Extended Abstract
Background and Objectives: Given Reconstruction, renovation, and reuse of existing buildings are key strategies for achieving a sustainable built environment. In light of rapid technological advancements, it is essential to assess the condition of existing buildings to ensure they can accommodate the new developments and conditions. Given the technological development, changes in educational methods, and the natural aging of school facilities, many schools across the country now require large-scale renovation. Managing the existing building stock, especially on a large scale, necessitates thoughtful decision-making and planning. Prioritizing buildings for adaptive reuse is a crucial aspect of this approach. Adaptive reuse involves repurposing buildings that have reached the end of their useful life, based on their condition and the needs of the community. This strategy helps create a sustainable built environment by extending the lifespan of existing structures and preserving their structural, social, economic, physical, environmental, and cultural value. This paper aims to provide an effective framework for prioritizing buildings for adaptive reuse and renovation.
Methods: The Adaptive Reuse Potential (ARP) model serves as the primary tool for prioritizing buildings for reuse. This model assesses a building’s potential for adaptive reuse by factoring in its useful life, current age, and predicted physical lifespan. The ARP model generates scores: an ARP score below 20% indicates low reuse potential, a score above 50% indicates high potential, and scores between 20% and 50% indicate moderate potential. The building’s potential for adaptive reuse and the optimal timing for reuse intervention are key criteria for prioritizing school renovations. Schools with higher adaptive reuse potential are prioritized for renovation, and those with less remaining time until the best reuse intervention point are given higher priority. In this study, eight schools in Gilan province are analyzed as case studies, focusing on schools between 25 to 35 years old, with consideration of their geographical distribution. Some of the necessary data and information about schools were obtained from the Organization for Development, Renovation and Equipping Schools of Gilan Province and some through field visits and interviews with the school principals.
Findings: According to the results, the maximum ARP score among the case studies is 78%, the minimum is 49%, and the average is 63%. This indicates that, overall, the case studies show a high potential for adaptive reuse. Building obsolescence, defined as the loss of utility and functionality, was significant in the school buildings. The maximum obsolescence rate observed was 0.0121, the minimum was 0.0073, and the average rate of obsolescence across all case studies was 0.0101. This high obsolescence rate contributes to a reduction in the buildings’ useful life. The research shows that the average effective useful life of the school buildings is 50%, which represents the percentage of the predicted lifespan that is actually utilized. This means that, on average, only half of the buildings’ expected life will be realized, with the other half lost. Therefore, these findings highlight the importance of reusing such buildings.
Conclusion: Renovation and adaptive reuse of older buildings are essential strategies for preserving the existing building stock. However, addressing these buildings requires careful decision-making. Prioritizing buildings for adaptive reuse is a key challenge, particularly on a large scale, as each building has a different priority based on its condition and potential. This paper utilizes the Adaptive Reuse Potential (ARP) model, focusing on building reuse potential and the optimal time for reuse intervention as the two main evaluation criteria, to offer a robust decision-making model for prioritizing buildings for adaptive reuse and renovation. Considering both criteria simultaneously allows for a more precise and comprehensive prioritization. In cases where multiple buildings have the same adaptive reuse potential, evaluating the available time until the optimal reuse intervention can enhance prioritization accuracy. Conversely, if several buildings share the same available time, their reuse potential can be reassessed to determine the best reuse ranking.