Search Results (1,177)

This study proposes a design method for near-slope roads along multi-level slopes that integrates excavation requirements and post-construction ecological restoration through sprayed eco-protection. Firstly, the design principles and procedural steps for near-slope roads are established. The planar layouts of multi-level slopes are categorized, including mixing areas, turnaround areas, berms, and access ramps. Critical technical parameters, such as curve radii and widths of berms and ramps, as well as dimensional specifications for turnaround areas, are systematically formulated with corresponding design formulas. The methodology is applied to the ecological restoration project of multi-level slopes in the Huamahu mountainous area, and a comparative technical-economic analysis is conducted between the proposed design and the original scheme. Results demonstrate that the optimized design reduces additional maintenance costs caused by near-slope roads by 6.5–8.0% during the curing period. This research advances the technical framework for multi-level slope governance and enhances the ecological design standards for slope protection engineering. Full article

Urban areas are at the forefront in addressing global challenges such as climate change and biodiversity loss. Among the key responses are nature-based solutions, which are increasingly being integrated into policy frameworks but which require strong community engagement for their effective implementation. This paper presents the findings of surveys conducted within the Place Lab in Katowice, Poland, an initiative developed as part of an international project and used as a participatory tool for co-creating and implementing green infrastructure. The project applies both place-based and people-centred approaches to support European cities in their transition towards regenerative urbanism. Place Lab activities encourage collaboration between local authorities and residents, enhancing awareness and fostering participation in environmental initiatives. The survey data collected during the project allowed for the evaluation of changes in public attitudes and levels of engagement and for the identification of broader societal phenomena that may influence the implementation of nature-based solutions. The findings revealed, for instance, that more women were interested in supporting the project, that residents tended to be sceptical of governmental actions on climate change, and that views were divided on the trade-off between urban infrastructure such as parking and roads and the presence of green areas. Furthermore, questions of responsibility, awareness, and long-term commitment were frequently raised. Building on the survey results and the existing literature, the study proposes a set of indicators to assess the contribution of citizen participation to the adoption of nature-based solutions. While the effectiveness of nature-based solutions in mitigating climate change impacts can be assessed relatively directly, evaluating civic engagement is more complex. Nevertheless, when conducted transparently and interpreted by experts, indicator-based assessment can offer valuable insights. This study introduces a novel perspective by considering not only drivers of engagement but also the obstacles. The proposed indicators provide a foundation for evaluating community readiness and commitment to nature-based approaches and may be adapted for application in other urban settings and in future research on climate resilience strategies. Full article

The European Union is committed to reducing greenhouse gas emissions across all sectors, including the transportation sector. It is possible to assume that road freight transport will need to undergo technological changes, leading to greater use of alternative powertrains. This article builds on previous research on the energy consumption of battery electric trucks (BETs) and assesses the economic efficiency of electric vehicles in freight transport through a cost calculation. The primary objective was to determine the conditions under which a BET becomes cost-effective for a transport operator. These findings are practically relevant for freight carriers. Unlike other studies, this article does not focus on total cost of ownership (TCO) but rather compares the variable and fixed costs of BETs and conventional internal combustion engine trucks (ICETs). In this article, the operating costs of BETs were calculated and modeled based on real-world measurements of a tested vehicle. The research findings indicate that BETs are economically efficient, primarily when state subsidies are provided, compensating for the significant difference in purchase costs between BETs and conventional diesel trucks. This study found that optimizing operational conditions (daily routes) enables BETs to reach a break-even point at approximately 110,000 km per year, even without subsidies. Another significant finding is that battery capacity degradation leads to a projected annual operating cost increase of approximately 4%. Full article

Background/Objectives: Vertebral fractures (VFs) are a global health issue caused by traumatic or pathological factors that compromise spinal integrity. The burden of VFs is increasing, particularly in older adults. Methods: Data from the Global Burden of Disease 2021 were analyzed to estimate the prevalence, mortality, and years lived with disability due to VFs from 1990 to 2021. Estimates were stratified according to age, sex, and region. Bayesian meta-regression models were used to generate age-standardized rates, and projections for 2050 were calculated using demographic trends and the sociodemographic index. Das Gupta’s decomposition assessed the relative contributions of population growth, aging, and prevalence changes to future case numbers. Results: In 2021, approximately 5.37 million people (95% Uncertainty Interval [UI]: 4.70–6.20 million) experienced VFs globally, with an age-standardized prevalence of 65 per 100,000. Although the rates have declined slightly since 1990, the absolute burden has increased owing to population aging. VF prevalence was the highest in Eastern and Western Europe and in high-income regions. Males had higher VF rates until 70 years of age, after which females surpassed them, reflecting postmenopausal osteoporosis. Falls and road injuries were the leading causes of VF. By 2050, the number of VF cases is expected to increase to 8.01 million (95% UI: 6.57–8.64 million). Conclusions: While the age-standardized VF rates have decreased slightly, the global burden continues to increase. Targeted strategies for the early diagnosis, osteoporosis management, and fall prevention are necessary to reduce the impact of VFs. Full article

