Search Results (306)

In Turkey, flexible pavement containing bituminous material is widely preferred on highways. Rigid pavement, which is based on concrete, is generally used in small-scale, specific projects. This situation, which has arisen due to historical and technical reasons, has also brought with it certain prejudices against rigid pavement applications. A review of the literature reveals that many factors influence the choice of highway pavement type, but decision-makers tend to make their selection based on the most important factors, disregarding other parameters. The lack of a systematic factor analysis is a shortcoming in this regard. In this research, a combined multi-criteria decision-making study was conducted, including the neglected factors, to address this technical deficiency in the pavement type selection process. Through detailed analysis, parameters likely to influence pavement type selection were identified and analyzed using the hybrid AHP-TOPSIS approach, guided by the opinions of experts in the field. The analysis shows that comfort (user ride quality), financial, and environmental factors are the most effective main criteria, while maintenance and repair costs, eco-friendliness, and initial construction costs were identified as the most critical sub-criteria influencing the choice of pavement type. Based on the analysis results, a detailed decision support system was presented to decision-makers according to the characteristics of the alternatives obtained. The results highlight the need for decision-making frameworks that prioritize both long-term cost efficiency and user safety, contributing to more sustainable and resilient pavement applications. Full article

Ageing building stock, shrinking budgets, and inspector shortages hinder timely façade safety inspections. This research develops an automated damage detection and risk prioritization system for aging concrete structures. Five YOLOv11 variants were trained on 130,838 high-resolution images from 25 Seoul districts to detect three critical damage types: cracks, exposed rebar, and spalling. The proposed framework integrates YOLOv11 detection with a novel Damage Criticality Index (DCI) that transforms five visual-spatial cues—area, multiplicity, confidence, density, and spread—into continuous severity scores, subsequently categorized into low, medium, and high risk via K-means clustering. YOLOv11x achieved 0.78 mAP@0.5 at 101 FPS, enabling real-time processing suitable for field deployment. Field trials confirmed robust detection and consistent risk ranking in both uncluttered and cluttered urban environments, substantially reducing inspection time and minimizing missed defects compared to conventional manual methods. The framework provides scalable, data-driven support for city-wide monitoring and transparent, risk-prioritized maintenance of aging infrastructure. Full article

Urban year-round green exercise (YRGE)—defined as moderate to vigorous physical activity performed regularly in natural urban settings throughout all seasons and weather conditions—has the potential to promote health, well-being, and social connectedness. This study investigates the relationship between YRGE and individuals’ perceived health status, psychological well-being, and reasons for engagement. A cross-sectional online survey was conducted with 408 adult participants engaged in urban green exercise. The findings indicate that physical activity in adverse meteorological conditions, such as rain, cold, and wind, is positively associated with perceived current health, health over the past 12 months, and well-being. Social connectedness is particularly influenced by environmental factors like sun exposure and heat. The study also reveals key motivational factors for YRGE participation, including improving health and fitness, disconnecting from everyday life, enjoying nature, and experiencing tranquility, with significant variation depending on age and individual nature connectedness. These results suggest that YRGE serves as an accessible and inclusive public health intervention with consistent benefits across socio-demographic groups. Urban planning and health promotion initiatives should prioritize the maintenance and accessibility of urban green spaces and offer guided YRGE programs to encourage sustainable participation across the population. Full article

State Departments of Transportation (DOTs) face challenges with traditional bridge inspections that are time-consuming, inconsistent, and paper-based. This study focused on an existing research gap regarding automated methods that streamline the bridge inspection process, prioritize maintenance effectively, and allocate resources efficiently. Thus, this paper introduces a digitalized bridge inspection framework by integrating Building Information Modeling (BIM) and Business Intelligence (BI) to enable near-real-time monitoring and digital documentation. This study adopts a Design Science Research (DSR) methodology, a recognized paradigm for developing and evaluating the innovative SmartBridge to address pressing bridge inspection problems. The method involved designing an Autodesk Revit-based plugin for data synchronization, element-specific comments, and interactive dashboards, demonstrated through an illustrative 3D bridge model. An illustrative example of the digitalized bridge inspection with the proposed framework is provided. The results show that SmartBridge streamlines data collection, reduces manual documentation, and enhances decision-making compared to conventional methods. This paper contributes to this body of knowledge by combining BIM and BI for digital visualization and predictive analytics in bridge inspections. The proposed framework has high potential for hybridizing digital technologies into bridge infrastructure engineering and management to assist transportation agencies in establishing a safer and efficient bridge inspection approach. Full article

