This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R² = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. Experimental results indicate that up to 20 % WIS replacement maintains adequate strength, whereas higher proportions reduce structural integrity. A ranking score index and cost analysis confirmed the technical and economic feasibility of using WIS in concrete. Cost analysis demonstrated substantial cost savings with 25 % WIS incorporation, confirming its economic feasibility. Integrating experimental data with ML predictions highlights WIS's potential for sustainable concrete applications, enabling optimized mix designs and reduced reliance on physical testing. Future work should address limitations, including dataset expansion and the exploration of additional durability and mechanical properties to validate WIS's practicality in construction further.

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km² at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka.

This research evaluates the interfacial friction angles of sand-structural materials (i.e. concrete, steel, and wood) from direct shear and model pile tests data. Some variable parameters are considered for performing laboratory tests such as shearing rates (0.05, 0.5, 1.0, 2.0, and 2.5 mm/min.), pile diameters (38.1, 76.2, and 101.6 mm), and normal stresses (27, 55, and 81 kPa). The validated shearing rates are found to be 8.7% and 4.3% in cases of direct shear and model pile tests respectively, which may represent a good accuracy of experimental tests. According to the direct shear test results, the interfacial sand-concrete and internal friction angles are equal at shearing rate of 2 mm/min. The interfacial friction angles are increased with the increment of pile diameters because of slip dilation impact of small diameter pile. The interfacial friction angle of sand-wood pile shows the higher value because of lower self-weight and stiffness of wood compared to the other structural materials. There is a scope for further research by conducting numerical analysis and deriving analytical formulations for obtaining explicit solution.

Natural aggregate scarcity and the environmental burden of extraction have pushed researchers toward industrial by‐products as viable alternatives in concrete production. Steel slag, a by‐product of steelmaking, has drawn attention as a coarse aggregate substitute due to its mechanical properties and availability. This study examines the compressive strength and in situ strength prediction of concrete where steel slag partially or fully replaces coarse aggregates in both stone and brick aggregate systems. A total of 150 cylindrical specimens were cast at replacement levels ranging from 0% to 100% and tested for 28‐day compressive strength. Ultrasonic Pulse Velocity and Rebound Hammer tests were carried out alongside destructive testing to develop correlations with compressive strength. Strength and non‐destructive responses improved up to 50% slag replacement, then declined at higher levels. This reduction is attributed to increased void formation and deterioration of the interfacial transition zone. Linear and exponential regression models were fitted to the data, yielding strong coefficients of determination. A scaling factor was also introduced to account for the effect of slag content on these correlations. To further improve prediction accuracy, four machine learning models were developed: Artificial Neural Network, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. These models were trained using non‐destructive test results, mixture proportions, and combined inputs. Models using combined input consistently outperformed those relying on a single source, with Extreme Gradient Boosting and Artificial Neural Network showing the strongest results. SHAP analysis identified fine aggregate and cement content as the most influential predictors. Uncertainty analysis confirmed the stability of the proposed framework. The combined use of non‐destructive testing, regression modeling, and machine learning offers a practical and reliable method for estimating in situ compressive strength of steel slag concrete, supporting its application in sustainable construction.

Rapid urban expansion in Dhaka, Bangladesh, has driven extensive development on reclaimed hydraulic fills that are highly susceptible to seismic liquefaction. Standard Penetration Test data from five reclaimed fill sites were used to assess liquefaction hazard. A peak ground acceleration of 0.20 g and earthquake magnitude of M _w = 7.5 were applied uniformly. For 182 depth intervals within the 20 m liquefaction potential index (LPI) integration depth, the normalized blow count was used to compute the factor of safety, liquefaction probability. For the 88 sand classified intervals among these, three grain‐scale fabric descriptors: relative density, coordination number, and strong‐contact fraction were additionally derived. LPI values range from 0.3 at Bashundhara Block M to 32.1 at Jhauchar; most sand‐dominated blocks fall in the High to Very High severity class. Liquefaction probability and the grain‐scale parameters share a common blow count input, so their inverse relationship constitutes a physically grounded translation into grain‐contact language rather than an independent finding. All five sites are classified as silty sand, with a mean fines content of 13.6%–19.3%. Applying an equivalent granular void ratio correction reveals that the coordination number is overestimated by 33.5%–52.4% and the strong‐contact fraction by 10%–15%. An aging sensitivity analysis for South Keraniganj shows that a 30% increase in cyclic resistance reduces its LPI from 22.0 to 8.6, reclassifying six depth intervals as nonliquefiable. Densification to a relative density of 60% is identified as the critical ground improvement target, raising the coordination number by 0.3–0.8 and reducing mean liquefaction probability by 1–18 percentage points across sites. These targets follow from interpretive, uncalibrated equations and should be treated as indicative rather than validated design values pending site‐specific laboratory confirmation.