Search Results (66)

The goal of this paper is to optimize mission schedules for vertical airports (vertiports in short) to satisfy the different needs of stakeholders. We model the problem as a resource-constrained project scheduling problem (RCPSP) to obtain the best resource allocation and schedule. As a new approach to solving the RCPSP, we propose answer set programming (ASP). This is in contrast to the existing research using MILP as a solution to the RCPSP. Our approach can take complex scheduling restrictions and stakeholder-specific requirements. In addition, we formalize and include stakeholder needs using a knowledge representation and reasoning framework. Our experiments show that the proposed method can generate practical schedules that reflect what stakeholders actually need. In particular, we show that our approach can compute optimal schedules more efficiently and flexibly than previous approaches. We believe that this approach is suitable for the dynamic and complex environments of vertiports. Full article

Stream–aquifer interactions, as well as surface water/groundwater interactions within wetlands, require a solution of complex partial differential equations of flow and contaminant transport, namely a deterministic approach. Groundwater level (GWL) responses to precipitation, particularly for extreme value events such as annual maxima, require a probabilistic approach to evaluate potential climate trends. It is commonly assumed that the distribution of annual maxima series (AMS) precipitation follows the generalized extreme value distribution (GEV). If the extremes of the data are nonstationary, it is possible to incorporate this knowledge into the parameters of the GEV. This approach is also applied to the computed annual maxima of daily groundwater level data. Nonstationary versus stationary time series for both groundwater level and AMS 24-h duration precipitation are compared for National Oceanic and Atmospheric Administration (NOAA) stations with nearby wells. Predicted extreme value analysis (EVA) climate trends for wells penetrating limestone aquifers directly beneath rainfall monitoring stations at major airports indicate similar GWL response. Groundwater levels at wells located near coastlines are partially impacted by sea level rise. An extreme value analysis of the GWL is shown to be a useful tool to confirm hydrologic connections and long-term climate trends. Full article

Mega-facility management has long been inefficient due to manual, reactive approaches. Current facility management systems face challenges such as fragmented data integration, limited predictive systems, use of traditional methods, and lack of knowledge of new technologies, such as Building Information Modeling and Artificial Intelligence. This study examines the transformative integration of Artificial Intelligence and Digital Twin technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. Unlike previous research, this study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed ability of the platform to integrate the Internet of Things and Building Information Modeling technologies, shifting facilities management from being reactive to proactive. This paper presents a demo platform that integrates BIM with Digital Twins to improve the predictive maintenance of HVAC systems, equipment, security systems, etc., by recording data from different assets, which helps streamline asset management, enhance energy efficiency, and support decision-making for the buildings’ critical systems. Full article

Clusters of knowledge-intensive industries and manufacturing industries form industrial agglomeration in Step I and activate innovation in Step II. Industry clusters are formed by building segments. “Construction sequencing” in the construction industry refers to the process of determining the sequence of segments to optimize a project’s resources, budget, and scheduled timeline. The process usually begins by dividing a project into segments. Urban segments consist of public spaces, airports, factories, health, housing, etc. A “segment” is a component of a cluster; the organization of a cluster consists of constructing segments. These segments can be divided into four main categories: human resources, physical infrastructure, institutions, and the living environment. Each segment has a specific function in the process of building a cluster. This study focused on innovation in Step II and extended the Fujita–Thisse model of spatial economics to hypothesize that research expenditure per researcher leads to value being added. The Granger causality was tested for the knowledge and manufacturing industries in nine major countries including China and the U.S. The results showed that the hypothesis was significant in identifying the starting segment of innovation in Step II. Accordingly, it can be concluded that research funding is the start switch that triggers innovation. The policy implication is that activating innovation in cluster policies begins with the establishment of a research fund for researchers in its assigned clusters. Full article

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability. Full article

This article contributes to the existing literature by modeling and automating the learning process from previous aviation construction projects (ACPs) using artificial intelligence tools, where it will be easier to characterize aviation construction projects and identify the specifications of different aspects of the projects throughout their entire life cycle. An artificial intelligence (AI) framework is proposed for the categorization of aviation construction projects using different machine-learning (ML) methods with a focus on the UAE as a source of data. Airport construction projects have been seen to share a good deal of similar attributes, which should simplify the decision-making process regarding layouts, design, equipment, labor, budget, complexity, etc. However, the gap in reality is that the huge and scattered sources of data, project specifications, characteristics, and the knowledge from past projects are not utilized in an automated way that could simplify the navigation through projects for better future decision-making. The utilization of AI/ML tools is expected to be useful here in order to reduce the revisions of design and construction rework by classifying the projects and the elements that managers need to consider. The planning, design, and construction of new projects can be improved by identifying the attributes of past projects and categorizing them according to similarities, differences, and complexities. Specifically speaking, a framework of hierarchical clustering and neural networks is integrated together to form the classification model. Upon implementing hierarchical classification and neural networks, it was found that neural networks could demonstrate remarkable classification results; the error in classification was minimal in most of the cases. The advantage of such classification is to help decision-makers utilize best practice from the groups of previous projects, which were classified using both hierarchical and neural networks models. With this classification, rework can be minimized, overhead costs may be reduced, and past best practices can be utilized. Full article

