Infrastructures, Volume 9, Issue 12 (December 2024) – 5 articles

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

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