Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles

Abstract

Defining and utilizing Neutrosophic Multinomial Logistic Regression (NMLR) is significant in architecture because it introduces a novel approach to prioritizing the optimal alternative for adaptively reusing a historic building. This is particularly crucial in post-conflict recovery. NMLR presents an intelligent classification system and decision-making tool that optimizes the evaluation process for adaptive reuse projects, even under conditions of uncertainty. The integration of Neutrosophic sets and digital technologies provides decision-makers with a more accurate and reliable tool to make rational decisions regarding functional spaces reuse. The effectiveness of this approach is demonstrated through a case study of the Castle of Aleppo. The study determined that the most suitable alternative for the castle is a multi-purpose facility that caters to tourism. This approach can be adapted to various restoration projects, and the assessment of proposed alternatives can be customized according to the weight of the criteria by creating desktop application which contributes to the sustainability and improvement of post-disaster reconstruction efforts.