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Application of CNT/CF Cement Composite Sensor for Corrosion Monitoring in Chloride-Exposed Reinforced Concrete
No full textThis study investigates the use of a conductive cement composite containing carbon nanotubes (CNTs) and carbon fibres (CFs) as a reference electrode for corrosion monitoring in reinforced concrete. For comparison, stainless-steel reference electrodes were also prepared. Both CNT/CF cement composite and stainless-steel electrodes were immersed in a 10% NaCl solution for three months to simulate a chloride-rich marine environment. The open circuit potential (OCP) of both reference electrodes was measured against a standard silver/silver chloride electrode (SSCE) over time. Two reinforced mortar samples and one reinforced concrete sample were tested under different exposure conditions. In Experimental Program 1, a mixed-in NaCl contamination approach was employed by incorporating NaCl into cylindrical concrete samples at concentrations ranging from 0% to 1.2% by binder weight. Corrosion current was measured using the linear polarization resistance (LPR) method with the three reference electrodes. In Experimental Program 2, mortar blocks were immersed in a NaCl solution following the NT Build 443 standard, and corrosion initiation was monitored through OCP measurements over time. In Experimental Program 3, reinforced concrete blocks with varying cover depths were subjected to rapid chloride ingress using a 30 V potential for 24 hours, following the NT Build 492 standard. The OCP of embedded rebars was then measured using all three electrodes. Results indicate that the CNT/CF cement composite electrode maintained a stable potential (~-220 mV vs. SSCE) regardless of chloride exposure, while the stainless-steel electrode exhibited potential variation over time. Corrosion monitoring using CNT/CF electrodes demonstrated reliable OCP measurements, faster stabilization, and consistent correlation with SSCE, confirming their suitability for long-term monitoring in chloride-exposed structures
Enhancing the Durability of Concrete Structures through Digital Twins and Advanced Numerical Simulation
No full textThis study applies a digital twin–based multi-scale and multi-physics simulation framework for durability assessment of reinforced concrete (RC) structures. The framework integrates thermo-hygro-chemical and structural analyses using DuCOM–COM3 to capture time-dependent deterioration processes such as fatigue and corrosion, and to assess their effects on structural performance under realistic environmental and loading conditions. Two case studies illustrate the methodology. The first addresses the fatigue behaviour of RC bridge slabs under moving wheel loads in both dry and wet environments. Numerical simulations reproduced field-observed deterioration patterns, including top-surface disintegration in wet conditions, and led to the development of a simplified predictive equation to support inspection planning and maintenance scheduling. The second case focuses on a corroded RC jetty superstructure in a marine environment. Field-calibrated chloride ingress and corrosion models were applied to evaluate long-term performance under alternative repair scenarios. Analyses showed that surface coating can be highly effective when both moisture and oxygen ingress are restricted, and that unmitigated corrosion not only reduces load-carrying capacity but also alters failure modes despite apparent structural redundancy. In both cases, the calibrated digital twin reproduced the present damage state and projected future deterioration under different intervention strategies, providing a rational basis for determining the scope and timing of repairs. By embedding inspection data and site-specific environmental histories into a unified material–structure simulation from the time of construction, this approach supports evidence-based lifecycle management and the development of durability-focused design provisions. These findings highlight the potential of combining digital twin technology with advanced numerical simulation to bridge the gap between material degradation modelling and structural performance assessment in concrete infrastructure
Machine-Learning PCI Forecasts for Concrete Pavements: Evidence from a Nigerian Airfield
