Infrastructures

3D concrete printing (3DCP) offers a transformative approach to industrialized construction and housing solutions. However, its reliance on high Portland cement (PC) mixes raises sustainability concerns. This study explores alkali-activated materials (AAM) as low-carbon alternatives, leveraging an industrial by-product (ground granulated blast furnace slag—GGBFS) to enhance environmental performance of 3DCP mortars. The novelty of this study lies in demonstrating that, despite equivalent activation parameters, one-part (“just-add-water”) and two-part AAM systems exhibit substantially different fresh-state, rheological, and printing behavior in extrusion-based 3DCP—an activation-method-dependent divergence that, to the best of the authors’ knowledge, has not been previously reported. Rheological behavior (dynamic yield stress, plastic viscosity, and structural build-up), flow table spread evolution, hydration kinetics (via isothermal calorimetry), and mechanical performance (cast and extruded specimens of selected mixes) were systematically evaluated. Results reveal that one-part mixes exhibit greater flowability when compared to two-part mixes (140–239 mm vs. 135–210 mm), inferior dynamic yield stress (35.94–93.61 Pa vs. 52.91–152.30 Pa), and slower structural build-up, while maintaining comparable compressive and flexural strengths as the equivalent two-part mortars. These findings demonstrate the potential viability of AAM for extrusion-based 3DCP and highlight the critical influence of activation method and water content on extrudability and performance.

Traffic loading strongly influences the structural design of flexible pavements, yet in many developing countries the electronic toll collection (ETC) systems that record heavy-vehicle movements continuously are used almost exclusively for revenue administration. This study asks how continuously collected toll records can be converted into inputs for flexible pavement design and asset management, and answers it with a Data-to-Design Implementation Framework and a four-tier data readiness classification. A single embedded case study was conducted at the Shimabala Toll Plaza on the Lusaka–Chirundu corridor (road T2), Zambia, using electronic toll records for 2023 and 2024 and an automatic axle classification count. Load equivalency factors were derived from the SADC legal axle-group loads for two load states. The 2023 base-year Annual Average Daily Truck Traffic was 1538 heavy vehicles per day, the annual design-lane loading was 1.33 × 10⁶ Equivalent Single Axle Loads (ESALs) in a fully laden, legal-limit scenario, and cumulative design traffic over 20 years was 2.66 × 10⁷ ESALs at zero growth and 4.39 × 10⁷ at 5% compound growth. A layered AASHTO (1993) solution requires structural numbers of 5.46 to 5.84, satisfied by 220 to 240 mm of asphalt concrete over granular base and subbase. A Monte Carlo analysis gives a median required structural number of 5.51 (90% interval 4.83 to 6.46); subgrade strength dominates and overloading ranks highest among the traffic parameters, while the order of load state and growth depends on the assumed ranges. The take-away for the pavement industry is that toll data supply a complete, low-cost record of demand but not of axle load, so road authorities should retain axle counts in toll transactions and calibrate equivalency factors with weigh-in-motion data before relying on toll-derived loading.

Corrosion and conductor breaks in grounding grids can compromise the safe dissipation of substation fault currents. To diagnose these faults using only a limited number of accessible nodes, this study groups frequencies and selects reference-port impedances based on the distribution of injected currents throughout the grounding grid at different frequencies. The topology-tearing method retains all nodal voltages and employs equivalent sources to approximate the effects of currents in fixed branches. For a grounding grid comprising 49 branches, this method reduces the number of unknowns from 79 at low frequencies to 47 at high frequencies. The proposed hybrid algorithm (ERS-DBO-SQP) augments dung beetle optimization with reflection and scattering moves during stagnation and uses sequential quadratic programming for local refinement. In the selected timed run on this grid, ERS-DBO-SQP achieved a geometric mean of sensitivity and specificity of 0.8563 in 91.25 s of solver time. Three alternative solvers achieved 0.3801–0.4282 in 120.74–129.47 s. Numerical simulations, CDEGS simulations, and field tests demonstrate that the proposed method can effectively identify corrosion and conductor-break faults with high diagnostic accuracy.