Search Results (998)

Sequential twin-tunnel excavation has become increasingly common as urban rail networks expand, making both deformation control and construction-phase carbon management essential for sustainable underground development. This study investigates the spatiotemporal development of ground settlement induced by parallel Earth Pressure Balance shield tunnelling in a twin-tunnel section of the Hangzhou Metro, based on long-term field monitoring. The settlement process is divided into three stages—immediate construction settlement, time-dependent additional settlement, and long-term consolidation—each associated with distinct levels of energy input, grouting demand, and embodied-carbon release. Peck’s Gaussian function is used to model transverse settlement troughs, and Gaussian superposition is applied to separate the contributions of the leading and trailing tunnels. The results indicate that the trailing shield induces ahead-of-face settlement at approximately two excavation diameters and produces a deeper–narrower settlement trough due to cumulative disturbance within the overlapping interaction zone. A ratio-type indicator, the Twin-Tunnel Interaction Ratio (TIR), is proposed to quantify disturbance intensity and reveal its environmental implications. High TIR values correspond to amplified ground response, prolonged stabilization, repeated compensation grouting, and increased embodied carbon during construction. Reducing effective TIR through coordinated optimization of shield attitude, face pressure, and grouting parameters can improve both deformation control and carbon efficiency. The proposed framework links geotechnical behaviour with environmental performance and provides a practical basis for risk-controlled, energy-efficient, and low-carbon management of sequential shield tunnelling. Full article

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

Remote sensing image change captioning (RSICC) fuses computer vision and natural language processing to translate visual differences between bi-temporal remote sensing images into interpretable text, with applications in environmental monitoring, urban planning, and disaster assessment. Multimodal Large Language Models (MLLMs) boost RSICC performance but suffer from inefficient inference due to massive parameters, whereas lightweight models enable fast inference yet lack generalization across diverse scenes, which creates a critical timeliness-generalization trade-off. To address this, we propose the Dual-Constrained Transformer (DCT), an end-to-end lightweight RSICC model with three core modules and a decoder. Full-Level Feature Distillation (FLFD) transfers hierarchical knowledge from a pre-trained Dinov3 teacher to a Generalizable Lightweight Visual Encoder (GLVE), enhancing generalization while retaining compactness. Key Change Region Adaptive Weighting (KCR-AW) generates Region Difference Weights (RDW) to emphasize critical changes and suppress backgrounds. Hierarchical encoding and Difference weight Constrained Attention (HDC-Attention) refine multi-scale features via hierarchical encoding and RDW-guided noise suppression; these features are fused by multi-head self-attention and fed into a Transformer decoder for accurate descriptions. The DCT resolves three core issues: lightweight encoder generalization, key change recognition, and multi-scale feature-text association noise, achieving a dynamic balance between inference efficiency and description quality. Experiments on the public LEVIR-CC dataset show our method attains SOTA among lightweight approaches and matches advanced MLLM-based methods with only 0.98% of their parameters. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

A large share of the global population now lives in urban areas, which creates growing challenges for city life. Local authorities are seeking ways to enhance urban livability, with transportation emerging as a major focus. Developing smart public transit systems is therefore a key priority. Accurately estimating travel times is essential for managing transport operations and supporting strategic decisions. Previous studies have used statistical, mathematical, or machine learning models to predict travel time, but most examined these approaches separately. This study introduces a hybrid framework that combines statistical regression models and machine learning algorithms to predict public bus travel times. The analysis is based on 1410 bus trips recorded between November 2021 and July 2022 in Kayseri, Turkey, including detailed meteorological and operational data. A distinctive aspect of this research is the inclusion of weather variables—temperature, humidity, precipitation, air pressure, and wind speed—which are often neglected in the literature. Additionally, sensitivity analyses are conducted by varying k values in the K-nearest neighbors (KNN) algorithm and threshold values for outlier detection to test model robustness. Among the tested models, CatBoost achieved the best performance with a mean squared error (MSE) of approximately 18.4, outperforming random forest (MSE = 25.3) and XGBoost (MSE = 23.9). The empirical results show that the CatBoost algorithm consistently achieves the lowest mean squared error across different preprocessing and parameter settings. Overall, this study presents a comprehensive and environmentally aware approach to travel time prediction, contributing to the advancement of intelligent and adaptive urban transportation systems. Full article

