Infrastructures

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

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

Cutterhead torque is a key operational parameter for earth pressure balance (EPB) TBM tunneling in soil strata. The effective management of cutterhead torque can significantly maintain face stability and ensure the tunneling machine operates steadily. The Shenzhen Metro Line 12 project at Shasan Station utilized the world’s largest rectangular pipe jacking machine for constructing the subway station. This project has enabled the collection of relevant data to analyze the factors influencing cutterhead torque and to establish a predictive model. The data encompass an abundant array of cutterhead design parameters, operational parameters, properties of the excavated soil, and environmental factors, revealing the distribution characteristics of cutterhead torque during tunneling. The correlation between various factors and cutterhead torque has been examined. By employing multiple regression analysis and a Levenberg–Marquardt (L-M) algorithm-based neural network, an optimal prediction model for EPB cutterhead torque has been developed. This prediction model incorporates various factors, including cutterhead diameter, RPM, soil chamber pressure, soil shear strength, and the soil consistency index. And the degree of influence of each factor on the cutter torque was also revealed. The prediction results demonstrated good accuracy compared to previous models, providing valuable insights and guidance for EPB TBMs or pipe jacking machines operating in soil strata. The current limitations of this model and suggestions for future work have also been addressed. Full article

A simple model to evaluate the effects of relative displacements between the ground anchors of a suspension bridge is proposed. An equation system is defined, which allows for the evaluation of the structural response under a general displacement set of the ground anchor points. Then, the most interesting and likely cases are analyzed in detail with reference to a suspension bridge having geometrical and mechanical characteristics typical of a long-span bridge. A simple procedure for the assessment of variation in cable stress is also given, which can be used to choose the optimum values for stress in cables under dead loads, as a percentage of their strength. The results obtained showed that expected movements do not significantly impact the structure in its lifetime and that the effects become negligible for very long-span bridges. Finally, the results obtained can be easily used for the condition monitoring of suspension bridges. Full article

As part of the Horizon 2020 InfraROB project aimed at enhancing road safety through innovative robotic solutions, a compact autonomous vehicle has been developed to repair asphalt potholes. Central to this system is a 3D printer capable of extruding a novel cold-asphalt mixture, specifically designed for patching road surfaces. The printer is mounted on a small robot that autonomously navigates to potholes, while the human operator controls the operation from a secure location outside the traffic area. The system’s development involved several key steps: designing the repair mixture, constructing the 3D printer for mixture extrusion, implementing a photogrammetric technique to accurately measure pothole geometry for printing, and integrating the extrusion system with the robotic platform. Two preliminary tests were conducted in controlled environments at Sapienza University of Rome to check the reliability of calculation of the amount of material needed to fill in the potholes. Finally, the entire procedure was tested on an Italian motorway, demonstrating the system’s functionality without encountering operational issues. Full article

The Deep Cement Mixing Integrated Drilling, Mixing and Jetting (DMJ) method represents a novel approach to the construction of slurry piles in the Yellow River Floodplain, offering the potential to enhance the quality of these structures. In order to investigate the pile strength and its distribution characteristics under different conditions, an unconfined compressive strength test was conducted on DMJ pile core samples. The Kolmogorov-Smirnov (K-S) test was employed to evaluate the normal distribution characteristics of the strength, and the fluctuation of the pile strength was evaluated by the autocorrelation function method to elucidate the distribution characteristics. Moreover, a resistivity non-destructive test was conducted to ascertain the correlation between strength and resistivity and to perform supplementary strength testing. The distribution of the pile body strength is normal at the 5% level of significance. A clear correlation was observed between the strength of the pile core and depth, while the correlation between the strength of the pile outer side and depth was less pronounced. Additionally, a positive linear correlation was observed between resistivity and strength, which can be used to evaluate the strength of DMJ piles. Full article

