Engineered Material Arresting System (EMAS) is a critical safety installation at the end of a runway toarrest an overrunning aircraft. Key parameters such as vertical load and wheel-EMAS interaction mode significantly influence arresting performance. Since aircraft overrun involves combinations of these parameters, understanding their coupled effect is essential for predicting arresting behavior. This study investigates the combined effects of loads and wheel-EMAS interaction modes, namely rolling, sliding, and combined rolling-sliding, on wheel velocity. A finite element model simulating the wheel’s translation across the EMAS bed was developed using LS-DYNA, a commercial software. The wheel was modeled as a rigid aluminum body, whereas the EMAS bed was represented as a glass foam. The simulation model was defined using comprehensive inputs, including geometry, material models, boundary conditions, and contact parameters, with the initial wheel entry velocity kept constant across all simulations. For each load, the various wheel-EMAS interaction modes were examined. Results indicate that both higher loads and sliding-dominated interaction significantly enhance velocity decay.Among the investigated cases, the greatest velocity decay occurs for pure sliding motion under the highest applied load, where the velocity is reduced by 99.43% of the initial velocity. In contrast, rolling motion under the reference load exhibits the least decay, with only a 29.27% of the initial velocity, while all other cases fall between these two extremes. This trend indicates that sliding-dominated motion dissipates a larger portion of kinetic energy, resultingin greater velocity decay. On the other hand, rolling motion limits energy loss, and the combined rolling-sliding mode exhibits transitional behavior between these two modes. This velocity decay is amplified under higher loads, where the increased normal force enhances energy dissipation. Hence, the combined effect of interaction mode and load is critical for the accurate evaluation and design of EMAS systems.

In response to global emission reduction goals set by the Paris Agreement (2015), the construction industry has adopted strategies to reduce cement consumption by using sustainable materials. Sugarcane bagasse ash (SCBA), an agro-waste by-product, has shown its potential for use as a sand or cement replacement in concrete. However, its nature and complex interactions across mixed designs make predicting its compressive strength challenging using conventional approaches. This study presents the use of k-nearest neighbors (KNN), a machine-learning model for predicting the compressive strength of concrete incorporating SCBA as a sand or cement replacement. Three databases, Web of Science, Scopus, and Google Scholar, were used in the collection of 844 experimental data points from published articles between 2015 and 2025. Independent variables include SCBA usage, SCBA replacement level, cement content, fine and coarse aggregate contents, water-to-binder ratio, superplasticizer dosage, and curing age, while compressive strength served as the dependent variable. Prior to model development, SCBA usage, the only categorical variable, was encoded using one-hot encoding. Feature scaling using StandardScaler was applied to ensure consistency in distance-based features. The dataset was split into 75%-25% train-test subsets, and 5-fold cross-validation was carried out to enhance model generalization and robustness. Hyperparameter tuning was optimized using Optuna to enhance predictive accuracy. The optimized KNN model achieved a coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 1, 0.329, and 0.063 in the training phase and 0.81, 7.378, and 5.674 in the testing phase, respectively. Optuna improved the result of the mean cross validated coefficient of determination from 0.78 to 0.84 upon hyperparameter tuning. While the results indicate KNN capability of capturing localized patterns in the heterogeneous SCBA concrete dataset, robust ML models such as eXtreme Gradient Boosting and Random Forest may be used to capture enhanced generalization of the dataset. KNN serves as a reliable baseline model for data-driven prediction of compressive strength, highlighting the potential of simple, interpretable machine-learning models in supporting sustainable concrete design aligned with global climate mitigation goals.

