Search Results (22)

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used International Roughness Index (IRI) has attracted much attention due to its importance in pavement maintenance planning. This study focuses on predicting future IRI values using traditional regression models and neural networks, specifically Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, on two highway sections, each analyzed in two experimental setups. The models consider only traffic and structural road characteristics as variables. The results show that the LSTM method provides significantly lower prediction errors for both highway sections, indicating better performance in capturing roughness trends over time. These results confirm that ANNs are a useful tool for engineers by predicting future IRI values, as they help to extend pavement life and reduce overall maintenance costs. The integration of machine learning into pavement evaluation is a promising step forward in ongoing efforts to optimize pavement management. Full article

Assessing the condition of airport pavements is essential to ensure operational safety and efficiency. This study presents an innovative, fully automated approach to calculate the Pavement Condition Index (PCI) by combining UAV-based aerial photogrammetry with advanced Artificial Intelligence (AI) techniques. The method follows three key steps: first, analyzing orthophotos of individual pavement sections using a custom-trained AI model designed for precise crack detection and classification; second, utilizing skeletonization and semantic mask analysis to measure crack characteristics; and third, automating the PCI calculation for faster and more consistent evaluations. By leveraging high-resolution Unmanned Aerial Vehicle (UAV) imagery and advanced segmentation models, this approach achieves superior accuracy in detecting transverse and longitudinal cracks. The automated PCI calculation minimizes the need for human intervention, reduces errors, and supports more efficient, data-driven decision-making for airport pavement management. This study demonstrates the transformative potential of integrating UAV and AI technologies to facilitate infrastructure maintenance and enhance safety protocols. Full article

With the transformation of urban development patterns and profound changes in population structure in China, outdoor activity spaces in residential areas are facing common issues such as obsolete infrastructure, insufficient barrier-free facilities, and intergenerational conflicts, which severely impact residents’ quality of life and hinder high-quality urban development. Guided by the principles of all-age friendly and inclusive design, this study innovatively integrates eye-tracking and multi-modal physiological monitoring technologies to collect both subjective and objective perception data of different age groups regarding outdoor activity spaces in residential areas through human factor experiments and empirical interviews. Machine learning methods are utilized to analyze the data, uncovering the differentiated response mechanisms among diverse groups and clarifying the inclusive characteristics of these spaces. The findings reveal that: (1) Common Demands: All groups prioritize spatial features such as unobstructed views, adequate space, diverse landscapes, proximity accessibility, and smooth pavement surfaces, with similar levels of concern. (2) Differentiated Characteristics: Children place greater emphasis on environmental familiarity and children’s play facilities, while middle-aged and elderly groups show heightened concern for adequate space, efficient parking management, and barrier-free facilities. (3) Technical Validation: Heart Rate Variability (HRV) was identified as the core perception indicator for spatial inclusivity through dimensionality reduction using Self-Organizing Maps (SOM), and the Extra Trees model demonstrated superior performance in spatial inclusivity prediction. By integrating multi-group perception data, standardizing experimental environments, and applying intelligent data mining, this study achieves multi-modal data fusion and in-depth analysis, providing theoretical and methodological support for precisely optimizing outdoor activity spaces in residential areas and advancing the development of all-age friendly communities. Full article

Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management. Full article

Harnessing the power of phase change materials (PCMs) in asphalt pavements proposes a sustainable solution for addressing temperature-related issues, affording more robust and energy-efficient infrastructure. PCMs hold enormous potential for reforming various industries due to their ability to store and release large amounts of thermal energy, offering noteworthy benefits in energy efficiency, thermal management, and sustainability. The integration of PCMs within pavements presents an increasingly exciting field of research. PCMs have the ability to efficiently manage the changes in and distribution of temperature in asphalt pavements via the release and absorption of latent heat that occurs during the phase shifts of PCMs. Asphalt pavements experience less severe temperatures and a slower rate of temperature fluctuation as a result of this, which in turn reduces the amount of stress caused by temperature. In addition, the function of temperature adjustment that PCMs provide is natural, intelligent, and in line with the direction in which the development of smart pavements is heading in the future. This study aims to explore the impact of organic, inorganic, and mixed organic–inorganic PCMs on diverse surface characteristics of asphalt. In addition, this review addresses current challenges associated with using PCMs in asphalt and explores potential advantages that could facilitate future research in addition to broadening the implementation of PCMs in construction. Full article