The deployment of autonomous vehicles (AVs) depends on the readiness of both physical and digital infrastructure. However, existing national and city-level indices often overlook deficiencies along specific routes, particularly in developing contexts such as Thailand, where infrastructure conditions vary widely. This study develops and applies a corridor-level framework to assess AV readiness on five controlled-access roads in western Bangkok. The framework evaluates key infrastructure dimensions beyond conventional vehicle requirements. In this study, infrastructure readiness means the extent to which essential physical (EV charging capacity, traffic sign visibility, and lane marking retroreflectivity) and digital (5G speed and coverage) subsystems meet minimum operational thresholds required for AV deployment. Data were collected through field measurements and secondary sources, utilizing tools such as a retroreflectometer, a handheld spectrum analyzer, and the Ookla Speedtest application. The results reveal significant contrasts for physical infrastructure, showing that traffic signage is generally satisfactory, but EV charging capacity and road marking retroreflectivity are insufficient on most routes. On the digital side, 5G coverage was generally adequate, but network speeds remained less than half of the global benchmark. Kanchanaphisek Road demonstrated comparatively higher digital readiness, whereas Ratchaphruek Road exhibited the weakest road marking conditions. These findings point out the need for stepwise enhancements to EV charging infrastructure, lane marking maintenance, and digital connectivity to support safe and reliable AV operations. The proposed framework not only provides policymakers in Thailand with a practical tool for prioritizing corridor-level investments but also offers transferability to other rapidly developing urban regions experiencing similar infrastructure challenges for AV deployment. Full article

Vertical greening (VG) delivers measurable urban ecosystem benefits, yet maintenance is constrained by at-height safety risks, heterogeneous facade geometries, and low labor efficiency. Although unmanned aerial vehicles show promise, most studies optimize isolated modules rather than providing a user-oriented, system-level pathway. This paper proposes a closed-loop, multi-method framework integrating the Decision-Making Trial and Evaluation Laboratory-Analytic Network Process, the Functional Analysis System Technique, and the Theory of Inventive Problem Solving. DEMATEL-ANP models causal interdependencies among requirements and derives prioritized weights,; FAST decomposes functions and localizes conflicts, and TRIZ converts those conflicts into principle-guided structural concepts—establishing a traceable requirements → functions → conflicts → structure pipeline. We illustrate the approach at the prototype level with Rhino–KeyShot visualizations under near-facade constraints, showing how prioritized requirements propagate into candidate UAV architectures. The framework structures the identification and resolution of tightly coupled technical conflicts, supports adaptability in facade-proximal scenarios, and provides a transparent mapping from user needs to structure-level concepts. Claims are restricted to methodological feasibility; comprehensive quantitative field validation remains for future work. The framework offers a reproducible methodological reference for the systematic design and decision-making of intelligent UAV maintenance systems for VG. Full article

The construction and maintenance of ecological corridors not only facilitate species migration and gene flow but also enhance ecosystem stability and resilience, providing critical support for achieving global carbon neutrality goals. Despite their importance, research on urban ecological corridors—specifically their role in carbon sequestration and emission reduction within urban environments—remains insufficiently explored. To address this gap, we employed bibliometric and network analysis methods, utilizing the CiteSpace6.3.1 visualization tool to systematically review existing literature from the Web of Science Core Collection database. This study examines the research progress and trends in urban ecological corridors from 2000 to 2023, focusing on their role and significance in the context of global carbon neutrality. The findings reveal the following: (1) Research attention has grown steadily from 2000 to 2023, with climate change, carbon emission dynamics, and biodiversity emerging as core themes, reflecting increasing global focus on the carbon neutrality functions of urban ecological corridors. (2) CiteSpace analysis identified key research hotspots through keywords including climate change, carbon cycle, ecosystem services, model simulation, and ecological network analysis, revealing the functional mechanisms and pathways of urban ecological corridors in carbon neutrality contexts. (3) Current scientific challenges focus on understanding three core aspects of urban ecological corridors, the compositional elements, spatial structural design, and functional capacity assessment, requiring systematic theoretical breakthroughs. (4) Future research should prioritize exploring mechanisms to enhance urban ecological corridor functions and constructing low-carbon urban ecological networks, providing theoretical guidance and practical pathways for achieving urban emission reduction and climate goals. This study contributes to integrating research on the effectiveness of urban ecological corridors and carbon sinks, offering theoretical insights and practical guidance for reducing urban emissions and achieving climate goals. Full article