No full textThis study develops data-driven Pavement Condition Index (PCI) forecasting for rigid (jointed concrete) airfields to support maintenance planning, risk mitigation, and lifecycle analysis. Traditional PCI surveys per ASTM D5340 are manual, time-consuming, and subjective, motivating machine-learning alternatives. Using panel-level observations, five models predict next-year PCI from readily available variables: prior-year PCI, annual aircraft traffic, temperature, and rainfall. The models include ordinary least squares (linear), ridge, lasso, random forest (RF), and a feedforward artificial neural network (ANN). All models performed well on held-out validation data; the ANN was best, achieving ��2 = 0.985, MSE = 0.286, MAE = 0.397, and RMSE = 0.534. The ANN architecture comprised one input layer, three hidden layers (128, 64, 32 neurons), and one output layer, with diagnostics indicating no material overfitting. Despite limited data, results show strong potential for accurate short-term PCI prediction in rigid airfields. Main limitations stem from the small sample size and potential generalisability issues. Future work should expand datasets, add predictors (e.g., soil type, traffic mix, slab age, joint spacing, deflection data), and assess ensemble and probabilistic models. The approach can help authorities estimate short-term deterioration and prioritise budgets for timely interventions
Feature Extraction of Steel Corrosion based on XCT Scanning and Deep Learning Model
No full textAccurate quantification and analysis of steel corrosion is crucial for reliability assessment studies of in-service reinforced concrete structures. However, the pixel-level cross-sectional data provided by X-ray computed tomography (XCT) proves difficult to quantify, especially for the amorphous corrosion products filled in mortar, due to the absence of robust feature extraction methods. In this study, multiple deep learning models were trained to automatically identify corrosion products from a large number of XCT images. The database comprised XCT images obtained from a RC component subjected to chloride-rich environment for four years. The results indicate that deep learning models can segment different regions of XCT images with high accuracy. Among the models, the K-Net model performed the best on this dataset, achieving an accuracy of 94.60%, and a mean Precision (mPrecision) of 88.21%. This advance makes it possible to automatically extract parameters that characterise steel corrosion and to assess the damage to RC structures caused by corrosion
Mortal Writing: Toward Braver Concepts of “Better Writers,” Peerness, and Nationality
No full textReflecting on experiences with two Afghan students writing in response to events following the U.S. withdrawal from Afghanistan in 2021, this essay challenges traditional writing center practices in response to the evolving and urgent writing needs of diverse (international) student populations. Focusing on the intersectional identities of student writers and the geopolitical realities they face, we develop further the call to transform writing centers into “brave spaces.” Deploying this framework of bravery, we call for a reevaluation of the concept of “better writers,” of empathy constructed primarily through peerness, and of the current conceptualization of nationality in writing center scholarship. Writing centers as a discipline must reconceptualize these constructs of our theory and practice if they are to become brave(r) spaces that support students as they fight for social justice and survival
A simple approach to delineating field boundaries using satellite imagery
No full textAccurate delineation of agricultural field boundaries is crucial for farm management, research, and policy development. However, publicly available boundary datasets are often limited in accuracy, very expensive, or use deep learning architectures that require extensive annotated data that are not available, limiting access. This project presents a simple, image-processing-based method for delineating field boundaries using openly available satellite images. Our method manipulates the images using a variety of image processing techniques and is able to generate an accurate boundary for a selected field. By providing a simple and accessible solution, this approach has the potential to transform boundary delineation practices, increasing the efficiency of farmers, advisors, and researchers. Our method’s simplicity and accuracy highlight a path from innovation to a real-world impact
An AI-enhanced Soft Robotic System for Selective Strawberry Harvesting
No full textStrawberry harvesting is labor-intensive and requires delicate, selective handling. Current robotic solutions rely mostly on rigid arms, which lack flexibility and often cause fruit damage or require complex mechanisms. To address this, we propose an intelligent soft robotic system for efficient and gentle strawberry harvesting. The system combines an AI-powered computer vision module to detect ripe strawberries, a soft silicone-based robotic arm to handle fruit without damage, and a data-driven control method for smooth, adaptive movement. An adjustable ground vehicle supports flexible field navigation. Initial results show a harvest success rate of 66.7% and an average speed of 240 strawberries per hour, demonstrating the potential of soft robotics for safer and more effective automated harvesting
Embedded two-phase cooling in an additively manufactured stator prototype for a novel high-power-density electric motor concept
