Strength prediction of rotary brace damper using MLR and MARS

• Main Authors: Mansouri, I., Safa, M., Ibrahim, Z., Kisi, O., Tahir, M. M., Baharom, S., Azimi, M.
• Format: Article
• Published: Techno Press 2016
• Subjects: TH434-437 Quantity surveying
• Online Access: http://eprints.utm.my/id/eprint/71916/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991435097&doi=10.12989%2fsem.2016.60.3.471&partnerID=40&md5=610b6c7e9ae7ec0a23009ce838af5b20

This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled wit...