Lava caves commonly occur in basaltic ground and can compromise roadway stability when present beneath pavements; however, their long-term effects remain insufficiently characterized. This study quantitatively evaluates how lava caves influence pavement behavior using numerical analyses in ABAQUS/CAE. The parameters examined include the presence/absence of a cave, cave width, cover depth, pavement thickness, and load range. Load–settlement curves under a uniformly distributed surface load show that narrower load ranges concentrate stresses and produce larger settlements, whereas wider load ranges disperse stresses and reduce deformation. Classification of deformation behavior using a rutting criterion indicates that plastic soil response dominates under most conditions. A Peak Load Reduction (PLR) index further demonstrates that structural resistance decreases markedly with shallow cover, larger cave width, and narrower load range. Overall, pavement stability above lava caves is governed primarily by cover depth, load range, and cave width, while the effect of pavement thickness is negligible. These findings suggest that, in basaltic terrains, design and maintenance should prioritize subsurface conditions and loading characteristics over pavement thickness. Full article

This work aims towards the automatic detection of faults in industrial air-cooling equipment used in a production line for staple fibers and ultimately provides maintenance scheduling recommendations to ensure seamless operation. In this context, various deep learning models are tested to ultimately define the most effective one for the intended scope. In the examined system, four vibration and temperature sensors are used, each positioned radially on the motor body near the rolling bearing of the motor shaft—a typical setup in many industrial environments. Thus, by collecting and using data from the latter sources, this work exhaustively investigates the feasibility of accurately diagnosing faults in staple fiber cooling fans. The dataset is acquired and constructed under real production conditions, including variations in rotational speed, motor load, and three fault priorities, depending on the model detection accuracy, product specification, and maintenance requirements. Fault identification for training purposes involves analyzing and evaluating daily maintenance logs for this equipment. Experimental evaluation on real production data demonstrated that the proposed ResNet50-1D model achieved the highest overall classification accuracy of 97.77%, while effectively resolving the persistent misclassification of the faulty impeller observed in all the other models. Complementary evaluation confirmed its robustness, cross-machine generalization, and suitability for practical deployment, while the integration of predictions with maintenance logs enables a severity-based prioritization strategy that supports actionable maintenance planning. Full article

In pharmaceutical manufacturing, the reliability of machines and utility assets is critical to ensuring product quality, regulatory compliance, and uninterrupted operations. Traditional Risk-Based Maintenance (RBM) models quantify asset criticality using the Risk Priority Number (RPN), calculated from the probability and impact of failure alongside detectability. However, these models often neglect the practical challenges involved in diagnosing and resolving equipment issues, particularly in GMP-regulated environments. This study proposes an enhanced RPN framework that replaces the conventional detectability component with Maintainability Complexity (MC), quantified through two practical indicators: Ease of Diagnosis (ED) and Ease of Resolution (ER). Thirteen Key Performance Indicators (KPIs) were developed to assess Probability, Impact, and MC across 185 pharmaceutical utility assets. To enable objective risk stratification, Jenks Natural Breaks Optimization was applied to group assets into Low, Medium, and High risk tiers. Both multiplicative and normalized averaging methods were tested for score aggregation, allowing comparative analysis of their impact on prioritization outcomes. The enhanced model produced stronger alignment with operational realities, enabling more accurate asset classification and maintenance scheduling. A 3D risk matrix was introduced to translate scores into proactive strategies, offering traceability and digital compatibility with Computerized Maintenance Management Systems (CMMS). This framework provides a practical, auditable, and scalable approach to maintenance planning, supporting Industry 4.0 readiness in pharmaceutical operations. Full article

Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation. Full article

Non-communicable diseases (NCDs), including cancer and autoimmune, metabolic, cardiovascular, and neurodegenerative diseases, represent the leading cause of death globally and a growing healthcare burden. The gut microbiota (GM) has been recognized as a key biological component of host health that contributes to the maintenance of immune regulation, metabolic homeostasis, and epithelial barrier function. Several studies are now demonstrating that biological sex has an influence on both GM composition and function, which might explain sex differences in disease predisposition, course, and treatment response. Evidence from both clinical and experimental studies indicates that sex hormones, genetics, and lifestyle-related exposures interact with GM to influence the development and progression of most common NCDs. Some research suggests that estrogens promote diversity in GM with anti-inflammatory immune responses, while androgens and male-abundant taxa are associated with pro-inflammatory conditions. However, the evidence in humans is largely confounded by other variables (such as age, genetics, and lifestyle) and should be interpreted with caution. Unique GM metabolites, such as short-chain fatty acids and secondary bile acids, can have distinct, sex-specific effects on inflammation, metabolic regulation, and even antitumor immunity. While the existence of a sex–gut microbiota axis is gaining increased support, most studies in humans are cross-sectional epidemiological studies with limited mechanistic evidence and little consideration for sex as a biological variable. Future works should prioritize longitudinal, sex-stratified studies and utilize multi-omics integrated approaches to identify causal pathways. Ultimately, integrating sex differences into GM-based approaches could provide new avenues for personalized strategies for the prevention and treatment of NCDs. Full article