Ultra-shallow prefabricated underpass tunnel technology has been widely adopted in urban transportation construction owing to its advantages of rapid construction and minimal environmental impact. However, the deformation behavior of tunnel joints under long-term vehicular dynamic loads remains unclear, which constrains the reliability and durability of this technology. To address this, this study focuses on a large cross-section tunnel with five bidirectional lanes. A combined methodology of “refined numerical simulation + long-term cyclic loading model tests” was employed to systematically investigate the dynamic response and cumulative deformation patterns of tunnel joints under different burial depths (3 m, 5 m, and 8 m) and prestress levels (0–0.5 MPa). First, based on the analysis of structural bending moment distribution, various division principles such as zero-moment points and maximum-moment points were compared, leading to the determination of a joint layout scheme primarily adopting a two-segment division. On this basis, a refined numerical model integrating pavement excitation and vehicle dynamic coupling was established, supplemented by a model test with 2 million loading cycles, to reveal the deformation mechanism of joints under both moving vehicle loads and long-term loading. The results indicate the following: (1) burial depth is the decisive factor controlling overall joint deformation—increasing the depth from 3 m to 8 m can reduce the maximum joint opening and slip by approximately 60%; (2) prestress serves as a key measure for restraining joint opening and ensuring waterproofing performance, with its effect being particularly pronounced under shallow burial conditions; (3) based on the dynamic attenuation coefficient, the concept of “sensitive burial depth” (approximately 3.7 m) is proposed, providing a quantitative criterion for identifying tunnels susceptible to surface traffic loads; (4) the recommended two-segment structural division scheme effectively controls deformation while considering construction convenience and waterproofing reliability. The methodological framework of “numerical simulation + model testing” established in this study can provide theoretical support and engineering reference for the long-term performance design and assessment of ultra-shallow prefabricated tunnels. Full article

Urban parks are essential components of sustainable cities, providing vital health, social, and environmental benefits. Using weekly smartphone-based visitation data for 182 parks in Las Vegas from 2019 to 2022, this study quantifies how the COVID-19 pandemic altered park use and identifies the socio-economic, environmental, and infrastructural determinants of these changes. Park visitation in Las Vegas showed a marked early pandemic decline followed by uneven recovery, with socially vulnerable neighborhoods lagging behind. Ordinary Least Squares (OLS) and Random Forest (RF) models were used to capture both linear and nonlinear relationships. The RF model explained 81% of the variance in standardized visitation (R² = 0.81, RMSE = 0.0415), substantially outperforming the OLS benchmark (R² = 0.24, RMSE = 0.0656). Domain-specific RF models show that socio-economic variables alone achieve an R² of 0.88, compared with about 0.70 for housing, environmental/health, and lighting variables, while demographic variables explain only 0.39, indicating that social vulnerability is the dominant driver of visitation inequalities. Phase-specific analyses further reveal that RF performance increases from R² = 0.84 before the pandemic to R² = 0.87 after it, as park visitation becomes more strongly coupled with socio-economic and health-related burdens. After COVID-19, poverty, uninsured rates, and asthma prevalence emerge as the most influential predictors, while the relative importance of demographic composition and environmental exposure diminishes. These findings demonstrate that pandemic-era inequalities in park visitation are driven primarily by reinforced socio-economic and health vulnerabilities, underscoring the need for targeted, equity-oriented green-infrastructure interventions in disadvantaged neighborhoods. Full article

