Search Results (118)

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

Rapid urbanization has intensified global settlement density, significantly increasing the importance of urban street environmental quality, which profoundly affects residents’ physical and psychological well-being. Traditional methods for evaluating urban environmental quality have largely overlooked dynamic perceptual changes occurring throughout the day, resulting in incomplete assessments. To bridge this methodological gap, this study presents an innovative approach combining advanced deep learning techniques with time-series street view imagery (SVI) analysis to systematically quantify spatio-temporal variations in the perceived environmental quality of pedestrian-oriented streets. It further addresses two central questions: how perceived environmental quality varies spatially across sections of a pedestrian-oriented street and how these perceptions fluctuate temporally throughout the day. Utilizing Golden Street, a representative living street in Shanghai’s Changning District, as the empirical setting, street view images were manually collected at 96 sampling points across multiple time intervals within a single day. The collected images underwent semantic segmentation using the DeepLabv3+ model, and emotional scores were quantified through the validated MIT Place Pulse 2.0 dataset across six subjective indicators: “Safe,” “Lively,” “Wealthy,” “Beautiful,” “Depressing,” and “Boring.” Spatial and temporal patterns of these indicators were subsequently analyzed to elucidate their relationships with environmental attributes. This study demonstrates the effectiveness of integrating deep learning models with time-series SVI for assessing urban environmental perceptions, providing robust empirical insights for urban planners and policymakers. The results emphasize the necessity of context-sensitive, temporally adaptive urban design strategies to enhance urban livability and psychological well-being, ultimately contributing to more vibrant, secure, and sustainable pedestrian-oriented urban environments. Full article

Urban commercial districts, with their unique characteristics, serve as a reflection of broader urban development patterns. However, only a handful of studies have harnessed point-of-interest (POI) data to model the intricate relationship between retail commercial space types and other factors. This paper endeavors to bridge this gap, focusing on the influence of urban development factors on retail commerce districts through the lens of POI data. Our exploration underscores how commercial zones impact the density of residential neighborhoods and the coherence of pedestrian pathways. To facilitate our investigation, we propose an ensemble clustering technique for identifying and outlining urban commercial areas, including Kernel Density Analysis (KDE), Density-based Spatial Clustering of Applications with Noise (DBSCAN), Geographically Weighted Regression (GWR). Our research uses the city of Manchester as a case study, unearthing the relationship between commercial retail catchment areas and a range of factors (retail commercial space types, land use function, walking coverage). These include land use function, walking coverage, and green park within the specified areas. As we explore the multiple impacts of different urban development factors on retail commerce models, we hope this study acts as a springboard for further exploration of the untapped potential of POI data in urban business development and planning. Full article

LiDAR-based 3D object detection is fundamental in autonomous driving but remains challenging due to the irregularity, unordered nature, and non-uniform density of point clouds. Existing methods primarily rely on either graph-based or tree-based representations: Graph-based models capture fine-grained local geometry, while tree-based approaches encode hierarchical global semantics. However, these paradigms are often used independently, limiting their overall representational capacity. In this paper, we propose density-aware tree–graph cross-message passing (DA-TGCMP), a unified framework that exploits the complementary strengths of both structures to enable more expressive and robust feature learning. Specifically, we introduce a density-aware graph construction (DAGC) strategy that adaptively models geometric relationships in regions with varying point density and a hierarchical tree representation (HTR) that captures multi-scale contextual information. To bridge the gap between local precision and global contexts, we design a tree–graph cross-message-passing (TGCMP) mechanism that enables bidirectional interaction between graph and tree features. The experimental results of three large-scale benchmarks, KITTI, nuScenes, and Waymo, show that our method achieves competitive performance. Specifically, under the moderate difficulty setting, DA-TGCMP outperforms VoPiFNet by approximately 2.59%, 0.49%, and 3.05% in the car, pedestrian, and cyclist categories, respectively. Full article

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments. Full article

Historic cities face a dual challenge of preserving cultural authenticity and adapting to modern urbanization, yet existing studies often overlook the multidimensional coupling mechanisms critical for sustainable urban renewal. This research has proposed a replicable framework to balance heritage conservation, ecological restoration, and pedestrian mobility. Focusing on the historic city of Shenyang, this study evaluated spatial dynamics via the Walkability Index (WI), Green View Index (GVI), and Cultural Heritage Index (CHI), and quantified their coupling coordination patterns. Multisource datasets including OpenStreetMap road networks, POIs, and Baidu street-view imagery were integrated. A Coupling Coordination Degree (CCD) model was developed to assess system interactions. Results revealed moderate overall walkability (WI = 42.66) with stark regional disparities, critically low greening (GVI = 10.14%), and polarized heritage distribution (CHI = 18.73) in Shenyang historic city. Tri-system coupling was moderate (CCD = 0.409–0.608), constrained by green-heritage disconnects in key districts. This work could contribute to interdisciplinary discourse by bridging computational modeling with human-centric urban design, providing scalable insights for global historic cities. Full article

