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- Use of Unmanned Aerial Vehicles (UAVs) for Transport Pavement InspectionPublication . Santos, Bertha; Gavinhos, Pedro; Almeida, Pedro G.; Nery, Dayane; Rujikiatkamjorn, C.; Xue, J.; Indraratna, B.Technological evolution has allowed the use of unmanned aerial vehicles (UAVs) in an easier and more diversified way, creating opportunities for its application in various fields of engineering, namely in the inspection of transport infrastructures. The present study begins with the analysis of the main practices that resort to the use of UAVs, in order to frame its application in the field of transport pavement inspection. A review of studies and other available literature served as a starting point to define the methodology adopted for the development of the case study presented. The methodology includes the collection of images of a flexible road pavement section, its processing, and the creation of an orthoimage and a 3D model from which it was possible to identify and characterize the distresses present on the pavement surface. The main results obtained point to planimetric and altimetric deviations of less than 2 and 10 mm, respectively, for the images collected by theMavic 2 Pro drone at 3 and 20mhigh.With the collected data, itwas also possible to calculate the global quality index PCI for the inspected pavement section. Under these conditions, it is possible to conclude that the accuracy is very good and suitable for the intended purpose, allowing fast data collection at low cost. This new technological approach supports infrastructure managers in the design of maintenance programs and in the scheduling of interventions, thus contributing to the increase of the durability and safety levels of the inspected pavements.
- Automated Geographic Information System Multi-Criteria Decision Tool to Assess Urban Road Suitability for ActiveMobilityPublication . Santos, Bertha; Ferreira, Sandro Alfaro ; Lucena, Pollyana;The planning of greener, more accessible, and safer cities is the focus of several strategies that aim to improve the population’s quality of life. This concern for the environment and the population’s quality of life has led to the implementation of active mobility policies. The effectiveness of the mobility solutions that are sought heavily depends on the identification of the main factors that favor their use, as well as how adequate urban spaces are in minimizing existing difficulties. This study presents an automated geographic information system (GIS) decision support tool that allows the identification of the level of suitability of urban transportation networks for the use of active modes. The tool is based on the determination of a set of mobility indices: walkability, bikeability, e-bikeability, and active mobility (a combination of walking and cycling suitability). The indices are obtained through a spatial multi-criteria analysis that considers the geometric features of roads, population density, and the location and attractiveness of the city’s main trip-generation points. The treatment, representation, and study of the variables considered in the analysis are carried out with the aid of geoprocessing, using the spatial and network analysis tools available in the GIS. The Model Builder functionality available in ArcGIS® was used to automate the various processes required to calculate walking, cycling, and e-biking travel times, as well as the mobility indices. The developed tool was tested and validated through its application to a case study involving the road network of the urban perimeter of the medium-sized city of Covilhã, Portugal. However, the tool is designed to be applied with minimal adaptation to different scenarios and levels of known input information, providing average or typical values when specific information is not available. As a result, a flexible and automated GIS-based tool was obtained to support urban space and mobility managers in the implementation of efficient measures compatible with each city’s scenario.
- Assessment of Airport Pavement Condition Index (PCI) Using Machine LearningPublication . Santos, Bertha; Studart, André; Almeida, Pedro G.Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices.
- A GIS-Based Approach to Fostering Sustainable Mobility and Combating Social Isolation for the Rural ElderlyPublication . Branco, Luís; Santos, BerthaThe growing demographic trend of an aging population, particularly in remote rural areas, exacerbates social isolation and limits access to essential goods and services. This vulnerability highlights a pressing need to develop sustainable solutions for their mobility and support. Using Geographic Information Systems (GISs) and network analysis, a workflow was developed to optimize road-based transport for the elderly. The analysis utilized an electric vehicle, with its range limitations, influenced by road slopes, being a critical variable for assessing route efficiency. Two potential solutions were investigated: (1) the delivery of goods and medicines and (2) the transport of passengers and medicines. The methodology was tested using the Municipality of Seia, Portugal, as a case study, with a defined weekly visit frequency. The results demonstrate that both proposed solutions are technically viable for implementation, with the transport of passengers and medicines being the most effective option. This study provides a foundational framework for developing practical, demand-oriented, sustainable transport and logistics services to support isolated elderly populations.
- Statistical analysis of an in-vehicle image-based data collection method for assessing airport pavement conditionPublication . Feitosa, Ianca Teixeira ; Santos, Bertha; Gama, Jorge; Almeida, Pedro G.This study presents a comprehensive comparative statistical analysis to validate a novel in-vehicle image-based method for collecting pavement condition data in airport environments. It highlights the method’s potential to address key challenges faced by airport pavement managers, such as the need for continuous maintenance and the demand for fast, effective, and reliable inspection procedures. The in-vehicle system integrates laser scanning systems, image capture, and georeferencing devices to collect pavement distress data, and its accuracy and reliability are evaluated statistically. The primary objective is to validate and enhance this novel inspection approach, which shows strong potential as an effective alternative for comprehensive pavement evaluation, enabling continuous, rapid monitoring and the analysis of trends. Validation was performed by means of a detailed statistical comparison of pavement distress density on the main runway of Amílcar Cabral International Airport, Sal Island, Cape Verde, based on data collected using the proposed in-vehicle and the traditional on-foot inspection methods. Non-parametric repeated measures analysis (nparLD) showed statistically similar results between methods for 9 of 12 distress type-severity combinations (4 types × 3 levels), especially for medium and high severity cases, and that pavement section and method-section factors were significant in 10 and 9 of 12 cases, respectively, indicating spatial variability. Kruskal-Wallis tests were applied to each method separately. Significant section-based differences were found in 11 of 12 cases for the traditional method and in 2 of 12 cases for the in-vehicle image-based method, indicating greater sensitivity of the on-foot inspection to spatial variation in distress distribution. These findings support the statistical validation of the proposed method for practical application in airport pavement management. Furthermore, the comprehensive analysis, which included correlation and autocorrelation studies, revealed a bias in severity level assignment during traditional on-foot inspections. The findings highlight time-efficiency gains with the image-based method and suggest improvements, such as enhancing image quality and providing inspector training to increase the accuracy of severity level classification. These results offer valuable insights for airport pavement managers, contributing to improved safety, operational efficiency, and resilience in the face of growing air traffic demands.