Airport areas generate significant air pollution from both air and surface traffic. Policy makers often address this by considering single contributions, either from rubber-tired vehicles or aircraft, leading to an underestimation of the non-considered-mode’s impact. Similarly, literature on airport pollution often focuses on specific case studies, evaluating either surface or air traffic. Understanding the overlap of these contributions requires calculation of emissions from both traffic modes. This raises two research questions: which is the major contributor, and what mitigation measures can be applied? This paper addresses these questions through two Italian case studies. In the first, we estimated emissions from passenger cars, buses, and aircraft in a medium-sized airport representative of similar facilities across Italy and Europe, calculating emissions using COPERT for surface modes and ICAO methodologies for each LTO cycle. Results showed that aircraft emissions were significantly higher than those from surface vehicles. To address this, the second case study examined four mitigation measures at take-off and landing at another Italian airport, recalculating emissions via the same methodologies. The paper details the methodology process, presents results, and discusses the management of air-operations’ effects at urban airports within local mobility policies and practice, all within the research goal of advancing knowledge farther afield. Full article

Hybrid threats exploit vulnerabilities in digital infrastructures, posing significant challenges to democratic countries and the resilience of critical infrastructures (CIs). This study explores integrating data governance with business process management in response actions to hybrid attacks, particularly those targeting CI vulnerabilities. This research analyzes hybrid threats as a multidimensional and time-dependent problem. Using the Business Process Model and Notation, this investigation explores data governance to counter CI-related hybrid threats. It illustrates the informational workflow and context awareness necessary for informed decision making in a cross-border hybrid threat scenario. An airport example demonstrates the proposed approach’s efficacy in ensuring stakeholder coordination for potential CI attacks requiring cross-border decision making. This study emphasizes the importance of the information security lifecycle in protecting digital assets and sensitive information through detection, prevention, response, and knowledge management. It advocates proactive strategies like implementing security policies, intrusion detection software tools, and IT services. Integrating Infosec with the methodology of confidentiality, integrity, and availability, especially in the response phase, is essential for a proactive Infosec approach, ensuring a swift stakeholder response and effective incident mitigation. Effective data governance protects sensitive information and provides reliable digital data in CIs like airports. Implementing robust frameworks enhances resilience against hybrid threats, establishes trusted information exchange, and promotes stakeholder collaboration for an emergency response. Integrating data governance with Infosec strengthens security measures, enabling proactive monitoring, mitigating threats, and safeguarding CIs from cyber-attacks and other malicious activities. Full article

By exploring the top three airports in Asia, this study explores the area of smart airport systems. With the goal of analyzing the significant elements of airport services that captivate travelers’ attention through online reviews and establishing a correlation between sentiment in reviews and numerical ratings given by travelers, the study analyzes what captivates travelers’ attention. Data mining, frequency analysis, sentiment analysis, and linear regression are employed in this study in order to analyze a dataset of 10,202 online reviews. The results indicate that the most common attributes of airport services significantly impact customer satisfaction, as well as how the sentiment expressed in online reviews correlates with the numerical ratings. A significant contribution of this study lies in its contribution to understanding the dynamics of customer satisfaction in the field of airport services as well as in identifying areas for improvement that could enhance the overall traveler experience in the burgeoning field of smart airports. In the context of smart airport systems, the analysis of exploratory and predictive data provides valuable insights into the optimization of airport operations, thus enriching the body of knowledge in this rapidly evolving area and providing the foundation for future research. Full article

