Infrastructures

Landslides are a common occurrence that results in both human and financial losses each year around the world. The conventional methods use a variety of techniques, such as the application of lime, cement, and fly ash, for slope stabilization. Nevertheless, all these materials, to some extent, have their own shortcomings. In this study, multi-walled carbon nanotubes (MWCNTs) application was investigated for slope stabilization. Extensive saturated and unsaturated laboratory testing as well as numerical analyses were conducted in this study for both scenarios of soil with and without MWCNTs. The result from unsaturated testing demonstrates that the air-entry value and saturated volumetric water content of soil with MWCNTs increased compared to soil without MWCNTs, while the unsaturated permeability of soil stabilized with MWCNTs decreased. The result from the SEEP/W analysis during rainfall shows that the pore-water pressure (PWP) in the slope without carbon nanotubes was higher than the PWP in the slope with MWCNTs in the surface area. During rainfall, the factor of safety (FoS) of the slope without MWCNTs declined rapidly and at a high rate according to the Slope/W analysis, whereas the FoS of the slope with MWNCTs only changed slightly and remained safe when compared to the non-stabilized slope. Full article

The structural integrity of concrete arch dams is critical to ensuring their long-term stability and safety, particularly under seasonal thermal variations. In this study, the effect of contraction and construction joint quality on the static performance of concrete arch dam safety is evaluated. To achieve this, two different approaches are followed: the first approach focuses on global stability through the calculation of the shear strength reduction factor for certain structural joints, while the second approach assesses the local stress–deformation behavior after incorporating physical gaps into the specific joints. The proposed methodology is applied to the numerical model of a typical concrete arch dam. The results of the global safety evaluation reveal that, in the winter and summer scenarios, the joints in the upper half and middle third of the dam height, respectively, have the most significant influence on the dam stability. The local approach demonstrates that while one type of performance index might indicate acceptable performance, another could reveal underlying safety concerns. Both approaches aim to provide a comprehensive understanding of the dam’s structural integrity under varying conditions, supporting informed decision-making for maintenance and safety measures. Full article

In the current paper, the dynamic mechanical properties of sustainable lightweight aggregate concrete (SLAC) were numerically studied with a newly developed mesoscopic model. In the model, a fissure-based filling method was utilized for placing spherical aggregates, in which the aggregate geometric data were collected from specimen cross-profiles. The interfacial transition zone (ITZ) was also created in the meso-scale finite element model. The model was then utilized to simulate the Split Hopkinson Pressure Bar (SHPB) test of SLAC. The results indicated that the waveforms, dynamic compression strength, and strain rate effects obtained from the simulation closely matched the experimental ones, which demonstrated the effectiveness of the established mesoscopic model. The parametric analysis showed that the aggregate content and ITZ thickness had an important effect on the dynamic mechanical behavior of SLAC. It is believed that the current study can provide a valuable reference for the numerical study of the failure mechanism of sustainable lightweight aggregate concrete. Full article

Transfer learning techniques for structural health monitoring in bridge-type structures are investigated, focusing on model generalizability and domain adaptation challenges. Finite element models of bridge-type structures with varying geometry were simulated using the OpenSeesPy platform. Different levels of damage states were introduced at the midspans of these models, and Gaussian-based load time histories were applied at mid-span for dynamic time-history analysis to calculate acceleration data. Then, this acceleration time-history series was transformed into grayscale images, serving as inputs for a Convolutional Neural Network developed to detect and classify structural damage states. Initially, it was trained and tested on datasets derived from a Single-Source Domain structure, achieving perfect accuracy (1.0) in a ten-label multi-class classification task. However, this accuracy significantly decreased when the model was sequentially tested on structures with different geometry without retraining. To address this challenge, it is proposed that transfer learning be employed via feature extraction and joint training. The model showed a reduction in accuracy percentage when adapting from a Single-Source Domain to Multiple-Target Domains, revealing potential issues with non-homogeneous data distribution and catastrophic forgetting. Conversely, joint training, which involves training on all datasets except the specific Target Domain, generated a generalized network that effectively mitigated these issues and maintained high accuracy in predicting unseen class labels. This study highlights the integration of simulation data into the Deep Learning-based SHM framework, demonstrating that a generalized model created via Joint Learning utilizing FEM can potentially reduce the consequences of modeling errors and operational uncertainties unavoidable in real-world applications. Full article

