov 2018 is analyzed to quantify aeolian transport, and a flood in Jan–Feb 2025 is studied for river plumes that meet with stormy seas. Management efforts to limit development and recreational access have contributed to a sustainable coastal environment despite rising tides and inland temperatures. Full article

Building extraction plays a crucial role in a variety of applications, including urban planning, high-precision 3D reconstruction, and environmental monitoring. In particular, the accurate detection of tall buildings is essential for reliable modeling and analysis. However, most existing building-detection methods are primarily trained on datasets dominated by low-rise structures, resulting in degraded performance when applied to complex urban scenes with high-rise buildings and severe occlusions. To address this limitation, we propose TBFH (Total-Building-Focused Hybrid), a novel dataset specifically designed for building detection in remote sensing imagery. TBFH comprises a diverse collection of tall buildings across various urban environments and is integrated with the publicly available WHU Building dataset to enable joint training. This hybrid strategy aims to enhance model robustness and generalization across varying urban morphologies. We also propose the KTC metric to quantitatively evaluate the structural integrity and shape fidelity of building segmentation results. We evaluated the effectiveness of TBFH on multiple state-of-the-art models, including UNet, UNetFormer, ABCNet, BANet, FCN, DeepLabV3, MANet, SegFormer, and DynamicVis. Our comparative experiments conducted on the Tall Building dataset, the WHU dataset, and TBFH demonstrated that models trained with TBFH significantly outperformed those trained on individual datasets, showing notable improvements in IoU, F1, and KTC scores as well as in the accuracy of building shape delineation. These findings underscore the critical importance of incorporating tall building-focused data to improve both detection accuracy and generalization performance. Full article

This study examined the effect of build orientation on the surface finish of micro-optofludic (MoF) devices fabricated via a polydimethylsiloxane (PDMS)-based 3D-printing primary–secondary fabrication protocol, where an inkjet 3D-printing technique was implemented. The molds (i.e., primaries) for fabricating the MoF devices were 3D-printed in two orientations: along $XY$ (Dev-1) and across $YX$ (Dev-2) the printhead direction. Next, the surface finish was characterized using a profilometer to acquire the primary profile of the surface along the microchannel’s edge. The results indicated that the build orientation had a strong influence on the latter, since Dev-1 displayed a tall and narrow Gaussian distribution for a channel width of 398.43 ± 0.29 µm; Dev-2 presented a slightly lower value of 393.74 ± 1.67 µm, characterized by a flat and broader distribution, highlighting greater variability due to more disruptive, orthogonally oriented, and striated patterns. These results were also confirmed by hydrodynamically testing the two MoF devices with an air–water slug flow process. A large experimental study was conducted by analyzing the mean period trend in the slug flow with respect to the imposed flow rate and build orientation. Dev-1 showed greater sensitivity to flow rate changes, attributed to its smoother, more consistent microchannel geometry. The slightly narrower average channel width in Dev-2 contributed to increased flow velocity at the expense of having worse discrimination capability at different flow rates. This study is relevant for optimizing 3D-printing strategies for the fabrication of high-performance microfluidic devices, where precise flow control is essential for applications in biomedical engineering, chemical processing, and lab-on-a-chip systems. These findings highlight the effect of microchannel morphology in tuning a system’s sensitivity to flow rate modulation. Full article

The demand for super-tall buildings and long-span bridges has driven concrete development toward higher strength and durability. Therefore, this study investigated the impact of composition of materials (aggregates, admixtures, and steel fibers) on the mechanical performance and economic feasibility of coarse aggregate reactive powder concrete (CA-RPC). The goal is to identify optimal combinations for both performance and cost. Scanning electron microscopy (SEM) and pore structure analysis were used to assess microstructural characteristics. The results demonstrated that replacing quartz sand with yellow sand as the fine aggregate in CA-RPC effectively reduced construction costs without compromising compressive strength. The use of basalt as the coarse aggregate led to higher mechanical strength compared to limestone. Incorporating 20% fly ash reduced the 7-day compressive strength, while the 28-day strength remained unaffected. The addition of 10% silica fume showed no obvious effect on the early strength but significantly improved the 28-day strength and workability of the concrete. Moreover, the incorporation of steel fibers improved the flexural strength and structural integrity of CA-RPC, shifting the failure mode from brittle fracture to a more ductile cracking behavior. SEM observations and pore structure analyses revealed that the admixtures altered the hydration products and pore distribution, thereby affecting the mechanical performance. This study provides valuable insights into the strength development and underlying mechanisms of CA-RPC, offering a theoretical basis for its practical application in bridge deck pavement and tunnels. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

