{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:53:29Z","timestamp":1762196009325,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Early cost assessment is an essential part of building construction strategy; however, preliminary estimates are occasionally unreliable given incomplete data, which causes budgetary overruns. In general, traditional prediction techniques are imprecise and sluggish, particularly while the project scope is still unclear. By introducing a hybrid framework that utilizes ANNs for renovation cost estimation and features enhancements by the TOPSIS method to guarantee contextual relevance and input accuracy, the present research overcomes these drawbacks. Utilizing data from projects that are structurally and contextually comparable enhances the model\u2019s predicted reliability and robustness. The study builds, trains, and tests two ANN models using IBM SPSS Statistics software, which is based on a thorough literature review and actual renovation data from construction businesses. One model utilized 53 data points from prior building renovation projects, whereas the second model employed 11 data points from post-TOPSIS technique building renovation projects. The Radial Basis Function (RBF) procedure is the basis for models that include 14 input data such as total initial cost, estimated completion time, initial demolition drainage cost, initial cost of plumbing work, initial heating cost, initial cost of electrical work, initial cost of masonry coatings, initial cost of plasterboard construction, initial bathroom cost, initial flooring costs, initial frame cost, initial door cost, initial paint cost, and initial kitchen construction cost, and one output data, the total final cost. The models show excellent performance with near 0.5 relative error and up to 0.3 monetary units sum of squares error before applying the TOPSIS method and nearly 0.6 relative error and up to 0.8 monetary units sum of squares error after the TOPSIS implementation, proving the usefulness and demonstrating the speed of the ANN in estimating overall renovation costs in combination with the TOPSIS approach. By employing this hybridized approach, the entire contingent procedure is expedited and accomplished more rapidly.<\/jats:p>","DOI":"10.3390\/a18110696","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:21:46Z","timestamp":1762194106000},"page":"696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Innovative Approach Regarding Efficient and Expedited Early Building Renovation Cost Estimation Utilizing ANNs and the TOPSIS Methodology"],"prefix":"10.3390","volume":"18","author":[{"given":"Vasso E.","family":"Papadimitriou","sequence":"first","affiliation":[{"name":"Laboratory of Planning and Project Management, School of Civil Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Aristotle University of Thessaloniki Campus, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9248-3454","authenticated-orcid":false,"given":"Georgios N.","family":"Aretoulis","sequence":"additional","affiliation":[{"name":"Laboratory of Planning and Project Management, School of Civil Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Aristotle University of Thessaloniki Campus, 54124 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref_1","first-page":"39","article-title":"Analysis of Cost Control Challenges in the Construction Industry, Using Artificial Neural Network","volume":"49","author":"Abam","year":"2024","journal-title":"Poljopr. 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