With the accelerating pace of urbanization, historic districts are increasingly confronted with the dual challenge of coordinating heritage preservation and sustainable development. This study proposes an intelligent evaluation framework that integrates spatiotemporal street view imagery, affective perception modeling, and scene recognition to reveal the evolutionary dynamics of built environment spatial quality in historic districts. Empirical analysis based on multi-temporal data (2013–2020) from the Wudadao Historic District in Tianjin demonstrates that spatial quality is shaped by a complex interplay of factors, including planning and preservation policies, landscape greening, pedestrian-oriented design, infrastructure adequacy, and equitable resource allocation. These findings validate the framework’s effectiveness as a tool for monitoring urban sustainability. Moreover, it provides actionable insights for the development of resilient, equitable, and culturally vibrant built environments, effectively bridging the gap between technological innovation and sustainable governance in the context of historic districts. Full article

Sustainability is increasingly prioritized in infrastructure design; however, its integration into the conceptual design phase remains limited, particularly for pedestrian bridges, where structural performance plays a critical role. While existing frameworks address environmental and economic impacts in later stages, they typically fail to incorporate structural performance and sustainability holistically at the outset. To address this gap, this study introduces a quantitative decision-support framework tailored for the conceptual design of footbridges. The methodology integrates five key indicators, Global Warming Potential (GI), Total Cost (TC), Robustness (RO), Inspection (IN), and Maintenance (MA), using a Multi-Criteria Decision Making (MCDM) approach, specifically the Weighted Sum Model (WSM), supported by Pearson correlation analysis, to identify trade-offs and interdependencies among metrics. The framework is tested on two real-world case studies involving steel pedestrian bridges in different urban contexts. The results reveal a strong correlation between inspection and maintenance, suggesting that designs optimized for inspection accessibility can significantly reduce life cycle maintenance efforts and costs. Robustness appears to be largely independent from environmental impact, indicating the potential to improve structural resilience without compromising sustainability. Furthermore, cost–sustainability relationships are shown to be highly context-dependent. The practical implications of these findings are substantial: by offering a structured, data-driven tool for early-stage evaluation, the framework enables engineers, urban planners, and policymakers to make informed design choices that align with long-term sustainability goals. It offers a methodological basis for comparing design options based on quantifiable sustainability and structural metrics, contributing to evidence-based decision making in line with evolving standards for sustainable infrastructure. Full article

Streets, as critical public space nexuses, require synergistic quality–utilization alignment—where quality without use signifies institutional inefficiency, and use without quality denotes operational ineffectiveness. Focusing on high-frequency pedestrian commuting streets (HFPCSs) that not only crucially mediate metropolitan mobility patterns but also shape citizens’ daily urban experiences and satisfaction, this study proposes a data-driven diagnostic framework for street quality–utilization assessment, integrating multi-source urban big data through a case study of Shenzhen. By integrating multi-source urban big data, we identify HFPCSs using LBS data and develop a multi-dimensional evaluation system that incorporates 1.07 million Points of Interest (POIs) for assessing convenience, utilizes DeepLabv3+ for the semantic segmentation of street view imagery to evaluate comfort, and leverages 15,374 km of road network data for accessibility analysis. The results expose dual mismatches: merely 2.15% of HFPCSs achieve balanced comfort–convenience–accessibility benchmarks, while over 70% of these are clustered in northern districts, exhibiting systematically inferior quality metrics across dimensions. Diagnostic analysis reveals specific planning and spatial configurations contributing to these disparities, informing targeted retrofitting strategies for priority street typologies. This approach establishes a replicable model for megacity street renewal, deploying supply–demand diagnostics to synchronize infrastructure upgrades with pedestrian flow realities. By bridging data insights with human-centric urban improvements, this framework demonstrates how smart city technologies can concretely address the quality–utilization paradox—advancing sustainable urbanism through evidence-based street transformations. Full article

The Musée des Civilisations de l’Europe et de la Méditerranée (MuCEM) in Marseille represents a paradigm shift in sustainable architecture, integrating heritage conservation with cutting-edge material technology. Designed by Rudy Ricciotti, the museum utilizes Ultra-High-Performance Concrete (UHPC) to optimize structural efficiency, environmental resilience, and architectural aesthetics. This study highlights how UHPC contributes to reducing resource consumption and enhancing durability, in line with global sustainability goals. MuCEM’s lattice facade, modular supports, and pedestrian bridge showcase innovative engineering solutions that extend the building’s lifespan while ensuring seismic resilience and energy efficiency. Furthermore, UHPC’s longevity reduces maintenance requirements, contributing to lower life cycle costs and carbon footprint. The findings underscore how advanced materials and sustainable design principles can redefine the role of cultural landmarks in the built environment. Full article

Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and decision-making. However, such a paradigm is not scalable, possesses limited spatial resolution, and typically entails high effort and cost. Recently, mobile sensing-based paradigms have demonstrated promise in laboratory and field settings as an alternative. These methods can leverage big data from crowdsourcing vibration data acquired from smartphone devices belonging to pedestrians and passengers traveling over a bridge, constituting a significantly large data stream of indirectly sensed bridge response. Although the efficacy of such a paradigm has been demonstrated for a limited set of case studies, ubiquitous implementation requires analyzing the impact of vehicle dynamics and quantifying data sources that can be used for the purpose of bridge modal identification. This paper presents a road map for achieving this through dynamically diverse datastreams such as passenger cars, buses, bikes, and scooters. Existing datastreams point towards the implementation of crowdsourced mobile sensing paradigms in urban settings, which would facilitate effective decision-making for enhanced transportation infrastructure resilience. Full article

Realistically modeling interactions between road users—like those between drivers or between drivers and pedestrians—within experimental settings come with pragmatic challenges. Due to practical constraints, research typically focuses on a limited subset of potential scenarios, raising questions about the scalability and generalizability of findings about interactions to untested scenarios. Here, we aim to tackle this by laying the methodological groundwork for defining representative scenarios for dyadic (two-actor) interactions that can be analyzed individually. This paper introduces a conceptual guide for operationalizing controlled dyadic traffic interaction studies, developed through extensive interdisciplinary brainstorming to bridge theoretical models and practical experimental design. It elucidates critical trade-offs in scenario selection, interaction approaches, measurement strategies, and timing coordination, thereby enhancing reproducibility and clarity for future traffic interaction research and streamlining the design process. The methodologies and insights we provide aim to enhance the accessibility and quality of traffic interaction research, offering a guide that aids researchers in setting up studies and ensures clarity and reproducibility in reporting, bridging the gap between theoretical traffic interaction models and practical applications in controlled experiments, thereby contributing to advancements in human factors research on traffic management and safety. Full article

Person re-identification (ReID) plays a crucial role in advancing UAV-based surveillance applications, enabling robust tracking and event analysis. However, existing methods in UAV scenarios primarily focus on individual pedestrians, requiring cumbersome annotation efforts and lacking seamless integration with ground-based surveillance systems. These limitations hinder the broader development of UAV-based monitoring. To address these challenges, this paper proposes an Unsupervised Aerial-Ground Re-identification from Pedestrian to Group (UAGRPG) framework. Specifically, we introduce a neighbor-aware collaborative learning (NCL) and gradual graph matching (GGC) strategy to uncover the implicit associations between cross-modality groups in an unsupervised manner. Furthermore, we develop a collaborative cross-modality association learning (CCAL) module to bridge feature disparities and achieve soft alignment across modalities. To quantify the optimal group similarity between aerial and ground domains, we design a minimum pedestrian distance transformation strategy. Additionally, we introduce a new AG-GReID dataset, and extensive experiments demonstrate that our approach achieves state-of-the-art performance on both pedestrian and group re-identification tasks in aerial-ground scenarios, validating its effectiveness in integrating ground and UAV-based surveillance. Full article

Saudi Arabia has developed initiatives to transform wide street median islands into vibrant linear gardens that enhance city aesthetics, pedestrian safety, and social cohesion. Despite the significance of these spaces, urban designers often focus on physical aspects (Pedestrian Quality Needs) while ignoring social ones (Users’ Needs) which lead to the deterioration of these spaces, weakening their capability to create a positive user experience. This research aims to bridge the gap between the two aspects and highlight the potential of transforming street median islands into friendly linear spaces. The researcher conducts structured experts’ questionnaires to analyze the pedestrians’ needs and measure the impact of PQN attitudes on them to indicate the most effective indicator needed for each need. Additionally, a quantitative technique using the Statistical Package for the Social Sciences was employed to verify and weigh the importance of the PQN indicators that contribute to developing the suggested integrated framework. This framework is developed to be used for evaluating and improving the performance of these spaces. The study findings emphasize the importance of considering PQN and their impact on creating livable street median islands. Moreover, the results highlight the most effective design considerations to ensure the success of these spaces in generating social sustainability. Full article

This study investigates the application of computer vision techniques in Structural Health Monitoring (SHM). The advantages of multiple synchronized camera setups in capturing and analyzing the dynamic behavior of bridges are researched. The proposed methodology encompasses approach, setup, and data analysis techniques, with the final scope of extracting modal parameters from videos of a vibrating bridge. An operational pedestrian footbridge forced by human-induced vibrations serves as a case study. The findings demonstrate that computer vision techniques employing a multiple synchronized camera approach offer a precise, cost-effective, efficient, and safe alternative to conventional SHM approaches for the dynamic identification of bridges. Full article