Infrastructures, Volume 9, Issue 10 (October 2024) – 22 articles

Proper road network maintenance is essential for ensuring safety, reducing transportation costs, and improving fuel efficiency. Traditional pavement condition assessments rely on specialized equipment, limiting the frequency and scope of inspections due to technical and financial constraints. In response, crowdsourcing data from connected and autonomous vehicles (CAVs) offers an innovative alternative. CAVs, equipped with sensors and accelerometers by Original Equipment Manufacturers (OEMs), continuously gather real-time data on road conditions. This study evaluates the feasibility of using CAV data to assess pavement condition through the International Roughness Index (IRI). By comparing CAV-derived data with traditional pavement auscultation results, various thresholds were established to quantitatively and qualitatively define pavement conditions. The results indicate a moderate positive correlation between the two datasets, particularly in segments with good-to-satisfactory surface conditions (IRI 1 to 2.5 dm/km). Although the IRI values from CAVs tended to be slightly lower than those from auscultation surveys, this difference can be attributed to driving behavior. Nonetheless, our analysis shows that CAV data can be used to reliably identify pavement conditions, offering a scalable, non-destructive, and continuous monitoring solution. This approach could enhance the efficiency and effectiveness of traditional road inspection campaigns. Full article

Concrete-filled steel tube (CFST) beams have shown their flexural effectiveness in terms of stiffness, strength, and ductility. On the other hand, composite bridge girders demand durable and ductile girders to serve as tension members, while the concrete deck slab resists the compression stresses. In this study, six composite CFST beams with concrete slab decks with a span of 170 cm were investigated under a four-point bending test. The main variables of the study were the compressive strength of the concrete deck, the size of CFST beams, and the composite mechanism between the CFST girder and the concrete deck. The results showed that the flexural strength and ductility of the composite system increased by 20% with increasing concrete compressive strength. The study revealed that the higher-strength concrete slab deck enabled the CFST beam to exhibit improved flexural behavior with reduced deflections and enhanced resistance to cracking. The findings also highlighted the importance of considering the interactions between the steel tube and concrete slab deck in determining the flexural behavior of the composite system revealed by strain distribution along the composite beam profile as determined using the digital image correlation DIC technique, where a 40% increase in the flexural strength was obtained when a channel section was added to the joint of the composite section. Full article

Climate change significantly impacts transportation infrastructure, particularly asphalt pavements. Similarly, the heat absorption of paved surfaces, especially conventional black pavements, significantly intensifies the urban microclimate. Paved surfaces, including asphalt pavements, account for over 30% of the covered surfaces and are vulnerable to rising temperatures, which cause not only pavement distress, such as rutting and cracking, but also urban heat islands (UHI). Sustainable pavement solutions, specifically colored pavements, have been investigated for their potential to mitigate these effects. This review presents an extensive overview of current pavement technologies, emphasizing conventional asphalt’s economic, environmental, and functional characteristics. A discussion of the benefits and challenges of colored pavements is also provided, including their ability to reduce UHI, enhance safety, and contribute to sustainable urban growth. This paper discusses advancements in pavement material science, the use of recycled materials, and the application of reflective coatings, providing insights into sustainable infrastructure development. Transitioning from conventional black pavements to sustainable colored alternatives is not merely a matter of material choice but a strategic transition toward resilient urban planning. Increasing demand for environmentally friendly infrastructure could prompt the construction industry to adopt colored pavements as a tool to promote environmental stewardship. Full article

This research work aims to compare the strength and fracture mechanics properties of plain concretes, obtained from different coarse aggregates. During the study, mechanical parameters including compressive strength (f_cm) and splitting tensile strength (f_ctm), as well as fracture parameters involving critical stress intensity factor $\left(K_{\mathrm{I}\mathrm{c}}^{\mathrm{S}}\right)$ and critical crack tip opening displacement (CTOD_c) were evaluated. The effect of the aggregates used on the brittleness of the concretes was also analyzed. For better understanding of the crack initiation and propagation in concretes with different coarse aggregates, a macroscopic failure surfaces examination of the tested beams is also presented. Crushed aggregates covered were basalt (BA), granite (GT), and limestone (LM), and natural peeble gravel aggregate (GL) were used in the concrete mixtures. Fracture toughness tests were performed on an MTS 810 testing machine. Due to the high strength of the rock material, the rough surface of the aggregate grains, and good bonding in the ITZ area between the aggregate and the paste, the concretes with crushed aggregates exhibited high fracture toughness. Both of the analyzed fracture mechanics parameters, i.e., ${ K}_{\mathrm{I}\mathrm{c}}^{\mathrm{S}}$ and CTOD_c, increased significantly in the case of concretes which were manufactured with crushed aggregates. They amounted, in comparison to concrete based on gravel aggregate, to levels ranging from 20% for concrete with limestone aggregate to over 30% for concrete with a granite aggregate, and to as much as over 70% for concrete with basalt aggregate. On the other hand, the concrete with gravel aggregate showed the lowest fracture toughness because of the smooth surface of the aggregate grains and poor bonding between the aggregate and the cement paste. However, the fracture process in each series of concrete was quasi-plastic in the case of gravel concrete, semi-brittle in the case of limestone concrete, and clearly brittle in the case of the concretes based on granite and basalt aggregates. The results obtained help to explain how the coarse aggregate type affects the strength parameters and fracture toughness at bending. Full article

Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user behavior and optimize traffic flow. This systematic literature review aims to address this gap by examining the effectiveness of VMS in shaping user interactions and enhancing traffic management systems. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a thorough analysis of relevant studies was conducted to identify key factors influencing VMS impact, including message content and characteristics, complementary sources of information, user demographics, VMS location, and users’ reliance on these signs. Additionally, the review explores the implications of displaying non-critical information on VMS and introduces virtual dynamic message signs (VDMSs) as an innovative approach for delivering public traveler information. The study identifies several research gaps, such as the integration of VMS with vehicle-to-everything (V2X) technologies, navigation systems, the need for validation in real-world scenarios, and understanding behavioral responses to non-critical information on VMS. This review highlights the importance of optimizing VMS for improved user engagement and traffic management, providing valuable insights and directions for future research in this evolving field. Full article

Because of their improved properties, FRP composites are vastly used in the strengthening of aged concrete infrastructures. However, it has been observed that their performance is highly compromised when exposed to high temperatures, as expected during fire incidents, which critically affects FRP–concrete bond behavior, hence affecting the overall efficiency of the strengthening system. This paper critically presents the available literature concerning the degradation of bond strength between FRP systems with concrete substrates due to increased temperatures. Both analytical and numerical bond–slip models developed for the prediction of bond strength degradation under such conditions are reviewed. A generally confirmed fact is that exposure to high temperatures, especially those reaching glass transition temperature (T_g) for epoxy adhesives, leads to bond degradation. Therefore, cement mortar-bonded CFRP textiles display better performance in fire endurance. This present paper also utilizes machine learning algorithms for the prediction of bond strength under elevated temperatures based on an experimental database of 37 beams. The nonlinear relationships and variable interactions in the developed model provide a reliable method for the estimation of bond strength with reduced extensive experimental testing, where the critical role of temperature in bond behavior is identified. This paper emphasizes the use of advanced predictive models to ensure the durability and safety of FRP-strengthened concrete structures in thermally challenging environments. Full article

The increasing frequency and severity of climate variability poses substantial challenges to the sustainability and reliability of transportation infrastructure worldwide. Transportation systems, vital to economic and social activities, are highly vulnerable to extreme weather, sea-level rise, and temperature fluctuations, which can disrupt their structural integrity, operational efficiency, and maintenance needs. The aim of this study is to explore the scholarly landscape concerning the effects of climate variability on transportation systems, analyzing 23 years of scientific publications to assess research trends. Utilizing bibliometric methods, this analysis synthesizes data from numerous scientific publications to identify key trends, research hotspots, influential authors, and collaborative networks within this domain. This study highlights the growing acknowledgment of climate variability as a crucial factor affecting the design, maintenance, and operational resilience of transportation infrastructure. Key findings indicate a notable increase in research over the last decade, with a strong focus on the effects of extreme weather events, sea-level rise, and temperature changes. The analysis also shows a multidisciplinary approach, incorporating perspectives from civil engineering, environmental science, and policy studies. This comprehensive overview serves as a foundational resource for researchers and policymakers, aiming to enhance the adaptive capacity of transportation systems to climate variability through informed decision-making and strategic planning. Full article

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R²) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R² of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices. Full article

This article investigates the challenges of maintaining the Machico Cable-Stayed Bridge in a marine environment, focusing on its implications for urban mobility. The primary problem addressed is the impact of harsh marine conditions on the structural integrity of the bridge, which poses significant challenges for ongoing maintenance and safety. The research highlights unique aspects such as the effects of saltwater exposure on materials and the interplay between infrastructure and urban transit dynamics. By emphasizing these critical issues, this study aims to provide insights into effective maintenance strategies and contribute to the broader discourse on urban mobility in coastal regions. Full article

Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment. Full article

This study explores the influence of different concentrations of recycled concrete aggregate (RCA) on the fresh and hardened properties of self-consolidating concrete (SCC) in order to assess the structural suitability of the use of RCA in a precast concrete plant. The study particularly emphasizes the early strength of the produced concrete. The RCA was sourced from crushed concrete used in roadway applications and was sieved to replicate the characteristics of natural aggregate. Five different SCC mixes were produced, with RCA substituting 0%, 10%, 30%, 50%, and 70% of the natural coarse aggregate (NCA) by weight. For each different mix design, the hardened properties tested were the compressive strength and tensile strength. The fresh properties investigated were the passing and filling ability. Additionally, aggregate properties including grain size distribution and absorption of coarse aggregate were studied. The selected mix design follows a typical well-graded self-consolidating concrete mix with 28-day strength of 8000 psi (55.16 MPa). It was found that replacing up to 50% of the NCA with RCA improves the early strength of concrete without a significant impact on the fresh and hardened concrete properties. Full article

In this paper, a discussion on the applicability and limitations of the limit equilibrium method is presented. In this manner, the reliability of a concrete arch dam-foundation system under static loading is evaluated by considering a set of potentially moveable rock wedges in the foundation. The safety of the system is assessed utilizing a quasi-analytical method, which employs the limit equilibrium method and numerical analysis to calculate the sliding safety factors and the dam trust forces, respectively. The reliability is evaluated using the Latin Hypercube Sampling method. Random variables in the system are the friction angle, cohesion, and the Grout Curtain Efficiency Coefficient. In the end, the influence of two parametric variables of discontinuities, elastic slip and rock mass deformability modulus, on the rock wedges’ sliding safety factor is evaluated by comparing the results of the quasi-analytical method with the purely numerical method. The results show that in the case of complicated geotechnical conditions, the limit equilibrium method may not reflect real-world failure scenarios. Full article

In railway stations, queues often form in front of the ticketing booths that provide ticketing services. Proper design of service systems is key to effectively managing these queues, as waiting time is a critical factor affecting customer satisfaction. This research focuses on optimising the location and configuration of queues in front of ticket booths to minimise waiting times and increase service efficiency. Passenger flow management at the station can be understood as the planning and implementation of the orderly movement of the crowd through the infrastructure. Using operational Markov chain analysis, we evaluate different queue configurations and the number of service providers in urban railway stations. The study specifically focuses on the case of the Poprad-Tatry railway station in Slovakia, where we propose the introduction of a common queue for all ticket booths. We propose the distribution of lines and their schedule, based on mathematical analyses, by creating designated service zones with a common queue in front of the ticket booths. The results show that this approach significantly reduces waiting times and improves overall system efficiency. This research focusses on solving the shortcomings in the design of queues in railway stations, specifically on the use of a common queue, thereby contributing to the improvement of passenger movement management. Full article

This study investigates the impact of the attitudes of the elderly on the acceptance of Thailand’s high-speed rail technology according to the technology readiness index (TRI) and technology acceptance model (TAM) theories as guidelines for policies or strategies to enhance passengers’ intentions to use high-speed rail. A self-administered questionnaire was used to collect data from 3200 elderly people aged over 60 years in the surveyed areas along high-speed rail routes in Thailand, before the use of statistical analysis and multigroup structural equation modeling (SEM) to analyze variations in the participants’ attitudes toward urban and rural areas. The results that were thus obtained from both groups showed their differing attitudes toward the acceptance of technology. The TAM theory considers the attitude toward high-speed rail use in urban areas to be important, while, in rural areas, attitudes and perceived usefulness are important. With respect to the ease of use of high-speed rail, the most important factors were attitudes toward use and perceived usefulness. For the TRI theory, innovativeness features as the most positive influence on the perceived ease of high-speed rail use in both groups. Optimism and innovativeness were positive influences, but discomfort and insecurity carried a negative influence with respect to the perceived ease of use and usefulness. Full article

Moisture-induced damage is a serious problem that severely impairs asphaltic pavement and affects road serviceability. This study examined numerous variables in asphalt concrete mixtures to assess their impact on moisture damage resistance. Mix design parameters such as the asphalt content (AC) and aggregate passing sieve No. 4 (PNo. 4) were considered as variables during this study. Additionally, hydrated lime (HL) was utilized as a partial substitute for limestone dust (LS) filler at 1.5% by weight of the aggregate in asphalt concrete mixtures for the surface layer. This study also investigated the potential enhancement of traditional asphalt binders and mixtures by adding nano-additives, specifically nano-silica oxide (NS) and nano-titanium dioxide (NT), at rates ranging from 0% to 6% by weight of the asphalt binder. To quantify the moisture damage resistance of the asphalt concrete mixes, two types of laboratory tests were employed: the tensile strength ratio (TSR) and the index of retained strength (IRS). The former characterizes moisture damage using tensile strength, whereas the latter uses compression strength. The physical properties of the asphalt binder, such as its penetration, softening point, and ductility, were also evaluated to identify the effects of the nanomaterials. The results indicated that variations in the mix design variables significantly affected the moisture damage resistance of the asphalt concrete mixtures. The maximum improvement values were obtained at the optimum asphalt content (OAC) and PNo. 4 (mid-range + 6%) with TSR values of 80.45 and 82.46 and IRS values of 74.39 and 77.14, respectively. Modifying asphalt concrete mixtures with 1.5% HL resulted in improved moisture resistance compared with mixtures without HL (0% HL) at each PNo. 4 level, reaching superior performance at PNo. 4 (mid-range + 6%) by 4.58% and 3.96% in the TSR and IRS tests, respectively. Additionally, both NS and NT enhanced the physical properties of the asphalt binder, leading to substantial enhancements in asphalt concrete mixture performance against moisture damage. A 6% dosage of NS and NT showed the best performance, with NS performing slightly better than NT. TSR was increased by 14.72 and 11.55 and IRS by 15.60 and 12.75, respectively, with 6% NS and NT compared with mixtures without nanomaterials (0% NM). Full article