This study aimed to investigate land-use and land-cover change and the associated change in the ecological stability of the model area Dobrá–Opatová (district of Trenčín, Slovakia), where increasing landscape transformation has raised concerns about declining ecological resilience. Despite the importance of sustainable land management, few studies in this region have addressed long-term landscape dynamics in relation to ecological stability. This research fills that gap by evaluating historical and recent LULC changes and their ecological consequences. Four time horizons were analysed: 1850, 1949, 2009, and 2024. Although the selected time periods are irregular, they reflect key milestones in the region’s land development, such as pre-industrial land use, post-war collectivisation, and recent land consolidation. These activities significantly altered the structure of the landscape. To assess future trends, we used the MOLUSCE plug-in in QGIS to simulate ecological stability for the future. The greatest structural landscape changes occurred between 1850 and 1949. Significant transformation in agricultural areas was observed between 1949 and 2009, when collectivisation reshaped small plots into large block structures and major water management projects were implemented. The 2009–2024 period was marked by land consolidation, mainly resulting in the construction of gravel roads. These structural changes have contributed to a continuous decrease in ecological stability, calculated using the coefficient of ecological stability derived from LULC categories. To explore future trends, we simulated ecological stability for the year 2030 and the simulation confirmed a continued decline in ecological stability, highlighting the need for sustainable land-use planning in the area. Full article

The accurate early-stage estimation of environmental load (EL) and construction cost (CC) in road infrastructure projects remains a significant challenge, constrained by limited data and the complexity of construction activities. To address this, our study proposes a machine learning-based predictive framework utilizing artificial neural networks (ANNs) and deep neural networks (DNNs), enhanced by autoencoder-driven feature selection. A structured dataset of 150 completed national road projects in South Korea was compiled, covering both planning and design phases. The database focused on 19 high-impact sub-work types to reduce noise and improve prediction precision. A hybrid imputation approach—combining mean substitution with random forest regression—was applied to handle 4.47% missing data in the design-phase inputs, reducing variance by up to 5% and improving data stability. Dimensionality reduction via autoencoder retained 16 core variables, preserving 97% of explanatory power while minimizing redundancy. ANN models benefited from cross-validation and hyperparameter tuning, achieving consistent performance across training and validation sets without overfitting (MSE = 0.06, RMSE = 0.24). The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. DNN models, with their deeper architectures and dropout regularization, further improved performance—achieving 27.1% (EL) and 17.0% (CC) average error rates at the planning stage and 24.0% (EL) and 14.6% (CC) at the design stage. These results met all predefined accuracy thresholds, underscoring the DNN’s advantage in handling complex, high-variance data while the ANN excelled in structured cost prediction. Overall, the synergy between deep learning and autoencoder-based feature selection offers a scalable and data-informed approach for enhancing early-stage environmental and economic assessments in road infrastructure planning—supporting more sustainable and efficient project management. Full article

The cold recycling (CR) technique is gaining traction, with an increasing demand for sustainable pavement construction practices. Cold in-place recycling (CIR) and cold central plant recycling (CCPR) are two strategies under the umbrella of cold recycling. These techniques use reclaimed asphalt pavement (RAP) to rehabilitate pavement, and CCPR offers the added advantage of utilizing stockpiled RAP. While many agencies have expertise in cold recycling techniques including CCPR, the lack of pavement performance data prevented the largescale implementation of these technologies. Recent studies in high-traffic volume applications demonstrate that CCPR technology can be implemented on the entire road network across all traffic levels. This reignited interest in the widespread implementation of CCPR. Therefore, the purpose of this study is to provide agencies with the most up-to-date information on CCPR to help them make informed decisions. To this end, this paper comprehensively reviews the mix-design for CCPR, the structural design of pavements containing CCPR layers, best construction practices, and the agency experience in using this technology on high-traffic volume roads to provide in-depth information on the steps to follow from project selection to field implementation. The findings specify the suitable laboratory curing conditions to achieve the optimum mix design and specimen preparation procedures to accurately capture the material properties. Additionally, this review synthesizes existing quantitative data from previous studies, providing context for the comparison of findings, where applicable. The empirical and mechanistic–empirical design inputs, along with the limitations of AASHTOWare Pavement ME software for analyzing this non-conventional material, are also presented. Full article

To explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based on the Mamba neural network. Using monitoring data collected using on-site automated monitoring robots as the data foundation, the prediction results of the improved transformer, long short-term memory (LSTM), time-series dense encoder (Tide), and decomposition-linear (Dlinear) neural networks are compared. The research results show that the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the proposed Bi-Mamba model are 0.279 and 0.276, respectively, demonstrating higher prediction accuracy than comparative models such as iTransformer and LSTM. Additionally, ablation experiments verify that the attention gating module in the model reduces the MSE by 9.1%, serving as a key component for improving accuracy. This study provides an advanced data-driven prediction method for subgrade settlement forecasting, offering technical references for similar engineering projects. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

Rural road programs are essential for enhancing connectivity in remote areas, yet they frequently encounter schedule delays and budget overruns. This study explores the extent to which specific project characteristics influence these deviations in Colombian rural road contracts. A dataset comprising 229 projects was extracted from the national SECOP open-procurement platform and processed using the CRISP-DM protocol. Following the cleaning and coding of 14 project-level variables, statistical analyses were conducted using Spearman correlations, Kruskal–Wallis tests, and post-hoc Wilcoxon comparisons to identify significant bivariate relations I confirm I confirm I confirm hips. A Random Forest model was subsequently applied to determine the most influential multivariate predictors of cost and time deviations. In parallel, a directed content analysis of contract addenda reclassified 22 recorded deviation descriptors into ten internationally recognized categories of causality, enabling an integrated interpretation of both statistical and documentary evidence. The findings indicate that contract value, geographical region, and contractor configuration are significant determinants of cost and time performance. Additionally, project intensity and discrepancies between awarded and bid values emerged as key contributors to cost escalation. Scope changes and adverse weather conditions together accounted for 76% of all documented deviation triggers, underscoring the relevance of robust front-end planning and climate-risk considerations in rural infrastructure delivery. The findings provide information for stakeholders, policymakers, and professionals who aim to manage the risk of schedule and budget deviations in public infrastructure projects. Full article

The health of road tunnel linings directly impacts traffic safety and requires regular inspection. Appearance defects on tunnel linings can be measured through images scanned by cameras mounted on a car to avoid disrupting traffic. Existing tunnel lining mobile scanning methods often fail in image stitching due to the lack of corresponding feature points in the lining images, or require complex, time-consuming algorithms to eliminate stitching seams caused by the same issue. This paper proposes a mobile scanning method aided by collimated lasers, which uses lasers as corresponding points to assist with image stitching to address the problems. Additionally, the lasers serve as structured light, enabling the measurement of image projection relationships. An inspection car was developed based on this method for the experiment. To ensure operational flexibility, a single checkerboard was used to calibrate the system, including estimating the poses of lasers and cameras, and a Laplace kernel-based algorithm was developed to guarantee the calibration accuracy. Experiments show that the performance of this algorithm exceeds that of other benchmark algorithms, and the proposed method produces nearly seamless, measurable tunnel lining images, demonstrating its feasibility. Full article