Current practices in the construction industry could negatively affect the long lifecycle of building management due to the lack of information and stakeholder management. The purpose of this paper is to identify the critical success factors (CSFs) of integrating BIM models into facility management systems (FMS). This paper conducted a series of semi-structured interviews with industry experts in the FM sector. It used a structured questionnaire to identify the hierarchy arrangement of the identified CSFs using statistical analogies. The findings demonstrated a robust consistency with significant correlation, alongside a strong correlation established using Spearman’s rank correlation coefficient and strong agreement using Kendall coefficient. Additionally, the Relative Importance Index (RII) was employed to prioritize factors according to the professionals’ assessments, yielding the subsequent impact ranking: (1) define the OIR, AIR, and FM information requirements; (2) acquire correct files, data, and formats; and (3) update of information requirements during the defect liability period (DLP). These findings would help in assisting the management of information during FM operations by establishing clear guidelines to be added into the EIR in the early project initiation stages for a successful integration of BIM-FMS for more efficient life cycle management, operation, and maintenance by the FM. Full article

Software development projects are highly susceptible to risks during the design phase, which plays a crucial role in shaping the architecture, functionality, and quality of the final product. Decisions made during the design stage significantly affect the outcomes of the subsequent phases, including coding, testing, deployment, and maintenance. However, the complexities and uncertainties inherent in the design phase are often inadequately addressed by traditional risk management tools as they rely on deterministic models that oversimplify interdependent risks. This research introduces a fuzzy logic-based risk assessment model tailored specifically for the design phase of software development projects. The proposed fuzzy model, unlike the existing state-of-the-art models, regards the iterative nature of the design phase, the interaction between diverse stakeholders, and the potential inconsistencies that may arise between the initial and final version of the software design. More specifically, it develops a customized fuzzy model that incorporates design-specific risk factors such as evolving architectural requirements, technical feasibility concerns, and stakeholder misalignment. Finally, it integrates expert-driven rule definitions to enhance model accuracy and real-world applicability, ensuring that risk assessments reflect actual challenges faced by software design teams. Simulations conducted across diverse real-world scenarios demonstrate the model’s robustness in predicting risk levels and supporting mitigation strategies. The simulation results confirm that the proposed fuzzy logic model outperforms conventional approaches by offering greater flexibility and adaptability in managing design-phase risks, assisting project managers in prioritizing mitigation efforts more effectively to improve project outcomes. Full article

The increasing complexity of Water Distribution Systems (WDSs), driven by urbanization, climate change, and aging infrastructure, necessitates robust methods for risk assessment and visualization. This study presents a practical methodology for mapping the risk of water supply disruption or reduction using five parameters: Probability (P), Consequences (C), Water Pipe category (WP), Inhabitants exposed (I), and response Efficiency (E). The approach enables comprehensive analysis of the risk associated with specific pipeline segments within an Analyzed Supply Area (ASA). The method integrates statistical and operational data, allowing utilities to evaluate vulnerability, identify Critical Infrastructure (CI), and prioritize maintenance. The investigation conducted during the study revealed that cast iron and steel pipes with large diameters (e.g., 400 mm) show the highest failure probability and impact. Despite a calculated risk value (r_LW = 80), effective response measures—including specialized repair teams and equipment—kept the risk acceptable. The results demonstrate that historical failure and response data enhance risk identification and management. The generated risk maps facilitate spatial visualization of high-risk areas, supporting decision-making processes, renovation planning, and emergency preparedness. Integration with GIS tools, including GeoMedia and Google Earth programmes, enables dynamic map creation and simulation of response scenarios. The methodology is scalable and adaptable to any WDS, and potentially to other municipal systems such as wastewater and heating networks. By accounting for both technical and social dimensions of risk, the method supports improved water safety planning and infrastructure resilience. Future development should include real-time data integration and climate-related risk scenarios to increase predictive accuracy and system adaptability. Full article