Effective road pavement management is vital for maintaining the functionality and safety of transportation infrastructure. This review examines the integration of Machine Learning (ML) into Pavement Management Systems (PMS), presenting an analysis of state-of-the-art ML techniques, algorithms, and challenges for application in the field. We discuss the limitations of conventional PMS and explore how Artificial Intelligence (AI) algorithms can overcome these shortcomings by improving the accuracy of pavement condition assessments, enhancing performance prediction, and optimizing maintenance and rehabilitation decisions. Our findings indicate that ML significantly advances PMS capabilities by refining data collection processes and improving decision-making, thereby addressing the intricacies of pavement deterioration. Additionally, we identify technical challenges such as ensuring data quality and enhancing model interpretability. This review also proposes directions for future research to overcome these hurdles and to help stakeholders develop more efficient and resilient road networks. The integration of ML not only promises substantial improvements in managing pavements but is also in line with the increasing demands for smarter infrastructure solutions. Full article

This study examines the integration of epistemological principles into road infrastructure risk management, emphasizing the need for adaptive strategies in the face of inherent climate uncertainties, particularly flash floods. A systematic review of peer-reviewed articles, industry reports, and case studies from the past two decades was conducted, focusing on the application of epistemological approaches within the infrastructure sector. The research employs a mixed methods approach. Quantitatively, the risk of pavement failure is measured by analyzing the relationship between pavement serviceability rates and Intensity–Duration–Frequency (IDF) data in areas frequently affected by flash floods. For example, rainfall intensities during flood events on the BR-324 highway in Brazil were significantly higher than monthly averages, with maximum values reaching 235.73 mm for a 5 min duration over a 50-year return period. These intensities showed an increase of approximately 15% over 5 to 10 years and 8% over 50 to 75 years. Qualitatively, traditional risk management methods are combined with epistemological concepts. This integrated approach fosters reflective practice, encourages the use of both quantitative and qualitative data, promotes a dynamic management environment, and supports sustainable development goals by aligning risk management with environmental and social sustainability. This study finds that incorporating epistemological insights can lead to more fluid and continuously improving risk management practices in construction, design, and maintenance. It concludes with a call for future research to explore the integration of emerging technologies such as artificial intelligence to further refine these approaches and more effectively manage complexity and uncertainty. Full article

In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R² value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management. Full article

In order to maintain a suitable road pavement level with limited resources, a management system must be established. In order to achieve this goal, a program using AI (artificial intelligence) was developed to manage and evaluate a sizable volume of survey data. A national highway pavement data management system (PDMS) built on the WEB was also constructed. By connecting several artificial neural networks, the AI crack analysis algorithm was created and taught to automatically recognize cracks in road photos and calculate crack rates. In the PDMS, the current condition of a national highway can be shown on a map, and all the data are updated to allow for verification in increments of 100 m for each lane. The system was also improved to enable the collection of information on the detailed survey section’s pavement repair specifics according to survey year. Full article

Maintaining an acceptable durability and satisfactory in-service condition for pavements is a crucial and relatively complex task, which otherwise can have considerable economic, environmental, and social consequences. Design and management of pavements have traditionally relied mainly on empirical models. However, pavements have been undergoing drastic changes, especially during the new millennium, which can compromise the reliability of the empirical models which were developed based on relatively stagnant historical data. Climate change, traffic loading growth and advancements in pavement materials are some of the main drivers of moving towards more mechanistic-empirical methods which would allow for a better understanding of pavement performance evolution in the future. To this end, this paper discusses the opportunities and challenges of a proposed framework for developing smart pavements in Canada, as well as a summary of the efforts that so far have been made in this regard. The goal of the study is to enable autonomous monitoring and data collection from the instrumented pavement sections in a suitable manner to allow for training Artificial Intelligence models, improving interpretation of the pavement responses and, ultimately, future pavement performance predictions. Full article