Facing the increasingly severe challenges of global climate change, China has established clear “dual carbon” goals, with the core objective of achieving carbon peak by 2030 or earlier. However, carbon emissions from the road freight industry have remained higher for many years; understanding and estimating the characteristics of truck carbon emissions are critical for developing a low-carbon transportation system. This study takes Heilongjiang Province, a typically cold region, as a case study. By employing the growth curve method, we predicted the time for achieving carbon peak and constructed an improved STIRPAT model to identify key drivers and pathways for emission reduction in the road freight system. The research results show that only by committing to using the economy to reduce carbon emissions and improve energy intensity can the overall carbon emissions of Heilongjiang Province’s cargo transportation system achieve the “dual carbon” goals as soon as possible. If we develop according to the optimistic scenario proposed in this article, by 2030, the total quantity of trucks will reach about 933,720, and the carbon emissions per vehicle will reach about 178.14 t. If we actively increase the proportion of new energy trucks in the overall quantity of trucks, the peak time is expected to be achieved around 2030. The improvement of technological efficiency (e.g., lowering energy intensity) and the advancement of economic development have been identified as effective pathways for carbon emission reduction. Empirical studies indicate that these measures can achieve emission reduction impacts that are approximately 60 times and 10 times greater, respectively, in terms of efficiency, compared to baseline scenarios. Furthermore, energy intensity improvements and structural shifts toward low-carbon vehicles are critical to expediting peak attainment. This study provides a methodological framework for cold-region emission projections and offers actionable insights for policymakers to design tailored emission reduction pathways in the road freight transportation industry. Full article

Road infrastructure is the foundation of the predominant modes of transport, and its effective management is crucial to meet mobility needs. Although necessary for reconstruction, maintenance, and expansion projects, roadworks produce negative impacts, resulting in further risk for workers and drivers and failing to ensure sustainable development. The objective of this paper is twofold: Firstly, investigate the contributing factors to the occurrence of crashes in roadworks. Secondly, develop a model to estimate crash numbers in these areas. The results, which could support municipalities at the planning stage and implement policies for safe and sustainable development, are achieved by examining 121 sites, where 549 crashes occurred, and 25 contributing factors. The variables are divided into three categories: technical characteristics of the site, infrastructural, and environmental. Besides the conventional variables, a risk-increasing factor is calibrated. It assesses the impact of roadworks according to the manoeuvres imposed and the number of lanes. Consistent with previous findings, several variables related to the work zone layout, traffic conditions, infrastructure, and surrounding environment are correlated with the crash number. After performing a further statistical analysis, a multiple linear regression model, statistically significant (0.000) and suitable for accurately estimating the possible number of crashes (R²_adj = 0.41), is determined. Full article

Basalt is prevalent in the Earth’s crust and makes up about 90% of all volcanic rocks. The earth is warming at an alarming rate, and there is a search for a long-term solution to this problem. Geologic carbon storage in basalt offers an effective and durable solution for carbon dioxide sequestration. Basaltic rocks are widely used for road and building construction and insulation, soil amendment, and in carbon storage. There is a need to understand the parameters that affect this process in order to achieve efficient carbon mineralization. This review systematically analyzes peer-reviewed studies and project reports published over the past two decades to assess the mechanisms, effectiveness, and challenges of carbon mineralization in basaltic formations. Key factors such as mineral composition, pH, temperature and pressure are evaluated for their impact on mineral dissolution and carbonate precipitation kinetics. The presence of olivine and basaltic glass also accelerates cation release and carbonation rates. The review includes case studies from major field projects (e.g., CarbFix and Wallula) and laboratory experiments to illustrate how mineralization performs in different geological environments. It is essential to maximize mineralization kinetics while ensuring the formation of stable carbonate phases in order to achieve efficient and permanent carbon dioxide storage in basaltic rock. Full article

Delays in road construction projects pose significant challenges in the Philippines, resulting in increased costs, project overruns, and unmet infrastructure goals. Common causes include poor financial management, inadequate subcontractor performance, deficient planning, and regulatory bottlenecks. This study aims to develop a comprehensive and data-driven framework to mitigate construction delays using the Analytical Hierarchy Process (AHP). The methodology integrates literature review, expert surveys, and pairwise comparisons to identify and prioritize critical delay factors. Experts from the Department of Public Works and Highways (DPWH), private contractors, and academia contributed to the AHP model. The results highlight seven major factor groups: client-related, contractor-related, consultant-related, materials, labor and equipment, contractual issues, and external influences. AHP analysis identified financial management, planning and scheduling, and regulatory coordination as the most impactful causes. Based on these findings, a strategic framework was developed and visualized using a Fishbone Diagram to present mitigation strategies tailored to each factor. While environmental engineering principles—such as material efficiency, energy use optimization, and impact assessments—are acknowledged, they serve as guiding themes rather than formal components of the framework. The study offers practical, stakeholder-validated recommendations for both pre- and post-construction phases, including real-time monitoring, risk anticipation, and improved multi-agency coordination. This framework provides a scalable tool for DPWH and related agencies to improve infrastructure delivery while supporting long-term sustainability goals. Full article