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Almeida, Pedro Gabriel de Faria Lapa Barbosa de
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- Radon concentration assessment in water sources of public drinking of Covilhã's county, PortugalPublication . Inácio, M.; Soares, S.; Almeida, PedroRadon, the heaviest of the noble gases on the periodic table of elements, is a natural radioactive element that can be found on water, soils and rocks. The main goal of this work is to present an evaluation of radon concentration on samples of water, used for human consumption, collected on uranium-rich granitic rock areas. Once the geological features of the sampling region evidence the presence of this natural radionuclides, their slow dissolution steadily increases concentration in ground water. Although, the most important contribution of natural radiation, for most populations, is from inhaled radon (generic term used commonly to refer to the isotope 222Rn), in some circumstances, exposure to natural radionuclides, through drinking water, could exceed acceptable levels, and also present a hazard. Despite the fact that radon can be reduced if the water is boiled, this gas, dissolved in ground water, can be released into the air during household activities such as showering, dishwashing and laundry. So, the short lived radon decay products will contribute to increase the number of those which are present in particles suspended in the indoor air and can be accumulated up to dangerous concentrations. Once the radon progeny emits highly ionizing alpha-radiation, they may cause substantial health damage after long-term exposure. Radon concentration measurements were performed on thirty three samples collected from water wells at different depths and types of aquifers, at Covilhã's County, Portugal with the radon gas analyser DURRIDGE RAD7. Twenty three, of the total of water samples collected, gave, values over 100 Bq/L, being that 1690 Bq/L was the highest measured value.
- GIS-based inventory for safeguarding and promoting Portuguese glazed tiles cultural heritagePublication . Santos, Bertha; Gonçalves, Jorge H. G.; Almeida, Pedro G.; Martins-Nepomuceno, Ana M. T.Innovative, non-invasive, digital, and cost-effective instruments for systematic inventory, monitoring and promotion are a valuable resource for managing tangible and intangible cultural heritage. Due to its powerful and effective inventory and analysis potential, which allows supporting central and local entities responsible for cultural heritage management, Geographic Information Systems (GIS) have proven to be an appropriate information technology for developing these kinds of instruments. Given the above, this work aims to introduce a GIS-based instrument to support inventorying, safeguarding, tourism, and cultural promotion of the traditional Portuguese glazed tile (‘azulejo’, in Portuguese) to raise general awareness of the importance of this unique Portuguese heritage. To the best of the authors’ knowledge, there is no other instrument available with inventory and safeguarding management functions that is accessible and affordable, developed to be used at a municipal level and that contributes to the enrichment of the cultural and tourist information. Information from 70 tile works located in the Portuguese city of Covilhã was used to test the proposed GIS tool, resulting in a georeferenced alphanumeric, graphical, image and drawing inventory and in three pedestrian routes for touristic and cultural heritage promotion. The results were validated by both the research team and the municipality of Covilhã, foreseeing its expansion and daily use in the management of the heritage of the traditional Portuguese glazed tile. The proposed instrument can be replicated in other locations and easily implemented and managed by municipalities or institutions dealing with the protection of cultural heritage.
- Radon Concentration Potential in Bibala Municipality Water: Consequences for Public ConsumptionPublication . Kessongo, Joaquim; Bahu, Yoenls; Inácio, M.; Almeida, Pedro; Peralta, Luis; Soares, SandraThe primary motivation for this work is the evaluation of the radon concentration in portable water for human consumption in Bibala, a municipality in Angola, where granitic rocks are common, and contain a high concentration of uranium that can be mobilized in underground water. Radon is the largest contributor of radioactive pollution in underground water.Its concentration in water, represents a public health risk due to the fact that the gas can easily escape into the air, adding to the total indoor concentration of radon.On the other hand, ingestion of water with a high radon concentration represents an additional risk to the stomach. Measurements of radon concentration, in Bibala municipality's water, were performed on 16 samples obtained from wells of various depths and analyzed with DURRIDGEs' RAD7 equipment. Measured concentrations are in the range from 39.5 to 202 Bq/L , with 2 of the recovered samples presenting values over 100 Bq/L.
- 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.
- 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.
- Validation of an indirect data collection method to assess airport pavement conditionPublication . Santos, Bertha; Almeida, Pedro G.; Feitosa, Ianca; Lima, DéboraIn this study the authors compare two methods for airport asphalt pavement distress data collection applied on the main runway of Amílcar Cabral international airport, located at Sal Island in Cape Verde. The two methods used for testing were traditional visual inspection (on-foot) and an indirect method using a vehicle equipped with image capture and recording, lasers and geolocation devices (in-vehicle inspection). The aim of this research is to contribute to the validation of the proposed low-cost in-vehicle pavement distress inspection system with semiautomatic data processing in order to be considered in the implementation of the pavement condition assessment component of airport pavement management systems (APMS). This is a particularly important component as from the collected distress data it is possible to assess the condition of the pavements and define intervention strategies. Validation of the indirect data collection method is evaluated by statistical comparison of the collected distress data and pavement condition index (PCI) obtained from both methods. Statistically non-significant differences between the result sets validate the proposed indirect method, however the analysis evidenced two aspects that need improvement in the proposed system, namely the quality of the captured images to identify distresses with lower severity level and inspector training for proper allocation of severity levels during image analysis. This results in significant advantages considering that the total amount of the runway pavement area is inspected. Inspection time is reduced and data collection cost can be reduced. Processing and results visualization on GIS environment allows revaluation of the dataset on the in-vehicle method. Data interpretation and measurements quality control becomes simpler and faster.
- 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.
- Data Collection Methodology to Assess Road Pavement Condition Using GNSS, Video Image and GISPublication . Santos, Bertha; Almeida, Pedro G.; Maganinho, Leonor Graciete de OliveiraTraffic loads, along with the environment, damage pavement over time. The degradation of pavement quality is reflected in the development of a diversity of pavement distresses, such as cracking, deformation or deterioration. These distresses may occur on the surface and/or in the pavement structure, having a determinant role in pavement’s quality. Aiming to increase the degree of reliability of the pavement distress data and reduce pavement observation time and visual inspection operations cost, this work presents the main steps proposed for a methodology to observe, record and evaluate flexible road pavement distresses to assess the quality of road pavements. This methodology is based on an in-vehicle inspection using GNSS and video image capture devices and in the use of Geographic Information System (GIS). Validation of the proposed methodology was made through a case study by comparing the results obtained on the in-vehicle inspection to those from a traditional visual inspection performed on foot. The similarity of results obtained by the two approaches allowed to conclude about the feasibility of the proposed methodology. Among the main advantages of the proposed methodology a highlight is on the possibility to identify, quantify and locate the most severe pavement distresses through the use of spatial tools available on GIS, producing information maps and reports that can be used in the decision-making process about road pavements rehabilitation and conservation.
- 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.