This research assesses the economic impact of Autonomous Vehicles (AVs) on the logistics network of the China-Pakistan Economic Corridor (CPEC), a vital part of China’s Belt & Road Initiative (BRI). Using a scenario simulation, researchers evaluated the effects of AV adoption on logistics costs, the environment, and traffic efficiency. Widespread adoption of fully AV is estimated to reduce logistics costs by 57%, CO2 emissions by 50%, and traffic congestion by 30%, aligning with global economic and environmental goals. The findings offer decision-makers at national and local levels valuable insights into necessary infrastructure investments, regulations, and policies to support AV integration. The study also addresses issues such as labour displacement and public acceptance, highlighting mechanisms to achieve equitable economic and environmental outcomes. This feedback provides a framework for policymakers and stakeholders to refine future transport logistics and resource allocation across cross-border corridors.

Temperature variations have a significant impact on the performance and durability of rigid (concrete) pavement. As concrete is subjected to daily and seasonal temperature changes, it experiences thermal expansion and contraction. These movements, if not properly managed, can lead to cracking, joint deterioration, and loss of structural integrity. The pavement system is adversely affected by intense heat and significant flooding. This study aims to analyze the impact of several parameters on the performance of rigid pavement under typical, thermal, and flooding situations. This study investigates the properties of concrete and the dimensional design of rigid pavement with FEACONS IV software to assess their impact on the performance of concrete pavement during thermal and flooding conditions. The main conclusions of this study derived from the FEACONS IV analysis are as follows. Rigid pavement can enhance load-carrying capacity due to a lower elastic modulus, adequate flexural strength, and aggregates with a lower coefficient of thermal expansion. Increased thickness of concrete slabs and shorter slab lengths assist in minimizing load- and temperature-induced stresses. The increase in the subgrade modulus reaction value during flooding conditions improves pavement strength. However, in higher thermal conditions, a higher subgrade reaction modulus can increase the stress induced by temperature and load. Rigid pavement using porous limestone aggregate exhibits a reduced elastic modulus and coefficient of thermal expansion, suggesting higher resilience compared to rigid pavement composed of river gravel or granite. The findings suggest that higher thermal conditions will cause pavement damage. Agencies need to account for higher temperatures while designing and maintaining pavement. Flooding saturates the concrete pavement and subgrade layer, adversely affecting its performance over time.

The incorporation of fracture mechanics into structural engineering has underscored the importance of fracture energy in understanding crack propagation in quasi-brittle concrete. Nevertheless, conventional laboratory testing remains labor-intensive, time-consuming, and costly. This study applies hybrid machine learning (ML) algorithms, including ANN-GA, SVR-GA, XGB-GA, GBR-GA, and a Stacking Ensemble, to efficiently predict key concrete properties such as initial fracture energy (IFEC), modulus of elasticity (ME), tensile strength (TS), water absorption (WA), and abrasion resistance (AR). Among individual models, IFEC-XGB-GA and IFEC-GBR-GA demonstrated the most stable performance, while the Stacking Ensemble improved overall prediction accuracy and robustness by approximately 5–10 compared with single models. For ME prediction, the ensemble approach reduced bias and variance, although GBR performed better under extreme conditions. TS-ANN-GA exhibited overfitting, whereas TS-Stacking Ensemble achieved the lowest testing errors (MAE0.145, RMSE0.232) and the highest explained variance (≈1.014) and correlation (0.973), confirming the advantages of model aggregation. Moreover, GBR-GA effectively predicted water absorption (R ² 0.998) and classified abrasion resistance with about 84 accuracy, highlighting strong potential for durability evaluation. A graphical user interface (GUI) was developed to integrate these models, providing engineers with a practical, data-driven, and physics-informed tool for accurately predicting the fracture energy of concrete.

This study evaluates the performance of foamed geopolymer concrete (FGC) incorporating rigid polyurethane (PU) waste as a partial sand replacement and aluminum powder (AP, 1%) as a foaming agent. The mixtures were based on metakaolin, fly ash, and silica fume. Fresh and hardened properties were assessed, including workability, setting time, density, compressive strength, flexural strength, splitting tensile strength, elastic modulus, water absorption, porosity, gas permeability, and chloride ion penetration. Microstructural characteristics were examined using scanning electron microscopy (SEM). The results show that moderate PU incorporation significantly enhances mechanical performance. The optimal mixture (PU30) achieved a compressive strength of 47.25 MPa at 180 days, representing a 15.6% increase compared to the control. Flexural and splitting tensile strengths improved by 19.9% and 16.7%, respectively, while the elastic modulus increased by 33.8% to 0.95 GPa. These improvements are attributed to enhanced particle packing and more efficient stress transfer within the matrix. In contrast, higher PU contents (>30%) reduced mechanical performance due to increased total porosity and weakened interfacial bonding. Durability-related properties indicated that mixtures PU20–PU30 exhibited reduced permeability and optimized pore structure, characterized by lower pore connectivity. SEM observations confirmed a denser matrix with uniformly distributed pores at optimal PU levels. Additionally, the integration of Random Forest regression with GLCM-based texture analysis demonstrated strong capability in predicting mechanical properties from SEM images. Overall, the combined use of PU waste and AP enables the production of lightweight, structurally efficient, and sustainable FGC with improved mechanical and durability performance.

UHPC has been found to have excellent freeze–thaw durability in cold regions. Previous UHPC testing performed has mostly focused on concrete with compressive strength above 21 ksi (145 MPa). In this study, testing was conducted to determine at what strength level concrete transitions to provide excellent freeze–thaw (F–T) performance. Non-proprietary concrete samples were made for freeze–thaw durability from four different concrete mixture designs: 12–15 ksi, 15–18 ksi, 18–21 ksi, and 21+ ksi (83–145+ MPa), and these were tested according to ASTM C666, using 1.5% steel fibers. The samples were made for three different curing regimens: limewater curing in a fog room, simulated precast curing, and steam curing. Low-temperature differential scanning calorimetry (DSC) and mercury intrusion porosimetry (MIP) tests were carried out to reveal the freeze–thaw mechanism of the concrete samples. All mixtures with compressive strength above 15 ksi (103 MPa) performed excellent in freeze–thaw testing with no damage seen. Steam curing was found to negatively affect the freeze–thaw performance at the lowest strength level tested.