The systematic review studied the effectiveness of flexible pavement preservation using the Pavement Condition Index method, the scientific literature of the last 05 years was analyzed through an exhaustive search in databases and those that met the inclusion criteria were selected. The objective of the research is to examine previous studies on the application of the PCI method in flexible pavement preservation programs to determine its effectiveness. An advanced search was carried out in databases such as Scopus and Web of Science in order to obtain a bibliometric record with background information to facilitate the research. The main focus was on articles that respond to the general question, present coherence, show the research objectives and have a methodology oriented to the central theme; As a result of the research, 19 articles were selected and entered in the search matrix and it was identified whether they met the relevance criteria such as the dimensions similar to the general topic. The review analyzed some limitations and areas for improvement in the application of the PCI method, these include the need to establish clear principles for the definition of the different categories of the index, as well as the standardization of the evaluation procedures, the importance of planning and continuous monitoring of the conservation measures to guarantee their long-term effectiveness was highlighted.

This study investigates the application of several machine learning models using PyCaret to forecast the monthly demand for electricity consumption; we analyze historical data of monthly consumption readings for the Cuajone Mining Unit of the company Minera Southern Peru Copper Corporation, recorded in the electricity yearbooks from the decentralized office of the Ministry of Energy and Mines in the Moquegua region between 2008 and 2018. We evaluated the performance of 27 machine learning models available in PyCaret for the forecast of monthly electricity consumption, selecting the three most effective models: Exponential Smoothing, AdaBoost with Conditional Deseasonalize and Detrending and ETS (Error-Trend-Seasonality). We evaluated the performance of these models using eight metrics: MASE, RMSSE, MAE, RMSE, MAPE, SMAPE, R2, and calculation time. Among the analyzed models, Exponential Smoothing demonstrated the best performance with a MASE of 0.8359, an MAE of 4012.24 and an RMSE of 5922.63; among the analyzed models, Exponential Smoothing demonstrated the best performance with a MASE of 0.8359, an MAE of 4012.24 and a RMSE of 5922.63, followed by AdaBoost with Conditional Deseasonalize and Detrending, while ETS also provided competitive results. Forecasts for 2018 were compared with actual data, confirming the high accuracy of these models. These findings provide a robust energy management and planning framework, highlighting the potential of machine learning methodologies to optimize electricity consumption forecasting.

The increase in CO2 emissions is a global problem, which is why it is necessary to improve the mechanical properties of self-compacting concrete by replacing a percentage of the cement with the incorporation of ashes from agricultural waste. This research aims to evaluate the replacement of cement with rice husk ash and ichu straw ash, in percentages of 2 %, 4 % and 6 % and determine its influence on the fresh properties and compressive strength of self-compacting concrete considering concrete cores at different curing ages. The results showed that the compressive strength increased by 53 % and 40 % with the addition of rice husk ash. It is concluded that replacing 2 % of CCA in the CAC increases its compression resistance from days 3,7,14,21, and 28 days, being a great alternative solution for a high-strength and eco-friendly concrete. with the environment.

Self-compacting concrete is one of the most innovative materials of the moment in the construction industry, being one of the most required due to its high resistance. In this systematic review, the behavior of self-compacting concrete with the incorporation of new materials was analyzed. The objective is to determine which material, when incorporated into self-compacting concrete, has presented a large improvement in its physical-mechanical properties in a fresh and hardened state, based on the search for documents, of which there were 50, of which the following bases stand out. digital data such as: DYNA, ELSEVIER, IOPSCIENCE, MDPI, SCIELO, SCOPUS among others. Thus, obtaining results in Spanish and English, from this, through eligibility criteria such as temporality, access, its results, its focus on the object of study, 30 were selected. Where, the following conclusion was reached, for buildings self-compacting concrete with the addition of hooked hybrid fibers and micro steel have presented better results in eliminating cracking, likewise the incorporation of ashes in self-compacting concrete at a structural level has presented great benefits in compression resistance, this is supported by research and His studies carried out on the properties in the fresh state such as workability and in the hardened state improving resistance to compression and bending.

Sugarcane bagasse ash (CBCA) is a byproduct of the sugar industry that is considered as a pozzolanic material due to its high content of silica and aluminum. The study evaluates the effect of CBCA on the mechanical properties of permeable concrete. Tests were conducted on the concrete's compressive strength using 2 %, 4 %, and 6 % percentages of CBCA. The results showed that adding 2 % CBCA achieved a compressive strength of 234,61 Kg/cm2, higher than the standard sample which achieved 218,37 Kg/cm2. Adding 4 % and 6 % CBCA resulted in compressive strengths of 214,88 Kg/cm2 and 206,91 Kg/cm2, respectively. It was determined that the higher the incorporation of CBCA, the lower the compressive strength of the concrete, likely due to the low percentage of Sodium (Na) in the chemical composition of CBCA, which starts to affect the mechanical properties of the permeable concrete from day 21 of curing. Finally, it is demonstrated that CBCA is a promising additive, but further studies are suggested to evaluate its long-term effects and economic viability in large-scale concrete production.

The fatal consequences of dangerous natural events throughout the history of mankind, which have exceeded the response capacity of countries such as Japan, have led to the evaluation, design and monitoring of disaster risk management models. The general objective of this research was to analyze the planning and development of disaster risk management in Peru, with a focus on the seismic vulnerability of Metropolitan Lima. For this purpose, a case study was carried out based on a review of technical and scientific literature, as well as on the verification of the criteria suggested by the United Nations Office for Disaster Risk Reduction. The PLANAGERD 2014-2021, the PLANAGERD 2022- 2030 and the evaluation report carried out by the Presidency of the Council of Ministers were taken as the basis for analysis. The results obtained showed that there are two criteria that have not been fully considered. However, disaster risk management in the Municipality of Lima is being carried out at an acceptable level. In conclusion, there is an urgent need for effective coordination among all the actors involved to address the weaknesses detected and thus mitigate the risk in the event of a major earthquake. In addition, it is essential to reduce the worrying vulnerability of the various structures that house almost 12 million people in Metropolitan Lima.