If the production and construction information of asphalt mixture are tightly coupled and quality control is performed in real time, it is possible to minimize quality degradation and solve problems early. For these objectives, a cloud-based IoT (Internet of Things) PQMS (Pavement Quality Management System) was developed in this study. As a result, drivers and managers can monitor construction information and identify problems using monitors and apps. In 2023, it will be applied to national road construction sites to verify the effectiveness of the proposed cloud-based IoT PQMS and address potential problems. Full article

In the construction of asphalt pavement, poor quality is often the main reason for damage to the pavement, which necessitates the use of monitoring systems during the construction stage. Therefore, this study focuses on building an asphalt concrete pavement construction monitoring system to monitor the construction phase. Through a literature review and semi-structured interviews with industry experts, this paper provides an in-depth understanding of the goals and obstacles of asphalt pavement monitoring and discusses directions for improvement. Subsequently, based on the analysis of the interview results, a system framework for asphalt concrete pavement construction monitoring was constructed, and the system was successfully developed and applied to a highway construction project. The results show that the monitoring system significantly improves the construction quality of asphalt concrete pavement, improves the intelligent level of pavement construction management, and promotes the digital development of highway construction. Full article

Intelligent systems, the Internet of Things, smart factory, and artificial intelligence are just some of the pillars for the 4th industrial revolution. Engineering is the driving force behind this new industrial renaissance and transportation plays a leading role for the new challenges in mobility needs. In this scenario, intelligent transportation systems (ITS) represent an innovative solution for various transport issues, such as traffic congestion, air pollution, long travel time, and accidents. In parallel, transportation is going through a novel way of thinking for road pavements: a multi-functional infrastructure able to harvest energy and exploiting the solar radiation or the traffic load. As the main hurdle in ITS is to find reliable energy sources, the energy harvesting roads could be a great step in installing and managing ITS as an electricity supplier. The aim of this paper is to review the key elements of ITS and energy harvesting pavements, and investigate their coexistence. This paper describes different harvesting techniques that could be used to power various ITS solutions. A case study evaluates the power output of a road section equipped with a solar road, piezoelectric material, and thermoelectric generators. Finally, the coexistence between ITS and energy harvesting pavements is critically evaluated, taking into account the advantages and disadvantages. Full article

Pavement maintenance plays a significant role in megacities. Managing complaints and scheduling road reviews are the two maintenance concerns under the intelligent pavement management system (PMS) plan. In contrast, if the damages are not treated immediately, they will increase over time. By leveraging accurate data from sensors, smart PMS will improve management capability, support sustainability, and drive economic growth in the road network. This research aimed to elaborate on the different modules of an intelligent city pavement network to advance to a sustainable city. First, a 3D mobile light detection and ranging (LiDAR) sensor, accompanied by a camera, was applied as the data collection tool. Although 3D mobile LiDAR data have gained popularity, they lack precise detection of pavement distresses, including cracks. As a result, utilizing RGB imaging may help to detect distresses properly. Two approaches were integrated alongside conducting the data analysis in this paper: (1) ArcGIS pro, developed by Esri Inc., which includes noise removal, digital elevation model (DEM) generation, and pavement and building footprint extraction; (2) the Mechanistic-Empirical Pavement Design Guide (AASHTOWare PMED), which was used to assess site specifications such as traffic, weather, subbase, and current pavement conditions in an effort to design the most appropriate pavement for each road section. For the 3D visualization module, CityEngine (a software from Esri) was used to provide the 3D city model. After implementing the research methodology, we drew the following conclusions: (1) using the AASHTOWare PMED method to make decisions about road maintenance and rehabilitation(M&R) actions can significantly speed up the decision-making process, essentially saving time and money and shortening the project’s duration; and (2) if the road conditions are similar, the smart geographical information system (GIS)-based PMS can make consistent decisions about road M&R strategies, i.e., the interference from human factors is less significant. Full article

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating. Full article