Tall buildings are vulnerable to wind loads, which can cause significant displacements that can affect their stability, strength, and serviceability. Their structural configuration can significantly influence their behavior to wind loads. There are not enough comparative studies in the literature examining the effects of wind loads on different structural configurations. This study examines the response of tall buildings to wind loads by varying their structural forms. Twelve models of tall buildings of different heights and structural configurations were analyzed using the finite element method. Wind loads were applied to the models as equivalent static forces, according to existing codes. The maximum displacements were calculated for each model, and the results were compared. It was found that a considerable reduction in the response was achieved by including shear walls at specific locations in the building’s layout, thereby identifying the optimal location. However, the effectiveness of the different configurations converges at building heights greater than 120 m. In addition, the maximum displacement on the same floor in buildings with the same structural form may vary depending on the building’s total height. An increase in wind velocity results in an almost linear increase in the maximum displacements of the buildings. The findings of this study can assist designers in optimizing shear wall placement in tall building designs. Full article

This study investigates the effects of the combined addition of MgO expansive agent (MEA) and rice husk ash (RHA) on the performance of concrete. Results show that MEA absorbs water and competes with superplasticizers for adsorption, reducing early-age fluidity. In the later stages, its reaction with RHA generates M-S-H gel, accelerating slump loss. At early ages (up to 7 days), due to the slow hydration of MEA and partial replacement of cement, fewer hydration products are formed. Additionally, the pozzolanic reaction of RHA has not yet developed, resulting in the low early strength of concrete. In the later stages, Mg(OH)₂ fills pores and enhances compactness, while the pozzolanic reaction of RHA further optimizes the pore structure. The internal curing effect also provides the moisture needed for continued MEA hydration, significantly improving later-age strength. Moreover, in the post-cast strip of a tall building, the internal curing effect of RHA ensures the effective shrinkage compensation by MEA under low water-to-cement ratio conditions. The restraint provided by reinforcement enhances the pore-filling effect of Mg(OH)₂, improving concrete compactness and crack resistance, ultimately boosting long-term strength and durability. Full article

The development of beyond 5G (B5G) future wireless communication networks (FWCN) needs novel solutions to support high-speed, reliable, and low-latency communication. Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are promising techniques that can enhance wireless connectivity in urban environments where tall buildings block line-of-sight (LoS) links. However, existing UAV-assisted communication strategies do not fully address key challenges like mobility management, handover failures (HOFs), and path disorders in dense urban environments. This paper introduces a deep deterministic policy gradient (DDPG)-based UAV-RIS framework to overcome these limitations. The proposed framework jointly optimizes UAV trajectories and RIS phase shifts to improve throughput, energy efficiency (EE), and LoS probability while reducing outage probability (OP) and HOF. A modified K-means clustering algorithm is used to efficiently partition the ground users (GUs) considering the newly added GUs as well. The DDPG algorithm, based on reinforcement learning (RL), adapts UAV positioning and RIS configurations in a continuous action space. Simulation results show that the proposed approach significantly reduces HOF and OP, increases EE, enhances network throughput, and improves LoS probability compared to UAV-only, RIS-only, and without UAV-RIS deployments. Additionally, by dynamically adjusting UAV locations and RIS phase shifts based on GU mobility patterns, the framework further enhances connectivity and reliability. The findings highlight its potential to transform urban wireless communication by mitigating LoS blockages and ensuring uninterrupted connectivity in dense environments. Full article

Mass timber construction is gaining popularity in mid-rise and tall buildings due to its sustainability, aesthetics, versatile prefabrication, light weight, and faster construction time compared to conventional building materials such as concrete and steel. One of the challenges with timber construction is a potential fire hazard, and the risk is even aggravated in taller buildings due to the increased evacuation period. Several researchers have identified and reported important parameters that will have direct influence over mass timber fire performance behaviour. However, the current findings from the literature do not provide a correlation between the key parameters and the fire performance behaviour. This paper presents a review of experimental fire testing of mass timber structures and analyses the fire performance results output obtained from the experimental testing. This paper attempts to identify several key parameters that influence the fire performance behaviour of mass timber structures, such as peak temperature, charring rate and decay behaviour. The correlation between the key parameters and the fire performance behaviour of mass timber structures will enhance in developing a rational model to determine the time to reach the fire growth, peak temperature, charring behaviour, structural integrity (strength and stiffness reduction) and decay behaviour of the exposed timber. Full article

