Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.

This study introduces the Slit Hybrid Frictional-Yield Damper (SHFYD), an innovative energy-dissipating device designed to reduce seismic forces during earthquakes. Combining frictional and yielding mechanisms, the SHFYD provides a two-phase response: frictional behavior for minor seismic events and yielding behavior for severe earthquake. Using finite element (FE) modeling in ABAQUS, a parametric analysis was conducted to evaluate its hysteretic behavior, failure modes, and the effects of key geometric and frictional parameters, including rib thickness (t), strip width (b), number of strips (n), strip height (h), bolt clamping force (Fp), radius (R), and slot width (S). Energy dissipation primarily occurs through the controlled plastic deformation in the steel strips, aligning with the capacity design principles. Increasing Fp enhances the damping ratio by up to 51%, but it remains below the strip's yield strength to maintain the two-stage mechanism. Thicker plates (8–20 mm) increase energy dissipation during the yielding phase by 113.96%, wider strips (7.5–22.5 mm) would yield the greater absorption by 344%, increasing the number of strips (1−6) results in a 375% rise in cumulative energy dissipation, and shorter strip heights (65–95 mm) improve energy dissipation by 67%. A numerical framework using nonlinear time-history analyses was developed in OpenSees to assess a 20-story high-rise building equipped with the hybrid system. The performance of the SHFYD was compared with the bare frame (BF) and a conventional damper (CSFYD) under suites of ground motions at Design Basis Earthquake (DBE) and Maximum Considered Earthquake (MCE) levels. Results show that the SHFYD outperforms both reference systems, reducing inter-story drifts and residual deformations. At the DBE level, the maximum mean IDR decreased from 2.68% (BF) to 1.20% (SHFYD), and at the MCE level from 5.44% to 1.70%, satisfying performance targets. A two-phase analytical framework was developed to predict the friction-to-yielding transition, confirming the damper’s hysteretic behavior.