The use of hydrogen as an energy carrier has gained widespread adoption in warehouses and industrial environments. This increasing adoption necessitates the need for stringent safety evaluations specifically for enclosed fueling systems where the potential of accidental discharges may result in explosions or jet fires. This study demonstrates a quantitative risk assessment of an indoor hydrogen dispenser positioned in a warehouse facility using the Hydrogen Risk Assessment Models (HyRAM+) version 6.1 software developed by Sandia National Laboratories. A representative 120 m x 120 m x 10.62 m housing a 70 MPa gaseous hydrogen dispenser used in the refueling of hydrogen-powered vehicles in an enclosed space scenario was modeled Based on realistic industry standard assumptions system geometry conditions of operation and component inventory were defined. HyRAM+ was employed to evaluate deterministic and probabilistic models to determine potential loss of life (PLL), fatal accident rate (FAR), and average individual risk (AIR). The PLL, FAR, and AIR values of 1.05 x 10-4 fatalities per year 0.02 fatalities per 100 million working hours and 5.99 x 10-7, respectively indicate negligible individual risk under the modeled conditions. Additionally flame and leak analysis revealed smaller sizes of leaks being likely to result in no ignition and safe shutdown with larger leak size resulting in the probability of explosion or jet fire. Furthermore radiative heat flux analysis of a 6.223 mm leak also revealed the occurrence of peak heat flux along the jet axis with a decrease in distance demonstrating that thermal exposure is highly directional. These findings demonstrate that the assessed indoor hydrogen dispenser operates within acceptable safety limits and highlight the effectiveness of HyRAM+ as a tool for hydrogen safety designs.

The application of machine learning in concrete technology has expanded rapidly, yet its reliability is often constrained by limited experimental data, heterogeneous testing conditions, and inconsistencies across published studies. This study investigates the integration of machine learning and synthetic data augmentation to predict the compressive strength of concrete incorporating biochar as a partial replacement for cement. An experimental dataset was compiled from peer-reviewed journal articles indexed in Web of Science, focusing on biochar-modified concrete mixtures. Input variables included cement content, fine and coarse aggregates, biochar dosage, water to binder ratio, superplasticizer content, and curing age, with compressive strength as the target variable. Extreme Gradient Boosting was adopted due to its strong performance on nonlinear tabular data. Model performance was evaluated using the mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²), alongside five-fold cross-validation. Hyperparameter optimization was performed using Optuna. To address data scarcity, a synthetic dataset of 1000 samples was generated using ChatGPT. the large language model approach relied solely on natural language prompts. Only feature definitions and the target variable were provided, without exposing the original data or implementing data generation algorithms. Three modeling strategies were examined. First, model trained and tested solely on experimental data achieved a testing R² of approximately 0.91. Second, model trained on synthetic data and evaluated exclusively on experimental data showed reduced generalization, achieving a testing R² of about 0.42, indicating pronounced domain shift effects. Third, synthetic and experimental data were combined through data augmentation and jointly modeled, a testing R² of 0.93 was achieved. The result showed that the use of LLMs for augmentation improved the performance of the model.

The addition of fiber sto concrete matrix has been a norm to enhance the mechanical strength of concrete. However, the use ot synthetic fibers (artificial fibers) is rampant compared to natural fibers due to a low mechanical strength of some natural fibers. The study added cellulose fiber made from jute at 0.2%, 0.25, and 0.3% of cement weight to concrete matrix to determine their influence on the early strength development. It was observed that compressive strength and flexural strength increases as the proportion of fiber added to the concrete increased. Further observation showed that the compressive strength had its optimum point at 0.3% fiber addition. However, the optimum point of the flexural strength lied at 0.25% fiber addition. It was concluded that cellulose fiber is capable of enhancing the mechanical strengths of concrete matrix.

The purpose of this study is to review the available literature on the effectiveness of fibers in preventing early-age shrinkage cracking on cementitious concrete. The overview describes the widely used ASTM C1579 (Standard Test Method for Evaluating Plastic Shrinkage Cracking of Restrained Fiber Reinforced Concrete (Using a Steel Form Insert) for plastic shrinkage cracking. The past literature used crack length, width, or area to describe and quantify cracks on concrete specimens. To keep things simple, this review expresses the length, width or area as a percentage of the control specimen. Finally, the study establishes a relationship between fiber volume and aspect ratio on plastic shrinkage and compressive strength of concrete. It was concluded that fiber is sufficient enough to mitigate plastic shrinkage cracking. An increase in fiber volume and aspect ratio reduces the early-age cracking of concrete but harm its compressive strength.