This paper analyzes the performance of the TMD-Inerter for tall building damping. The analysis is performed by simulation to ensure ideal working behaviour of the inerter, i.e., the inerter produces a force in proportion to the relative acceleration of its terminals without any friction of real inerter devices such as fly wheels. For the study, the most realistic TMD-Inerter configuration is considered where the inerter is grounded to the TMD mass and the structural mass next to the TMD mass, i.e., the TMD-Inerter is installed in the top floor room of the structure. Approximate closed-form solutions for the tuning of the TMD-Inerter parameters are derived based on the characteristics of the inerter force. The resulting frequency response functions for different inertance ratios are compared to those of the classical TMD with same mass ratio. The results clearly demonstrate that the TMD-Inerter worsens the tall building damping compared to the classical TMD for the realistic situation that the inerter is grounded to the structural mass next to the TMD. There are two physical reasons why the inerter worsens the efficiency of the TMD. First, the inerter force is per definition in proportion to the relative acceleration of its two terminals, i.e., it is not in proportion to the damper mass (absolute) acceleration whereby it does not increase the damper mass. Second, for harmonic excitation the inerter force characteristics show negative stiffness behaviour which explains why the TMD stiffness must be designed by taking into consideration both the TMD physical mass and the inertance to ensure the correct tuning of the TMD-Inerter natural frequency. Full article

This study investigates the reliability of tall buildings subjected to dynamic across-wind loading, focusing on the Tuned Mass Damper Fluid Inerter (TMDFI). While existing literature emphasises the effectiveness of TMDFI in mitigating seismic hazards, research on its reliability regarding wind hazards remains limited. A wind-sensitive benchmark 76-storey building is modeled to compare the performance of the TMDFI against a traditional tuned mass damper (TMD) and an uncontrolled structure. A Monte Carlo Simulation (MCS) approach comprising 31,500 simulations is employed to assess reliability under uncertain damping ratios and varying turbulence intensities at reference wind speeds of 20 to 40 m/s. Key performance metrics, including peak acceleration and root mean squared (RMS) displacement responses, are derived through spectral analysis in the frequency domain. Results indicate that the TMDFI offers superior reliability, allowing an additional 6–7 m/s in reference velocity before reaching significant failure at the ISO limit state. Peak acceleration and RMS displacement are reduced by up to 64% to the uncontrolled structure. The TMDFI consistently outperforms both the TMD and uncontrolled configurations across all turbulent cases and wind velocities examined. Full article

The building drainage system (BDS) is a critical building component and must be designed to protect public health by maintaining safe and hygienic conditions within the indoor environment. The recent COVID-19 pandemic and the emergence of other wastewater-related issues, such as the spread of anti-microbial resistance (AMR), place the BDS at the centre of the public health agenda. To understand the complex characteristics of the BDS and its performance, the numerical simulation model AIRNET was used to model whole system responses to discharging events. In this study, the model’s effectiveness and accuracy were evaluated through its application in a case study system representative of a real-world tall building. Data reflecting actual conditions were collected using the drainage test rig at the National Lift Tower (NLT) in Northampton. The data show a strong correlation between the measured and modelled air pressures in the system over time and along the drainage stack height. More importantly, a sample dataset representing various ventilation configurations, flow rates, and water usage combinations shows a strong linear relationship between the simulated and measured pressure values. These results confirm the accuracy and reliability of the AIRNET model in modelling the BDS, even when applied to high-rise buildings. This is crucial for addressing drainage challenges in high-rise building design. Full article

Disparity estimation in satellite stereo images is a highly challenging task due to complex terrain, occlusions caused by tall buildings and structures, and texture-less regions such as roads, rivers, and building roofs. Recent deep learning-based satellite stereo disparity estimation methods have adopted cascade multi-scale feature extraction techniques to address these challenges. However, the recent learning-based methods still struggle to effectively estimate disparity in the high ambiguity regions. This paper proposes a disparity estimation and refinement method that leverages variance uncertainty in the cost volume to overcome these limitations. The proposed method calculates variance uncertainty from the cost volume and generates uncertainty weights to adjust the cost volume based on this information. These weights are designed to emphasize geometric features in regions with low uncertainty while enhancing contextual features in regions with high uncertainty, such as occluded or texture-less areas. Furthermore, the proposed method introduces a pseudo volume, referred to as the 4D context volume, which extends the reference image’s features during the stereo-matching aggregation step. By integrating the 4D context volume into the aggregation layer of the geometric cost volume, our method effectively addresses challenges in disparity estimation, particularly in occluded and texture-less areas. For the evaluation of the proposed method, we use the Urban Semantic 3D dataset and the WHU-Stereo dataset. The evaluation results show that the proposed method achieves state-of-the-art performance, improving disparity accuracy in challenging regions. Full article