Nilthon Arce Fernández

Estimating construction costs constitutes a strategic component of project management, because it provides fundamental information for assessing its economic feasibility. The present research aims to develop artificial intelligence models for estimating the construction cost of reinforced concrete housing. The database was constructed from 276 technical files corresponding to the period 2019–2025. Random Forest achieved the highest average R² across the three input-variable groups (R² = 0.8520), followed by XGBoost and Multiple Linear Regression, both with an average R² of 0.8473, whereas MLP Regressor obtained a lower value (R² = 0.8197). Likewise, it presented the lowest average standard deviation across the three groups (σ = 0.0530), compared with XGBoost (σ = 0.0547), Multiple Linear Regression (σ = 0.0719), and MLP Regressor (σ = 0.1023). Compared with Groups 1 and 2, the Random Forest model, using Group 3, presented lower MAE values by S/ 3174.91 and S/ 3185.34, respectively, while the MAPE values were lower by 4.19 and 4.40 percentage points, respectively. The main scientific contribution of this research lies in establishing a reproducible methodology, which made it possible not only to identify the model with the highest predictive capacity but also to characterize the conditions under which its accuracy decreases.

El artículo aborda los efectos de la aplicación de nanomateriales (NM) en el comportamiento mecánico del concreto, considerando su aplicación en forma de adición y reemplazo parcial del cemento. Mediante una exploración exhaustiva en diversas plataformas científicas entre ellas Scopus, Scielo, Dialnet, Latindex y Google Scholar, publicados entre 2021-2025. Se identificaron los tipos de nanomateriales más utilizados, sus proporciones óptimas y sus efectos sobre el rendimiento del hormigón. Los resultados muestran que no existe una dosis universal, la eficacia varía según el tipo y forma de incorporación. Entre los hallazgos más relevantes destacan la nanoarcilla (NC) con proporciones óptimas entre 4% y 5% en sustitución, el óxido de grafeno (GO) con niveles máximos del 1% y el nano-TiO₂ (NT) con una alta variabilidad dependiente del contexto experimental. La nanosílice (NS) evidenció mejoras notables en resistencia y durabilidad, aunque reduce la trabajabilidad del concreto fresco. Se concluye que el uso dosificado de nanomateriales mejora la resistencia del concreto frente a compresión, tracción y flexión, reduce la permeabilidad y mejora la durabilidad, constituyendo una estrategia tecnológica clave para mitigar la huella ambiental del concreto y extender su vida útil.

Temperature abuse during storage represents a critical factor influencing microbial behavior in meat products, particularly in non-conventional matrices such as guinea pig meat. This study aimed to characterize and compare the primary growth kinetics of Aerobic mesophilic bacteria and Staphylococcus aureus (S. aureus) in guinea pig meat burgers under controlled temperature abuse conditions (30, 35, and 40 °C). Microbial growth was monitored over 96 h and described using the modified Gompertz model to estimate key kinetic parameters, including maximum specific growth rate (µ_max) and lag phase duration (λ). Aerobic mesophilic bacteria exhibited increasing µ_max values with temperature, indicating enhanced metabolic activity under elevated thermal conditions. In contrast, S. aureus showed reduced µ_max and prolonged λ at 40 °C, suggesting stress-induced modulation of growth dynamics. These findings demonstrate that temperature increases do not uniformly accelerate microbial proliferation across different populations within the same food matrix. The contrasting kinetic responses indicate that Aerobic mesophilic bacteria and S. aureus respond differently to temperature abuse conditions, highlighting that total aerobic counts alone may not reliably predict pathogen behavior in guinea pig meat burgers.

This study addresses the formation, detection, and repair of cracks in concrete elements exposed to temperatures above 25 °C, where accelerated evaporation compromises their structural strength. An automated intelligent curing system with embedded sensors (DS18B20, HD-38) and Arduino controllers was developed and applied to solid slabs, columns, and concrete test specimens (1:2:3.5 mix ratio). The electronic design was simulated in Proteus and validated experimentally under tropical conditions. Data with normal distribution (p > 0.05) showed a significant correlation between internal and ambient temperature (r = 0.587; p = 0.001) and a low correlation in humidity (r = 0.143; p = 0.468), indicating hygrometric independence. The system healed cracks of 0.01 mm observed two hours after pouring the mixture, associated with an evaporation rate of 1.097 mL/s in 4 m². For 28 days, automated irrigation cycles were applied every 30 to 60 min, with a total of 1680 L, achieving a 20% reduction in water consumption compared to traditional methods. The system maintained stable thermal conditions in the concrete despite ambient temperatures of up to 33.85 °C. A critical evaporation range was identified between 11:00 and 16:00 (UTC-5). The results demonstrate the effectiveness of the embedded system in optimizing curing, water efficiency, and concrete durability.