When Hurricane Maria struck the island of Puerto Rico in September, 2017, it devastated the island’s critical infrastructures, including the well-documented total loss of electric power systems. The strong interdependencies or associations among critical infrastructures in modern society meant that the failure of power systems propagated to and exacerbated the failure of other infrastructure systems. Moreover, these associations impact systems recovery just as they impact system failure. This study is a follow-up of previous research by the first author on Hurricane Maria. In this research authors extracted and quantified the recovery associations of Hurricane Fiona (September 2022) made landfall in Puerto Rico and inflicted considerable damage to its critical infrastructures. The recovery efforts following the disaster provided an opportunity to follow up on the previous research and examine the recovery associations. Significant money and efforts have gone into upgrading the infrastructures of Puerto Rico to make them more resilient to natural disasters such as hurricanes or tropical storms following Hurricane Maria. This paper explores the new recovery resiliency characteristics of Puerto Rico’s critical infrastructure systems (CISs) that the recovery efforts following Hurricane Fiona illustrate. This research shows that the power systems and other CISs of Puerto Rico are much more resilient when compared to their state of resiliency in 2017. Moreover, examining the recovery interdependencies reveals that some of the CISs are strongly dependent on power systems recovery. Outcomes of this study suggest that CIS relationships based on recovery data from Puerto Rico, are transferable to similar disaster-prone areas such as the Caribbean islands or other island nations, as they have similar characteristics and challenges. Full article

The development of sustainable infrastructure is essential to address the challenges of climate change and reduce CO₂ emissions. The use of alternative materials, such as agro-industrial ashes and silica fume, emerges as a promising option to enhance the durability of concrete and diminish its environmental impact. These materials can partially replace conventional cement, contributing to the construction of more sustainable infrastructure without compromising performance, even under adverse environmental conditions. In this study, we present an analysis of the use of sugarcane bagasse ash (SBA) and silica fume (SF) as a 15% cement replacement. The behavior of these materials was investigated under coastal conditions, analyzing climatic variables and degrading gases such as CO₂, CH₄, and N₂O. Electrochemical techniques were employed to measure corrosion rate and potential, in addition to conducting carbonation and compressive strength tests. The mixtures with a 15% addition of SBA and SF showed improvements compared to conventional mixes. SBA reduced the corrosion rate by 25% and increased compressive strength by 12% after 150 days, while SF enhanced carbonation resistance by 20% and compressive strength by 25%. The incorporation of SBA and SF provides significant durability in coastal environments, contributing to the sustainability of infrastructure exposed to adverse weather conditions. Full article

This study investigated the combined effects of calcium carbide waste (CCW) and lateritic soil (LS) on sustainable concrete’s fresh and mechanical properties as a construction material for infrastructure development. The study will explore the possibility of using easily accessible materials, such as lateritic soils and calcium carbide waste. Therefore, laterite soil was used to replace some portions of fine aggregate at 0% to 40% (interval of 10%) by weight, while CCW substituted the cement content at 0%, 5%, 10%, 15%, and 20% by weight. A response surface methodology/central composite design (RSM/CCD) tool was applied to design and develop statistical models for predicting and optimizing the properties of the sustainable concrete. The LS and CCW were input variables, and compressive strength and splitting tensile properties are response variables. The results indicated that the combined effects of CCW and LS improve workability by 18.2% compared to the control mixture. Regarding the mechanical properties, the synergic effects of CCW as a cementitious material and LS as a fine aggregate have improved the concrete’s compressive and splitting tensile strengths. The contribution of LS is more pronounced than that of CCW. The established models have successfully predicted the mechanical behavior and fresh properties of sustainable concrete utilizing LS and CCW as the independent variables with high accuracy. The optimized responses can be achieved with 15% CCW and 10% lateritic soil as a substitute for fine aggregate weight. These optimization outcomes produced the most robust possible results, with a desirability of 81.3%. Full article

Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure in general, especially critical infrastructure such as bridges. SHM systems allow the real-time monitoring of structural conditions and early detection of abnormalities. This enables managers to make accurate decisions during the operation of the infrastructure. However, for various reasons, data from SHM systems may be interrupted or faulty, leading to serious consequences. This study proposes using a Convolutional Neural Network (CNN) combined with Gated Recurrent Units (GRUs) to recover lost data from accelerometer sensors in SHM systems. CNNs are adept at capturing spatial patterns in data, making them highly effective for recognizing localized features in sensor signals. At the same time, GRUs are designed to model sequential dependencies over time, making the combined architecture particularly suited for time-series data. A dataset collected from a real bridge structure will be used to validate the proposed method. Different cases of data loss are considered to demonstrate the feasibility and potential of the CNN-GRU approach. The results show that the CNN-GRU hybrid network effectively recovers data in both single-channel and multi-channel data loss scenarios. Full article