This study investigates the impact of an innovative mineral additive, ICRETE, on steel-reinforced concrete’s compressive strength and corrosion resistance. Nineteen concrete mixes were designed incorporating recycled industrial by-products, including Ground Granulated Blast Furnace Slag (GGBS) and Pulverized Fuel Ash (PFA), with varying dosages of ICRETE. Compressive strength was tested using cube specimens, cured, and assessed at 3, 7, and 28 days following IS 516-2018 standards. Corrosion behavior was evaluated in accordance with ASTM G109, employing macrocell potential monitoring and electrochemical methods, including Tafel extrapolation and linear polarization resistance. The results revealed that ICRETE-enhanced mixes achieved compressive strengths of 56.93 MPa at a water–cement ratio of 0.35 and 50.61 MPa at 0.38, surpassing the control mix’s 50.9 MPa at 0.33. Microstructural analysis via X-ray diffraction (XRD) and scanning electron microscopy (SEM) showed that ICRETE improved hydration, reduced porosity, and refined the microstructure, contributing to more excellent durability. Meanwhile, results demonstrated that the ICRETE additive reduced corrosion rates, displaying lower corrosion current densities and higher polarization resistance values where the corrosion rate dropped from 0.01 mmpy in control samples to 0.0081 mmpy with ICRETE. Environmental assessments indicated that ICRETE could significantly lower CO₂ emissions, reducing up to 46.50 kg CO₂ per cubic meter of concrete. These findings highlight ICRETE’s potential to enhance strength and durability, supporting its use in sustainable, eco-friendly concrete applications. Full article

Concrete columns are considered critical elements with respect to the stability of buildings during earthquakes. To improve the accuracy of column damage and collapse risk estimates using numerical simulations, it is important to develop a methodology to quantify the effect of displacement history on column force–deformation modeling parameters. Addressing this knowledge gap systematically and comprehensively through experimentation is difficult due to the prohibitive cost. The primary objective of this study was to develop guidelines to simulate the lateral cyclic behavior and axial collapse of concrete columns with different modes of failure using continuum finite element (FE) models, such that wider parametric studies can be conducted numerically to improve the accuracy of assessment methodologies for critical columns. This study expands on existing FEM research by addressing the complex behavior of columns that experience multiple failure modes, including axial collapse following flexure–shear, shear, and flexure degradation, a topic which has been underexplored in previous works. Nonlinear FE models were constructed and calibrated to experimental tests for 21 columns that sustained flexure, flexure–shear, and shear failures, followed by axial failure, when subjected to cyclic and monotonic lateral displacement protocols. The selected columns represented a range of axial loads, shear stresses, transverse reinforcement ratios, longitudinal reinforcement ratios, and shear span-to-depth ratios. Recommendations on optimal material model parameters obtained from a parametric study are presented. Metrics used for optimization include crack widths, damage in concrete and reinforcement, drift at initiation of axial and lateral strength degradation, and peak lateral strength. The capacities of shear–critical columns calculated with the optimized numerical models are compared with experimental results and standard equations from ASCE 41-17 and ACI 318-19. The optimized finite element models were found to reliably predict peak strength and deformation at the onset of both lateral and axial strength failure, independent of the mode of lateral strength degradation. Also, current standard shear capacity provisions were found to be conservative in most cases, while the FE models estimated shear strength with greater accuracy. Full article