- Automated and Intelligent Inspection of Airport Pavements: A Systematic Review of Methods, Accuracy and Validation ChallengesPublication . Feitosa, Ianca; Santos, Bertha; Almeida, Pedro G.; mdpiAirport pavement condition assessment plays a critical role in ensuring operational safety, surface functionality, and long-term infrastructure sustainability. Traditional visual inspection methods, although widely used, are increasingly challenged by limitations in accuracy, subjectivity, and scalability. In response, the field has seen a growing adoption of automated and intelligent inspection technologies, incorporating tools such as unmanned aerial vehicles (UAVs), Laser Crack Measurement Systems (LCMS), and machine learning algorithms. This systematic review aims to identify, categorize, and analyze the main technological approaches applied to functional pavement inspections, with a particular focus on surface distress detection. The study examines data collection techniques, processing methods, and validation procedures used in assessing both flexible and rigid airport pavements. Special emphasis is placed on the precision, applicability, and robustness of automated systems in comparison to traditional approaches. The reviewed literature reveals a consistent trend toward greater accuracy and efficiency in systems that integrate deep learning, photogrammetry, and predictive modeling. However, the absence of standardized validation protocols and statistically robust datasets continues to hinder comparability and broader implementation. By mapping existing technologies, identifying methodological gaps, and proposing strategic research directions, this review provides a comprehensive foundation for the development of scalable, data-driven airport pavement management systems.
- Evaluation of Pedestrian Crossing Accidents Using Artificial Neural NetworkPublication . Santos, Bertha; Gonçalves, Jorge; Amin, Shohel ; Vieira, Sandra Cristina Gil ; Lopes, Carlos Manuel Valença MartinsMost of European cities face increasing problems caused by excessive traffic of conventional fuel-based transport modes. To reverse this situation, sustainable urban mobility policies have been promoting soft modes of transport, such as walking. Despite the advantages of walking in reducing traffic congestion and pollution, cities have not always evolved to accommodate the needs of pedestrians. According to the European Commission, in 2020, 20% of road fatalities in the European Union (EU) and 21% in Portugal were pedestrian. Pedestrian fatality rates per million population was 9.7 for all EU countries and 13.1 for Portugal. In European and Portuguese urban areas, 36% and 27% of the fatalities were pedestrians’ and 49% and 56% of all pedestrian fatalities were elderly’s (respectively). In pedestrian infrastructures, crossings are considered the most critical element due to conflicts between vehicles and pedestrians. It is then essential to identify and minimize risk factors that increase the probability of accidents in these locations. The proposed work intends to assess this challenge by using Artificial Neural Network (ANN) to create pedestrian severity prediction models and identify road and pedestrian risk factors for accident occurred in or near urban crossings. The official Portuguese database on run over pedestrian accidents occurred between 2017–2021 was analyzed with ANN considering two scenarios: pre-Covid-19 and during Covid-19 period. Results obtained demonstrate that the use of ANN can promote a proactive infrastructure management, suggesting that crossings traffic lights operation, lighting, shoulders and pavement conditions, high speed limits (51–90 km/h) and pedestrians moving in soft modes are critical factors.
- Matrices - Book of Abstracts: II International Congress Architecture and GenderPublication . Pedrosa, Patrícia Alexandra Dias Santos ; Santos, Eliana Sousa; Matos, Maria João Pereira de ; Alvarez Lombardero, NuriaThe Second International Congress on Architecture and Gender will address the theme of Matrices. This concept has several definitions and they are all inclusive by nature. Matrices are environments where things develop, the models or patterns that shape formations, and they can also reinvent an environment. These images are suited to address the current patterns of change regarding architecture and gender.
- Teoria critica de apoio pedagógico à unidade curricular de Desenho IPublication . Sequeira, João Manuel Barbosa Meneses deO presente manual insere-se no âmbito da unidade curricular de Desenho I, leccionada no 1.º ano do Mestrado Integrado em Arquitectura da Universidade da Beira Interior. Resulta de uma prática pedagógica continuada que, ao longo dos últimos anos, tem procurado articular a formação técnica do desenho com uma reflexão crítica e sensível sobre o papel da representação na génese do projecto arquitectónico. Embora concebido como instrumento de apoio ao ensino, o seu horizonte ultrapassa o contexto estrito da unidade curricular, visando propor um contributo mais amplo para a pedagogia do desenho em arquitectura.
- Investigação em artes e arquitectura: Natureza, metodologias, validade e impactoPublication . Sequeira, João Manuel Barbosa Meneses deA discussão sobre a Investigação nas artes e na arquitectura, enquanto práticas dotadas de metodologias próprias e com potencial para serem consideradas formas válidas de produção de conhecimento, é central em muitos debates contemporâneos nas humanidades e ciências sociais. Para abordar esta hipótese, podemos dividir a discussão em três áreas principais: Da Natureza: Investigação em Artes e Arquitectura Das Metodologias: Regras Próprias Da Validade e Valor do Conhecimento Produzido