Predictive maintenance can help infrastructure managers to reduce costs and improve railway availability while ensuring safety. However, its accuracy depends on reliable data from various sources, especially track measurement data. When analysing track data over time, historical maintenance actions must be considered, as otherwise the interpretation of the data would be misleading. This research aims to address inconsistencies in recorded maintenance data by detecting unrecorded track works through track geometry evaluations. The main goal is to provide the foundations for accurate descriptions of track behaviour, supporting the implementation of effective predictive maintenance regimes. As part of the research, three different approaches are analysed and evaluated, whereby two of them are based on cross-sectional analyses and the third one detects track works in longitudinal track dimension. The results show that the CRAB algorithm produces the most statistically significant results. Conversely, the cumulative track geometry-based algorithm provides a homogeneous representation of past maintenance work and a result that is statistically only marginally inferior. Consequently, these two methods are best suited to build the foundation for making accurate cross-sectional conclusions about track geometry behaviour. This allows for the verification and enhancement of existing maintenance databases. Full article

One of the main priorities of airport authorities is to maintain a high level of serviceability of runway pavements due to the high safety requirements for aircraft at high speeds. Accordingly, the assessment of the functional condition of airfield pavements is crucial for the proper operation of an airport. The most critical functional parameter appears to be pavement roughness. It characterizes the condition of the runway surface and is directly related to the safety of aircraft flights, as it affects the handling characteristics and braking performance of the aircraft, the increase in operating costs, and the wear of the aircraft. Worldwide, there are several indices for assessing the roughness of airfield pavements. This study aims to compare some of these indices to assess their ability to capture the characteristics of airfield pavement roughness. For this purpose, roughness data were collected along a runway with flexible pavement at a regional airport in southeast Europe and corresponding indices were estimated. The analysis of the data leads to the most efficient index for assessing the roughness of airfield surfaces to date. However, the need for a new index that expresses the response of the aircraft remains a critical issue. Full article

This paper proposes a method to investigate the design properties of geotechnical seismic isolation (GSI). This technique has been the object of many research contributions, both experimental and numerical. However, methods that may be used by practitioners for design procedures are still unavailable. The formulation presented herein may be used for preliminary assessments of two important properties: the thickness and the shear wave velocity. Three-dimensional advanced numerical simulations were performed with the state-of-the-art platform OpenSees in order to verify the analytical formulation on a benchmark case study. The elongation ratio has been taken as the relevant parameter to discuss the efficiency of GSI in decoupling the soil from the structure. The main findings consist of assessing the dependency of the elongation ratio on two parameters: the thickness and the shear velocity of the GSI layer. In this regard, a novel formulation was proposed in order to make preliminary design assessments that can be used by practitioners for practical applications. Full article

Global warming has led to an increase in extreme rainfall events, which often result in landslides, posing significant threats to infrastructure and human life. This study evaluated the effectiveness of the Capillary Barrier System (CBS) in enhancing slope stability along a vulnerable section of India’s National Highway 10 (NH10) during maximum daily rainfall. The GEOtop model was employed to conduct water balance simulations and obtain the pore–water pressure (PWP), which was then used to calculate the Factor of Safety (FoS). Results showed that CBS effectively delayed the rise in PWP, leading to lower peak values and smaller areas of very high and high risk levels. Spatial distribution mapping further confirmed that CBS minimized very high risk zones. At three historical landslide points, CBS slopes generally maintained FoS values above 1, demonstrating enhanced stability and improved resilience to extreme rainfall. These findings highlight the potential of CBS as a viable strategy for slope reinforcement in regions susceptible to heavy rainfall. Full article

E-waste toner (EWT), which is produced in large quantities by modern industries, has the potential to be utilized as a bitumen modifier to improve engineering properties and save costs. The current study focuses on exploring the optimization of EWT content to identify the most optimal proportions for achieving desirable levels of mechanical properties. This study also examined the effects of E-waste toner contents ranging from 0% to 30% on the fresh consistency of toner-modified and unmodified binder. The study utilized a central composite design (CCD) together with the response surface methodology (RSM) to optimize the mix design variables, specifically the binder-toner ratio (BT) and mixing efficiency ratio (MER). The objective of this study was to examine the combined effects of these variables on the mechanical characteristics of EWT-modified asphalt mixtures. The mechanical responses were obtained through the performance of four responses such as Marshall stability (MS), Marshall flow (MF), indirect tensile strength (ITS), and stiffness tests. The findings suggest that the combined interaction of BT and MER ratios has an impact on their mechanical characteristics. However, the BT ratios had a significant impact on the volumetric attributes compared to MER. The RSM-based prediction models had an R-square over 0.86 across each response. This demonstrates that the inclusion of BT and MER ratios were accountable for a minimum of 86% of the alterations in the mechanical characteristics of EWT-modified asphalt. The multi-objective optimization analysis determined that the optimal proportions for the EWT-modified asphalt, in order to obtain the ideal consistency, were 0.249 ratio of BT and 1.63 ratio of MER with a desirability value of 0.97. Overall, it was found that RSM is a reliable technique for precisely forecasting the mechanical properties of EWT-modified asphalt, including BT and MER ratios. Full article