The “bare runway” principle aims to ensure passenger and employee safety by making runways more usable during winter conditions, allowing for easier removal of contaminants like snow and ice. Maintaining runway operations in winter is essential, but it involves considerable cost and environmental impacts. Greater knowledge about the de-icing and anti-icing performance of runway de-icing products (RDPs) optimizes operations. The ice melting test, as per the AS6170 standard, gauges the rate at which an RDP dissolves an ice mass to determine RDP effectiveness. Here, we introduce a novel integrated methodology for assessing RDP-related ice melting. We combine laboratory-based procedures with infrared thermography and Raman spectroscopy to monitor the condition of RDPs placed on ice. The plateau of maximum efficiency, marked by the most significant Raman peak intensity, corresponds to the peak minimum temperature, indicating optimal RDP performance. Beyond this point, RDP efficacy declines, and the system temperature, including melted contaminants and RDP, approaches ambient temperature. Effective RDP performance persists when the ambient temperature exceeds the mixture’s freezing point; otherwise, a freezing risk remains. The initial phases of RDP–ice contact involve exothermic reactions that generate brine, followed by heat exchange with surrounding ice to encourage melting. The final phase is complete ice melt, leaving only brine with reduced heat exchange on the surface. By quantifying these thermal and chemical changes, we gain a deeper understanding of RDP-related ice melting, and a more robust assessment can be provided to airports using RDPs. Full article

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport–Beach–Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach. Full article

No theoretical model effectively explains the blistering process, which provokes functional distress in asphalt pavements worldwide. This study focuses on the possible causes of blistering, the physical processes that drive blistering, the role of asphalt properties, and the uncertainties and gaps in the current knowledge. This paper analyzes peer-reviewed studies on pavement blistering published between 1959 and 2022 retrieved in a systematic literature review to justify and model this distress observed on sidewalks, airports, and bridges. According to the scientific literature, high surface temperatures due to solar radiation are the common factor responsible for uplifting, but several causal mechanisms have been investigated. Indeed, chemical reactions, evolutionary materials, thermal buckling, and physical reactions are the generally recognized causes. Their effects on pavement smoothness vary according to the various interdependent geometrical, physical, and mechanical properties of asphalt mixtures and the boundary conditions. Both the mix design and construction processes can hinder the blistering process that occurs during daytime hours of the hot season, right after the work is finished or a few years later. Further research should identify measures to prevent bulges whose management after uplift is difficult but necessary to avoid safety and functional issues. Full article

Urban airports are typically large infrastructures with significant cultural, economic, and ecological impacts; meanwhile, abandoned airports are common worldwide. However, there is limited knowledge regarding transformation strategies for the renewal of abandoned airports and their surrounding regions in historically and culturally rich areas. We use Beijing’s Nanyuan Airport as a case study, combining the historic urban landscape approach, land use and land cover change, and counterfactual simulations of land use patterns to construct a comprehensive analytical framework. Our framework was used to analyze the long-term land use patterns of the study area, determine its value, and improve perception from a macro- and multi-perspective. We discovered that the traditional knowledge and planning systems in the study area have largely disappeared, but Nanyuan Airport’s impact on the surrounding land use patterns is unique and significant. By considering the characteristics and mechanisms of land use in the study area, we aimed to find a balance point between the historical context and future potential. As such, we propose optimized recommendations with the theme of connection and development engines. Our findings supplement the planning knowledge of relevant areas and provide a springboard for interdisciplinary research in landscape planning. Full article

Hydrogen-fueled aircraft are a promising innovation for a sustainable future in aviation. While hydrogen aircraft design has been widely studied, research on airport requirements for new infrastructure associated with hydrogen-fueled aircraft and its integration with existing facilities is scarce. This study analyzes the current body of knowledge and identifies the planning challenges which need to be overcome to enable the operation of hydrogen flights at airports. An investigation of the preparation of seven major international airports for hydrogen-powered flights finds that, although there is commitment, airports are not currently prepared for hydrogen-based flights. Major adjustments are required across airport sites, covering land use plans, airside development, utility infrastructure development, and safety, security, and training. Developments are also required across the wider aviation industry, including equipment updates, such as for refueling and ground support, and supportive policy and regulations for hydrogen-powered aircraft. The next 5–10 years is identified from the review as a critical time period for airports, given that the first commercial hydrogen-powered flight is likely to depart in 2026 and that the next generation of short-range hydrogen-powered aircraft is predicted to enter service between 2030 and 2035. Full article

With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve conflicts. In this paper, we introduce a conflict resolution framework that incorporates prior knowledge by integrating a Multi-Layer Perceptron (MLP) neural network into the Monte Carlo Tree Search (MCTS) approach. The neural network is trained to learn the allocation strategy for waiting time extracted from actual aircraft taxiing trajectory data. Subsequently, the action probability distribution generated with the neural network is embedded into the MCTS algorithm as a heuristic evaluation function to guide the search process in finding the optimal conflict resolution strategy. Experimental results show that the average conflict resolution rate is 96.8% in different conflict scenarios, and the taxiing time required to resolve conflicts is reduced by an average of 42.77% compared to the taxiing time in actual airport surface operations. Full article