The expansion of urban areas in geotechnically complex regions has led to the imperative for more precise assessments of ground stability and the optimization of foundation design. To this end, a methodological framework was developed that integrates numerical modeling using the coupled Finite Element Method (MEF), real-time monitoring with Internet of Things (IoT) technologies, and stability analysis using the Shear Strength Reduction Method (SRM). This methodology was implemented in five urban sites with contrasting geotechnical conditions, characterized by different types of surface soil and risk levels defined by the probability of increased pore pressure (upsilon omega). The advent of IoT sensors has enabled the calibration of critical parameters, including permeability (k), and the detection of critical events that were previously unanticipated, such as earthquake-induced pressure peaks or extreme water variability. The findings indicated that sites A and E exhibited safety factor reductions exceeding 30% under maximum saturation, with critical values falling below 1.05, suggesting the imminent occurrence of a failure condition. In response, the pile length was optimized to achieve an FS >= 1.30, implying increases of 42.00% and 48.33%, respectively. The other sites maintained critical FS above 1.40, without requiring geometric adjustments, allowing the design to focus on compliance with the Limit of Service State (ELS). The efficacy of coupled modeling in simulating dynamic scenarios and validating adaptive structural solutions was demonstrated, while the indispensability of IoT monitoring in adjusting the design based on actual ground conditions was confirmed. The proposed methodological framework is a comprehensive strategy for assessing geotechnical stability in at-risk urban areas. It involves the optimization of deep foundations and the reduction of uncertainty through continuous observation. This approach promotes the development of safer, more efficient, and resilient designs in critical conditions.
The increasing adoption of educational software in higher education has intensified interest in gamification as a strategy to enhance student engagement and academic performance. However, the effectiveness of gamification largely depends on the strategic selection of game elements under conditions of uncertainty regarding student responses. This study proposes and empirically validates a minimax optimization approach, grounded in game theory, to design robust gamification strategies for university-level educational software. A quantitative quasi-experimental design was employed with a sample of 240 undergraduate students, divided into a control group using traditional gamification and an experimental group using gamification optimized through a minimax criterion. The interaction between the system designer and students was modeled as a strategic game, and the optimal strategy was obtained via linear programming. Academic performance and student engagement (behavioral, emotional, and cognitive) were measured using validated instruments and system logs. Results revealed that students exposed to the minimax-optimized gamification achieved significantly higher academic performance and engagement levels compared to the control group (p < .001), with large effect sizes. Moreover, the minimax approach reduced performance variability and increased minimum achievement levels, indicating greater robustness against adverse student responses. Regression analysis showed that behavioral engagement was the strongest predictor of academic performance. These findings demonstrate that minimax optimization provides a robust and effective framework for gamification design in higher education, contributing theoretically by integrating game theory into educational technology and practically by offering evidence-based guidelines for developing resilient digital learning environments.
Piles are frequently employed in low-strength soils and are often subjected to lateral loads from various phenomena, including wind, waves, earthquakes, and lateral ground pressure. To ensure stability, the lateral strength of the piles must be analysed through theoretical methods or on-site tests, which are essential for accurate design. This research draws parallels between the deflection outcomes of a lateral load test and the responses in the ultimate limit state, p-y, as well as the finite element method. It demonstrates that a range of procedures, with varying degrees of effectiveness, offer a genuine approximation to the behaviour of the pile under lateral loading, thereby facilitating an optimal design. The trial to analyse the experimental behaviour was carried out in Chilca, located in the southern region of Lima, Peru. The full-scale load testing conducted in this location provides a solid basis for understanding the problem. Geotechnical characterization was conducted using standard methods, including SPT field tests and seismic refraction analysis. The properties of the A572 grade 50 tubular steel pile are known to be standardized. The pile that was subjected to lateral load testing was in accordance with the guidelines of the ASTM D3966 standard. A comparison was made between the analytical and numerical calculations and the experimental measurements. This was accomplished by means of displacement plots along the pile. A three-dimensional numerical analysis was performed using the commercial program Abaqus (R). This analysis employed an elastoplastic constitutive model with a Mohr-Coulomb failure criterion for the finite element method (FEM). The reaction problem of the subbase (p-y) was solved using GEO5 (R). The Matlock and Reese approximation was used as the soil modulus. Finally, the Broms and Meyerhof formulations were used for flexible piles.
This paper investigates the optimal control of a bilinear damped wave equation over an infinite time horizon. We establish the well-posedness of the controlled system and derive uniform energy estimates. The existence of optimal controls is proven by constructing a minimizing sequence. We prove that the control-to-state mapping is twice continuously Fr & eacute;chet differentiable, which enables the derivation of first-order necessary optimality conditions in the form of a variational inequality and a pointwise projection formula. Furthermore, we establish second-order necessary and sufficient conditions: the nonnegativity of the Hessian of the cost functional is shown to be a necessary condition for local optimality, while the coercivity of this Hessian constitutes a sufficient condition. These results provide a complete characterization of local optimality for bilinear hyperbolic control systems over infinite time horizons on bounded spatial domains.
This paper presents a formal and algorithmic approach to the classification of network and seminetwork structures using Boolean algebra. While earlier research has explored logical properties of networks, this study introduces a previously unestablished classification model comprising five symbolic classes (Gk, Uk, Tk, Hk, Bk), each defined by distinct algebraic criteria. The algorithm developed in this paper represents a new contribution, enabling automated recognition and structural categorization of networks based on Boolean operations. In addition, a custom-built application in Python and MATLAB provides a concrete implementation of the model, offering visual insights and interactive classification of complex logical systems. These results establish a novel link between Boolean logic, graph structure interpretation, and algorithmic classification, opening new directions for research in digital logic, lattice modeling, and intelligent information systems.
