This research work focuses on the recycling of gypsum, a key component of drywall panels commonly used in construction. It proposes this practice as a technically and economically viable strategy that also aligns with the circular economy model. In this context, waste loses its status as refuse and is transformed back into raw material. A technical feasibility analysis was conducted based on a sample extracted from a specific project, which was subjected to characterization tests and mechanical behavior assessments in both laboratory and real construction conditions. Additionally, an economic feasibility analysis was performed by comparing the budgets of the same project in two scenarios: the traditional plastering process and the use of recycled gypsum. This analysis highlighted the fiscal and legal benefits that adopting the circular model could offer to stakeholders in the construction industry. This study began with a market analysis to determine the availability of recyclable material and to assess the multiple benefits that its reuse can provide. Based on the characterization of the material obtained from the construction site and the mechanical tests conducted, the economic advantages were evaluated for contractors as well as for the potential establishment of companies focused on gypsum recycling. All of this analysis was framed within the context of sustainability, emphasizing the positive environmental impacts of this practice, as well as the development of a strategy that serves as a valuable proposal for the construction sector. This work concludes that recycling gypsum in construction projects represents a technically, environmentally, and economically sustainable alternative that can positively transform the industry.

This study develops a landslide susceptibility zoning map for the Zipaquirá–Pacho road corridor in Cundinamarca, an area prone to frequent landslides. Two statistical methods—Weight of Evidence (WoE) and Information Value (IV)—were used alongside various causal factors to generate the map using GIS software (ArcGIS Pro 3.5.0 software.). A landslide inventory with 101 points was compiled through fieldwork and Google Earth image analysis. Of these, 70% were used to build the models, while the remaining 30% were reserved for validation, ensuring spatial representativeness. The resulting susceptibility maps classified the area into five categories: “very high”, “high”, “moderate”, “low”, and “very low.” For WoE, 19.62% of the area was classified as “very high” and 19.71% as “high”, while for IV, the respective values were 17.57% and 26.55%. Notably, 88% of the identified landslides occurred in “high” and “very high” zones. Model validation using the AUC (Area Under Curve) metric yielded an efficiency of 81%, confirming the reliability of both methods for landslide prediction. The study’s findings are essential for supporting mitigation strategies and serve as valuable input for local authorities and stakeholders involved in risk management and infrastructure planning.

This study develops a landslide susceptibility zoning map for the Zipaquirá–Pacho road corridor in Cundinamarca, an area prone to frequent landslides. Two statistical methods—Weight of Evidence (WoE) and Information Value (IV)—were used alongside various causal factors to generate the map using GIS software. A landslide inventory with 101 points was compiled through fieldwork and Google Earth image analysis. Of these, 70% were used to build the models, while the remaining 30% were reserved for validation, ensuring spatial representativeness. The resulting susceptibility maps classified the area into five categories: "very high," "high," "moderate," "low," and "very low." For WoE, 19.62% of the area was classified as "very high" and 19.71% as "high," while for IV, the respective values were 17.57% and 26.55%. Notably, 88% of the identified landslides occurred in "high" and "very high" zones. Model validation using the AUC (Area Under Curve) metric yielded an efficiency of 81%, confirming the reliability of both methods for landslide prediction. The study's findings are essential for supporting mitigation strategies and serve as valuable input for local authorities and stakeholders involved in risk management and infrastructure planning.