Evaluating the efficiency of membrane’s refurbishment solutions to perform vertical extensions in old buildings using a multicriteria decision-support model

Abstract

The initial premise of this research is that the relative efficiency of refurbishment solutions with architectural membranes needs to be measured in order to allow its comparison with conventional solutions, helping decision makers to select the most efficient solutions. The evaluation of this efficiency depends on economic features, but also on functional, technological and environmental ones. This study presents a model to solve this problem, using decision trees, multicriteria decision-making methods (SAW and AHP) and a sensitivity analysis. The selection of the criteria and the assignment of the corresponding weights was attained through an expert group survey for a baseline scenario, aiming maximizing functional performance (such as energy savings) and minimizing employed resources (materials, costs, etc.). The most efficient refurbishment solution among the set of alternatives was reached using the developed model. The methodology was applied to a case study-an old building from the 19th century, located in Portugal, which was refurbished with a vertical extension. The result reveals that the proposed model is successful and illustrates the potential of this evaluation methodology to compare and quantify the efficiency of a series of different lightweight constructive solutions. It also underlines the advantages of using lightweight building technologies, especially with architectural membrane materials, in building refurbishments.

Integration of the Hybrid Decision Support System and Machine Learning Algorithm to Determine Government Assistance Recipients: A Case Study in the Indonesian Funding Program

“…One hybrid method that is popular and interesting to research is the integration of AHP and SAW. AlNawaiseh et al [1]; Cahyapratama and Sarno [6]; Saputra, Sitompul and Sihombing [26]; Angelina et al [2]; Wijayanto et al [29]; Melvin, Sutrisno and Herwindiati [20]; Noviyanto, Tarmuji and Hardianto [22]; Chen [7]; Macieira et al [19] widely used in assessment and selection issues is the merging of AHP and SAW. Therefore, in this study, we combine the AHP method with SAW in a hybrid way and integrate it into machine learning modelling using a logistic regression algorithm for the problem of selecting recipients of the BRIN Innovation Village program funding incentives.…”