Bitlis Eren University

This paper presents an explainable data-driven framework for reliable prediction and multi-objective optimization design of compressive strength (CS) of green and low-carbon geopolymer concrete (GPC). A comprehensive literature-based database containing 2281 instances with 16 defined input features was employed to develop both individual and stacked ensemble learning models. All stacked ensemble models demonstrated strong predictive capability; meanwhile, stacked learning consistently improved generalization performance. The best stacked ensemble model, SCM-17 (ETR + GBM + RF), achieved a testing accuracy of R2 = 0.967, RMSE = 5.83 MPa, MAE = 3.76 MPa, and MAPE = 9.87%, confirming the robustness of the proposed stacked strategy for CS prediction. Further, to enhance interpretability and extract engineering insight, SHAP-based sensitivity analysis was conducted. The SHAP analysis indicates that precursor-related variables, particularly ground granulated blast furnace slag and fly ash contents, together with curing age and sodium silicate dosage, exhibit the strongest influence on the model-predicted CS. Beyond prediction, a target-strength-oriented multi-objective optimization framework was implemented to identify optimal GPC mixture proportions while simultaneously minimizing carbon emissions, cost, and energy consumption. The optimized solutions demonstrated balanced sustainability-performance trade-offs and confirmed that feasible high-strength mixtures can be obtained without extreme material configurations. Furthermore, selected optimized mixtures were experimentally validated, and the measured CSs showed good agreement with the corresponding target and predicted values, thereby providing independent verification of the proposed inverse-design framework. Lastly, a graphical user interface was developed to enable rapid CS prediction together with sustainability assessment within validated parameter limits. All in all, the proposed integrated prediction-interpretation-optimization workflow provides a reliable decision-support framework for data-driven and sustainability-oriented GPC design.

This paper presents an innovative and explainable AI framework for predicting the mechanical performance of three-dimensionally printed strain-hardening cementitious composites (3DP-SHCC), focusing on compressive strength (CS) and flexural strength (FS). A rigorously curated database of 202 mechanical performance records collected from state-of-the-art literature was used to develop AI models. To enhance robustness and overcome the limitations of conventional tuning methods, GBM was integrated with four metaheuristic optimization algorithms-GWO, WOA, HHO, and SSA-combined with five-fold cross-validation. The outcomes show that HHO-GBM achieved the highest accuracy for CS prediction (R2 = 0.951) during the test phase, while SSA-GBM performed best for FS prediction (R2 = 0.897). SHAP and ICE analyses identified binder composition, loading direction, and fiber parameters as key drivers. Additionally, a user-friendly, real-time decision-support interface was developed and validated using independent unseen mixtures. Overall, the proposed framework offers a reliable and engineering-ready tool for 3DP-SHCC design.

The 2023 Kahramanmara & scedil; earthquakes caused unprecedented structural damage across South-Eastern T & uuml;rkiye, highlighting the critical need for rapid post-disaster assessment and understanding the root causes of failure in reinforced concrete (RC) structures. This study provides a comprehensive comparative analysis of 207 RC buildings located in Ad & imath;yaman, Hatay, and Kahramanmara & scedil;. A novel methodological approach was employed by integrating post-earthquake field observations with pre-earthquake digital data obtained via Google Street View to identify structural irregularities and damage patterns. The investigated buildings were classified based on their damage levels, with 11.1% categorized as heavily damaged, 34.3% as to-be-demolished, and 54.6% as collapsed. Significant structural irregularities, including soft stories (ranging from 64.9% to 82.7%), heavy overhangs, and vertical discontinuities, were found to be the primary drivers of severe damage. Furthermore, pounding and short-column effects were identified as the most prevalent damage types across all three provinces. The results demonstrate that pre-existing structural irregularities significantly exacerbated the seismic vulnerability of the RC building stock. This research emphasizes the importance of stringent adherence to design codes and suggests that integrating digital imagery into post-disaster surveys can significantly enhance the accuracy of damage classification for future earthquake resilience.

This theoretical study investigates the frequency-dependent impedance behavior of vertical parallel-junction silicon (Si) solar cells, focusing on the impact of base depth under modulated illumination. Using dynamic impedance spectroscopy (DIS) and analyzing Bode and Nyquist diagrams, key electrical parameters, including series and parallel resistances, capacitance, and inductance, are extracted. The results reveal that increasing the base depth leads to a decrease in both the real and imaginary components of capacitance, a reduction in parallel resistance, and a transition from predominantly capacitive to more inductive behavior. The impedance spectra show a quarter-circle arc at low frequencies and a near-linear response at high frequencies, reflecting changes in dominant charge transport and recombination mechanisms. The cutoff frequency decreases significantly with depth, highlighting spatial limitations in minority carrier generation. These findings emphasize the critical role of base depth in shaping the solar cell's internal electrical characteristics and validate IS as a powerful tool for optimizing device design, absorber thickness, and equivalent circuit modeling in high-efficiency photovoltaic (PV) systems.

This paper introduces a novel cascaded softsign function-based PID (CSoft-PID) controller designed for precise pressure regulation in highly nonlinear shell-and-tube steam condenser systems. For the first time in the literature, the classical PID control structure is enhanced through a cascaded nonlinear transformation using the softsign function, which dynamically adjusts the controller input according to the magnitude of the error. This architecture allows for high sensitivity near the setpoint while gracefully limiting excessive control efforts during larger deviations, thereby improving stability and transient performance. To optimally tune the six parameters of the proposed controller, a new hybrid optimization algorithm, termed hGASO-PS, is proposed. This method synergistically integrates an adaptive gbest-guided atom search optimization (ASO) strategy with the precision of the pattern search (PS) technique, ensuring both effective global exploration and fine-tuned local exploitation. The controller parameters are optimized by minimizing the integral of time-weighted absolute error (ITAE), subject to a step change in the condenser pressure setpoint. Extensive simulations and statistical evaluations demonstrate the superiority of the proposed approach. The hGASO-PS-based CSoft-PID controller achieved the lowest ITAE value of 2.1608, with an average of 2.2746 across 30 runs. It also demonstrated the fastest settling time (12.51 s) and the lowest overshoot (1.98%) among all tested controllers. Comparisons with recent PI, FOPID, and cascaded PI-PDN controllers confirm the consistent outperformance of the proposed method in both transient response and control precision, making it a promising candidate for industrial condenser applications.