As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure. Full article

This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems. Full article

The depletion of petroleum reserves and increasing environmental concerns have driven the development of eco-friendly asphalt binders. This research investigates the performance of natural asphalt (NA) modified with waste engine oil (WEO) as a sustainable alternative to conventional petroleum asphalt (PA). The study examines NA modified with 10%, 20%, and 30% WEO by the weight of asphalt to identify an optimal blend ratio that enhances the binder’s flexibility and workability while maintaining high-temperature stability. Comprehensive testing was conducted, including penetration, softening point, viscosity, ductility, multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). The results reveal that WEO effectively softens NA, improves ductility, and enhances workability, with the 20% WEO blend achieving the best balance of physical and rheological properties. Chemical analysis indicates that WEO increases carbon content and reduces sulfur and impurities, aligning NA’s composition closer to PA. However, excessive WEO (30%) compromises thermal stability and deformation resistance. The findings underscore the potential of WEO-modified NA for sustainable pavement applications, with 20% WEO identified as the optimal content to achieve performance comparable to conventional petroleum asphalt while promoting environmental sustainability. Full article

The global rise in vehicle ownership has led to a significant accumulation of waste tires, with many ending up in landfills or incinerated, resulting in considerable environmental impacts. Several end-of-life solutions have been developed to repurpose these tires, and one promising approach is converting them into crumb rubber for use in road infrastructure. Crumb rubber has been incorporated as a stabilizing agent in asphalt mixtures, Portland cement concrete, base and sub-base granular layers, and subgrades. This application not only mitigates environmental harm but also often enhances the mechanical performance of these materials. Additionally, crumb rubber (CR) serves as a low-carbon material, offering environmental benefits such as reduced carbon footprint. This study provides a comprehensive literature review on the use of crumb rubber in road infrastructure materials, examining aspects such as treatment methods, mix design, mechanical properties, durability, and environmental impacts. It also highlights knowledge gaps and potential research directions to advance the application of crumb rubber in the road infrastructure industry. The findings suggest that, at appropriate dosages (in asphalt mixtures, for example, it is between 15–20% by weight of asphalt binder), crumb rubber can shift from being an environmental burden to a valuable resource across numerous road infrastructure applications. This review aims to guide agencies, designers, engineers, and other stakeholders in informed decision-making. Full article

Speeding is one of the main contributing factors to road crashes and their severity; therefore, this study aims to investigate the complex dynamics of speeding and uses a multivariable analysis framework to explore the diverse factors contributing to exceeding vehicle speeds on rural roads. The analysis encompasses diverse measured variables from Croatia’s secondary road network, including time of day and supplementary data such as average summer daily traffic, roadside characteristics, and settlement location. Measuring locations had varying speed limits ranging from 50 km/h to 90 km/h, with traffic volumes from very low to very high. In this study, modeling of influencing factors on speeding was carried out using conventional and more advanced methods with speeding as a binary dependent variable. Although all models showed accuracy above 74%, their sensitivity (predicting positive cases) was greater than specificity (predicting negative cases). The most significant factors across the models included the speed limit, distance to the nearest intersection, roadway width, and traffic load. The findings highlight the relationship between the variables and speeding cases, providing valuable insights for policymakers and law enforcement in developing measures to improve road safety by determining locations where speeding is expected and planning further measures to reduce the frequency of speeding vehicles. Full article

Traditional forecasting models for bridge conditions, such as ARIMA and Markov chains, often fail to adequately capture nonlinear and dynamic relationships among critical variables like age, traffic patterns, and environmental factors, leading to suboptimal maintenance decisions, increased long-term maintenance costs, and heightened safety risks. This study addresses these limitations by developing recurrent neural network (RNN) models utilizing Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures with a TimeDistributed output layer. This novel approach enables accurate forecasting of the Bridge Health Index (BHI) and condition ratings for key components—deck, superstructure, and substructure—while effectively modeling temporal dependencies. Applied to bridge data from Georgia, USA, the regression models (BHI) achieved R² values exceeding 0.84, while the classification models (components condition ratings) demonstrated accuracy between 84.78% and 87.54%. By modeling complex temporal trends in bridge deterioration, our method processes time-dependent data from multiple bridges simultaneously, revealing intricate relationships that influence bridge performance within a state’s inventory. These results provide actionable insights for maintenance planning, optimized resource allocation, and reduced risks of unexpected failures. This research establishes a robust framework for bridge performance prediction, ensuring improved infrastructure safety and resilience amid aging assets and constrained maintenance budgets. Full article