As global infrastructure systems face increasing environmental, social, and operational challenges, enhancing their resilience through digital and intelligent technologies has become a strategic priority. Digital Twin (DT) and Artificial Intelligence (AI) technologies offer transformative capabilities for monitoring, predicting, and optimizing infrastructure performance under stress. However, research on their integration within resilience frameworks remains fragmented. This study presents a comprehensive bibliometric analysis to clarify how DT and AI are being applied to strengthen infrastructure resilience (IR). Using data exclusively from the Web of Science (WoS) database, co-occurrence and overlay visualizations were employed to map thematic structures, identify research clusters, and track emerging trends. The analysis revealed six interconnected research domains linking DT, AI, and resilience, including artificial intelligence and industrial applications, digital twins and machine learning, cyber–physical systems, smart cities and sustainability, data-driven resilience modeling, and methodological frameworks. Overlay mapping revealed a temporal shift from early work on sensors and cyber–physical systems toward integrated, sustainability-oriented applications, including predictive maintenance, urban digital twins, and environmental resilience. The findings underscore the need for adaptive and interoperable DT ecosystems incorporating AI-driven analytics, ethical data governance, and sustainability metrics, providing a unified foundation for advancing resilient and intelligent infrastructure systems. Full article

Generative adversarial networks (GANs) can rapidly predict urban environmental performance. However, most existing studies focus on a single target and lack cross-performance comparisons under unified conditions. Under unified urban-form inputs and training settings, this study employs the conditional adversarial model pix2pix to predict four targets—the Universal Thermal Climate Index (UTCI), annual global solar radiation (Rad), sunshine duration (SolarH), and near-surface wind speed (Wind)—and establishes a closed-loop evaluation framework spanning pixel, structural/region-level, cross-task synergy, complexity, and efficiency. The results show that (1) the overall ranking in accuracy and structural consistency is SolarH ≈ Rad > UTCI > Wind; (2) per-epoch times are similar, whereas convergence epochs differ markedly, indicating that total time is primarily governed by convergence difficulty; (3) structurally, Rad/SolarH perform better on hot-region overlap and edge alignment, whereas Wind exhibits larger errors at corners and canyons; (4) in terms of learnability, texture variation explains errors far better than edge count; and (5) cross-task synergy is higher in low-value regions than in high-value regions, with Wind clearly decoupled from the other targets. The distinctive contribution lies in a unified, reproducible evaluation framework, together with learnability mechanisms and applicability bounds, providing fast and reliable evidence for performance-oriented planning and design. Full article

Urban inefficient land redevelopment (UILR) is crucial for sustainable urban development, yet its progress is driven by the interplay of multiple factors. To systematically uncover the driving mechanisms and dynamic patterns of these factors, an integrated analytical approach combining Structural Equation Modeling (SEM) and Fuzzy Cognitive Map (FCM) is developed in this study. Based on 222 valid survey responses from professionals across eight cities in China’s Yangtze River Delta region, five key factors are identified within the “drivers–pressure–enablers” conceptual framework: economic incentives, environmental objectives, social needs, policy guidance, and implementation conditions. SEM is first employed to examine static causal relationships, and the quantified pathway effects are subsequently incorporated into an FCM model to simulate the long-term evolution. The results reveal the following: (i) All five factors exert significant direct effects, with economic incentives, environmental objectives, and policy guidance also demonstrating notable indirect effects. (ii) The factors exhibit distinct temporal characteristics: policy guidance acts as a “fast variable” enabling short-term breakthroughs; economic incentives serve as a “strong variable” driving medium-term progress; and social needs function as a “slow variable” with long-term benefits. (iii) Policy guidance is essential, as its absence leads to persistently low effectiveness, while its synergy with implementation conditions can achieve satisfactory performance even without economic incentives. The combined SEM–FCM approach validates static hypotheses and simulates dynamic scenarios, offering a new perspective for analyzing complex driving mechanisms of UILR and providing practical insights for targeted redevelopment strategy design. Full article