No full textElectrification of transportation in the aviation industry is challenging in part due to the high power densities necessary for propulsion using electric motors. A commercial, narrow-body aircraft would require electric motor systems having \u3e12 kW/kg power density, over twice the current state-of-the-art. One major limitation to increasing power density are limits on the operating temperature. Electric motor windings, which are wrapped in insulation, produce the majority of the heat in the motor. Positioning the coolant closer to the windings so as to decrease the overall thermal resistance between the heat source and sink is therefore a promising route toward enabling higher power densities. In this study, an additively manufactured stator subsection prototype with embedded microchannels is used to demonstrate two-phase cooling at different mass flow rates of R1233zd(E). Compared to single-phase cooling, utilizing two-phase flow provides higher heat transfer coefficients, which have increasing importance on reducing the overall resistance when the coolant is embedded close to the heat source, as well as offering a nearly isothermal coolant at the saturation temperature independent of mass flux. This prototype test section, which has been demonstrated for continuous operation at 30.4 A/mm2, is experimentally characterized at five flow rates between 0.33 g/s and 0.83 g/s. The average coil temperature is demonstrated to be insensitive to mass flow rate, as is desired for practical operation, owing to the high effective heat capacity rate when operating in the two-phase regime. Instrumentation of the test section with wall-embedded thermocouples enables decomposition of the total coil temperature rise into the conductive and convective thermal resistance components. The incorporation of two-phase flow reduced the convective thermal resistance by 77 %. Thermal models for each of these resistance components are developed to validate experimental findings, and further, to allow performance prediction in context of up-scaling to the full motor assembly and higher operating powers
Degradation Mechanisms and Microstructural Performance of 3D-Printed Engineered Cementitious Composites with Yellow River Sand under Chloride Ion Wet-Dry Cycles
No full text3D printing of Engineered Cementitious Composites (ECC) is an emerging, cutting-edge construction technology that enables layer-by-layer fabrication without the need for formwork or steel reinforcement. ECC exhibits superior tensile strength and crack resistance compared to conventional concrete. However, the durability of composite structures, especially in marine environments exposed to harsh conditions such as sulfate and chloride ions, remains a concern. This study investigated the performance of cast and 3D-printed specimens under chloride ion wet–dry cycles (0, 5, 10, 15, 20, 25, and 30 cycles) and utilised sustainable Yellow River sand (YRS) as a partial replacement for quartz sand to reduce material costs. Results showed that the compressive strength of both cast and 3DP-ECC specimens was highest in the Z direction. Among them, the R25 cast specimens exhibited better strength properties, starting at 34 and 32 MPa, respectively, and decreasing to 22 and 23 MPa after 30 cycles of chloride exposure. In comparison, compressive strength in the Y- and X-directions decreased by 20% and 23%, respectively. Scanning Electron Microscopy (SEM) images of cast ECC revealed a dense and relatively uniform microstructure, with well-bonded phases between the matrix and the aggregates. The interfacial transition zone (ITZ) between the cement paste and aggregates appeared smooth, indicating strong bonding with minimal porosity. This study highlighted that incorporating Yellow River Sand as a partial replacement in 3D-printed ECC not only enhances sustainability and reduces material costs but also maintains satisfactory mechanical performance, particularly at the 25% replacement level, under chloride ion wet–dry cycles
A Unified Rheological Parameter for Concrete: Application of the Shear Work Index in Flowability Optimization
No full textAdequate flowability is crucial to ensure proper homogeneity, mechanical properties, and durability of hardened concrete. A new parameter called the Shear Work Index (SWI), which combines yield stress and plastic viscosity, was recently proposed by the authors to evaluate concrete flowability. Factors that can influence SWI are analysed in this paper. Application methods of SWI are demonstrated through case studies involving the evaluation of rheology-modifying materials, the selection of the optimal manufactured sand replacement ratio in mixed sand, and the determination of concrete vibration time. The findings highlight the effectiveness of SWI as a comprehensive parameter for optimising concrete mix design and guiding concrete construction