Road restraint systems (RRSs) on European roads are provided by several manufacturers and, hence, lead to differences in geometry, material, and mode of operation. Focusing on the combination of soft steel RRSs with relatively stiffer concrete RRSs, it is vital to consider the potentially critical deformation kinematics during vehicle impacts, such as vehicle pocketing. Since a statutory test procedure was not introduced until mid-2024, much of the transition construction (TC) on Austrian roads has remained untested. Knowledge of the design features to be implemented during the refurbishment of such TCs is of great interest. The main focus of this study was to derive constructive measures (CMs) that increase traffic safety and are applicable to various TCs already installed on roads. The first step involved deriving design principles whose implementations in TCs reduce the risk of critical vehicle or RRS behavior. Based on finite element simulations, the functionality of a TC featuring all derived design principles was examined. The effect of each individual CM was analyzed in a parameter study. The results from a TB61 impact simulation on the derived TC showed the effectiveness of CMs, achieving smooth vehicle redirection. Vehicle pocketing was limited to a minimum, and neither penetration of the TC nor rollover of the vehicle was observed. The analysis of the influence of each CM indicated positive, and in some cases, negative effects. The working width was mainly positively influenced by the compaction of the posts, an additional steel bar, and the chamfering of the first concrete element. A rather diverse picture is drawn regarding the influence on the tensile forces in the guardrails. Some CMs had both positive and negative effects on the distribution of forces in the upper and lower guardrails. Nevertheless, all CMs had positive effects on the tensile forces in the coupling. The chamfering of the first concrete element was the most effective measure to prevent vehicle pocketing. However, through the combination of all CMs, the positive effects predominated, ensuring the functionality of the TC as a whole. This study provides basic insights into the effectiveness of constructive measures, which can serve as a reference for the renovation of in-service TCs or in the development phase of new TCs to be certified. Full article

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy. Full article

To enhance the seismic performance of reinforced concrete (RC) elements, it is essential to consider both strength and ductility post-yielding. This study proposed a novel method to improve the ductility of RC piers by using preformed inward-bending longitudinal reinforcements at the plastic hinges. Two full-scale model tests of standard and ductility-enhanced (DE) RC piers and numerical simulations were conducted. The lateral reversed cyclic loading experiments were conducted to assess the effectiveness of this new approach. The performance was evaluated regarding failure mode, plastic hinge distribution, hysteretic properties, normalized stiffness degradation, normalized energy dissipation capacity, bearing capacity, and ductility. Non-linear finite element method (FEM) analyses were also carried out to investigate the usefulness of the proposed method by DIANA, and simulation was validated against the experiment results by hysteretic curves, skeleton curves, failure mode crack pattern, ductility coefficient, and bearing capacity. The results indicated that the proposed method enhanced bearing capacity, resistance to stiffness degradation, energy dissipation capacity, and ductility. Additionally, it was observed that the preformed positions and curvature of the main steel bars influenced the plastic hinge location and the buckling of longitudinal reinforcements. FEM analysis revealed that it might be reasonable to deduce the other factors that influenced the ductility of the specimens by using the same material parameters and models. Full article

This study applies the first three levels of analysis outlined in the recent Italian Bridge Guidelines to a stock of prestressed concrete bridges located along the highways connecting the cities of Palermo, Messina and Catania in Sicily, south of Italy. The examined levels of analysis include census, visual inspection and determination of the structural–foundational and seismic Classes of Attention of bridges and viaducts. Data of the census and visual inspection activities were gathered using a custom-made web application. The details, the methodologies and all the features implemented in the web platform were illustrated and discussed. Furthermore, the collected data were described and critically analyzed, offering insights into the strength and limitations of each of the three examined levels of analysis of the Italian Bridge Guidelines. Finally, based on the detected defects and their numerousness with respect to the total number of assessed bridges, the authors proposed a straightforward and practical methodology for prioritizing any subsequent repairing intervention on specific groups of bridges. Full article