Inaccurate cost and schedule estimations in road infrastructure projects continue to be a critical source of contractual disputes and financial inefficiencies, particularly in developing countries. While quantitative risk analysis (QRA) methods such as Monte Carlo simulation (MCS) and schedule risk analysis (SRA) are well-established in the literature, their practical adoption remains limited in contexts with low technical capacity and limited access to advanced modeling tools. This study addresses this gap by proposing a practical and accessible quantitative risk analysis framework tailored to the needs of professionals with limited expertise in probabilistic techniques. The framework combines MCS and SRA using probability distributions (PERT, triangular, and normal) and was empirically validated through three road projects in Peru. Results indicated substantial reductions in uncertainty, achieving cost contingency estimates between 1.34% and 11% which were significantly lower than documented overruns of up to 32.29%. Schedule contingencies ranged from 28.71% to 91.67%, markedly improving accuracy. The novelty of this research lies in its context-adapted implementation strategy, offering a robust and easily replicable approach for similar infrastructure environments in Latin America and beyond. This contribution bridges the gap between theoretical risk modeling and its practical adoption, thus enhancing the reliability of infrastructure planning under resource-constrained conditions.

In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays a critical role in ensuring the safety and operational efficiency of facilities. This case study uses a combination of the multi-criteria analysis approach and hydrological studies that use machine learning algorithms to simulate new rainfall events in order to estimate the risk of flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, and land use and land cover, will be weighed using the multicriteria approach. A methodical evaluation of the most vulnerable locations on the railroad network will be possible thanks to the analysis of these parameters based on the geographic information system (GIS) approach. In the meantime, historical precipitation, flow, and hydrological balance data will be used to calibrate and validate hydrological models. The database required for the machine learning model can be created with these hydrological data. The research regions are situated in the densely rail-networked state of Minas Gerais. The geographical and climatic diversity of Minas Gerais makes it the perfect place to test and validate the suggested approaches. The models evaluated included linear regression, random forest, decision tree, and support vector machines. Among the evaluated models, Linear Regression emerged as the best-performing model with an R² value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011).

This study examines the integration of epistemological principles into road infrastructure risk management, emphasizing the need for adaptive strategies in the face of inherent climate uncertainties, particularly flash floods. A systematic review of peer-reviewed articles, industry reports, and case studies from the past two decades was conducted, focusing on the application of epistemological approaches within the infrastructure sector. The research employs a mixed methods approach. Quantitatively, the risk of pavement failure is measured by analyzing the relationship between pavement serviceability rates and Intensity–Duration–Frequency (IDF) data in areas frequently affected by flash floods. For example, rainfall intensities during flood events on the BR-324 highway in Brazil were significantly higher than monthly averages, with maximum values reaching 235.73 mm for a 5 min duration over a 50-year return period. These intensities showed an increase of approximately 15% over 5 to 10 years and 8% over 50 to 75 years. Qualitatively, traditional risk management methods are combined with epistemological concepts. This integrated approach fosters reflective practice, encourages the use of both quantitative and qualitative data, promotes a dynamic management environment, and supports sustainable development goals by aligning risk management with environmental and social sustainability. This study finds that incorporating epistemological insights can lead to more fluid and continuously improving risk management practices in construction, design, and maintenance. It concludes with a call for future research to explore the integration of emerging technologies such as artificial intelligence to further refine these approaches and more effectively manage complexity and uncertainty.

Peru faces critical challenges in the development and maintenance of its national road infrastructure, comprising over 32,000 km, of which only 26% are classified as being in good condition. This infrastructural deficit significantly elevates logistics costs and undermines national competitiveness, particularly in key sectors such as agriculture and mining. In this context, improving the accuracy and reliability of cost estimation in road infrastructure projects is imperative to optimize resource allocation and mitigate the risk of cost overruns. This study proposes a stochastic cost estimation framework that integrates Monte Carlo simulation with correlation matrices, enabling the modeling of uncertainty and the complex interdependencies among critical cost drivers. The methodology was applied to the Oyon Ambo highway in Peru. Historical input cost databases were analyzed to define probabilistic distributions, and correlation coefficients were employed to represent the dependencies between variables such as material prices, labor productivity, and equipment efficiency. The stochastic model produced probabilistic cost forecasts with associated confidence intervals and quantified risk exposure. The findings demonstrate that the proposed integrated approach significantly enhances the precision and robustness of cost estimates, providing project managers and decision-makers with a rigorous, data-driven tool for risk-informed budgeting and strategic financial planning in complex infrastructure projects.

This study presents a stochastic approach to assess bending moment demand in reinforced concrete beams subjected to vertical loads, incorporating uncertainties in material properties, geometry, and loading conditions. A Monte Carlo simulation framework was developed in Python version 3.9.3 using the OpenSeesPy library to analyze the variability of internal forces based on probabilistic input parameters. The analysis focuses on a four-span continuous beam representative of typical structural configurations in buildings. Probability distributions were assigned to key structural design parameters such as the unit weight of concrete ($\rho$), beam dimensions (b, h), column dimension (a), and applied loads, based on standard statistical assumptions and design guidelines. A total of 10,000 simulations were performed to obtain statistical descriptors of bending moment demand across the different spans. The results reveal significant variability in moment magnitudes, underscoring the importance of accounting for uncertainty in structural design. The proposed methodology enables the estimation of demand distributions and the identification of critical spans with higher sensitivity to parameter variations. Although the study does not evaluate structural capacity or failure probability, it contributes to the integration of stochastic techniques in the preliminary stages of design. Future work may include the incorporation of reliability 16 indices and comparisons with design code values.

This study delves into the relationship between the warping of concrete slabs and the geometric design of roads with the International Roughness Index (IRI), specifically within the Oyon Ambo Road project. Through a detailed analysis that incorporates advanced measurements and design evaluations, the significant contributions of slab deformations and geometric features to the perception of road surface roughness are explored. It is identified that variations in slab warping, attributed to environmental influences and vehicular loads, directly impact the increase of the IRI, affecting the quality of travel and road safety. Moreover, it highlights how the geometric configuration, including curvature, slope, and transversality, plays a crucial role in the driving experience, with direct implications for the planning and maintenance of road infrastructures. This work not only provides empirical evidence on the factors influencing road roughness but also suggests design and maintenance practices to mitigate negative effects on user comfort and safety. The proposed recommendations aim to guide engineers and planners towards optimizing geometric characteristics and managing slab warping, in order to improve the longevity and functionality of roads. This comprehensive approach underscores the importance of considering both structural and geometric aspects in pavement design, contributing to the advancement of knowledge in transportation engineering and the development of better practices in the construction and maintenance of road infrastructures.