Pentamodes are lattice structures composed of beams. Their main property is the low ratio of the shear to bulk modulus, making them suitable for aerospace, antiseismic, and bioengineering applications. At first, in our study, pentamode structures were fabricated using three-dimensional printing and were tested in a laboratory. Then, computational analyses of bulk strength have been performed. In addition, several preliminary computational analyses have been considered, comparing different pentamodes’ dimensions and topologies in order to understand their behaviour under different loading conditions. Experimental results have been compared with the numerical results in order to validate the forces applied to the lattice structures. Our new contribution is that for the first time, the experimental and numerical results are investigated up to the failure of the specimens, the effective Young’s modulus has been calculated for different pentamode lattice structures, and our results are also compared with analytical equations. Full article

With the rise in construction costs and aging of existing concrete structures, retrofitting and strengthening have gained more popularity. Among all of the available techniques, adding new repairing layers on top of old concrete ones has proven to be highly effective. However, the efficacy of such method is dependent on the performance of the cold bond between old and new layers of concrete whose establishment requires different considerations, such as paying attention to the properties of concrete layers, namely their strength, permeability, aggregate size, density, etc., and the qualities of the interface between the layer, such as how wet it is or its roughness degree. In this paper, the factors which can impact shear and tensile bond strength are fully discussed while being categorized into two major groups of factors related to each concrete layer’s properties and those directly associated with the connection area. The durability of the bond after exposure to various environments in terms of temperature and relative humidity is also addressed and then a list and comparison of numerous tests that are commonly conducted to measure the bond strength are provided. The findings indicate the characterization of suitable materials and surface roughening techniques which can ensure an adequate bonding between substrate and overlay, along with recommendations for the scope of future research. Full article

This study presents a novel, machine-learning-based Automatic Incident Detection (AID) system for freeways. Through a comprehensive analysis of existing AID systems, the paper identifies their limitations and key performance metrics. VISSIM, a traffic simulation software, is employed to generate diverse, realistic traffic data incorporating factors significantly impacting AID performance. The developed system utilizes an Artificial Neural Network (ANN) trained via RapidMiner software. The ANN is designed to learn and differentiate normal and incident traffic patterns. Training yields a Detection Rate (DR) of 95.6%, a False Alarm Rate (FAR) of 1.01%, and a Mean Time to Detection (MTTD) of 0.89 min. Testing demonstrates continued effectiveness with a DR of 100%, a FAR of 1.29%, and a MTTD of 1.6 min. Furthermore, a sensitivity analysis is conducted to assess the influence of individual factors on system performance. Based on these findings, recommendations for enhancing AID systems are provided, promoting improved traffic safety and incident management. This research empowers transportation authorities with valuable insights to implement effective incident detection strategies, ultimately contributing to safer and more efficient freeways. Full article

Monitoring natural slopes, embankments, and unstable slopes is crucial to reducing predisposition to mass movements, especially in areas with geotechnical instability and high rainfall. This study proposes a methodology to identify geotechnical and pluviometric triggers of mass movements in railway slopes. It involves registering slopes and embankments along the railroad, recording accumulated rainfall indices, and documenting associated accidents. The experimental program included a cadastral survey at a pilot site on the MRS company’s railway network in the Paraopeba branch, Minas Gerais, Brazil. Surface and subsurface drainage conditions, anthropic interventions, and modifications affecting slope stability were also examined. Additionally, the history of accidents involving geotechnical and regional rainfall indices were incorporated to identify potential triggering events for mass movements. The study found a good correlation between landslide records and geotechnical risk mapping but a low correlation between landslide records and rainfall isohyets. The latter result is attributed to the low density and poor distribution of rainfall data and active pluviometers in the region. Overall, understanding the geological–geotechnical characteristics of slopes and the correlation between accidents and rainfall indices provides valuable insights for predicting potential landslide occurrences. Full article

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide. Full article