This research develops a city Branding–Competitiveness Index (BCI) that comprehend symbolic city branding elements with quantifiable aspects of urban competitiveness. It examines the effectiveness of branding strategies in emerging cities in MENA region to improve their competitiveness, focusing on King Abdullah Economic City in Saudi Arabia and New Alamein City in Egypt. This research employs a mixed-method approach that integrates systematic literature review, expert survey, and quantitative analysis using IBM SPSS Statistics 26. The BCI was built considering primary categories, then improved through expert review to make sure it is valid and relevant to the practice, then utilized on the two case studies to evaluate its efficacy and performance. Results indicated that both cities showed relatively better performance in the infrastructure, environmental planning, and accessibility indicating that government-led development models work well on some level. But they achieved lower scores in social cohesion, cultural identity, and participatory governance, highlighting the gap between urban development and the lifestyle in cities. The BCI helped identify these disparities and showed indicative insights for enhancing branding strategy. This empirical BCI provides a guiding framework for policymakers and urban planners to evaluate the strategic planning for city branding, and sustainable competitiveness. The findings demonstrate the potential applicability of BCI to emerging cities, while acknowledging that further testing in diverse international contexts is needed. Full article

Sustainable urban transport planning requires inclusive, high-quality mobility systems that address both user needs and broader environmental objectives. This study examines the service quality and user satisfaction of Chiang Mai University Transit using the SERVQUAL framework and structural equation modeling, based on survey data from 384 student respondents. Seven service quality dimensions—Tangibles, Reliability, Responsiveness, Assurance, Empathy, Environmental Performance, and Respiratory Disease Prevention—were systematically analyzed. The results indicate that Responsiveness has the strongest positive influence on perceived service quality, while Tangibles most significantly shape user expectations. Overall, higher service quality is found to enhance user satisfaction substantially. Nevertheless, a measurable service quality gap persists, especially in the areas of Responsiveness (gap = −0.34) and Respiratory Disease Prevention (gap = −0.26), which are identified as priority areas for targeted improvement. In contrast, Environmental Performance surpasses user expectations (gap = +0.12). These empirical insights underscore the importance of strategically investing in university transit systems to encourage modal shift away from private vehicles, reduce congestion, and improve air quality. The study provides an evidence-based foundation for policymakers and planners seeking to implement integrated and environmentally sustainable mobility strategies within both university and broader urban contexts. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

This study investigates the application of the Positive Energy District paradigm to two existing and morphologically diverse urban districts in Rome: Testaccio and Valco San Paolo. The research aims to evaluate the feasibility and effectiveness of district-scale energy retrofitting strategies, integrating dynamic simulation tools to model current energy behavior and assess future scenarios. The methodology combines a range of interventions including envelope insulation, high-performance glazing, HVAC system upgrades, efficient lighting solutions, and large-scale photovoltaic deployment. Additionally, the study explores the potential benefits of energy storage systems, with particular focus on the optimal sizing of lithium-ion battery solutions to enhance local self-consumption and reduce grid dependency. Key performance indicators are used to analyze the alignment between renewable energy generation and district demand, as well as the interaction with the electrical grid. By calibrating simulation models with real thermophysical and consumption data, the research ensures methodological robustness and enables the replicability of the proposed approach in other urban contexts. The study offers a comprehensive framework for planners and policymakers seeking to support the decarbonization and resilience of urban districts through the implementation of PEDs. Future developments will focus on optimizing storage management, assessing the environmental impact of battery life cycles, and integrating PEDs within broader urban energy ecosystems. Full article

In complex urban low-altitude (ULA) airspace, unmanned aerial vehicles (UAVs) face several safety challenges, such as building obstacles, airspace restrictions, and environmental uncertainties. In this study, these issues are addressed by adopting a novel quantitative method for evaluating UAV flight safety margins and integrating this method into a ULA airspace grid model. This method comprehensively considers critical factors such as airspace obstacles, environmental conditions, and UAV performance to compute a quantitative safety margin. Once safety buffer grids around restricted and potential conflict grids are introduced, dynamic constraints can be imposed on the trajectory planning process. The proposed model not only achieves a balance between path cost and safety redundancy but also significantly enhances UAV flight safety and the efficiency of airspace resource utilization in complex urban environments. The experimental results validate the effectiveness of this approach for planning multi-UAV trajectories, demonstrating its feasibility and potential for broader application. This research not only extends the safety implications of low-altitude airspace grid modeling but also provides a new technical pathway and theoretical foundation for future ULA airspace safety management, multi-UAV collaborative scheduling, and refined airspace governance. Full article