Crack formation and rehabilitation of concrete elements, such as slabs and columns, in buildings located in areas with temperatures above 25 °C, where accelerated water evaporation significantly reduces their structural strength, was addressed. To mitigate this effect, an intelligent automated monitoring and curing system was developed, using integrated sensors and embedded controllers. The study was applied, experimental and explanatory, based on the hypothetico-deductive method. Work was carried out with physical models (specimens, columns and solid slabs) with 1:2:3 dosage, in which thermal and humidity sensors were integrated, and a visualisation system with an LCD screen was designed in Proteus for data collection. Quantitative analysis, with 95 % confidence, revealed a moderate and significant correlation (r = 0.587; p = 0.001) between the environmental thermo-hygrometer and the embedded DS18B20 sensor, showing effective heat transfer. The low correlation with the HD-38 sensor (r = 0.143; p = 0.468) indicated little influence of ambient humidity on internal humidity. The system restored the initial screams, used 1680 litres of water in 28 days, reducing consumption by 20 % compared to traditional methods. Higher evaporation was identified between 11:00 and 16:00 hours (UTC-5), a critical phenomenon not reported in previous studies in tropical areas.

El término "posicionamiento" se ha convertido en una de las principales preocupaciones de las universidades; un buen posicionamiento influye en la preferencia, el valor y la lealtad de los solicitantes hacia la institución. La presente investigación estudió el posicionamiento de una universidad privada en la provincia de Jaén-Perú, identificando los elementos necesarios para visualizar el panorama adecuado hacia el cual debe dirigirse la institución. Se obtuvo una muestra representativa de 341 alumnos de quinto grado de secundaria y 340 padres de familia. La mayoría de los encuestados percibieron la calidad de la enseñanza como regular, por lo que se debió valorar la importancia de mejorar la calidad de la educación en todos los programas de estudio ofertados, considerando siempre el costo de la educación de acuerdo con el mercado. El estudio demuestra carencias en el posicionamiento de la UDCH - Sede Jaén durante su etapa operativa, que requerían atención inmediata para proyectar la marca y mejorar su posicionamiento en el mercado. Esta investigación es de importancia social ya que servirá de base para estudiar el posicionamiento de las universidades privadas y públicas, mejorando así la calidad de la educación superior en América Latina.

Scheduling for a construction project with a limited number of machines is a critical and well-studied problem. Most studies assume that task processing times are exact; in practice, delays frequently occur, rendering the initial work plan invalid. Therefore, adaptability is crucial to the success of a project. This work introduces a fuzzy optimization model for the planning of construction projects executed simultaneously and having only one backhoe. The model assumes imprecise task processing times, represented by triangular fuzzy sets, that accept delays up to a permitted degree of tolerance. The model solution obtains a fuzzy work plan. This is a robust plan that supports incidents (delays). A method to apply the model was created. The fuzzy model can help construction companies reduce delays in the delivery of their projects and avoid excessive penalties. The model was implemented in the CPLEX solver, which can quickly obtain an optimal solution for small and medium instances. For large instances, the model must be solved with metaheuristics. This scientific contribution is important for future work since it consists of the application of fuzzy optimization in a specific area of civil engineering.

Purpose: This work aims to minimize the penalty generated by delays in delivering construction projects operated by a single machine. Theoretical framework: The fundamental issues to develop this work are optimization and its application to delays in construction projects operated in their initial stage by a single machine. Design/Methodology/Approach: We presented a construction case study, developed an optimization model, implemented a computational optimization tool and obtained the optimal sequence to perform the tasks. Findings: The numerical results demonstrated the model’s usefulness in minimizing the penalty generated by delays in the delivery of projects. Research, practical & social implications: The benefit of this study is to help managers or decision-makers schedule their construction projects with limited resources and deadlines per activity to minimize penalty costs of delay. Originality/Value: Considering that the construction sector generates the largest labor force in Peru, the study has an important social value by providing a tool to improve operations and incentivize construction companies to continue operating. In addition, it provides a substantial basis for future work by applying optimization in a specific area of civil engineering. To our knowledge, no researcher or company in Peru has yet addressed this study.