Sabkha soils, widely distributed in arid coastal regions, are characterized by high salinity, metastable structure, and low bearing capacity, posing significant challenges for geotechnical applications. Although sustainable stabilization methods have been increasingly explored, the application of alkali-activated waste glass powder (WGP) to highly saline sabkha soils, with emphasis on linking engineering performance to microstructural characteristics, remains insufficiently investigated. This study investigates the effectiveness of alkali-activated WGP as a sustainable and environmentally beneficial stabilizer for sabkha soil. WGP was incorporated at 10-30% by dry soil mass and activated using a sodium silicate-sodium hydroxide solution (Na₂SiO₃: NaOH = 2:1). The treated soils were evaluated through Standard Proctor compaction, California Bearing Ratio (CBR) under soaked and unsoaked conditions, and unconfined compressive strength (UCS) at curing periods up to 90 days, complemented by Scanning Electron Microscopy coupled with Energy Dispersive Spectroscopy (SEM-EDS) analysis. The results reveal a property-dependent optimum WGP content. The highest CBR was achieved at 10% WGP (290% under soaked conditions and 180% under unsoaked conditions), while maximum UCS was obtained at 20% WGP, reaching 3550 kPa at 90 days. Although the untreated sabkha soil exhibited relatively high soaked CBR due to temporary salt-induced bonding, alkali activation provided a more stable cementation mechanism, resulting in improved mechanical performance. This distinction indicates that bearing resistance and compressive strength are governed by different mechanisms of stabilization. Excess WGP content (30%) led to reduced performance due to incomplete activation and disruption of particle interlocking. SEM-EDS observations indicated the development of a denser and better-bonded matrix that is consistent with the possible formation of sodium aluminosilicate hydrate (N-A-S-H) gel, accompanied by reduced pore connectivity and improved load transfer. The findings demonstrate that alkali-activated WGP provides an effective and more sustainable approach for stabilizing highly saline sabkha soils, highlighting the importance of property-dependent binder optimization for ground improvement in saline environments.
Samia Bouzouaid, H. Charrak, Ahmed Rafik Belakhdar et al.· Scientific Reports· 0 citations
Construction safety remains a critical concern due to accidents influenced by multiple interacting factors. While previous studies have identified key risk drivers, robust, data-driven approaches for prioritising them remain limited. This study proposes an integrated machine learning and sensitivity-based framework to systematically prioritise construction accident risk factors. A dataset of key safety-related variables was analysed using Random Forest Regression, Extreme Gradient Boosting, and Artificial Neural Networks. Model performance was evaluated using standard metrics, and sensitivity analysis quantified the relative importance of each risk factor. Within the available dataset, the Random Forest model achieved the highest observed test correlation coefficient of 0.813; however, statistical testing did not indicate significant differences between model prediction errors. Sensitivity analysis revealed that physical strain sub-factors, particularly mechanical and electrical strain, gravity-related tasks, and motion-related exertion, collectively account for 33.74% of the total influence on predicted accident risk. Cognitive demand factors, specifically perceived time pressure and workflow interruptions, represent the most influential secondary drivers. The top five sub-factors alone account for nearly half of the cumulative risk impact, providing a preliminary, data-driven basis for targeted safety interventions within the studied context, subject to further validation with larger, more geographically diverse datasets. The proposed framework offers a promising decision-support tool for construction stakeholders, enabling data-driven prioritisation of safety risks and supporting more informed resource allocation for risk mitigation, subject to further validation in real industrial environments.
A. Alotaibi, John Gambatese, Hossam Wefki et al.· Scientific Reports· 0 citations
The interfacial performance of advanced composites bars embedded in Ultra-High Performance Concrete (UHPC) is an important factor that controls load transfer and the performance of structural elements. Predicting bond strength is still difficult because it is affected by several factors, such as rebar type, bar profile, bar diameter, bonded length, cover depth, fiber content, UHPC compressive strength, and FRP tensile strength. Therefore, this study uses machine-learning models to estimate the the bonding capacity of FRP bars placed in UHPC Using a collected experimental database of 183 specimens from previous studies. Four machine-learning models were developed and compared, including Linear Regression, Random Trees, Multi-Layer Perceptron, and Locally Weighted Learning. The MLP model gave the best prediction performance, with a correlation coefficient of 0.9466, MAE of 2.3083 MPa, and RMSE of 3.0631 MPa. SHAP analysis showed that embedment length was the most influential variable, followed by bar surface condition, FRP tensile strength, and concrete cover. This confirms that FRP–UHPC bond behavior is controlled by the interaction between bonded length, surface condition, mechanical interlock, and confinement provided by UHPC. Overall, the developed explainable ML framework provides a useful tool for predicting FRP–UHPC bond strength and supporting future UHPC-specific bond models.
Abdulaziz Alqurashi· Islamic University Journal o...· 0 citations