Facility maintenance requires thorough inspections throughout a facility’s lifecycle to ensure structural integrity and longevity. A significant challenge lies in managing the semantic relationships between various inspection data across different lifecycle phases and effectively representing inspection results. While numerous studies have focused on identifying, analyzing, repairing, and preventing defects, organizing and integrating this information systematically for future use remains unaddressed. This paper introduces the Ontology for Concrete Surface Defects (OCSD), a unified knowledge model that enables stakeholders to access information systematically. OCSD aims to enhance future asset management systems by providing comprehensive knowledge about concrete surface defects, encompassing inspection, diagnosis, 3R (Repair, Rehabilitation, and Replacement), and defect concepts. Although the integration with Building Information Modeling (BIM) standards like the Industry Foundation Classes (IFC) is not undertaken in this study, OCSD provides a foundational framework that can facilitate such mappings in subsequent studies or applications. The methodology includes reviewing existing literature to define relevant concepts, outlining steps for developing OCSD, creating its basic components, and evaluating its effectiveness. The semantic representation of OCSD was assessed through a survey, confirming its ability to clarify concepts and relationships in this field. Full article

On 6 February 2023, two very large destructive earthquakes occurred in the East Anatolian Fault Zone (EAFZ), one of Türkiye’s primary tectonic members. The fact that these earthquakes occurred on the same day and in the same region increased the extent of the destruction. Within the scope of this study, twenty different settlements affected by earthquakes and located directly on the EAFZ were taken into consideration. Significant destruction and structural failure at different levels were induced in reinforced concrete (RC) structures, the dominant urban building stock in these regions. To determine whether the earthquake hazard is adequately represented, the PGA values predicted in the last two earthquake hazard maps used in Türkiye for these settlements were compared with the measured PGAs from actual earthquakes. Subsequently, the damage to reinforced concrete structures in these settlements was evaluated within the scope of construction and earthquake engineering. In the final part of the study, static pushover analyses were performed on a selected example of a reinforced concrete building model, and target displacement values for different performance levels were determined separately for each earthquake. For the 20 different settlements considered, the displacements were also derived based on the values predicted in the last two earthquake hazard maps, and comparisons were made. While the target displacements were exceeded in some settlements, there was no exceedance in the other settlements. The realistic presentation of earthquake hazards will enable the mentioned displacements predicted for different performance levels of structures to be determined in a much more realistic manner. As a result, the performance grades predicted for the structures will be estimated more accurately. Full article

In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process. Full article

This study explores the potential of a composite binder comprising cement bypass dust (CBD) and spent fluid catalytic cracking (FCC) catalyst for sustainable pavement base stabilization. Various CBD/FCC ratios (30:70, 50:50, 70:30) and binder contents (4%, 6%, 8%, 10%) were evaluated through laboratory testing. The 50:50 CBD/FCC mixture demonstrated optimal performance, achieving an unconfined compressive strength (UCS) of 15.6 MPa at 28 days with 10% binder content. The mix exhibited improved stiffness (E50 modulus up to 13,922 MPa) and resistance to degradation under wetting–drying cycles, attributable to synergistic cementitious and pozzolanic reactions. Microstructural analysis revealed a denser matrix, validating the enhanced performance. These findings suggest CBD and FCC, as promising materials for sustainable pavement construction, align with circular economy principles. Full article

Eco-friendly road infrastructure is vital for the advancement of sustainable transportation and promotion of efficient urban mobility. This systematic literature review explores the current state of research and development in the eco-friendly road infrastructure area. This review explored three electronic databases to gather pertinent studies using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study explored a wide range of research areas pertinent to eco-friendly road infrastructure. The findings highlight significant progress in the utilization of recycled materials, integration of photovoltaic, piezoelectric, and other energy harvesting technologies, regulatory frameworks, AI and machine learning for monitoring, predictive maintenance, and other technologies to enhance road sustainability and performance. This review analyzed the development of eco-friendly road infrastructure and identified several challenges such as high initial costs, technical performance issues, regulatory gaps, limited public acceptance, and the complexity of integrating advanced technologies. Addressing these challenges will require collaboration, further advancement in knowledge, and standardized regulations. This review serves to broaden the knowledge of the area and offer direction for future research and policy discussions, underscoring the need for continuous advancement in eco-friendly road infrastructure to meet sustainable development goals and address the challenges of climate change. Full article

According to statistics from the World Health Organization, traffic accidents are one of the leading causes of death among children and young people, and statistical indicators are even worse for the elderly population. Preventive measures require an approach that includes analyses of traffic infrastructure and regulations, users’ traffic behavior, and their interactions. In this study, a methodology based on traffic microsimulations was developed to select the optimal reconstruction solution for urban traffic infrastructure from the perspective of traffic safety. Comprehensive analyses of local traffic conditions at the selected location, infrastructural properties, and properties related to traffic users were carried out. The developed methodology was applied and tested at a selected unsignalized pedestrian crosswalk located in Osijek, Croatia, where traffic safety issues had been detected. Analyses of the possible solutions for traffic safety improvements were carried out, taking into account the specificities of the chosen location and the traffic participants’ behaviors, which were recorded and measured. The statistical analysis showed that children had shorter reaction times and crossed the street faster than the analyzed group of adult pedestrians, which was dominated by elderly people in this case. Using microsimulation traffic modeling (VISSIM), an analysis was conducted on the incoming vehicle speeds for both the existing and the reconstructed conflict zone solutions under different traffic conditions. The results exhibited a decrease in average speeds for the proposed solution, and traffic volume was detected to have a great impact on incoming speeds. The developed methodology proved to be effective in selecting a traffic solution that respects the needs of both motorized traffic and pedestrians. Full article

The rising population and demand for plastic materials lead to increasing plastic waste (PW) annually, much of which is sent to landfills without adequate recycling, posing serious environmental risks globally. PWs are grinded to smaller sizes and used as aggregates in concrete, where they improve environmental and materials sustainability. On the other hand, PW causes a significant reduction in the mechanical properties and durability of concrete. To mitigate the negative effects of PW, highly reactive pozzolanic materials are normally added as additives to the concrete. In this study, PW was used as a partial substitute for coarse aggregate, and graphene nanoplatelets (GNPs) were used as additives to high-volume fly-ash concrete (HVFAC). Utilizing PW as aggregates and GNPs as additives has been found to enhance the mechanical properties of HVFAC. Hence, this study employed two machine-learning (ML) models, namely Gaussian Process Regression (GPR) and Elman Neural Network (ELNN), to forecast the mechanical properties of HVFAC. The study input variables were PW, FA, GNP, W/C, CP, density, and slump, where the target variables are compressive strength (CS), modulus of elasticity (ME), splitting tensile strength (STS), and flexural strength (FS). A total of 240 datasets were employed in this study and divided into calibration (70%) and validation (30%) sets. During the prediction of the CS, it was found that GPR-M3 outperforms all other models with an R-value equal to 0.9930 and PCC value of 0.9929 in the calibration phase, and R-value = 0.9505 and PCC = 0.9339 in the verification phase. Additionally, during the modeling of FS, it was also noticed that GPR-M3 surpasses all other combinations with R = 0.9973 and PCC = 0.9973 in calibration and R = 0.9684 and PCC = 0.9428 in the verification phase. Moreover, in ME modeling, GPR-M3 is the best modeling combination and shows high accuracy with R = 0.9945 and PCC = 0.9945 in calibration and R = 0.9665 and PCC = 0.9584 in the verification phase. On the other hand, GPR-M3 outperforms all other models during the modeling of STS with R = 0.9856 and PCC = 0.9855 in calibration, and R = 0.9482 and PCC = 0.9353 in the verification phase. Further quantitative analysis shows that, in the prediction of CS, the GPR improves the prediction accuracy of ELNN by 0.49%, while during the prediction of the splitting tensile strength, it was also found that the GPR improved the accuracy of ELNN by 1.54%. In FS prediction, it was also improved by 7.66%, while in ME, it was improved by 4.9%. In conclusion, this AI-based model proves how accurate and effective it was to employ an ML-based model in forecasting the mechanical properties of HVFAC. Full article